Synapse 2008 - Volume 2

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UNDERGRADUATE JOURNAL OF NEUROSCIENCE Volume 2 2008 Editor-in-Chief Cina Sasannejad ‘09 Executive Editor Kevin K. Kumar ‘09 Managing Editors Nicholas Macpherson ‘09 Vinay Patel ‘10 Caroline Wee ‘09 Cornell Undergraduate Society for Neuroscience 2007-08 Executive Board President Cina Sasannejad ‘09 Vice President Aniq Rahman ‘09 Secretary Caroline Wee ‘09 Treasurer Vinay Patel ‘10 Faculty Advisor Dr. David P. McCobb The publication of this journal was made possible with funding from the Cornell University Student Assembly Finance Commission (SAFC). Views expressed in this publication may not necessarily reflect those of the SAFC or Cornell University.

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LETTER FROM THE EDITOR Dear Fellow Students, Faculty and Members of the Cornell Community, Last year, the Cornell Undergraduate Society for Neuroscience (CUSN) published the first volume of the Cornell Synapse.

In many respects, the creation of the Synapse was a bold venture: in a landscape already inhabited by a myriad of student publications, a membership of a nascent student organization wondered if there would exist contributors, a readership, and resources available to publish a journal devoted to undergraduate research in neuroscience. The success of the first volume of the Cornell Synapse represents a tribute to the tireless energy and limitless intellectual curiosity of the Cornell community. I extend my sincerest appreciation toward the students who submitted articles to last year’s journal, the faculty who generously contributed their advice, the readers who embraced a novel student publication, and my peers who helped create the journal – without these, the launch of the Synapse could not have been nearly as successful.

The success of the first volume of the Synapse is also a reflection of the vastly interdisciplinary nature of neuroscience.

The 2007 edition of the Synapse examined topics such as learning, memory, cognition, and courtship through a prism of biochemistry, genetics, behavior, and evolution. Where the idea of a neuroscience journal may have appeared in some ways limiting, the diversity of the content and the enthusiasm of the Cornell community demonstrated the universal appeal of neuroscience. To understand the mechanisms underlying our intellectual and sensorimotor experiences represents a fundamentally human goal, unlimited in questions, approaches, and tools.

The 2008 edition of the Synapse continues on this theme, covering a yet more diverse array of topics from authors integrating information from psychology, mathematics, chemistry, and physics to illustrate and analyze neurobiological phenomena.

This volume’s Reviews section examines how we make decisions and what happens when neurological disease obscures our decision-making ability, with articles on the neurobiology underlying morality and schizophrenia. The Articles section continues on the theme of schizophrenia, then branches out into studies involving electrical communication in fish, a rat model of autism, and a mathematical model of cortical neuron firing patterns. Finally, the Proposals section features novel approaches toward nicotine addiction, Parkinson’s disease, and stress.

I hope you enjoy the second volume of the Cornell Synapse. For more information about how to become involved with the Synapse and with CUSN, please visit http://www.cusn.org/. Thank you for your interest and support.

Sincerely,

Cina Sasannejad Biochemistry ‘09

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TABLE OF CONTENTS

REVIEWS 4

The neuroscience of morality Caroline Wee

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A case of Dr. Jekyll and Mr. Hyde: Revisiting schizophrenia Ugo Ihekweazu

ARTICLES 16

Effects of both smoking status and deficit syndrome diagnosis on specific eye tracking task performances by schizophrenia patients Ben Friedman, Natalie R. Pennywell, Elliot Hong, Matthew T. Avila, Gunvant K. Thaker

19

Electric Signaling and Electroreception Properties in Electric Fishes of the Genus Campylomormyrus (Mormyridae) Natalie Trzcinski, Carl D. Hopkins

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Gender Differences in Hypersensitivity Behavior in a Rat Model of Autism Sarah Kirsch, Patricia M. Whitaker-Azmitia

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Recurrent activity of UP and DOWN states in cortical networks Yvette Wong, G. Bard Ermentrout

PROPOSALS 35

Disruption to reinstatement of nicotine self-administration after induced lesions and glutamatergic receptor antagonism in the insular cortex of the rat brain Steven B. Sachs

39

Role of PINK1 in adolescent and adult substantia nigra pars compacta dopaminergic pacemaking neurons Julianna G. Marwell

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DHEA mediation of acute and chronic cortisol effects on CA1 subset hippocampal neurons Nishant J. Trivedi

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REVIEW The neuroscience of morality Caroline Wee

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

Morality has long been considered an integral aspect of our humanity, a characteristic of society that makes us uniquely human. For a long time, scientists have refrained from addressing questions pertaining to moral cognition and behavior, mostly because of the difficulties of defining, measuring and quantifying morality scientifically. In the past few years, increased interest has been focused on the issue of morality, integrating a broad range of academic fields, which include, among other areas, philosophy, psychology, anthropology and biology. Advances in neuroimaging technology have especially led to fascinating discoveries about the brain’s role in our moral thought and behavior. In this article I will highlight the current scientific theories and ideas related to the complex but exciting field of morality. The evolutionary origins of moral behavior and its strong ties to social interaction and cooperation will be discussed. I will also focus on three specific aspects of morality that have been extensively researched over the years, moral reasoning, the Theory of Mind (and social cognition) as well as moral emotions, and will explore their links to neural structure and function. Finally, I will emphasize the integrative role of the pre-frontal cortex (PFC) in unifying the various aspects of morality discussed in this article. Through providing a closer look at the human brain, this review aims to contribute to a better understanding of human nature and morality.

What is morality? How does it work? These questions appears to have been debated throughout history, with philosophers like Hobbes and Descartes, Kant and Hume, laying out insightful ideas that paved the study of morality all the way into the 21st century.

Charles Darwin declared morality “the most noble of all attributes of man,” that can lead him to “risk his life for that of a fellow creature; or after due deliberation, impelled simply by the deep feeling of right or duty, to sacrifice it in some great cause”1. Morality has been defined as a code of conduct present in all human societies2.

Some believe the moral faculty is a universal instinct that permeates human thought and behavior, in the same way that language does3.

The question of what shapes moral judgment and behavior has perpetually been at the heart of the study of morality. How do we decide what is right and wrong? How do we decide how to behave?

The two big players in this field, even till today, are cognition and affect. Recent findings and theories are calling for an integration of these two domains of morality, which appear to be more closely intertwined than ever imagined. At the same time, moral behavior appears to be intimately tied to our social and interpersonal inter©2008 Cornell Synapse | www.cusn.org

actions. Much of social behavior, like cooperation, helpfulness and honesty, is moral. Much of immoral behavior, like theft, cheating and murder, is anti-social. These beliefs are consistent across cultures and religions.

Certainly, morality is not always so clear-cut, and grey areas exist where cultures, religions or even individuals differ in their appraisals of moral situations like honor killings, aggression, and abortion3. However, many scientists believe that underlying principles of morality exist across all humanity. Because rudiments of morality are observed in newborn babies and animals like our primate relatives, the building blocks of morality appear to predate humanity, and thus rise above cultural or religious differences4. Furthermore, research on morality has shown that when posed with moral dilemmas, people across different cultural and religious backgrounds respond more similarly that one may have expected3.

The scientific study of morality attempts to tease apart the relative roles of reasoning, emotions and social cognitive skills play in influencing people’s moral judgment and actions, the fundamental components of morality that humans (and some animals), regardless of culture and religion, may share. This study has for long been approached using a variety of angles; the neuroscience of morality, however, is a fairly new field, made possible by recent advances in neuroimaging technologies, especially Functional Magnetic Resonance Imaging (fMRI) and Position Emission Tomography (PET). Findings stemming from these developments have brought the scientific world one step closer to understanding moral phenomena, by elucidating the regions of the human brain where moral processing may occur. The neural regions involved in moral reasoning, social cognition (especially Theory of Mind) and moral emotions have been recently brought to light, and discoveries on how they interplay in the human brain can give us clues as to how morality really “works” within each of our minds.

Moral Reasoning, Self regulation and Decision-making Philosophers and scientists alike have long perceived reason and rationality as having a very important influence on moral judgment and behavior. Moral reasoning is defined as an individual or collective practical reasoning about what one ought to do5. It operates through mechanisms of mental models and information processing, and tends to be rational and non-emotional6.

Lawrence Kohlberg and his cognitive-developmental theory implicated moral reasoning heavily in moral decision-making and judgment6,7. He theorized a six-stage model of the development of moral reasoning, and developed an approach to classify individuals into the various stages through the use of moral dilemmas6,7. Kohl4

Theory of Mind and Social Cognition In a recent review, Greene and Haidt aptly summarized the relationship between morality and social cognition as follows: “moral psychology is part of social psychology, but some social psychological processes are not moral. Some social psychological processes appear to make use of cognitive mechanisms specifically dedicated to processing social information, and it is likely that some, but not all, moral judgments fall in this category”6.

Theory of Mind (ToM) is a widely researched component of social and moral cognition. It refers to the ability to understand other people as intentional, perceptive and emotional agents, or to interpret their minds in terms of intentional, perceptual or feeling ©2008 Cornell Synapse | www.cusn.org

Permissible

states17. More commonly known as “putting yourself into someone else’s shoes,” ToM is a cognitive ability that many of us take for granted, but which only a limited few species of animals appear to possess.

ToM is an important influence on moral judgments. Humans often rely on their appraisals of other people’s beliefs, desires and intentions (belief attribution) to make moral judgments about situations, and such appraisals are highly associated with ToM3,18.

When an individual attempts harm, but is unsuccessful, he is still condemned for his prior belief, even by young children. The neural correlates of such judgments were studied by Dr. Young and his colleagues. Results of the study suggest that moral judgments depend primarily on processes mediated by the right temporoparietal junction (RTPJ), already previously associated with belief attribution, as well as the precuneus (PC), left temporoparietal junction (LTPJ) and medial PFC, regions associated with ToM18,19.

However, the outcomes and consequences of acts of wrongdoing also interact with belief attribution to influence moral judgments (See Fig 1). For example, subjects’ moral judgments were determined solely by belief attribution in the case of attempted (but failed) harm, but not in the case of unintentional harm, where the outcome was also considered18. Representing and integrating information about beliefs and outcomes are indeed crucial when 4

Neutral Belief Negative Belief

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...

berg was interested in how individuals resolved moral conflict. A well-known dilemma describes a man named Heinz who must decide whether he should break into a druggist’s store to steal a drug that may save the life of his dying wife7. He claimed that children begin with pre-conventional morality, making decisions based on consequences that they may face, like punishment or reward. As their cognitive abilities develop, they are able to “role-take”, or see situations from other peoples’ perspectives. They later progress to conventional and sometimes post-conventional moral reasoning.

At the highest stage, the individual forms a personal commitment to universal moral principles7,8. Only some adults ever reach this stage8.

Indeed, to solve classic moral dilemmas like those designed by Kohlberg, higher-order cognitive abilities such as planning, executive flexibility and strategy application are necessary9. An important brain region involved in undertaking these tasks is the prefrontal cortex (PFC, especially ventral and medial regions), as noted in studies of humans with focal brain damage and experimental lesions in monkeys10. The PFC is also associated with the ability to establish complex relations between causes and consequences, the establishment of “if then” types of reasoning11. Foremost among the foundations of moral reasoning is the ability to imagine the consequences of alternative courses of action. “What would have happened if Heinz had broken into the store instead of letting his wife die?” This capability of counterfactual reasoning, in concert with emotional input is also probably what gives us the capacity for regret, if we make the conclusion that we have made a morallycompromised decision11. In fact, anticipated regret is a powerful predictor of future decisions, and the amount of anticipated regret associated with a decision has been found to correlate with activity in the orbital medial PFC12.

The anterior cingulate cortex (ACC) and hippocampal structures could also play a role in moral decision making and selfcontrol. Hippocampal structures are essential for learning and memory. In making moral judgments, the hippocampus might facilitate conscious recollection of schemas and memories that allow past lessons to affect current moral decisions10. The ACC has been associated with conflict detection, selective attention and the regulation of motivation, and thus may influence moral behavior.

In fact, rostral ACC activation (along with the nucleus accumbens, the caudate nucleus and ventromedial OFC) is needed for cooperative behavior among subjects playing a version of the ‘prisoner’s dilemma’13. Other works support the ACC’s role in identifying times when an organism needs to be more strongly engaged in controlling its behavior14,15,16.

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1

Neutral Outcome

Negative Outcome

Figure 1: The effect of belief attribution (highly associated with ToM) on moral judgment. Moral judgments given by subjects on a four-point scale (1, forbidden; 4, permissible). Error bars correspond to standard error.

Adapted from Young, L., Cushman, F., Hauser, M, Saxe, R. The neural basis of the interaction between theory of mind and moral judgment. Proc. Natl. Acad. Sci. 104, 8235-8240. (2007)

making moral judgments; this to a large extent requires a ToM. Impairments associated with abnormal moral cognition are also observed in Autism and Asperger’s syndrome, which are typically associated with ToM impairments17. Studies of children with autism indicate that ToM might be subserved by the aggregate neural activity of the orbitofrontal and medial frontal cortex, medial structures of the amygdala and superior temporal sulcus (STS)20. Children with autism also demonstrate reduced activity in the frontal mirror neuron system, a system related to imitation and simulation of the behavior of others21,22. A recent experiment showed that premotor mirror neuron areas in humans– areas active during the execution and the observation of an action –are not only involved in action recognition but also in understanding the intentions of others23. In this study, fMRI imaging demonstrated that the human mirror neuron areas respond differently to the observation of the same “grasping” action, if the action was embedded 5

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A

B

Coronal

Axial

Saggital

Figure 2: Grasping intentions with mirror neurons. The observation of a grasping action embedded in two different contexts. 2A: Suggest two different intentions – drinking on the left and cleaning up on the right – elicits differential activity (greater for drinking) in the mirror neuron area located in the right posterior inferior frontal gyrus. 2B: This shows that the mirror neuron system does not simply code the observed action (“that’s a grasp”) but rather the intention associated with the action (“that’s a grasp to drink”).

This panel was reproduced from Iacoboni et al. The mirror neuron system and the consequences of its dysfunction. Nature Rev. Neuroscience. 7, 942-950 (2006), which was modified from Iacoboni et. al. Grasping the intentions of others with one’s own mirror neuron system. PLoS Biol. 3, e79 (2005).

Figure 3: Brain areas (indicated by Brodmann’s areas (BA)) exhibiting differences in activity in response to personal moral dilemmas as compared with impersonal and non-moral dilemmas. Areas exhibiting greater activity for personal moral dilemmas (as compared with impersonal and non-moral): medial frontal gyrus (BA 9/10); posterior cingulate gyrus (BA 31); superior temporal sulcus, inferior parietal lobe (BA 39). Areas exhibiting greater activity for impersonal moral dilemmas (as compared with personal): dorsolateral PFC (BA 46); parietal lobe (BA 7/40). Images are reversed left to right according to radiological convention.

Reproduced from Greene J. and Haidt J. How (and where) does moral judgment work? Trends in Cognitive Neuroscience 6 (12), 517-523, which was reprinted with permission from Greene et al. An fMRI investigation of emotional engagement in moral judgment. Science 293, 2105-2108. Copyright 2001 American Association for the Advancement of Science.

within different contexts that suggested different intentions, such as “drinking water” and “cleaning up” (See Fig 2). So, mirror neurons appear to contribute to the understanding of the intentions of others associated with everyday actions, and play an important role in ToM. Because ToM appears to be an inner simulation of other’s feelings, perceptions and intentions; some believe that it may underlie the human capacity for the moral feeling of empathy (See “Moral Emotions” section). Human beings are social in nature, thus an ability to take the perspective of another individual potentially allows a greater moral concern for their well-being3. Such a com©2008 Cornell Synapse | www.cusn.org

plex understanding of another person’s mental states, whether to empathize or to discern ulterior motives is crucial in both social interactions and moral behavior. Indeed, the contributions of ToM to both moral and social processes highlight the close relationship between these two aspects of human cognition.

It is important to note however that ToM, while impacting social cognition and behavior, is certainly not sufficient for morality.

ToM is relatively intact in psychopathy, perhaps even allowing for the deviousness of these individuals17. Furthermore, some autistic children have been found not to have the deficit in moral understanding that is found in psychopaths24. Indeed, as Greene and Haidt emphasized, moral cognition is only a part of social cognition, and not all moral “phenomena” can be explained by social mechanisms6.

Moral Emotions Is morality best achieved solely through mechanisms of rationalization and perspective-taking? Historically, this approach to morality was widely supported24; however research in the past thirty years has heavily implicated emotion as a crucial influence of both moral judgments and behavior. One of the key evidences used to support the importance of moral emotions in moral decisions and behavior comes from psychopaths who have intact reasoning faculty but who are unrepentant about serious crimes they commit. While these psychopaths do not seem to have any rational defect, they appear to lack an affective response to cues of distress in others, which is present in most other individuals, and probably forms the roots of empathy25. The callous and unemotional reactions of psychopaths to their crimes seem to suggest that affect and emotional motivation plays an important role in preventing an individual from acting immorally24.

While all emotions may contribute to moral judgments and behavior under specific circumstances, some emotions are more central to our moral lives than others6. Moral emotions differ from basic emotions in that they are intrinsically linked to the interests or welfare either of society as a whole or of persons other than the agent26. They motivate action, serve as markers of value, coordinate social behavior and influence moral judgments10. Some examples of moral emotions include guilt, compassion, embarrassment, shame, pride, contempt, disgust and gratitude10,17. Some researchers also categorize empathy as a complex moral emotion (or more accurately, a mirroring of emotions), which psychopaths appear to lack24,25. Empathy has been argued to form the root of our moral concern for others, and our moral behavior may have originated from our capacity to empathize10,27. In fact, both emphatic sadness and emphatic anger have been found to increase the odds of altruistic behavior towards others3.

Moral emotions strongly influence our moral judgments, especially when we are faced with moral dilemmas: You are driving along a deserted road when you hear a plea for help coming from some roadside bushes. You encounter a man whose legs are covered with blood. The man explains that he has an accident while hiking and asks you to take him to the nearest hospital. Your initial inclination is to help the man, who will lose his leg (but will not die) if he does not get to the hospital soon.

However if you give the man a lift, his blood will ruin the leather upholstery of your car. Is it right to leave the man by the roadside 6

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Figure 4: Areas of the brain that are activated by moral versus unpleasant conditions. These areas included the medial orbitofrontal cortex/medial frontal gyrus, superior temporal sulcus and posterior middle temporal gyrus of the right hemisphere (uncorrected p< 0.0001; adjacent voxels in the cluster with t>3.61, p<0.003; minimum cluster volume of 100 mm³).

Reproduced from Moll, J et al. The Neural Correlates of Moral Sensitivity: A Functional Magnetic Resonance Imaging Investigation of Basic and Moral Emotions.

The Journal of Neuroscience. 22(7), 2730–2736. (2002).

Figure 5: Brain areas implicate in moral cognition by neuroimaging studies (Brodmann’s areas in parantheses): 1. medial frontal gyrus (9/10); 2. posterior cingulate, precuneus, retrosplenial cortex (31/7); 3. superior temporal sulcus, inferior parietal lobe (39); 4. orbitofrontal, ventromedial frontal cortex (10/11); 5. temporal pole (38); 6. Amygdala; 7. dorsolateral PFC (9/10/46); 8. pariental lobe (7/40).

Reproduced from Greene J. and Haidt J. How (and where) does moral judgment work? Trends in Cognitive Neuroscience 6 (12), 517-523, which was adapted from Adolphs, R. Physiologie und Anatomie der Emotionen. In Handbuch der Neuropsychologie (Karnath, H-O. and Thier, P., eds), Chapter 47, Springer-Verlag.

to preserve your upholstery?

You are home one day when a letter arrives from a reputable international organization. The letter asks you to make a donation of two hundred dollars to their organization. The letter explains that a two-hundred dollar donation will allow this organization to provide needed medical attention to some poor people in the other part of the world. Is it right not to make a donation to this organization, in order to save money? (Adapted from Unger, 1996)28.

Most people claim that it would not be wrong to refrain from donating in this case29. On the other hand, most people agree that it would be seriously wrong to abandon this man out of concern for one’s car seat29. It can be argued though that both cases are ©2008 Cornell Synapse | www.cusn.org

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in fact strikingly similar. In both these cases, one has the option to give someone much needed medical attention and a relatively modest financial cost. Yet it seems brutal to ignore the man by the road, but simple to ignore the poor people halfway across the world. The reason for this judgment lies with the type of dilemma involved: the dilemma with the bleeding hiker is a “personal” moral dilemma, while the donation dilemma is “impersonal”30. A greater brain activity was found in areas associated with emotion and social cognition when subjects were faced with the “personal” dilemma, as compared to the “impersonal” one29. Research has demonstrated that cognitive control processes, afforded by the lateral PFC and ACC produce utilitarian, pragmatic responses to impersonal moral dilemmas (Would you rather kill one person or ten people?)31. However, when the dilemma becomes up close and personal (Would you smother a baby to save ten other lives?) emotional responses kick in and affect a person’s perception of the situation31. An fMRI study by Greene and colleagues mapped the brain areas that were specifically activated when participants were presented with personal (vs. impersonal) moral dilemmas (See Fig 3)32,33. Areas exhibiting greater activity for personal dilemmas included the medial frontal gyrus, posterior cingulate gyrus, STS and inferior parietal lobe, while areas exhibiting greater activity for impersonal moral dilemmas included the dorsolateral PFC and parietal lobe.

In another study, Moll and colleagues used functional magnetic resonance imaging to investigate the neural correlates of moral emotion, by contrast brain responses to moral visual stimuli (containing emotionally charged scenes of war, physical assault and abandoned children) with unpleasant but non-moral ones (e.g. body lesions, dangerous animals)34. The right medial orbitofrontal cortex (OFC) and the medial frontal gyrus (MedFG) and the cortex surrounding the right posterior STS had increased activation in the moral as compared with the non-moral unpleasant stimuli (See Fig 4)34. The medial OFC has been implicated in implicit socio-emotional appraisals and the representation of inner states (ToM)20,26. The lateral STS are also important in the perception of social signs21. In another study, the medial frontal and posterior cingulate regions were also implicated in empathy35. Such overlaps in the neural substrates of moral emotion and social behavior highlight the relevance of moral emotional processing in our social relationships.

How do moral emotions affect moral judgments? Damasio’s Somatic Marker Hypothesis proposes that bodily arousal may facilitate smoother, more automatic decision-making which leads to quick action, especially in situations where “right” and “wrong” can clearly be perceived26. Haidt’s Social-intuitionist theory also suggests that moral judgment is caused by quick moral intuitions, and is followed (when needed) by slow, ex-post facto moral reasoning6. Wheatley and Haidt recently conducted an experiment to test if an arbitrary induced gut-level bodily reaction (disgust) would be used as information for moral judgment, as predicted by the above two models36. Their findings showed that moral judgments can be made more severe by the presence of a flash of disgust, suggesting that moral judgments may be grounded in emotion. Some participants, confused as to why their judgments were so severe, even engaged post-hoc reasoning in order to justify them. As we can see, moral emotions, like disgust, are indeed integral influences in human morality.

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The evolutionary origins of morality We have now seen the roles that moral reasoning, social cognition and moral emotions play in affecting our moral thinking and behavior. A question that still lingers is how we, as humans, developed the deep and complex moral capabilities that we possess today, without which there would have been no “high civility”, as Emerson so convictedly believed. We have identified brain structures that appear to be relevant to morality, so it appears that morality is, to some extent, heritable and innate. Since this is the case, we would expect some aspects of morality to be observable even in young infants and across cultures. Furthermore, are we, as humans alone in such capabilities? Do animals, whom we share an evolutionary past with with, share with us some aspects of moral cognition and behavior?

Recent literature on the evolution of altruistic and cooperative behavior could perhaps give us a clue into how moral behavior emerged. While helping behavior could have originated from mechanisms of kin selection and direct reciprocity, a new form of reciprocity, that does not depend on kinship or returned favors soon emerged, known as indirect reciprocity. This form of cooperation, where an act of kindness is returned not by the recipient, but by other members of society, has been hailed as the “basis of all systems of morality”37. Wedekind and Milinski tested the mechanisms of indirect reciprocity in an experimental money donation game. In order to mimic the impact of “social reputation”, each player was given an image score, accessible to other players, which increased with the amount of money they donated. Results showed that helping behavior was widespread even though participants were unlikely to meet twice, and increased towards those with higher scores38. Players also refused to help other players who had low scores. Such discriminative behavior against defectors appears to encourage cooperation within the group38. Thus, the benefits of a good social reputation as well as discrimination against (or punishment of) defectors appear to be effective mechanisms for maintaining cooperation even in larger groups of unrelated strangers. Some researchers even suggest that an internalization of such mechanisms of cooperation may have led to what we now know as conscience37. Indeed a hallmark of moral behavior is that of cooperativity and helpfulness, which could have evolved within the highly social environment of our ancestors. Reciprocity appears to be at the core of the “Golden Rule”, or doing unto others what one would like for oneself, which appears in various forms in world cultures and major religions from Confucianism to Christianity3.

The evolution of ToM is also a source of interest. ToM appears to emerge in children from about four years of age3. Some researchers have questioned if animals too possess abilities to perceive the internal states of others. A little known fact is that the concept of ToM started with a 1970s primate study, where a chimpanzee would select a picture of a key if she saw a person struggling with a locked door, or a picture of a man climbing on a chair if she saw a man jumping to reach a banana. It was concluded that the chimpanzee could recognize others’ intentions4. Mirror neurons were also first discovered in monkeys22. Another test of ToM in animals assesses their abilities to recognize themselves in a mirror. Selfrecognition certainly does not imply ToM per se, however, mirror recognition results not only correlate with the ToM in children, but also with the breakdown of ToM in children with mental retardation, some autistics and patients PFC damage3. Some researchers ©2008 Cornell Synapse | www.cusn.org

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believe that being able to identify the image in the mirror requires an awareness of self, which is tied closely to ToM capabilities3,4.

This mirror test has been implemented on a variety of animals, and only a very limited number have shown signs of self-recognition.

They include chimpanzees, bonobos, orangutans and dolphins.

Recently, elephants have been added to the list39. All these animals are highly social and have been known to display helping or empathic behavior, even in the wild4 Such evidence, though open to other interpretations, suggest convergent cognitive evolution probably related to the complex sociality and cooperative behavior in these animal societies39.

Moral emotions, like all other emotions, also appear to have evolved to serve adaptive functions, especially within the social communities of our ancestors. The evolution of empathy has also been particularly well characterized in recent years. Evidence that empathy exists even in newborns suggests some extent of innateness. Within a few hours of life, newborns will cry in response to hearing others cry, though not to other equally annoying noises.

Nancy Eisenberg, a developmental psychologist, interprets such a response as a “rudimentary form of empathy”40. Animals too appear to show some degree of empathy. Apes will go out of their way to console distressed group members, by embracing and grooming them4. Rats will press a lever to lower another highly distressed rat suspended in mid-air, without any reward for doing so, or punishment for not3. Rhesus monkeys will forgo food for days on end if the means through which they can get food will cause its neighbor to receive a nasty shock. This is especially so if they had previously had the experience of being shocked, or if the neighbor was familiar3. Again, while there are other ways to interpret such results, empathy appears to be a viable explanation.

Though only certain aspects of human social cooperation, self (and other) awareness and moral emotions have been discussed in this section, it is important to note that an overwhelming amount of literature exists that deals with many other aspects of morality and its evolutionary basis. Numerous studies have been conducted testing concepts of fairness across cultures, reciprocity among animals (from scrub jays to chimpanzees), deception in young children and the self-control of delayed gratification, among others3. Strikingly, as much as human morality appears to extend from a continuum of evolving adaptations to a social environment, it maintains its uniqueness in that whereas many individual animal species exhibit only a subset of moral capacities, humans appear to have evolved a complete set, of unsurpassed intricacy3.

An integrative approach –A closer look at the PFC Emotion, reasoning and social cognition appear to converge in morality. What are the neural substrates and mechanisms behind this integration? While the answer is still not yet clear, a candidate region has emerged. Converging results from lesion and imaging studies implicate the PFC in everything from planning, decision making, attention, spatial-temporal memory and conflict recognition to ToM, social cooperativity, interpersonal relationships and emotions10,16,19,41. Thus, the PFC could perhaps be the crucial link between moral reasoning, moral emotion and social cognition (ToM), the three aspects of morality which have been discussed above10. Some evidence for this role comes from studies on patients with early-onset PFC damage (before 16 months), who perform relatively well on IQ tests and various cognitive tasks, but 8

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Levels

Adults

PFC-Damaged Patients

Achieved by a minority

Achieved by 1/6 of adultonset patients

Characteristic of most adults and adolescents

Achieved by 5/6 of adultonset patients

Characteristic of most children under 9

Both early-onset patients at this level (in adulthood)

Level 3 - Postconventional Stage 6: Personal commitment to universal moral principles Stage 5: Recognition that moral perspective may conflict with law.

Consider rights and welfare of all

Level 2 - Conventional Stage 4: Recognition of obligations to society. The individual is viewed within the system Stage 3: Reliance on the Golden Rule. Be a good person in your eyes and those of others

Level 1 - Preconventional Stage 2: Concrete reasoning that to serve one’s own needs, you must recognize others’ needs Stage 1: Egocentric perspective with decisions based on avoidance of punishment

Table 1: Performance in Kohlberg Moral Judgment Task.

Adapted from Anderson, S.W., Bechara, A., Damasio, H., Tranel, D. and Damasio, A.R. Impairment of Social and Moral Behavior Related to Early Damage in Human Prefrontal Cortex, Nature Neuroscience 2 (11), 1032–1037 (1999)

suffer from severe moral and social impairment almost resembling psychopathy8.

These patients, who had brain damage in the ventral, medial and polar areas of the PFC are irresponsible in planning decisionmaking, appear unemotional, and are unable to conduct themselves appropriately in social settings and interpersonal relations. Moreover, they failed to respond to programs aimed at correcting their inappropriate behavior during adolescence and young adulthood.

The only parts of their brains that suffered damage in these cases were the ventral, medial and polar regions of the PFC. While cases of adult-onset PFC damage have also been documented, damage at an early age seems to have much more severe consequences on personality and behavior. The adult-onset prefrontal-lesion patients that were studied also generally do not show the sort of antisocial behavior noted in the early-onset patients, for example, stealing, and violence against persons or property. Typically, the victims are the adult-onset patients themselves, not others, and their social and moral ineptitude can hardly be described as antisocial8.

Furthermore, while adult-onset patients also maintain knowledge of social conventions and factual rules, early-onset patients appear to be deficient in knowledge of social and moral norms8,26.

Unlike adult-onset patients, their moral reasoning was conducted at a preconventional stage, in which they solved moral dilemmas from an “egocentric” perspective of punishment avoidance (See Table 2).

Thus, the PFC appears to play an important role in the acquiring of moral knowledge and social skills in childhood. Such knowledge could be stored in a factual mode, namely a declarative knowledge of socially and morally-relevant facts, or an emotional mode, as emotional “tags” that influence judgment behavior automatically and quickly (as proposed by the Somatic Marker Hypothesis and Social-Intuitionist Model)8. The unemotional, guilt-free, empathy-lacking nature of the early-onset PFC patients (as well as neuroimaging evidence, see “Moral Emotions” section) point towards yet another role of the PFC, that of emotional processing.

The PFC, which has numerous neural connections to the limbic ©2008 Cornell Synapse | www.cusn.org

regions of the brain, appears to play a role in the incorporation of affect into decision-making processes, as seen in studies of both early and adult-onset PFC patients, using the Iowa Gambling Task.

This experiment seeks to simulate real-life decision-making in the way it factors uncertainty of rewards and punishments associated with various response options. Adult-onset PFC damage patients fail to develop a preference for the advantageous response options, persisting in choosing options which provide high immediate reward but higher long-term loss over options with low immediate reward but positive long-term gains, unlike normal controls. Unlike controls, they do not generate anticipatory electrodermal skin conductance responses (SCRs) when pondering the selection of a risky response. In other words, they do not seem to demonstrate anxiety or physiological arousal even when faced with potential (long-term) punishment. The two early-onset PFC damaged patients were found to behave in very similar ways. While the PFC has long been believed to play a role in rational problem solving and reasoning, results from these studies suggest that the PFC also plays a crucial role in the integration of affect into decisionmaking.

The PFC is indeed an interesting region that deserves a closer look, especially in the context of morality. Certainly though, it is neither the only moral part of the brain, nor does it participate solely in moral processes. Furthermore, even specific areas within the PFC appear to be associated with relatively different moral tasks.

For example, while the medial frontal gyrus appears to be activated in emotional planning, emotional recall and emotionally-charged stimuli, the orbitofrontal and ventromedial areas have been found to be absent in many PET studies of emotion6. Indeed how exactly the PFC works is still not fully understood. However, converging evidence appears to show that localized regions of the human brain act in concert under the coordination and regulation of the PFC, to produce individualized judgments to moral situations that we face in everyday life.

Morality – from a neural perspective In a comprehensive review of scientific literature on the neural components of moral cognition, Greene and Haidt identified eight regions of the brain, many of which have been discussed in this article, that have been implicated in studies of morality (See Fig. 5)6.

In their conclusion, they emphasized the fact that there does not appear to be any specifically moral part of the brain. In fact, every brain region that has been found to play a role in moral cognition has also been implicated in non-moral processes6.

We can probably admit that though much progress has been made in the area of moral cognitive neuroscience, we are still far from certain about the answers to many questions on the human capacity for morality. Furthermore, it is unlikely that we will ever obtain a definite answer. Why then do we even bother? Some believe that moral cognitive neuroscience can improve the assessment, prediction and treatment of behavioral disorders, as well as help us to shape environmental, psychological and even medical intervention aimed at resolving moral conflicts and promoting pro-social behavior and welfare17. Others hope that this will help us better develop and instill good moral judgment in ourselves10.

Perhaps though, greatest satisfaction may be derived from the appreciation that all the complexities of human morality are fundamentally a product, a fine handiwork, of the human brain.

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References 1. Darwin, C. The morality of evolution. Autobiography (Norton, London, 1958).

2. Gert, Bernard, “The Definition of Morality”, The Stanford Encyclopedia of Philosophy (Fall 2005 Edition), Edward N. Zalta (ed.), URL = <http://plato.stanford.edu/archives/fall2005/entries/ morality-definition/> 3. Hauser, M.D. Moral Minds: The Nature of Right and Wrong.

(HarperCollins, New York, 2006).

4. DeWaal, Frans. Our Inner Ape. (Penguin Group, New York, 2005).

5. Richardson, Henry S., “Moral Reasoning”, The Stanford Encyclopedia of Philosophy (Fall 2007 Edition), Edward N. Zalta (ed.), URL = <http://plato.stanford.edu/archives/fall2007/entries/ reasoning-moral/>.

6. Haidt, J. The emotional dog and its rational tail: a social intuitionist approach to moral judgment. Psychol. Rev. 108, 814–834 (2001).

7. Kohlberg, L. Stage and sequence: the cognitive-developmental approach to socialization. In Handbook of Socialization Theory and Research (Goslin, D.A., ed.) pp. 347-480 ( Rand McNally, 1969).

8. Anderson, S.W., Bechara, A., Damasio, H., Tranel, D. and Damasio, A.R. Impairment of Social and Moral Behavior Related to Early Damage in Human Prefrontal Cortex, Nature Neuroscience 2 (11a), 1032–1037 (1999).

9. Moll, J., Oliveira-Souza, R., Eslinger, P.J. Morals and the human brain: a working model. Neuroreport 14, 299-305 (2003).

10. Casebeer, W. D. Moral cognition and its neural constituents.

Nature Rev. Neuroscience. 4, 840-846 (2003).

11. Goldberg, E. The Wisdom Paradox (Penguin, New York, 2005).

12. Coricelli, G., Critchley, H.D., Joffily M., O’Doherty J.P., Sirigu, A., Dolan R.J. Regret and its avoidance: a neuroimaging study of choice behavior. Nature Neuroscience 8, 1255-1262 (2005).

13. Montague, P.R. et al. Hyperscanning: simultaneous fMRI during linked social interactions. Neuroimage 16, 1159-1164 (2002).

14. Van Veen, V. et al. Anterior cingulate cortex, conflict monitoring, and levels of processing. Neuroimage 14, 1302–1308 (2001).

15. Bunge, S. A. et al. Prefrontal regions involved in keeping information in and out of mind. Brain 124, 2074–2086 (2001).

16. MacDonald, A. W. III, Cohen, J. D., Stenger, V. A. & Carter, C. S. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science 288, 1835–1838 (2000).

17. Moll, J. et al. The neural basis of human moral cognition. Nature Rev. Neuroscience.6, 799- 809 (2005).

18. Young, L., Cushman, F., Hauser, M, Saxe, R. The neural basis of the interaction between theory of mind and moral judgment.

Proc. Natl. Acad. Sci. 104 (20), 8235-8240. (2007).

19. Amodio, D.M., Frith, C.D. Meeting of minds: the medial frontal cortex and social cognition. Nature Reviews Neuroscience 7, 268-273 (2006).

20. Baron-Cohen, S. Mindblindness: An Essay on Autism and Theory of Mind (MIT Press, Cambridge, Massachusetts, 1995).

21. Adolphs R. Social cognition and the human brain. Trends Cogn. Sci. 3, 469-479 (1999). ©2008 Cornell Synapse | www.cusn.org

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22. Iacoboni et al. The mirror neuron system and the consequences of its dysfunction. Nature Rev. Neuroscience. 7, 942-950 (2006).

23. Iacoboni, M. et al. Grasping the intentions of others with one’s own mirror neuron system. PLoS Biol. 3e, 79 (2005).

24. Nichols, Shaun. How Psychpaths Threaten Moral Rationalism, Is it Irrational to be Amoral? The Monist, 85: 285-304 (2002).

25. Blair, R.J., Jones, L., Clark, F., Smith, M. The Psychopathic Individual: A Lack of Responsiveness to Distress Cues? Psychophysiology 34: 192-198 (1997).

26. Damasio AR. Descartes’ error: emotion, reason, and the human brain. (Avon, New York, 1994).

27. Pizzaro, D. Nothing more than Feelings? The Role of Emotion in Moral Judgment. Journal for the Theory of Social Behavior 30 (4), 0021-8308 (2000).

28. Unger, P. Living High and Letting Die: Our Illusion of Innocence (Oxford Univ. Press, New York, 1996).

29. Greene, J.D., Sommerville R.B., Nystrom L.E., Darley J.M. & Cohen J.D. An fMRI investigation of emotional engagement in moral judgment. Science 293, 2105-2108 (2001).

30. Greene, J.D. From neural ‘is’ to moral ‘ought’: what are the moral implications of neuroscientific moral psychology? Nature Rev. Neuroscience. 4, 846-849 (2003).

31. Greene, J.D., Nystrom, L.E., Engell, A.D., Darley, J.M., & Cohen, J.D. (2004). The neural bases of cognitive conflict and control in moral judgment. Neuron 44, 389-400.

32. Greene, J.D. and Haidt, J. How (and where) does moral judgment work? Trends in Cognitive Neuroscience 6 (12), 517-523 (2002).

33. Greene, J.D. Sommerville, R.B., Nystrom, L.E., Darley, J.M., Cohen, J.D. An fMRI investigation of emotional engagement in moral judgment. Science 293, 2105-2108 (2001).

34. Moll, J., de Oliveira-Souza, R., Eslinger, P.J., Bramati, I.E., Mourao-Miranda, J., Andreiuolo, P.A. and Pessoa, L. The Neural Correlates of Moral Sensitivity: A Functional Magnetic Resonance Imaging Investigation of Basic and Moral Emotions. The Journal of Neuroscience. 22(7a), 2730–2736. (2002).

35. Farrow, T.F, Zheng, Y., Wilkinson, I.D., Spence, S.A., Deakin, J.F., Tarrier, N., Griffiths, P.D., Woodruff, P.W. Investigaating the functional anatomy of empathy and forgiveness. Neuroreport 12, 2433-2438 (2001).

36. Wheatley, T. and Haidt, J. Hypnotic Disgust Makes Moral Judgments More Severe. Psychological Science 16 (10a), 780-784 (2005).

37. Nowak, M.A., Sigmund, K. Shrewd Investments. Science 288, 819-820 (2000).

38. Wedekind, C., Milinski, M. Cooperation Through Image Scoring in Humans. Science 288, 850-852 (2000).

39. Plotnik, J.M., deWaal F.B., Reiss D. Self-recognition in an Asian Elephant. Proc Natl Acad Sci USA 103 (45), 17053-7 (2006).

40. Eisenberg, N., Losoya, S., Spinrad, T. (2003). Affect and prosocial responding. In: R.J. Davidson and K.R. Scherer and H.H. Goldsmith (Eds.), Handbook of Affective Sciences (pp. 787803). New York: Oxford University Press.

41. Adolphs, R. Cognitive neuroscience of human social behaviour. Nature Rev. Neurosci. 4, 165–178 (2003).

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Cornell Synapse

REVIEW A case of Dr. Jekyll and Mr. Hyde: Revisiting schizophrenia Ugo N. Ihekweazu

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

Introduction Schizophrenia is a psychiatric disorder that impairs cognitive awareness of reality and is present in both social and occupational settings (DSM-IV). A schizophrenic individual presents a number of distinct symptoms including but not limited to: auditory and visual hallucinations, disorganized behavior, flat affect, social withdrawal and cognitive deficits (DSM IV). Schizophrenia impairs 1% of the population1. The term schizophrenia or “splitting of the mind” was coined by Swiss psychiatrist Eugene Bleuler of the University of Zurich. Prior, psychiatrists thought of dementia praecox “early insanity” as a single disorder.

Though identified as early as ancient times, schizophrenia, through its many faces, has long remained elusive in both its characterization and its treatment. Currently, no pathophysiological tests are available to diagnose the disorder. It is believed that genetics, the environment, and social development, are common causes of the illness. Individuals with schizophrenia are also likely to be diagnosed with other cognitive dysfunctions, as co-morbidity with other mental disorders is high. An alternative approach to schizophrenia that stems from the anti-psychiatry movement suggests that the illness develops primarily from reactions to psychological stresses placed on sensitive individuals by traumatic events rather than conventional neural-pathologies. As such, schizophrenia is an extremely complex illness that is important to gain a sufficient understanding of.

The purpose of this review is to discuss the current understanding of schizophrenia. This paper will accomplish a number of tasks. First, a brief history of schizophrenia and other psychotic illnesses will be described. Next, the current diagnostic trends will be mentioned. Subsequently, the common causes of the illness as outlined in the literature will be discussed. Third, a brief discussion of the most common and some alternative treatments will be discussed in addition to prognostic information. Lastly, alternative approaches to understanding of the illness will be explored.

History Our understanding of psychotic behavior has matured over time. Before formal recognition and classification of schizophrenia occurred in the early 20th century, our construction of mental illnesses needed to evolve. Human acknowledgement of psychotic illness has its roots even in early civilizations. Yet, the perception and understanding of these diseases has drastically changed. From Greek and Roman society, to the European Enlightenment, and on to the Eugenics movement, our construction of schizophrenia ©2008 Cornell Synapse | www.cusn.org

and other mental illnesses has changed markedly. The following ideas that will be discussed are not nearly the only forms that schizophrenia has been shaped throughout history; they are simply discussed to provide a framework for how our perception of the disease has progressed.

In a recent study, literature from Greek and Roman societies that date back from the 5th century BC to the beginning of the 2nd century AD was analyzed for descriptions of schizophrenia and other associated mental illnesses2. The purpose of the study was to provide a base of historical knowledge concerning schizophrenia and other psychotic diseases2. Symptoms of the mental disorders were provided by the Hippocratic Corpus and the Roman writer Celsus. In addition, the DSM IV criterion for the diagnosis of schizophrenia was used to contrast the ancient methods of characterizing and diagnosing of the disease. The term ‘mad’, was often used in the ancient literature and could be used to describe a number of characterizations. Often, perceived psychotic individuals that were classified as ‘mad’, were also identified as ‘demented’, or ‘unhinged’ among others.

In ancient Roman and Greek societies, the etiology of the schizophrenia followed two diverse schools of thought: supernatural and scientific/medical. Rosen (1968) believes that the two could be held concurrently, or independently3. In addition, it is suggested that supernatural intervention was the most popular belief held by a majority of the non-medically educated citizens. On the other hand, the humoral theory, held by the medical profession maintained that mental dysfunction stemmed from an imbalance of the humors. In line with this theory, a disparity between the blood, phlegm, yellow bile, or black bile in the body was the primary cause of schizophrenia.

Further, physicians such as Celsus were able to diagnostically classify three forms of the disease based on the pervasiveness of its symptoms. Under these classifications, schizophrenia was often misrepresented as mania. It was concluded that because the symptoms were often described in various literatures, the general public in Greco-Roman societies had an awareness of psychotic disease2.

However, there was no written text that describes a condition that qualifies specifically as modern day schizophrenia. Although Schizophrenics do experience hallucinations and delusions, other symptoms (which will be discussed later) are involved. Explanations for these findings include an insufficient literature review, and the possibility of a lesser occurrence of schizophrenia in ancient societies.

Throughout the dark ages, medieval era, and European Renaissance, the perception of schizophrenia remained relatively constant between the supernatural and scientific constructs developed 11

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during the ancient Greek and Roman periods. However, during the enlightenment era of the 18th century, science evolved into the dominant form of medical knowledge construction4. Although the idea of an imbalance of the humors remained important to the characterization of the disease, research to prove otherwise was beginning to be actively pursued. The ideas of rationality and scientific pursuit held precedent, and paved the way for improved curiosity and inquiry into topics concerning schizophrenia. This active pursuit of knowledge improved upon the ancient understandings of the disease.

In the early quarter of the 20th century, the Eugenics movement attempted to improve the genetic quality of the human species by ‘better breeding’. In this age, schizophrenia was considered to be a hereditary defect. Researchers developed programs and projects to determine the extent to which genetics played a role in psychosocial diseases like schizophrenia. In the U.S., Nazi Germany, and Scandinavian societies, thousands of individuals were sterilized without consent in efforts to eradicate the illness. Additionally, in Germany, a significant number were murdered5. The study by Allen (1997), suggests that genetic arguments were used to disguise the social and economic disparity, which may be a more significant contributor to human behavioral deficits5. Together, these constructions of the disease, the Greek/Roman, Enlightenment, and Eugenics, among others, have helped to shape the way we think about schizophrenia today.

Diagnostic Trends Currently, no pathophysiological tests are available to definitively diagnose schizophrenia. As with other mental illnesses, its diagnosis remains a controversial topic, as no consistent or fully objective measure is able to diagnose the disease. At this time, two methods are most widely used, the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorder (DSM), and the World Health Organization’s International Statistical Classification of Diseases and Related Health Problems (ICD).

The most up to date versions are DSM-IV, and the ICD-10 6. A few studies have considered relationships and concordance between the two, while other researchers have focused on the understanding of weaknesses of the two models6,7. The hope is that one common worldwide psychiatric diagnostic tool will emerge.

According to the DSM-IV measure, to be diagnosed with schizophrenia, the patient must present in three areas: category (A) ‘characteristic symptoms’, category (B) ‘social/occupational dysfunction’, and category (C) ‘duration’8. Two or more of the following ‘characteristic symptoms’ from category (A) must be displayed during a one month period: delusions, hallucinations, derailed speech, grossly disorganized or catatonic behavior, and lastly negative symptoms8.

The specific diagnostic definitions that are provided for each term in the DSM-IV will now be summarized. A delusion is described as a “fixed false belief”. Hallucinations are listed as, “seeing (visual), hearing (auditory), smelling (olfactory), feeling (haptic, tactile), or tasting (gustatory) sensations that others would not sense and do not exist outside one’s perception” (DSM-IV).

Derailed speech and disorganized thought results when a patient has difficulty maintaining structured conversations and idea processes. The term catatonic refers to excessively excited, purpose©2008 Cornell Synapse | www.cusn.org

Cornell Synapse

less movement. If the patient is aware of their catatonic behavior, the case then becomes that the patient remains characteristically overly poised and calm. Lastly, negative symptoms, are defined as to the absence of normal behavior. The examples, alogia, apathy, and social withdrawal are given in the manual. Additionally, a note for category (A) maintains that only one symptom may be available to satisfy the criteria, if either of the following is displayed: delusions presented are “bizarre” , or two or more voices are conversing with one another in a hallucination. Under the category (B), the patient must exhibit a decreased level of achievement in the workplace, interpersonal relationships, or self-care (DSM IV). Symptoms in category (C) must be displayed for a minimum of six months; whereas symptoms from category (A) must be present for at least one month. Finally, the additional categories serve the purpose of excluding the possibility of other mental illnesses. Category (D) states that no major depressive, manic, or mixed episodes must occur concurrently with symptoms from category (A). If episodes have occurred, their duration must be brief relative to the length of displayed symptoms from category (A). Category (E) considers substance abuse as a factor, and states that psychotic episodes cannot be due to drug abuse.

Lastly, behavior cannot be caused by any illnesses from the Pervasive Developmental Disorders (PDD). Examples of PDD diseases include Autism, Asperger’s, and Rett’s syndrome8.

To recap, for a patient to be diagnosed with schizophrenia, (s) he must present with two or more symptoms from category (A), these symptoms must be present in work, school, or other social settings, and they must be displayed for at least one month. Also, categories D-F are used to exclude other mental illnesses (DSMIV). The DSM-IV calls on physicians and family members to determine to what degree patients satisfy these definitions. This freedom lends itself to subjectivity and inconsistency in diagnosis of the illness.

The DSM-IV recognizes four subtypes of schizophrenia: catatonic, disorganized, paranoid and residual8. Alternatively, the ICD-10 distinguishes seven, four of which are recognized by DSM-IV8,9. Although work is being done to amalgamate the ICD and DSM, the two still have distinct differences, as the ICD-10 recognizes three additional subtypes of the disease. According to the ICD-10, the subtypes of schizophrenia include: paranoid schizophrenia, hebephrenic schizophrenia, catatonic schizophrenia, undifferentiated schizophrenia, residual schizophrenia, simple schizophrenia, and other schizophrenia9.

The descriptions for each subtype are as follows. Paranoid schizophrenia refers to intense delusional and hallucinatory states; of significance is an auditory and perceptual disturbance. In hebephrenic schizophrenia, affective changes are prominent, while delusions and hallucinations are relatively moderate, e.g. a complete lack of motivation is often seen. Disorganization is prominent along with patient difficulty with coherent speech and social behavior. Catatonic schizophrenic patients transition between two states, hyper-excessive purposeless movement and extreme stillness in a dream-like state. Undifferentiated schizophrenia refers to a patient that does not conform to any one of the previous three subtypes, but exhibits behaviors similar to what can be classified as two or more of the three: paranoid, hebephrenic, and catatonic.

In residual schizophrenia, the patient is in what seems to be a transitional stage between early onset and the long-term symptoms of 12

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the illness. The patient exhibits negative features such as loss of coordination, or poor non-verbal communication. Simple schizophrenia mimics the residual subtype. However, the difference is that the negative features of simple schizophrenia develop without being preceded by any overt psychotic symptoms. Lastly, other schizophrenia refers to delusional or hallucinatory disorders that do not justify a diagnosis of schizophrenia9. As with the DSM-IV, the ICD-10 shaped to certain a degree by levels of subjectivity, however novel methods have emerged to combat these inconsistencies.

Wciorka et al conducted a study that utilized the CASS (Clinical Assessment of Schizophrenic Syndromes) system, a new multi-purpose and multi-level clinical diagnostic instrument, to determine its efficiency in diagnosing schizophrenia7. This instrument allows for the collaborated use of DSM-IV and ICD-10 for diagnosis7. The instrument utilizes statistics to identify patients as schizophrenic7. The hope is that the CASS system would allow for greater consistency in diagnosis of schizophrenia7. The researchers found that the CASS system was accurate in diagnosing schizophrenia compared to individual physicians7. They believe that the system would be useful in both clinical and experimental settings7.

In 2002, Bertelsen wrote a review article on the diagnostic criteriaâ&#x20AC;&#x2122;s of the DSM-IV and ICD-10. He suggests that both have yet to determine which subtypes or forms of schizophrenia are most common and prevalent6. Further, Bertelsen indicates that there is a paucity of literature on what constitutes certain symptoms6. The main suggestion from the article is more research on the subject, which is persistently needed6.

In summary, diagnosis of schizophrenia remains indistinct and relatively subjective. So far, the two criteriaâ&#x20AC;&#x2122;s, DSM-IV and the ICD-10 are the most relied upon systems, yet both contain degrees of subjectivity which result in inconsistent diagnoses. The CASS system has completed successful attempts at integrating the two systems to make accurate diagnoses. Yet, more research needs should be conducted before the system is more widely used. The hope is that over the next years, a common diagnostic tool will emerge that make diagnosis of schizophrenia more accurate and objective.

Etiology Although formal recognition of the disease occurred a century ago, understanding of its etiology is still imprecise. A number of entities have been implicated in the etiology of schizophrenia.

Neuropathology, genetics, traumatic events, and others are claimed to play a significant role. Today, research focuses on identification of the pathological and genetic causes of the disease. Additional knowledge in this realm would facilitate diagnosis and treatment of the disease.

Pathological studies are important because if findings relating abnormalities to symptoms are found to be definitive, physicians will be better equipped to treat the disease. Current studies suggest that anomalies exist in the ventricles and cerebral volume.

Meta-analyses have confirmed that signs of schizophrenia include ventricular enlargement and decreased cortical and hippocampal volume10. An increase in the size of the ventricles often corresponds to a reduction of the cortex. Š2008 Cornell Synapse | www.cusn.org

Cornell Synapse

Another cause that has been implicated in the neuro-anatomical abnormalities is a decrease in size of neutrophils and neurons.

Related to neuronal changes, immunocytochemical and ultrastructure findings are suggestive of alterations in synaptic, dendritic, and axonal organization10.

Neurotransmitters may also be involved in the onset of schizophrenia. Variance in dopamine levels has been shown to be related to increased rates of schizophrenia10. Further, dopamine is more closely related to the positive symptoms of schizophrenia. Typical anti-psychotics, which primarily modulate dopamine pathways, are routinely effective in limiting positive symptoms. However, this does not account for the negative symptoms associated with the disease, and also, a primary focus of solely dopamine does not translate well to all schizophrenic patients. Additional research has suggested that dopamine is not the only neurotransmitter involved in the disease. Glutamate, another neurotransmitter has been shown to play a role. Interruption of the Glutamate receptor NMDA can cause schizophrenic like symptoms11. Given that the neuropathology of schizophrenia remains indistinct, Harrison suggests that future research should focus on the relationship between pathophysiology and presented symptoms of delusions and hallucinations10. Other neurotransmitters that may play a role in the disease include Serotonin and Acetylcholine.

Genetic anomalies are also thought to play a role in the etiology of schizophrenia, as the relative chances of developing the disease are influenced by heredity. However, researchers have yet to definitively comprehend the relationship between susceptible genetic sequences and DNA variance, protein alteration, or other interruptions of biological processes associated with schizophrenia. Although inconclusive, a number of genes have been implicated as interruption candidates, or genes that if altered, lead to schizophrenic behavior. The first sets of genes found by Riley et al, (DTNBP1, NRG1, G72/G30, and TRAR4), are diverse and have yielded consistent, although dubious, data in expression12.

While each gene is in charge of encoding unrelated proteins, variance within their nucleotide sequence does result in similar phenotypes. Of that set, two genes have given more robust and consistent findings. Evidence for the genes DTNBP1, and NRG1, is considerable.

Although research on the topic remains unclear, according to Riley et al (2006), findings from current genetic studies on schizophrenia imply four conclusions12. First, traits and phenotypes for psychotic diseases are genetically influenced, not genetically determined12. Therefore, although involved, genetics do not solely confer schizophrenia12. Next, findings suggest that a number of genes are likely to be involved12. Further, the responsible genetic variances are likely to fall within the typical range of human variation12. This means that the rate and manner with which variance occurs in a gene is likely to be similar to that of normal, healthy individuals12. Therefore the risks that are associated and the potential for developing schizophrenia are similar to other illnesses where genetic mutations play a significant role12. Lastly, studies suggest that some of the variants may interact with others or with environmental risk factors12.

The current focus of genetics in schizophrenia is on replication of the above genes that have been implicated as interruption candidates. The hope is that over next years, these genes will be shown to be susceptibility genes for schizophrenia.

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A number of studies have considered environmental factors as causes of schizophrenia. A study by Schenkel et al aimed to determine if adverse rearing environments (neglect, physical, and sexual abuse), had a role in the development of schizophrenia13.

They found that individuals that have experienced unfavorable childhood environments are prone to having poorer peer relationships in school, more difficulty in school, an earlier age of first hospitalization, more frequent hospitalization, and elevated symptoms of anxiety, depression, and suicidality; which all are related to schizophrenia13. In addition, childhood maltreatment was positively correlated with hallucinations and delusions13.

Also, an analysis by Brown (2006) indicates that prenatal infections of rubella, influenza, and toxoplasmosis, may also be a cause of schizophrenia14. In addition, in pregnancies that give rise to schizophrenia, maternal cytokines such as interleukin-8, were found to be significantly increased14.

In another experiment that aimed to use a multivariate approach at predicting schizophrenia, seven parameters were studied: genetic risk, birth factors, abnormal autonomic response, poor cognitive functioning, adverse rearing, deviant personality traits, and socially incompetent school behavior (see Carter et al, 2002 for a review)15. The purpose of the study was to determine which factors, when distorted, best predict schizophrenia. Many studies have shown that genetics is the single most reliable predictor of psychotic behavior amongst families15. In addition, it has been suggested that genetic susceptibility has the aptitude to amplify environmental stresses12. The Carter et al review suggests that complications associated with child birth may have a role in psychotic susceptibility15. Although there is a paucity of literature on the subject, studies have indicated that children that are either hyperresponsive or hyporesponsive to unfamiliar or aversive stimuli are at higher risk for schizophrenia15. Further, studies focusing on cognitive processes have suggested that individuals with psychotic illnesses tend to have lower IQ’s than peers15. Additionally, studies have shown that children that are reared in dysfunctional homes or overcrowded environments are at a higher risk of being diagnosed with schizophrenia15. Furthermore, children that develop schizophrenia, are observed to exhibit less eye contact, are less responsive, and show less positive affects15. Lastly, retrospective studies have shown that adults with schizophrenia were rated less socially competent by their teachers when compared to classmates15.

Carter et al found that of the seven parameters, prediction of schizophrenia was the most accurate when genetics, rearing environment, and school behavior were considered15. This suggests that the most significant causes of schizophrenia are anomalies within the three parameters: Genetics, rearing environment, and social behavior15.

Treatments and Prognosis Currently, there is no cure for schizophrenia, only medications and treatments that are used to curb symptoms. Expectations from different methods vary and ideas on when to end treatments remain controversial. However, as with most illnesses, if the disease is identified early, the outlook for the future is improved.

Anti-psychotic medication is usually the first method of treatment in schizophrenic patients. Two forms of anti-psychotic drugs that have emerged are Typical and Atypical anti-psychotic ©2008 Cornell Synapse | www.cusn.org

Cornell Synapse

medications. The ‘Typical’ anti-psychotic medications were first developed in the 1950’s. These drugs work to block the mechanism of action of the neurotransmitter dopamine; hyperactivity of dopamine has been said to cause the ‘positive symptoms’ of schizophrenia. Common typical anti-psychotic drugs include Chlorpromazine, Haloperidol, Pimozide, and Haloperidol. Side effects of the ‘Typical’ drugs are numerous. A number of patients have reported Parkinsonian symptoms: stiffness and shakiness, in addition to retarded thinking and thought processes. Another common side effect is akathisia, or restlessness. Also, problems in sex life have been reported. Lastly, tardive dyskinesia (TD) is another common side effect. Individuals with TD routinely experience continuous involuntary movements of the mouth or tongue16.

Recently a newer class of drugs has been developed, ‘Atypical’ anti-psychotic medications. These drugs work on various other physiological aspects of the brain, primarily dopamine, yet differ from ‘typical’ medications by their extensive interaction with serotonin. Common atypical anti-psychotic drugs include Clozapine, Risperidone, Sertindole, and the most recently FDA approved, Paliperidone. Perhaps the greatest benefit of the atypical anti-psychotic medication is the relatively limited side-effects.

Patients are less likely to develop Parkinson’s-like symptoms and TD is much less prevalent. Also, research suggests that unlike ‘typical’ anti-psychotics, atypical anti-psychotics help to suppress the negative symptoms of schizophrenia16. However, side effects are still associated with the atypical anti-psychotics, they include: drowsiness, weight gain, sex life interference, and increased chances of developing type two diabetes16.

Statistics suggest that anti-psychotic drugs do help 4 in 5 patients, however for the effects to last; the medication must be taken routinely. Also, according to the Royal College of Psychiatrists, although medications are sometimes useful, it is important for the schizophrenic patient to utilize other forms of treatment, psychological and social therapies16.

In addition to medications, psychological and social treatments are also available to supplement other physiological treatments.

When combined with formal medication, psychological and social treatments have been shown to greatly benefit patients. These include: Cognitive Behavioral Therapy, counseling and supportive psychotherapy, and family work16. Cognitive behavioral therapy (CBT) is typically done by a clinical psychologist, psychiatrist, or nurse therapist. The aim of this approach is to improve patient’s self-confidence, while developing methods for problem solving.

In CBT sessions, the therapist and the patient identify disruptive thought processes, characterize behavioral habits, and explore new ways of thinking and behaving. According to the Royal College of Psychiatrists, for CBT to be effective, a patient must attend at least ten sessions in a period of six months16. Although counseling and other forms of psychotherapy may not directly influence symptoms, this form of treatment allows patients to manage emotions better16. Lastly, family meetings are designed to teach family members of schizophrenic patients to relate and interact with the ill16.

When left untreated, schizophrenia may end in dire circumstances. Suicide rates amongst the schizophrenic are much higher than in the general population. A study by Radomsky et al (1999) suggests that 30% of schizophrenic patients attempt suicide at least once in their lives17. Estimates indicate that 10-13% of in14

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dividuals diagnosed with schizophrenia will commit suicide16.

However, if schizophrenia is left untreated, the likelihood of suicide increases. Although medications and other forms of treatment are useful in controlling symptoms, the outlook for schizophrenic patients remains indistinct. The study by Robinson et al indicates that if schizophrenia is discovered and treated early, patients can ‘Recover’ from it18. The definition of what constitutes recovery is still highly debated. In this case, recovery refers to the concurrent remission of positive and negative symptoms and adequate social/vocational functioning. Even in early discovery, however, the overall recovery rates remain low18.

Alternative Approaches to Schizophrenia The Ross Institute for Psychological Traumas maintains that mental illnesses such as schizophrenia are primarily the effects of some sort of traumatic event. A common example is child abuse19.

This contrasts the primary focus of genetic and physiological anomalies that biomedicine attributes to schizophrenia. In Schizophrenia: an Innovative Approach to Diagnosis and Treatment, Ross contends that neurophysiological anomalies are not the primary causes of psychotic disease19. Under the ‘Trauma Treatment Model’, empirical evidence was given that validates that patients were positively responding to their method of treatment; the focus on treating psychological traumas20. Although the foundation for the traumatic model differs from the conventional psychiatric approach to thinking about schizophrenia, Ross still believes in the importance of statistical measures like the DSM-IV19. This suggests that although different in nature, alternative and conventional models of understanding schizophrenia can work together.

Conclusions Since the modern day characterization of the ‘schizophrenias’ by the 19th century psychiatrist Eugene Bleuler, researchers have sought to understand the disease. Many methods and forms of research have been utilized to define, consistently diagnose, and treat the psychotic disorder. As previously mentioned, in those respects, there is a lot we have yet to uncover about the disease. Currently the DSM and ICD criterions are still used for diagnosis, but research on the neurotransmitters involved proves to be promising.

Although there is no cure for schizophrenia, the combination of atypical anti-psychotic medications and counseling continues to provide positive effects in the treatment of symptoms. Lastly, as mentioned, alternative methods based on treatment of traumatic events have shown promise. A combination of the conventional and alternative forms of treatment may be useful to patients.

Perhaps the most promising line of study in the future is the understanding of the genes such as DTNBP1 and NRG1, in addition to the neurotransmitter glutamate and its receptor NMDA. Improved knowledge in these areas will enhance diagnostic and treatment methods for the disease.

References 1. Sawa, A., Snyder, S.H. (2002). Schizophrenia: Diverse approaches to a complex disease. Science, 296, 692-95.

2. Evans, K., McGrath, J., Milns, R. (2003). Searching for schizo©2008 Cornell Synapse | www.cusn.org

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phrenia in ancient Greek and Roman literature: a systematic review. Acta Psychiatr Scand, 107, 323-330.

3. Rosen, G. (1968) Madness in Society: chapters in the historical sociology of mental illness. Chicago: The University of Chicago Press.

4. Hamilton, Peter. “The Enlightenment and the Birth of the Social Sciences,” in Stuart Hall, et al., eds., Modernity: An Introduction to Modern Societies, 20-54.

5. Allen, G.E. (1997). The Social and economic origins of genetic determinism: a case history of the American eugenics movement, 1900-1940 and its lessons for today. Genetica, 99, 77-88.

6. Bertelsen, A. (2002). Schizophrenia and related disorders: experience with current diagnostic systems. Psychopathology, 35(23), 89-93.

7. Wciorka J, et al. (2000). Clinical assessment of schizophrenic syndrome (CASS): validity evaluation of the new diagnostic tool.

Psychiatry Poland, 34(2), 203-21.

8. American Psychiatric Association. (1994). Diagnostic and Statistical Manual of Mental Disorders, 4th Edition TR Washington, DC: American Psychiatric Association.

9. World Health Organization. 1992. International Statistical Classification of Diseases and Related Health Problems, ICD Version 2007. Accessed March 20, 2007. From http://www.who.int/classifications/apps/icd/icd10online/ 10. Harrison, P.J. (1999). The neuropathology of schizophrenia: A critical review of the data and their interpretation. Brain, 132, 593-624.

11. Javitt, D.C., Coyle, J.T. (2004). Decoding schizophrenia. Scientific America, 290 (1), 48-55.

12. Riley, B., Kendler, K.S. (2006). Molecular genetic studies of schizophrenia. European Journal of Human Genetics, 15, 669680.

13. Schenkel, L.S., Spaulding, W.D., DiLillo, D., Silverstein, S.M. (2005) Histories of childhood maltreatment in schizophrenia: relationships with premorbid functioning, symptomatology, and cognitive deficits. Schizophrenia Research, 76, 273-286.

14. Brown, A.S. (2006). Prenatal infection as a risk factor for schizophrenia. Schizophrenia bulletin, 32(2), 200-202.

15. Carter, J.W., Schulsinger, F., Parnas, J., Cannon, T., Mednick, S.A. (2002). A multivariate prediction model of schizophrenia.

Schizophrenia Bulletin, 28(4), 649-682.

16. The Royal College of Psychiatrists. Mental Health Information. Accessed March 23, 2007, from http://www.rcpsych.ac.uk/ mentalhealthinformation.aspx.

17. Radomsky, E.D., Haa, G.L., Mann, J.J., Sweeney, J.A. (1999).

Suicidal behavior in patients with schizophrenia and other psychotic disorders. American Journal of Psychiatry 156: 1590–5.

18. Robinson, D.G., Woerner, M.G., McMeniman, M., Mendelowitz, A., Bilder, R.M. (2004). Symptomatic and functional recovery from a first episode of schizophrenic or schizoaffective disorder. American Journal of Psychiatry, 161, 473-479.

19. Ross, Colin. (2004). Schizophrenia: An Innovative Approach to Diagnosis and Treatment. Binghamton: Haworth Press.

Van Os J, et al. (2006). Standardized remission criteria in schizophrenia. Acta Psychiatr Scand, 113, 91-95.

20. Ellason, J.W., Ross, C.A. (1997). Two-year follow-up of inpatients with dissociative identity disorder. American Journal of Psychiatry, 154, 832-839.

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ARTICLE Effects of both smoking status and deficit syndrome diagnosis on specific eye tracking task performances by schizophrenia patients Ben Friedman1,2, Natalie R. Pennywell1, Elliot Hong1, Matthew T. Avila1, and Gunvant K. Thaker1 1 2

Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD 21228 Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

Schizophrenic patients have among the highest rates of smoking of any single patient group. It has been suggested by postmortem findings and by linkage analysis that the region containing the alpha (7) nicotinic receptor found on chromosome 15q14 results in an abnormal regulation of nicotinic receptors in subjects with schizophrenia. These receptors have been shown to regulate the various cognitive tasks which Schizophrenia patients have difficulty with, such as P50 evoked response/sensory gating and eye tracking. We tested the hypothesis that Schizophrenia patients smoke at high rates in an effort to selfmedicate this abnormal nicotinic expression. In our study we examined eye-tracking performance in smoking as well as nonsmoking schizophrenia patients on three specific eye tracking tasks. We also examined how deficit syndrome schizophrenia is related to smoking and eye tracking deficits. Previous studies have shown that deficit patients perform worse on eye tracking than both non-deficit patients and controls. Thus, we expected to find that deficit-smoking patients perform at the lowest levels, while the non-deficit non-smoking patients perform at the highest levels. Our results were in fact consistent with our hypothesis for the deficit group, as the smokers preformed worse than the non-smokers. However, the results for the non-deficit group were not consistent with what we had anticipated, as the relationship between smoking and eye tracking tended to be in the opposite direction.

Introduction It has been confirmed numerous times in research studies that the percentages of schizophrenia patients that smoke are significantly greater than the rates found in non-schizophrenic patients, including some with other psychiatric illnesses. It is also well documented that schizophrenics are â&#x20AC;&#x153;heavierâ&#x20AC;? smokers in that they tend to have higher rates of nicotine use in general, and are more likely to smoke high tar cigarettes. Other studies have even shown that schizophrenia patients are more likely to smoke all the way to the end of the cigarette, the area containing the most nicotine.

Many theories have been generated, all with the same goal of trying to explain this phenomenon. The simplest of explanations provided is that smoking improves mood and anxiety, which is Š2008 Cornell Synapse | www.cusn.org

something that is most beneficial to the stressful lives of schizophrenia patients. Although this explanation must not be ruled out completely, it does not truly satisfy the overwhelming statistics that show approximately 80% of patients with Schizophrenia smoking, in contrast to figures well below that for the rest of the psychiatric population, and three times as less for the general population.

Many are now turning to neurobiology for answers. Schizophrenia patients may be more vulnerable to the effects of nicotine.

They may be more likely to become addicted to nicotine. Nicotine may reduce the symptoms of the schizophrenic illness. Finally, nicotine may also be used by patients in order to treat/minimize the side effects of anti-psychotic medications.

Schizophrenia can be partially characterized by deficits in sensory and memory processing, which bring out the cognitive symptoms of the disease. Such deficits are found in P50 auditory-evoked potential gating and smooth pursuit eye tracking, as schizophrenia patients have consistently preformed worse than non-schizophrenics on these two tasks. Scientific studies have pointed towards one of the neuronal nicotinic receptors, the alpha 7 nicotinic receptor, as the regulator behind these two processes. Trials were thus undertaken to see if the consummation of nicotine had any direct parallel to performance on such tasks. The overwhelming majority of statistics have shown nicotine, in the short term, to normalize the deficits in P50 response and eye tracking that characterize the schizophrenia illness. This finding has led many to the realization that the addictive smoking found in schizophrenia may be traced to a neurobiological deficit related to the alpha 7 nicotinic receptor gene found on chromosome 15.

Previous studies have examined performances by smokers vs. non-smokers on specific eye tracking tasks in an attempt to test the hypothesis that smokers perform at the lowest levels because they exhibit the nicotinic receptor abnormality. Our study attempted to take this a step further, as we examine how deficit syndrome schizophrenia is related to smoking and eye tracking deficits. Deficit syndrome schizophrenia patients exhibit primary enduring negative symptoms, which led us to believe that these patients would perform worse on cognitive tasks relative to non-deficit patients, independent of smoking status. We also hypothesized that smoking deficit patients would perform at the lowest level, as we assume that they exhibit the nicotinic receptor deficit in addition to their 16

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deficit syndrome. Moreover, we conjectured that the non-deficit nonsmokers would perform at the highest level relative to the other three groups, as we assumed that they lack the nicotinic receptor abnormality and do not exhibit primary negative symptoms.

Methods Recruitment Subjects were recruited from the outpatient clinics and an inpatient unit at the Maryland Psychiatric Research center. Subjects were excluded if they had a history of substance abuse within the past 6 months or a lifetime diagnosis of substance dependence (DSM-III-R). Individuals with lung disease, preexisting clinically significant cardiovascular disease, or mental retardation were excluded. All subjects were given clinical evaluations, which included the Structured Clinical Interview for DSM-IV1. Schizophrenic patients were also assessed using the Brief Psychiatric Rating Scale (BPRS)2. All subjects gave informed consent in accordance with the University of Maryland Institutional Review Board guidelines.

Before participating, subjects were interviewed by a non-investigator clinician (using a standardized form) to assess their ability to understand the procedures and provide valid consent. All subjects received $15/hour for their participation in the study. Deficit syndrome was diagnosed using the Schedule for the Deficit Syndrome (SDS), which requires the primary enduring presence of two or more of the following six negative symptoms: (1) restricted effect; (2) diminished emotional range; (3) poverty of speech; (4) curbing of interests; (5) diminished sense of purpose; and (6) diminished social drive3.

Eye Tracking Tasks Smooth Pursuit A fovea-petal step-ramp with unpredictable onset was presented, followed by 3-4 cycles of triangular waveform target motion. The digression of the target at a constant velocity from one side to the other is established as a ramp. After 4-6 of these ramps the target suddenly/unpredictably disappears (becomes invisible) for (mask) 500 msec. Subjects were instructed to follow the target with their eyes throughout the task, regardless of its visibility.

Twenty-five trials were taken at each ramp velocity. The mask occurred at the beginning of a ramp half the time, and in the middle of a ramp half the time.

Closed loop gain and peak predictive pursuit were both measured to be used as data in our study. Closed loop gain provides an index of the extent to which eye velocity matches target velocity.

Peak predictive pursuit was obtained from responses where a mask occurred at the beginning of a new ramp after the change in direction. Subjects stop moving in one direction and commence predictive pursuit in the opposite direction. The latency of the change in eye velocity from the time of change in direction was recorded.

Smooth pursuit response and mean acceleration were measured during the first 100 msec of smooth pursuit, which is referred to as the the Initiation Phase4,5.

Anti-Saccade Task Subjects must fixate on a centrally located cross hair for 1.5 to 2.5 s. In this task the center fixation disappears and the target is simultaneously presented 5 or 10 degrees to the right, or left Š2008 Cornell Synapse | www.cusn.org

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of the center point. Subjects are required to make an anti-saccade equidistant to the position error created by the target onset but in the opposite direction. The target stays for 2 seconds and then a feedback target appears at the correct location and remains for 1 s.

Two blocks of 40 trials each are administered.

The two measures of interest in this task were anti-saccade error (saccades made to the target rather than in the opposite direction) and final position error (accuracy of equidistance). Estimated marginal means of average anti-saccade percent error and antisaccade absolute final position error were thus obtained.

Data collection An infared technique filtered at 75 Hz low-pass filter and converted to digital signals using a 16 bit A-D converter captured eye tracking data used in the study. Interactive software was used in the analysis of the data to remove saccades, blinks, and slow compensatory pursuit after leading saccades. Successive analyses of the original data measured leading saccades.

Results Initiation Phase For the mean acceleration measures, the main effects of smoking status and deficit diagnosis were not statistically significant (F(1,52) =1.73, p>0.05, respectively). There was a marginally significant smoking status by deficit diagnosis interaction (F(1,52)= 3.10, p=0.08). A Post Hoc test showed that the difference in mean acceleration between smoking and non-smoking deficit patients was not statistically significant (F(1, 16) = 1.86, p>0.05). It must be noted that the effect size for this comparison is 0.77 (Cohenâ&#x20AC;&#x2122;s d)â&#x20AC;&#x201D;suggesting that a stable difference would be observed in a larger sample. Post Hoc tests also found no differences between deficit and non-deficit subjects, among both smokers (F(1,27) = 2.13, p>0.05) and non-smokers (F(1,25) = 1.35, p>0.05).

InitiationPhasePhase - Mean Inititation MeanAcceleration Acceleration 110 110 100 100 90 90 80 80 70 70 60 60 50 50

Smokers Smokers Non-Smokers Non-smokers

Non-deficit Non-deficit

Deficit Deficit

Figure 1: A comparison of the performances of deficit and non-deficit smokers and non-smokers during the Initiation Phase of the Smooth Pursuit test.

Smooth Pursuit Closed loop gain There were no main effects of smoking status or deficit diagnosis. However, there was a significant smoking status by deficit diagnosis interaction (F (1, 58) = 4.66m, p<0.05). A Post Hoc test showed that the difference in closed loop gain between non-deficit smokers and non-smokers was marginally significant (F(1, 43) =3.41, p=0.07). The difference in closed-loop gain between deficit 17

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Closed Loop CLosed LoopGain Gain 0.78 0.78 0.76 0.76 0.74 0.74 0.72 0.72 0.70 0.7 0.68 0.68 0.66 0.66 0.64 0.64 0.62 0.62 0.60 0.6

Smokers Smokers Non-Smokers Non-smokers

Discussion Non-deficit Non-deficit

Deficit Deficit

Figure 2: A comparison of the performances of deficit and non-deficit smokers and non-smokers during the Closed Loop Gain test.

Predictive Pursuit Predictive PursuitGain Gain 0.48 0.48 0.46 0.46 0.44 0.44 0.42 0.42 0.40 0.4 0.38 0.38 0.36 0.36 0.34 0.34

nificant (F(1,43)<1.00, p>0.05). The main effect of deficit diagnosis was also not significant (F(1,43)<1.00, p>0.05). There was a significant smoking status by deficit diagnosis interaction (F(1,43)=4.03, p=0.05). No difference was shown between nondeficit smokers and non-smokers, (F(1,31)=2.72, p>0.05, effect size = 0.56). Post Hoc tests revealed a marginally significant difference between deficit and non-deficit smokers, (F(1,22)=3.72, p=0.07) and no difference between deficit and non-deficit nonsmokers, (F(1,21)<1.00, p>0.05).

Smokers Smokers Non-Smokers Non-smokers

Non-deficit Non-deficit

Deficit Deficit

Figure 3: A comparison of the performances of deficit and non-deficit smokers and non-smokers during the Predictive Pursuit Gain test.

smokers and non-smokers was found to be not statistically significant in a Post Hoc test (F(1,15) =1.95, p>0.05) However, this difference was characterized by a moderate effect size (Cohen’s d = 0.69), suggesting that a stable difference would be observed in a larger sample. Among smokers, a Post Hoc test showed that deficit patients performed more poorly than non-deficit patients (F(1,32) = 5.73, p<0.05). Among non-smokers, however, a Post Hoc test showed that deficit and non-deficit subjects did not differ in closed loop gain (F(1,26)<1.00, p>0.05, effect size = 0.44).

Predictive Pursuit There were no main effects of smoking status or deficit diagnosis. However, there was a significant smoking status by deficit diagnosis interaction (F(1, 49) = 9.20, p<0.05). The difference in predictive pursuit gain between non-deficit smokers and non-smokers was statistically significant (F(1,43) = 5.55, p<0.05). Predictive pursuit gain was found to be significantly different between smoking and non-smoking deficit patients in a Post Hoc test (F(1,12) = 14.09, p<0.05). A Post Hoc test showed that among smokers, deficit patients performed more poorly than non-deficit patients (F(1,28) = 19.57, p<0.05). Deficit and non-deficit non-smokers did not differ significantly in predictive pursuit gain (F(1,21) = 1.27, p>0.05, effect size=0.72).

Our hypothesis was based on the following assumptions: 1) Smoking patients exhibit the nicotinic receptor abnormality. 2) Patients who don’t smoke do not exhibit the nicotinic receptor abnormality. From there we hypothesized that a combination of deficit diagnosis along with the nicotinic receptor abnormality would result in more errors in eye tracking, while the non-deficit patients with no nicotinic receptor abnormality would perform with the least amount of errors relative to the other four groups. In general, the results of both measures would neither support nor disprove our hypothesis as in most cases there simply were not enough subjects to suggest significance. Despite this, patterns throughout were in fact coherent with our hypothesis, as the deficit smokers tended to perform worse than the deficit non-smokers. However, the nondeficit group’s results tend to not be consistent with what we had expected. Within this group, smokers tended to do as well as nonsmokers on eye-tracking tasks. These results cannot be interpreted at this time as a greater comprehension of this unexpected phenomenon is needed. Another study with a larger group of patients will need to be preformed in order to advance understanding of the interesting relationships that are seen here.

References 1. Spitzer RL, Williams JB (1988). Having a dream: a research strategy for DSM-IV. Arch Gen Psychiatry 45(9): 871-874.

2. Hedlund JL, Vieweg BW (1980). The brief psychiatric rating scale (BPRS): a comprehensive review. J Operational Psychiatry 11: 48-64.

3. Kirkpatrick B, Buchanan RW, McKenney PD, Alphs LD, Carpenter WT Jr. The schedule for the deficit syndrome: an instrument for research in schizophrenia. Psychiatry Res. 1989 Nov;30 (2): 119-23.

4. Ross DE, Holcomb HH, Thaker GK, Buchanan R, Medoff DR, Tamminga CA. Functional neuroanatomy of smooth pursuit eye movements in normal contrls and schizophrenic patients. Schizophrenia Research 24(1):172. 1997.

5. Weiler MA, Thaker GK, Lahti AC, Tamminga CA. Ketamine effects on eye movements. Neuropsychopharmacology. 2000.

23(6): 645-653.

Anti-saccade task The main effect of smoking status was not statistically sig©2008 Cornell Synapse | www.cusn.org

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ARTICLE Electric Signaling and Electroreception Properties in Electric Fishes of the Genus Campylomormyrus (Mormyridae) Natalie Trzcinski, Carl D. Hopkins

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

Abstract Natural selection acts upon both signalers and receivers to ensure that communication signals are economically generated and efficiently detected and discriminated1. Mormyrid fish communicate using weak pulse-type electric signals (electric organ discharges, or EODs) that vary according to the species and sex of the signaler.

These fish sense EODs using the Knollenorgan, one class of tuberous electroreceptor which is specialized for communication. It has been proposed that this receptor’s filtering characteristics should enhance features of the species-specific EOD such that these fish may better discriminate between conspecifics and heterospecifics.

In particular, the receptor’s membrane electrical resonance allows the cell to respond best to a certain frequency2. Previous work has demonstrated electroreceptor tuning to conspecific EOD frequency in gymnotiforms (weakly electric fish from South America)1,3.

However, there has never been a comprehensive study comparing the electroreceptor tuning properties across several species of mormyrids that exhibit EODs of various durations.

This study sought to describe electroreceptor tuning properties in an unusual genus, Campylomormyrus, which contains species with EODs ranging from 0.2 msec to 10 msec long and peak frequencies from 6500-200 Hz. We sampled fish from four species of Campylomormyrus which had EOD durations of 0.2-2.5 msec and peak frequencies of 6500 to 500 Hz. A majority of Knollenorgans across four species of Campylomormyrus were preferentially tuned around 1 kHz, and tuning across these four species did not match the EOD peak frequency. Knollenorgans were found to be more broadly tuned than those observed in several species of gymnotiforms. We observed that two C. alces individuals’ EOD waveforms elongated and changed shape over the course of five months. In addition, we found an unusual receptor property in three species sampled: unlike the usual single spike-like receptor potential recorded in other mormyrid Knollenorgans, we saw receptor potentials exhibiting “spikelets,” depolarizations 0.1 msec after the initial spike.

We believe that this genus may be characterized by EOD alteration during a fish’s lifetime. A recent study suggests a similar phenomenon of EOD elongation in C. numenius, particularly during male sexual maturation4. Therefore, it may be advantageous for these fish to have electroreceptors broadly tuned, ver©2008 Cornell Synapse | www.cusn.org

sus sharply tuned to their current EOD peak frequency, if they are encountering conspecifics with EODs of varying durations. It may be also advantageous for these fish to have Knollenorgans tuned to lower frequencies if EOD elongation is common during sexual maturation. We are unsure of the exact purpose of the unusual receptor potentials, or “spikelets” or how they are related to altered electroreceptor morphology. They may be functioning to increase transmission of high frequency signals in those fish with very short EODs. This data encourages further investigation of the EOD stability across the entire genus of Campylomormyrus, and supports the hypothesis that Knollenorgan properties may be more complicated that previously envisioned and may filter the electric signal in ways not yet described.

Introduction Electric fish serve as a model for neural basis of communication; analysis of the methods of social communication and how natural selection may act on these methods can provide a direct link between neuroethology and evolutionary biology. Mormyrid electric fish from Africa generate weak electric discharges with an electric organ in the tail. Their electric organ discharges (EODs) are used for electrolocation of objects and electric communication.

These fish possess specialized tuberous electroreceptors for sensing EODs. Knollenorgan receptors are particularly used for communication and species recognition, while mormyromast receptors are used for electrolocation. All electroreceptors are believed to be derived from modified hair cells. Lacking mechanosensitivity, they have become specialized for sensing weak voltage gradients across the high resistance skin. Receptor cells in Knollenorgans generate spikes which are phase-locked to outside positive going voltage transitions2. Knollenorgan receptor organs are electrically tuned to a characteristic frequency by the combination of ion channels in the receptor cell membranes5.

For an independently evolved group of electric fish from South America, tuberous electroreceptors are tuned to match the frequency of their own electric discharges1,3,6. There has not been a comparable comprehensive survey of the relationship between Knollenorgan tuning and EOD frequency among a diverse group of mormyrids. Campylomormyrus is a genus of mormyrid fishes which currently consists of 14 recognized species, and demon19

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strates great EOD waveform diversity (Figure 1). EODs vary from pulses shorter than 500 microseconds to over 10 milliseconds. In addition, the genus is characterized by recent taxonomic instability. The alteration of the C. numenius EOD in sexually mature male fish has also been recently described4,7.

C. tshokwe C. compressirostris C. sp. B

Methods This study compared four species of Campylomormyrus all obtained through the aquarium trade. All fish originated from the Congo River. C. compressirostris, C. tamandua, and C. alces were identified by comparisons with type specimens and original species descriptions8. One species could not be positively identified and is referred to as Campylomormyrus species B in Hopkins, 19999.

Fourteen fish were used for Knollenorgan physiology experiments.

First, the EOD of these fish was recorded in a stable environment.

The power spectrum and peak frequency of the waveform was determined by performing a fast-Fourier transform (FFT).

Fish were immobilized and electrically silenced with Flaxedil (a curare-derivative), placed on a platform lightly restrained by plastic rods and fed freshly aerated water through a gravity-driven respirator. Extracellular Knollenorgan recording has been established, first by Bennett (1965)10. Using a wire electrode encased in 0.1 mm glass capillary tubing, we recorded Knollenorgan receptor potentials and responses to various stimuli. Knollenorgan receptor tuning characteristics were determined by using a reverse correlation (Revcor) method, which uses a spike-triggered average on

C. sp. B C. rhynchophorus C. sp. D C. numenius C. tamandua C. sp. E 1 msec

C. phantasticus

Figure 1: EOD diversity across the genus Campylomormyrus (figure modified from Hopkins, 1999).

1:1 Frequency

10000

C. compressirostris C. species B C. alces

Knollenorgan Best Frequency (Hz)

C. tamandua

1000

100 100

1000

10000

EOD Peak Frequency (Hz) Figure 2: Relationship between Knollenorgan best frequency and EOD peak frequency in four species of Campylomormyrus. Line represents expected relationship if receptor tuning sharply matched EOD peak frequency. N (fish)= 14, m (receptors)= 45. Š2008 Cornell Synapse | www.cusn.org

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Low Frequency 0-1000 Hz

Low Frequency, spikelets 0-1000 Hz

Medium Frequency 1001-1500 Hz High Frequency 1500+ Hz

Med. Frequency, spikelets 1001-1500 Hz

A

A

(x20)

n=3

High Frequency, spikelets 1500+ Hz

C. compressirostris

B B

C. species B

C

C. tamandua

n=2

C

n=1

D

n=1

1msec

C. alces Figure 3 (left): EODs and relative body position versus receptor best frequencies for four species of Campylomormyrus; EOD scale bars all to 1 msec. Pictures of a-c from Hopkins, pers.comm. Picture of d. by N. Trzcinski. For EODs of d, see Figure 4 for clarification.

Figure 4 (right): EODs from five C. alces demonstrating EOD alteration after 5 months. EOD scale bars all to 1 msec. 4A: EODs of three C. alces during 5 month period. 4B: EOD of 2 C. alces at initial trial. 4C: EOD of same C. alces in b. after five months.

Gaussian white noise stimulus to determine the average waveform that best stimulates the cell to fire11. The spectral components of this waveform were then determined with an FFT. The frequency most represented in this “best waveform” was determine as the receptor’s “best frequency” (BF).

Results The EODs sampled from these three fish varied in duration from 0.2 msec (for C. compressirostris) to 2.5 msec (for C. species B), corresponding to peak frequencies of 6500 to 500 Hz, respectively. Two species, C. compressirostris and C. species B, have biphasic waveforms throughout the trial, one species C. tamandua, had a triphasic waveform (that is, an initial head negative phase).

Figure 2 exhibits these typical EODs. For fish characterized as C. alces, three fish exhibited stable EODs lasting 0.4 msec. Two fish exhibited EODs which were altered five months after the initial experiment. The initial EOD was 1.25 msec long. After five months, the EOD for one fish was about 4 msec long and had a slower rising phase, while another fish had an EOD 3 msec in duration. Initially these two fish exhibited an EOD with an initial head-negative phase; after five months, this

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phase was absent (Figure 4).

Figure 2 summarizes the relationship observed between EOD peak frequency and receptor BF for these four species– the receptor tuning did not correspond with the expected direct relationship between these two variables. However, the cells did exhibit substantially “broader” tuning than observed in gymnotiforms1.

This was compared using the bandwidth of the FFT of the Revcor waveform at -5dB. “Low” (<1000 Hz) and “high”/ “medium” (>1000 Hz) frequency tuned cells were not found in the same pattern as described by Bass and Hopkins for Brienomyrus (1984) (Figure 3)12.

Fourteen cells exhibited an unusual receptor potential beyond the usual spike-like receptor observed in other mormyrid Knollenorgans (Figure 5). These cells showed 1-4 smaller depolarizations which occur consistently 0.1 msec after the initial spike.

These were designated as “spikelets”. They were associated with cells with lower BFs, but the spectrum of Revcor waveforms of these cells was broader, particularly for higher frequencies and for fish with EODs with higher peak frequencies.

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8kHz

B Magnitude (dB)

A

1msec

Frequency (Hz)

Figure 5: Example of an unusual receptor potential. 1A: FFT. 1B: The waveform corresponding to the FFT in figure 1A. Increasing number of spikelets increases power at 7-8 kHz.

Conclusions EOD Characteristics The four species sampled exhibited EOD diversity particularly in their EOD duration, with signals ranging from 0.2- 2.5 msec (6500-500 Hz peak frequencies). Three species had EODs that did not vary over time or across individuals, C. alces did show alterations in EOD duration and shape over five months. P. Feulner (2006) describes a similar case of EOD alteration in C. numenius, but attributed this to male sexual maturation4. We believe that these fish were not in breeding condition at any time during the course of these experiments. Bass and Hopkins (1985) described a case of Stomatorhinus corneti juveniles whose EOD altered as the juveniles/ females grew13. Smaller fish exhibited an initial head negative phase while larger fish exhibited no head-negative phase13.

Knollenorgan tuning All species showed little to no correlation between EOD peak frequency and receptor BF. We believe this could also be an adaptation to EOD alteration during development or sexual maturation that may be a feature of this genus. Knollenorgans of this genus show distinctly “broader” tuning than that observed in gymnotiforms; this could be an adaptation to encountering conspecifics with EODs of varying duration depending on maturity. Future experimentation with this genus should be careful not to assume the EOD is necessarily a stable characteristic.

3. Bastian, J. 1977. Variations in the frequency response of electroreceptors in weakly electric fish (Gymnotodei) with a pulse discharge. Journal of Comparative Physiology, 121: 53-64.

4. Feulner, P.G.D. et al, 2006. Electrophysiological and molecular genetic evidence for sympatrically occurring cryptic species in African weakly electric fishs (Teleostei: Mormyridae: Campylomormyrus). Molecular Phylogenetics and Evolution, 39: 198-208.

5. Bennett, M.V.L. 1967. Mechanisms of electroreception. In: P. Cahn (ed) Lateral line detectors. University Indiana Press, Bloomington: 313-393.

6. Bastian, J. 1976. Frequency response characteristics of electroreceptors in weakly electric fish (Gymnotoidei) with a pulse discharge. Journal of Comparative Physiology 112:165-180.

7. Feulner, P.G.D. et al. 2007. Adaptive radiation in African weakly electric fish (Teleostei: Mormyridae: Campylomormyrus): a combined molecular and morphological approach. Journal of Evolutionary Biology. 20: 403-414.

8. Poll, M. 1967. Contribution a la faune ichthvologique de I’ Angola. Publication Culturais Companhia de Diamantes de Angola, 75:1-381.

9. Hopkins, C. D. 1999. Signal evolution in electric communication. In: M. D. Hauser and M. Konishi (eds) The Design of Animal Communication. Cambridge, Massachusetts: M.I.T. Press: 461491.

10. Bennett, M.V.L. 1965. Electroreceptors in mormyrids. Cold Spring Harbor Symposia On Quantitative Biology, 30: 245:262.

11. deBoer, E. and de Jongh, H.R. 1978. On cochlear encoding: potentialities and limitations of the reverse- correlation technique.

Journal of the Acoustical Society of America. 63: 115-135.

12. Bass, A.H. and Hopkins, C.D. 1984. Shifts in Frequency tuning of electroreceptors in androgen-treated mormyrid fish. Journal of Comparative Physiology A, 155: 713-724.

13. Bass, A.H. and Hopkins, C. D. 1985. Hormonal Control of sex differences in the electric organ discharge (EOD) of mormyrid fishes. Journal of Comparative Physiology A, 156: 587-604.

Presence of spikelets Spikelets were correlated with cells with lower BFs, however, they slightly increased the representation of higher frequencies, especially in fish with very fast EODs. This spike pattern has been observed previously in Stomatorhinus ivendoensis and Pollimyrus adspersus (Hopkins, unpublished data), and these fish exhibit very fast EODs. References 1. Hopkins, C.D. 1976. Stimulus Filtering and Electroreception: Tuberous Electroreceptors in Three Species of Gymnotoid Fish.

Journal of Comparative Physiology A, 111: 171-207.

2. Bennett, M.V.L. 1971. Electroreception. In: Hoar W.S., Randall, D.J. (eds) Fish Physiology, vol. V. Academic Press, New York: 493-574. ©2008 Cornell Synapse | www.cusn.org

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ARTICLE Gender Differences in Hypersensitivity Behavior in a Rat Model of Autism Sarah Kirsch, Patricia M. Whitaker-Azmitia

State University of New York at Stony Brook, Department of Psychology, Stony Brook, NY 11794

Autism is a pervasive developmental disorder, with language, cognitive and social impairment, often accompanied by hypersensitivity. Hypersensitivity interferes with the ability to learn, reach one’s full potential, and enjoy a typical quality of life.

Injecting rats with 5-methoxytryptamine (5-MT), a serotonin agonist, has been used to produce a hyperserotonemic model of autism. This animal model can facilitate discovery of the etiology of such behaviors in humans. Human autistic females are more cognitively impaired than males. There is a dearth of research on autistic symptoms in females. This study utilizes the hyperserotonemic rat model of autism to analyze male and female differences in hypersensitivity to tactile, auditory and olfactory stimuli. Rats were tested in an open field, from postnatal day 137 through 147. Atypical behaviors in rats can be compared to atypical behavioral indicators of autism in humans. During the tactile test, hyperserotonemic female rats exhibited more discomfort than hyperserotonemic male rats.

The former kept their tails off the floor a longer time. In the auditory test, hyperserotonemic female rats crossed more grid lines than hyperserotonemic male rats. The former demonstrated more escape behavior in the presence of the auditory stimuli. In the olfactory test, hyperserotonemic female rats held their head directly over odors for more seconds than the hyperserotonemic male rats, indicating an increased self stimulatory behavior in the former. Hyperserotonemic female rats were found to be more hypersensitive than males to all three stimuli tested.

Introduction Autism is a pervasive developmental disorder, characterized by mild to severe deficits in social interaction and communication, often accompanied by hypersensitivity1. For example, autistic persons may gag when touching green vegetables or play-doh, or cover their ears during a fireworks performance. Autistic individuals lack creative play, repeat body movements (stereotypy), and carry out ritualistic behaviors2. Additionally, persons with autism have minimal emotional attachment to others and are hypersensitive to tactile and auditory stimuli2. Autism is four times more likely to occur in males than females3. Because more severe pathology is needed to cause autism in females, an autistic female has more pronounced manifestations1,4,5. Specifically, autistic females have more cognitive impairment than autistic male age matched counterparts1. It has not been previously studied whether ©2008 Cornell Synapse | www.cusn.org

autistic females are more blighted in specific symptoms of autism, or if they experience higher autistic symptoms across all excesses and deficits of the disorder. Of particular interest is whether autistic females experience more hypersensitivity (tactile, auditory, and olfactory) than autistic male counterparts. Hypersensitivity interferes with human attention span, ability to learn, and quality of life. A hypersensitive individual may be offended or distracted by an odor such as perfume or flowers, a sound of a specific tone/ pitch, or a clothing material, which does not bother the typical person. An animal model of autistic hypersensitivity could aid in understanding the etiology of such behaviors. In humans, identifying individuals’ hypersensitivity allows the environment to be modified to foster learning and improve life quality and comfort6.

Elevated levels of the neurotransmitter serotonin in blood platelets, is the most widespread biological finding in autistic persons2,7.

One third of all autistic individuals have hyperserotonemia, with serotonin levels fifty percent higher than individuals without autism8,9. When a fetus is developing, it does not have a blood brain barrier and therefore the high amounts of serotonin in the bloodstream may enter the brain. The increased serotonin in the brain causes a loss of serotonin nerve terminals due to a negative feedback response10. Thus autistic individuals have excessively high levels of serotonin in their blood and have disordered serotonergic pathways10.

Animal models are often used to study autism due to relatively short animal life spans. The animal model goes through all stages of life in a few years, whereas it takes decades for a human to go through all life stages. Observed animal behaviors are recorded and studied, to correlate atypical behaviors with specific symptomatic brain functions and features. Research utilizing a hyperserotonemic animal model of autism found behavioral manifestations including hypersensitivity to tactile and auditory stimuli, changes in motor development and lack of separation induced vocalization3.

Serotonin plays a key role in brain growth11. Elevated levels of serotonin in the blood and corresponding depleted levels in the brain are the most consistent finding in autism. I evaluated and compared the behaviors of male and female rats injected with 5-methoxytryptamine (5-MT), a serotonin agonist vs. male and female control rats injected with saline. Rats were injected before they developed a brain blood barrier. 5-MT injected into these pups, crossed into the brain. Serotonin auto-regulates its type and number of neurons. Increased serotonin levels due to 5-MT injections impaired serotonin terminal growth producing “autistic like” 23

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behaviors2.

Autistic individuals demonstrate behavioral changes in response to sensations, “which is described as a sensory defense to tactile modalities (high sensitivity to touch or texture), as well as oral, visual (high sensitivity to light), and mainly, acoustic modalities through a peculiar sensitivity to sounds”12. A rat model of autism was used to assess differences in the hypersensitivity response of male vs. female.

This study’s primary objective is to determine whether autistic females experience different degrees of hypersensitivity vs. autistic males. On a broader level, this study seeks to contrast male vs. female differences in autism and to study olfactory responses in hyperserotonemic rats. Three behavioral tests were used to assess autistic excesses and deficits: tactile, auditory, and olfactory hypersensitivity.

Tactile hypersensitivity was examined by observing rats walking on coarse sandpaper. Five specific behaviors are assessed for tactile hypersensitivity: (1) The total number of lines rats crossed signifies hyperactivity and agitation; (2) Number of seconds rats spent self grooming is classified as a stereotypic behavior13; (3) Number of times rats reared represents discomfort and need to escape; (4) Number of seconds the rat kept tail off the floor measures pain; (5)The number of fecal boli produced shows the rat’s level of anxiety.

Auditory hypersensitivity was studied by observing rats responding to a loud beeping sound. Seven behaviors were evaluated for auditory hypersensitivity: (1) The total number of lines rats crossed; (2) Number of seconds rats spent self grooming; (3) Number of seconds rats remained frozen during testing periods represents their fear level; (4) Number of times rats reared; (5) Number of seconds the rat kept tail off the ground; (6) Number of seconds rats spent in each of four quadrants signifies their sensitivity to the loud beeping; (7) The number of fecal boli produced. Olfactory hypersensitivity was analyzed by observing rats responding to odors presented.

Four behaviors were studied for olfactory hypersensitivity: (1) The number of seconds rats spent in each odor quadrant (control, peanut butter, and vanilla) represents sensitivity and tolerance levels to the odor stimuli; (2) The number of seconds the rat held head directly over the odors; (3) the number of seconds until the rats crossed from no odor quadrant (control) into the odor areas signifies attraction/repulsion to/from the odors; (4) The number of fecal boli produced.

Materials and Methods Subjects Stony Brook University laboratory has Institutional Animal Care and Use Committee (IACUC) approval to use rats and specifically has permission to induce the rats with a hyperserotonemic model of autism.

Six pregnant female rats were injected. Three rats were given 5-methoxytryptamine (5-MT), a serotonin agonist, at a dosage of 1mg/kg. The other three rats were given a dose of 0.9% saline at 1mg/kg. Postnatally all pups were injected with equal amounts of either saline or 5-MT, matching the solutions of their previous injections. The 5-MT was given at a dose known to hinder serotonin terminal growth, thus generating behavioral manifestations3. ©2008 Cornell Synapse | www.cusn.org

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Thirty-two rats, sixteen males and sixteen females raised in Stony Brook University Laboratory were tested and evaluated. Rat pups were cross fostered, meaning 5-MT and saline pups were raised by both 5-MT and saline moms. The rats were given water and rat chow ad libitum3.

When rats reached postnatal day (PND) 137, testing began.

Rats of this age are comparable to young adult age humans. Prior to and throughout experimentation, male rats were housed singly and female rats were housed in pairs. Male rats house solitarily because of their relatively large size and aggressive behavior. Females are much smaller in size and two can comfortably house in one cage.

Response to tactile stimulus On PND 137 all sixteen male rats were placed in the testing room at 11:30 am for thirty minutes to acclimate. Beginning at noon, one at a time, each male rat was placed in an open field (Figure 1A; 53.34 cm l x 53.34 cm w x 15.24 cm h) for three-hundred seconds. The grid lines in the open field measured 7.62cm x 7.62 cm. The bottom surface of the open field was thirty-six grade sandpaper, which served as a tactile stimulus. All rat behaviors were video recorded for later analysis. Rat responses measured include: total number of lines crossed; number of seconds spent self grooming; number of times reared; number of seconds rat elevated tail off floor; number of seconds until rat crossed out of the perimeter of open field; and number of fecal boli. Post threehundred seconds, the rat was returned to its home, all fecal boli were discarded, and the open field was cleaned with an alcohol solution and water.

On PND 138 the sixteen female rats were studied using the same protocol.

Response to auditory stimulus On PND 139 the sixteen male rats were brought inside the laboratory at 11:30 am, for thirty minutes, to acclimate to the testing environment. Starting at noon, one by one, each male rat was transported into the testing room. Rats were not permitted into the testing room prior to testing to prevent exposure to the beeping alarm clock stimulus. Simultaneously the alarm clock began beeping and the rat was placed in the open field (Figure 1B; 53.34 cm l x 53.34 cm w x 38.1 cm h) at the front right corner. The alarm clock was positioned just outside the right front corner of the open field. The alarm beeped once per second for three-hundred seconds. The alarm clock reached maximum loudness after beeping for twenty seconds. It maintained that volume for the test duration. All rat behaviors were video recorded for later analysis. Behavioral analysis included total number of lines crossed, number seconds spent self grooming, number seconds frozen, number of times reared, number of seconds tail elevated off floor and number of seconds spent in each of four quadrants. At three hundred seconds the rat was removed from the open field. All fecal boli were counted and removed and the open field was cleaned with an alcohol and water solution.

On PND 140 sixteen female rats were studied using the same protocol.

Response to olfactory stimulus On PND 147 sixteen male rats were brought into the testing 24

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A

B 4

3

means of multiple groups. This test highlights significance between groups, but does not show which groups are significantly different from the others. LSD Post Hoc test was run to demonstrate which groups were significantly different from one another.

All data is in the form mean ± standard error of the mean, n=8.

Results

2

1

Alarm Clock

C

Peanut Butter

Control

Vanilla

Figure 1: Apparati used in open field experiments. 1A: Sample open field used for tactile test. The entire bottom is covered with sand paper. 1B: Sample open field for auditory test. Thick lines represent tape separating the field into four quadrants.

1C: Sample open field used for olfactory test. The thick line represents a cardboard barrier separating control and odor quadrants. The thin line represents tape on the floor separating the peanut butter and vanilla areas.

room at 11:00 am, to acclimate to the testing room, for thirty minutes prior to testing. Beginning at 11:30 am, one at a time, each rat was placed in the open field in the middle of the control side, with head facing toward the center of the field. The open field (53.34 cm l x 53.34 cm w x 38.1 cm h) was divided into three sections (Figure 1C). Half the field (left side) was the control side. The other half of the field was divided into two sections. The right back quarter contained one tablespoon of peanut butter. The right front quarter contained one tablespoon of vanilla extract. Testing devices emitted respective scents. These smells were selected because they are known to appeal to rodents14. The rats were unable to eat the peanut butter and vanilla extract because edibles were enclosed in a container with only the top grated open to allow the odors to escape. For 180 seconds, the rat was placed in the testing field. Rat behavior was video recorded for analysis. Behavioral investigation included the number of seconds the rat spent in control area, the number of seconds in peanut butter area, the number of seconds in vanilla area, number of seconds self-grooming, number of times rearing, number seconds head directly over peanut butter and vanilla scents, and number of seconds till crossed into odor area. Post 180 seconds, the rat was returned to its home, all fecal boli were counted and discarded and the field was cleaned with an alcohol and water solution.

On PND 147 the female rats were brought into the testing room at 1:00pm. Beginning at 1:30pm, sixteen female rats were tested using the same protocol.

Statistical Analysis After all data was collected, the mean, standard deviation and standard error of the mean were computed. An ANOVA or Analysis of Variance test was run to examine the disparity between the ©2008 Cornell Synapse | www.cusn.org

Response to tactile stimulus Group mean total number of lines rats crossed is given in figure 2A (error bars are ± standard error of the mean, n=8). ANOVA between all four rat groups (male saline, male 5-MT, female saline, female 5-MT) revealed female rats more active (i.e. crossed more lines) than male rats (F(3,28)=6.348, p=.002). LSD post hoc test revealed significant difference male saline vs. female saline (p=.002), and male 5-MT vs. female 5-MT (p=.010). No significant differences male saline vs. male 5-MT, and female saline vs. female 5-MT (p>0.05).

Group mean total of number of seconds rats self-groomed is shown in figure 2B. ANOVA concluded male rats self groom significantly more than female rats (F(3,28)=2.766, p=.060). LSD post hoc test revealed male 5-MT rats self groom significantly more than female 5-MT rats (p=.008). No significant differences male saline vs. female saline, male saline vs. male 5-MT, and female saline vs. female 5-MT (p>0.05).

Group mean total number of times rats reared was: male saline 5.75±1.40 (mean ± standard error of the team, n=8), male 5MT 6.625±2.33, female saline 18.125±1.23, and female 5-MT 22.125±2.75. ANOVA between all four rat groups showed females reared more than males (F(3,28)=10.033, p=.000). LSD post hoc test showed significant differences male saline vs. female saline (p=.000), and male 5-MT vs. female 5-MT (p=.000). There were no significant differences male saline vs. male 5-MT, and female saline vs. female 5-MT (p>0.05).

Group mean number of seconds rat tail was off floor is shown in figure 2C. ANOVA revealed significant difference between all four rat groups (F(3,28)=10.033, p=.000). LSD post hoc test showed significant differences male saline vs. female saline (p=.004), and male 5-MT vs. female 5-MT (p=.000). Female lifted tail off the floor for more seconds than male. No significant differences male saline vs. male 5-MT, and female saline vs. female 5-MT (p>0.05).

Group mean number of seconds until rat crossed out of the perimeter of the open field: male saline 82.625±47.50, male 5-MT 22.750±5.89, female saline 19.250±2.93, female 5-MT 4.625±12.60. ANOVA and LSD post hoc test revealed no significant differences between the four groups (p>0.05).

Group mean number of fecal boli counted: male saline 0.6250±0.3239, male 5-MT 0.000±0.000, female saline 0.125±0.1250, female 5-MT 0.000±.000. ANOVA showed marginal significance between the four groups (F(3,28)=2.938, p=.05).

LSD post hoc test revealed that male saline rats produced significantly more fecal boli than male 5-MT rats (p=.017). There was no significant differences male saline vs. female saline, male 5-MT vs. female 5-MT, and female saline vs. female 5-MT (p>0.05).

Response to auditory stimulus Group mean number of lines rats crossed in the open field 25

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B Self-Grooming Time (s)

8

# Lines Crossed

A

0

Males

Females

Males

Time w/ Tail Off Floor (s)

C

Females

Key Saline 5-MT

Males

Females

Figure 2: Comparative performance across rat genders and treatments in three tactile stimulus tests. 2A: Number of lines crossed. 2B: Time spent self-grooming.

2C: Time spent with tail off the floor.

B Key

# Lines Crossed

Time Spent Frozen (s)

A

Males

Females

Saline 5-MT

Males

Females

Figure 3: Comparative performance across rat genders and treatments in auditory stimulus tests. 3A: Number of lines crossed. 3B: Time spent frozen during a 300-second open field test.

Time in Vanilla Quad. (s)

Key Saline 5-MT

Males

Females

B Time head over odors (s)

A

Males

Females

Figure 4: Comparative performance across rat genders and treatments in two olfactory stimulus tests. 4A: Time spent in the vanilla quadrant. 4B: Time spent with heads directly over the vanilla and peanut butter half of the field.

is depicted in figure 3A. ANOVA revealed major significance between the four groups, with females more active than males (F(3,28)=10.981, p=.000). LSD post hoc test showed significant differences male saline vs. female saline (p=.001), and male 5-MT vs. female 5-MT (p=.001). No significant differences male saline vs. male 5-MT, and female saline vs. female 5-MT (p>0.05).

Group mean number of seconds rats self-groomed: male control 1.875±1.875, male 5-MT 3.125±1.716, female saline 15.125±5.674, female 5-MT 7.188±1.889. ANOVA confirmed significant differences between the four groups. Females selfgroomed more than males (F(3,28)=3.467, p=.029). LSD post hoc test showed significant difference male saline vs. female saline ©2008 Cornell Synapse | www.cusn.org

(p=.007). No significant differences male saline vs. male 5-MT, female saline vs. female 5-MT, and male 5-MT vs. female 5-MT. Group mean number of seconds rats froze during the 300 seconds in the open field is shown in figure 3B. ANOVA between all four groups depicted males froze significantly more than females (F(3,28)=7.060, p=.001). LSD post hoc test revealed significant difference male saline vs. female saline (p=.002), and male 5-MT vs. female 5-MT (p=.011). No significant differences were found male saline vs. male 5-MT, and female saline vs. female 5-MT (p>0.05).

Group mean number of times rats reared was: male saline 1.000±0.500, male 5-MT 7.500±3.065, female saline 17.125±3.372, female 5-MT 24.250±3.483. ANOVA revealed females rear significantly more than males (F(3,28)=12.738, p=.001). LSD post hoc test showed significant differences male saline vs. female saline (p=.000), and male 5-MT vs. female 5-MT (p=.000). A trend was noted, female saline vs. female 5-MT (p=.091), female 5-MT rats reared more. There were no significant differences male saline vs. male 5-MT, and female saline vs. female 5-MT (p>0.05).

Group mean number of seconds rats lifted tails off the open field floor: male saline 4.375±2.017, male 5-MT 3.625±1.209, female saline 20.000±6.959, female 5-MT 14.500±3.311. ANOVA revealed females lift tail off the floor for more seconds than males (F(3,28)=3.922, p=.0190). LSD post hoc test showed a significant difference male saline vs. female saline (p=.011), and marginal significance male 5-MT vs. female 5-MT (p=.067). No significant differences male saline vs. male 5-MT, and female saline vs. female 5-MT (p>0.05).

Group mean number of seconds rats spent in the four quadrants were compared. ANOVA for each of the four quadrants showed no significant differences. LSD post hoc test also revealed no significant differences in any quadrants (p>0.05).

Group mean number of fecal boli counted: male saline 1.625±0.497, male 5-MT 0.750±0.526, female saline 0.125±0.125, female 5-MT 0.000±0.000. ANOVA between all four groups revealed males produced more fecal boli than females (F(3,28)=4.088, p=.016). LSD post hoc test revealed a significant difference male saline vs. female saline (p=.007). No significant differences male saline vs. male 5-MT, female saline vs. female 5-MT, and male 5-MT vs. female 5-MT. Response to olfactory stimulus Group mean number of seconds rats spent in the control, peanut butter, and vanilla quadrants were compared. ANOVA between the four rat groups for the three quadrants found significant differences in the vanilla quadrant. LSD post hoc test was run between groups in each respective quadrant. Male 5-MT rats spent significantly more seconds in the vanilla quadrant than male saline rats (p=.011) (figure 4A). Marginal significance male saline vs. female saline, with females spending more seconds in the vanilla quadrant (p=.054). Both the control and peanut butter quadrants showed no significant differences between any groups (p>0.05).

Group mean number of seconds rats held head directly over the peanut butter and vanilla odors is depicted in figure 4B. ANOVA between all four groups showed female 5-MT rats holding head over the odors for the most seconds (F(3,28)=8.064, p=.001). LSD post hoc test revealed female 5-MT rats spent significantly more time with heads over the odors than male 5-MT rats (p=.001). Fe26

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male 5-MT rats held head directly over the odors for significantly more seconds than female saline rats (p=.005). No significant differences male saline vs. male 5-MT, and male saline vs. female saline (p>0.05).

Group mean number of seconds until rats crossed from control area, into odor quadrants: male saline 63.500±28.610, male 5-MT 29.500±18.379, female saline 20.625±6.279, female 5-MT 11.250±4.174. ANOVA and LSD post hoc test show no significant differences (p>0.05).

Group mean number of fecal boli counted: male saline 1.000±0.627, male 5-MT 0.000±0.000, female saline 0.000±0.000, female 5-MT 0.000±0.000. ANOVA between all four groups exposed marginal significance (F(3,28)=2.545, p=.076). LSD post hoc test showed that male saline rats produced significantly more fecal boli than both female saline rats (p=.032) and male 5-MT rats (p=.032). No significant differences male 5-MT vs. female 5-MT, and female saline vs. female 5-MT (p>0.05).

Discussion Autistic females are overall more severely affected than male counterparts1. The degree of specific deficits and excesses analogous with autism are different in the two sexes5,15. There is a dearth of research identifying specific brain areas where autistic females are more severely affected than males. Previous studies may not have focused on female autism since male autism is four times as common3. This study specifically compares and contrasts hypersensitivity in autistic males and females. Tactile, auditory and olfactory stimuli were studied.

The tactile study suggests hyperserotonemic female rats have exaggerated atypical versions of similarly diagnosed males. These results are plausible because female rats have a lower threshold for uncomfortable stimuli (i.e. very coarse sandpaper)16. The number of line crosses, number of times rearing and number of seconds the tail is off the floor indicate that control females are more sensitive than control males. It appears reasonable that autistic female behavior demonstrates greater hypersensitivity to tactile stimuli than autistic male behavior. The number of lines the rats cross is a measure of discomfort induced by the rough sandpaper. In rat studies, increased line crossing corresponds to a higher state of agitation. The human equivalent to increased agitation in autistic individuals is repetitive motor activities such as hand flapping3. A rat’s tail is often used to measure pain (i.e. Tail flick test)17. Hyperserotonemic female rats lifting their tails off the floor longer than hyperserotonemic male rats represent increased hypersensitivity to the tactile stimulus.

Autistic individuals are hypersensitive to auditory stimuli due to their increased perception of loudness6. Auditory investigation confirmed hyperserotonemic females experience exaggerated male atypical autistic behaviors. Rat behaviors measured include number of total lines crossed, amount of seconds self grooming, and number of times rearing, show control females experience greater hypersensitivity than control males. Hyperserotonemic females experienced greater auditory sensitivity than hyperserotonemic males. Hyperserotonemic females crossed more lines than males, suggesting females have increased agitation and discomfort to loud beeping. Hyperserotonemic females displayed increased self-grooming compared to males. Females may have demon©2008 Cornell Synapse | www.cusn.org

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strated augmented self-grooming as a result of their anxiety from the deafening environment. The auditory stimulus (beeping alarm clock) caused the male rats to freeze significantly longer than the female rats. The hyperserotonemic female rats scurried across the open field, from wall to wall, rearing on their hind legs, seeking escape. Escape behavior indicates females appear more sensitive to loud noises than males.

Autistic individuals’ behavioral manifestations may be due to impairment in the upper processing level of the cerebral cortex, possibly involving the limbic system12. The limbic system is key for processing of emotion, motivation, and memory18. Serotonergic neurotransmission is prevalent in the limbic system18. Disorders related to abnormal limbic system function include anxiety, major depression, and schizophrenia18. Of note, drugs used to treat these disorders affect serotonergic neurotransmission18. The limbic system encompasses the hippocampus, amygdala and cortex. In autistic individuals these regions show “small cell size and increased cell packing density,” implying these regions develop abnormally19. Disturbances during early human development in brain regions including the amygdala and the hippocampus may alter “memory and emotion-guided motivational behavior,” causing a malfunction in the “processing of environmental stimuli,”20.

This hinders maturation of sensory discrimination, causing abnormal auditory processing in autistic persons such as increased sensitivity to loudness6,20.

There is a paucity of research on autism and olfactory hypersensitivity. This research serves as a baseline for future studies to explore further the relationship between olfactory hypersensitivity and the hyperserotonemic model of autism. Generally, rats like the smell of peanut butter and vanilla14. Therefore, I hypothesized hyperserotonemic rats would spend less time in the peanut butter and vanilla areas, due to hypersensitivity and repulsion to the scents.

This would mirror human autistic individuals. Perfume scents which typical humans find pleasing, autistic persons may find repulsive due to increased olfactory sensitivity. However, hyperserotonemic male rats spent significantly more time in the vanilla quadrant than control male rats. Additionally, hyperserotonemic females held their head directly over the peanut butter and vanilla odors for more time than hyperserotonemic males. One possibility for this unexpected outcome is that sniffing is a self stimulatory behavior. Hyperserotonemic rats are so hypersensitive to and distracted by the odors that they fixate and continue sniffing.

An unexpected result was that control male rats produced more fecal boli than hyperserotonemic males. Fecal boli was one measure of anxiety in rats. Since one of autism’s atypical traits is hypersensitivity, it was expected that hyperserotonemic rats would generate more fecal boli. However, this unexplained result may be due to the fact that fecal boli is a relatively poor measure of anxiety. In the auditory study, both control and hyperserotonemic females, lifted their tail off the floor significantly more seconds than respective male groups. This is surprising because there was no tactile stimulus making females tails hypersensitive. Thus, one would expect the tails of male and female rats to remain on the floor for the same amount of time. Also in the auditory test, there was no significant difference between the time period any rat group spent in the four quadrants. It was expected that hyperserotonemic rats would be hypersensitive to the loud beeping and would avoid it by moving as far away from the source as possible, remaining in 27

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quadrant number four for the longest time.

One possible drawback of this experiment is that it took place during daylight hours. Rats are nocturnal and thus have an active night period. Studies on humans have suggested differential performance when not well rested. Disturbing the rat during rest period may affect the rats’ initiation behavior. Rats may have responded differently if sleep was not interrupted. These experiments should be replicated during the rats’ active period.

In conclusion, hyperserotonemic female rats are more hypersensitive than hyperserotonemic males for tactile, auditory, and olfactory stimuli. However, the control female rat represents a heightened sensitivity state compared to its male control counterpart. Therefore, the difference between autistic female and male hypersensitivity response is reflective of the greater sensitivity state in the control female. Future research should focus on post mortem studies, correlating typical neurons in the auditory cortex, sensory cortex and olfactory bulb with impaired neurons in the hyperserotonemic rat model of autism.

References 1. Volkmar, F. (2003). Autism. The Lancet, 362, 1133.

2. Whitaker-Azmitia, P.M. (2001). Serotonin and brain development: Role in human developmental diseases. Brian and Research Bulletin, 56, 479-485.

3. Kahne, D., Tudorica, A., Borella, A., Shapiro, L., Johnstone, F., Huang, W., & Whitaker-Azmitia, P.W. (2002). Behavioral and magnetic resonance spectroscopic studies in the rat hyperserotonemic model of autism. Physiology &Behavior, 75, 403-410.

4. Wing, L. (1984). Some Questions On Sex Differences. Journal of Autism and Developmental Disorders, 14, 211-214.

5. Konstantareas, M., Homatidis, S., & Busch, J. (1989). Cognitive, Communication, and Social Differences Between Autistic Boys and Girls. Journal of Applied Developmental Psychology, 10, 411-424.

6. Khalfa, S., Bruneau, N., Roge, B., Georgieff, N., Veuillet, E., Adrien, J., Barthelemy, C., & Collet, L. (2004). Increased perception to loudness in autism. Hearing Research, 198, 87-92.

7. Janusonis, S. (2005). Statistical distribution of blood serotonin as a predictor of early autistic brain abnormalities. Theoretical Biology and Medical Modelling, 2.

8. Mulder, E., Anderson, G., Kema, I., DeBildt, A., VanLang, N., DenBoer, J., & Minderaa, R. (2004). Platelet Serotonin Levels in Pervasive Developmental Disorders and Mental Retardation: Diagnostic Group Differences, Within-Group Distribution, and Behavioral Correlates. Child Adolescent Psychiatry, 43, 491-499.

9. Chugani, D.C., Muzik, O., Rothermel, R., Behen, M., Chakraborty, P., Mangner, T., da Silva, E.A., & Chugani H.T. (1997). Altered serotonin synthesis in the dentatothalamocortical pathway in autistic boys. Annals of Neurology, 42, 666-669.

10. Whitaker-Azmitia, P.M. (2005). Behavioral and cellular consequences of increasing serotonergic activity during brain development: a role in autism? International Journal of Developmental Neuroscience, 23, 75-83.

11. Djavadian, R.L., Wielkopolska, E., & Turlejski, K. (2005).

Postnatal treatment with NAN-190 but not with 5-HT1A receptor agonists retards growth of the rat brain. International Journal of Developmental Neuroscience, 23, 485-493. ©2008 Cornell Synapse | www.cusn.org

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12. Gomes, E., Rotta, N., Pedroso, S., Sleifer, P., & Danesi, M. (2004). Auditory hypersensitivity in children and teenagers with autistic spectrum disorder. Arquivos de Neuro-Psiquiatria, 62, 797-801.

13. Navarro, M., Rodriguez De Fonseca, F., Hernandez, M.L., Ramos J.A., & Fernandez-Ruiz, J.J. (1994). Motor behavioral and nigrostriatal dopaminergic activity in adult rats perinatally exposed to cannabinoids. Pharmacology Biochemistry and Behavior, 47, 47-58.

14. Crawley, J. What’s Wrong With My Mouse? WILEY-LISS A John Wiley & Sons Inc. Publication, 2000.

15. Baron-Cohen, S. (2002). The extreme male brain theory of autism. Trends in Cognitive Sciences, 6, 248-254.

16. LaCroix-Fralish, M.L., Tawfik, V.L., Nutile-McMenemy, N., & DeLeo, J. (2006). Progesterone mediates gonadal hormone differences in tactile and thermal hypersensitivity following L5 nerve root ligation in female rats. Neuroscience, 138, 601-608.

17. Dashti-Rahmatabadi, M.H., Hejazian, S.H., Morshedi, A., & Rafati, A. (2006). The analgesic effect of Carum copticum extract and morphine on phasic pain in mice. Journal of ethnopharmacology.

18. Hensler, J. (2006). Serotonergic modulation of the limbic system. Neuroscience and Biobehavioral Reviews, 30, 203-314.

19. Bauman, M., and Kemper, T. (2004). Neuroanatomic observations of the brain in autism: a review and future directions. International Journal of Developmental Neuroscience, 23, 183-187.

20. Tecchio, F., Benassi, F., Zappasodi, F., Gialloreti L.E., Palermo, M., Seri, S., & Rossini, P.M. (2003). Auditory Sensory Processing in Autism: A Magnetoencephalographic Study. Biological Psychiatry, 54, 647-654.

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ARTICLE Recurrent activity of UP and DOWN states in cortical networks Yvette Wong1,2, G. Bard Ermentrout1 1 2

University of Pittsburgh, Department of Mathematics, Pittsburgh, PA 15260 Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

Experimental data have shown that cortical neurons undergo recurrent activity alternating between UP and DOWN states.

The input of external stimuli into in vitro networks has been found to alter the state of the network, provided that the strength of the stimuli falls in an appropriate range. The propagation of the UP state has also been found to spread through in vivo networks in the form of traveling waves. Using computer simulations, we modeled these findings in a cortical network by coupling excitatory and inhibitory neuron populations which contain white noise and can be externally stimulated. We later introduced a noise-fitted model which incorporates the amount of noise directly into the model for the network. We found that an entire network could be moved from the DOWN state to the UP state merely by self-propagation (an intrinsic property of the network), through adding an external stimulus, or by increasing the amount of white noise. By decreasing the strength of the connection between excitatory neurons (aee), it was possible to shift from a bistable state to a monostable phase plane where only the DOWN state existed. Conversely, increasing aee increased the speed of propagation towards the UP state for the entire network and reduced the threshold for the number of populations initially in the UP state required for this propagation. For aee above a certain threshold, increasing the amount of white noise caused the DOWN state to disappear. Increasing the strength of the inhibitory synapses Ď&#x201E;i for a fixed aee also caused the instability of the UP state as well as a change in the direction of the unstable manifold in the phase plane. In the two neuron population, increasing aee made the transition between the two states more feasible. A spiking model was also examined in which a stimulus was added to the network to test its affect on displacing the network from its original state (UP or DOWN). We found that an excitatory stimulus could shift the network from a DOWN to an UP state, as well as shifting it from an UP to a DOWN state. These simulations identically mimic those from previous in vitro and in vivo experiments concerning cortical recurrent activity.

Introduction Both excitatory and inhibitory networks of cortical neuron populations have long been known to exhibit recurrent activity1,2,3.

Such activity has been thought to aid in short term memory4,5 and Š2008 Cornell Synapse | www.cusn.org

neuronal excitability during attention6,7, as well as spontaneous neuronal activity during sleep8,9. Both Shu et al. and Luczak et al. have confirmed that local cortical circuits are capable of recurrent activity through a balance of excitation and inhibition10,11,12.

Recurrent activity is characterized by a period of intense network firing (17.1+11.1Hz) known as the UP state, alternating with a relative quiet period (3.6+3.0Hz) known as the DOWN state, as shown by Shu et al. using in vitro ferret prefrontal and occipital cortical neurons11. Luczak et al. also demonstrated the existence of UP and DOWN states in vivo when recording from rat somatosensory cortices12. From the experiments of Shu et al, single-shock electrical stimulation (mimicking a synaptic input) were found to be capable of initiating the UP state within the network, with an increase in shock intensity correlating to a decrease in the duration of the UP state. Thus, strong electrical stimuli (greater than four times the threshold) caused the network to remain briefly in the UP state (20-30ms) before returning back to the DOWN state. It was also found that strong stimuli over a certain threshold were capable of terminating the firing of a network already in the UP state. Shu et al. noted that inhibitory mechanisms, attributed to both synaptic input as well as the intrinsic properties of the network, may be responsible for this termination of activity and return to the DOWN state11,13. In addition, Luczak et al. showed that the appearance of an UP state within a cortical network is related to an organized progressive spread of activity throughout the network in the form of traveling waves12. However, each specific neuronâ&#x20AC;&#x2122;s response to this increase in activity is distinct from that of other neurons in the same network. This difference is suggested to arise from intrinsic neuronal properties, such as the diversity of ion channels14.

The UP and DOWN states characteristic of cortical recurrent activity were examined computationally. We also examined the ability to transition between these two states and the intrinsic network properties which might affect this transition. We studied the self-propagating property of the neuronal network, the input of a stimulus, and the introduction of white noise into the network to test if such transitions were plausible12. Specific intrinsic properties of the network were also examined, including the strength of the connection between neurons, the amount of white noise, and the ratio between the excitatory and inhibitory neuron population strengths. Finally, we simulated the experiments of Shu et al. by adding excitatory and inhibitory stimuli during both UP and DOWN states11. Overall, our results closely mimicked those presented by Luczak et al. and Shu et al11,12.

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Methods Computer simulations were conducted to model the cortical neurons described in Shu et al.11. The most basic model consists of an excitatory (U) and an inhibitory (V) neuron population which have synapses onto themselves and each other. The respective equations used to describe the mean voltage within each population are given by:

u’ = (-u + fe (aeeu - aiev - te))/τe v’ = (-v + fi (aeiu - aiiv - ti))/τi

(1)

where f(x)=√(max(x,0)). The variable aee represents the synaptic current strength from an excitatory population to itself, and aie, aei, and aii represent the synaptic current strength from an inhibitory to excitatory population, excitatory to inhibitory population and between two inhibitory populations respectively. The variables te and ti represent the threshold value for firing for the two neuron types and τe and τi help scale the difference in strength between excitatory and inhibitory populations.

These populations were then placed into a network comprising of 200 populations each of excitatory and inhibitory neurons, and the strength of their coupling was described by the distance2 dependent equation: e-(t/s) /√πs with s=8 for excitatory neurons and s=4 for the inhibitory neurons.

A stimulus was also added to each population given by the equation: stim(t,x) = amp*heav(t - ton)*heav(ton + wid - t)*heav(xx - x) (2) and was added to the right side of the voltage equation as shown below:

u’ = (-u + fe (aeeu - aiev - te + cestim(t,u)))/τe v’ = (-v + fi (aeiu - aiiv - ti + cistim(t,u)))/τi

(3) (4)

where ce and ci controlled the strength of the excitatory and inhibitory stimuli respectively. These could be set to zero to turn off the stimulus or increased in order to stimulate the respective networks.

To better replicate the properties of cortical neurons as described by Luczak et al, white noise was also added to this model in the form of:

ze [0..199]’ = -ze[j]/tne + normal(0,.25)/√tne

(5)

for tne=1 and tni=1 and added into the voltage equation (1) as shown below:

u’ = (-u + fe (aeeu - aiev - te + cestim(t,u) + σeze))/τe v’ = (-v + fi (aeiu - aiiv - ti + cistim(t,u)+ σizi))/τi

(6) (7)

with σe and σi controlling the amount of noise in excitatory and inhibitory neuron populations respectively.

Using this first model, the amount of noise which affected the neuron voltage was manually found to fit the function

l(x,σ) = x/ 1-e(-x/.195σ)

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(8)

where σ represents the amount of noise added into the network.

This function was discovered by adding noise to the original theta quadratic model. Thus, the square root function previously used was switched to

fe = √l (x, σe) fi = √l (x, σi)

(9) (10)

for the excitatory and inhibitory neuron populations respectively.

This model was also placed into a network consisting of 200 of each type of population, which were all coupled to one another.

A spiking model was also used to simulate the cortical neurons, which followed the equation: xe = 1- cos(xe) + (1 + cos(xe))( -te + aee k([j]) – aie k([j])) (11) to which stimulus and noise were also added as described above.

The synapse voltage between the populations was modeled using

se [0..99]’ = (-se [j]+qe(-b*(1 + cos(xe[j]))))/ τe

(12)

and set into a network of 100 excitatory and inhibitory neuron populations each. These populations were connected to one another through sparse coupling described by (ran(1)<p)/(100p) where p was the proportion of the network to which one population was coupled to. All simulations were conducted using XPP and AUTO. Results From the first model (new-fx), an analysis of the phase plane shows that two stable equilibrium points exist, representing the UP and DOWN states as described in experimental findings. The phase plane of an inhibitory vs. excitatory neuron population without any noise or stimulus added (Figure 1A). A third unstable equilibrium point is also shown in which contains both unstable manifolds (which extend to the UP and DOWN states) and stable manifolds (which define the range of the UP and DOWN states’ basin of attraction) (Figure 1B). The result of decreasing the strength of the inhibitory population τi on the phase plane (Figure 1C). For τi<5.3, the stable manifold will extend upwards from the unstable equilibrium point, altering the desired basins of attraction of the two stable states.

A stimulus was added to the two neuron populations to transition them from one state to the other. The phase plane of both populations originally in the DOWN state and a stimulus of ce=3.0 being added which bring them to the UP state is shown (Figure 1D). Furthermore, the phase plane of both populations originally in the DOWN state and an even larger stimulus (ce=5.0) which bring them back to the DOWN state. For 2.0<ce<4.1 (where ce represents the strength of the excitatory stimulus), it was possible to bring the population from the DOWN state to the UP state. Likewise, the phase plane of the populations originally in the UP state and a stimulus of ce=2.0 bringing it to the DOWN state (Figure 1F). For ce>1.8, it was possible to bring the population from the UP state to the DOWN state.

Noise was added into the two neuron population model (newfx-noise-hist) and was found to be capable of propagating the net30

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A

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D Figure 2: Array of 200 coupled excitatory neuron populations vs. time (ms) using the square-root function with no stimulus or noise added. The first 100 populations were initially set to be in the DOWN state (U=0 and V=0) while the other 100 were set to the UP state (U=1.4498 and V=0.77438). The blue represents the original DOWN state and the yellow represents the UP state. The network propagates to the UP state.

A E

F

Figure 1: Phase plane, V(inhibitory) vs. U(excitatory), of a two neuron population model using the square-root function with no stimulus or noise added. All simulations using this model have aee = 5.5, aie = 5, aei = 7, aii = 2, τe = 10, τi = 13, te =2 and ti =8. 1A: Nullclines and stable equilibrium points. The red (blue) and green (purple) lines represent the excitatory and inhibitory nullclines respectively. The open circles represent the two stable equilibrium points, with the DOWN state occurring at U=0 and V=0 and the UP state occurring at U=1.4498 and V=0.77438.

1B: Stable and unstable manifolds. The open triangle represents the one unstable equilibrium point, which occurs at U=0.3915 and V=0. The blue (red) and yellow (dark blue) lines represent the stable and unstable manifolds, respectively, which extend from the unstable point. 8C: Phase plane, V(inhibitory) vs. U(excitatory), of a two neuron population model using the square-root function with no stimulus or noise added. τi = 5 while all other parameters remain as in Figure 1A. The stable manifold extends upwards in this case. In 1D-F, stimulus is added to the squareroot function.. The path of the two populations is traced by the black line. For all stimuli, the simulations have amp =1, xx = 100, wid =10. 1D: Network initially set to the DOWN state (U=0, V=0) and stimulus added with ce = 3.0. The network propagates to the UP state. 1E: Network initially set to the DOWN state and stimulus added with ce = 5.0. The network returns to the DOWN state. 1F: Network initially set to the UP state (U=1.4498, V=0.77438) and stimulus added with ce =2.0.

The network goes to the DOWN state.

work between the UP and DOWN states. As aee was increased, the transition between both states occurred more frequently. As a result, the time spent in one state before it transitioned to the other state decreased.

By putting these neuron populations in a network (2-convnew), it was observed that any one population initially in the UP state was sufficient to bring all the other populations originally in the DOWN state to the UP state as well. Two hundred excitatory neuron populations of one hundred were initially set into the UP state (Figure 2). The first 100 which were originally in the DOWN state linearly propagate to the UP state and remain there. It was also observed that the rate at which the DOWN states propagated up remained constant regardless of how many populations were ©2008 Cornell Synapse | www.cusn.org

B

Figure 3: Array of 200 coupled excitatory neuron populations vs. time (ms) using the square-root function with stimulus and no noise added. All populations were initially set to be in the DOWN state and a stimulus was added at ton =50.

For all stimuli in this model, the simulations have amp=1, xx=100, wid=10. The blue represents the original DOWN state and the green represents the UP state. 3A: Excitatory stimulus added with ce = 2.5 and ci = 0. 3B: Excitatory stimulus added with ce = 4.5 and ci = 0.

originally in the UP state (i.e. the slope of the line always remained the same).

A stimulus was also applied to this network, which was initially set to be in the DOWN state. It was found that a stimulus of at least ce=2.1 was required to bring the entire network from the DOWN state to the UP state, for ce=0. The affects of adding stimuli of ce=2.5 and ce=4 respectively are shown (Figure 3A). For larger values of ce (a stronger stimulus added onto excitatory neurons, ce>3.86), the first 100 populations return to the DOWN state after the stimulus is added, and only proceed to propagate up once the remaining populations have reached the UP state (Figure 3B).

White noise was added to this network of 200 neuron populations (conv-new-noise), and was found to be capable of propagating all populations initially in the DOWN state to the UP state if above a threshold value of σe=33 for σi=0.01. This propagation for σe=33 and σe=40 respectively is shown (Figure 4). Under these circumstances, the σe was increased (beyond the threshold value), the neurons propagated to the UP state earlier.

Using the first model, a fitted function was introduced which incorporated the amount of noise (σe,σi) directly into the equation for mean neuron population voltage (2-noise-fit). For values of aee below the lower limit point, only a stable DOWN state is found. Similarly, for values above the higher limit point, only a stable UP state exists. For values of aee between these two points (3.193<aee<19.23), both the stable UP and DOWN states exist 31

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along with an unstable equilibrium point between the two states.

Two-parameter diagrams comparing σ, aee, and τi were also created using this fitted model. For values of aee and σ above the cusp, only a stable UP state exists, since increasing the white noise (σ) above this line shifts the nullclines and causes them to only intersect once (creating the UP state). For values of aee and σ below the cusp, both stable UP and DOWN states exist.

The two parameter diagram of τi vs. aee, for σ=1, q=1. For any τi, if aee<3.193, only a stable DOWN state will exist (Figure 6A).

For higher values of aee, τi must be below the homoclinic trajectory (the lower curve in Figure 5) for both stable equilibrium states to exist (Figure 6B). For τi between the homoclinic trajectory and the hopf bifurcation, both stable states exist, but the unstable manifold never reaches the UP state (Figure 6C). For τi above the hopf bifurcation, the UP state becomes an unstable equilibrium point (Figure 6D).

The phase planes for V(inhibitory) vs. U(excitatory) for σ=1, q=1 using this fitted model, with various values of aee and τi (Figure 6). As previously mentioned, for aee>3.193, both an UP state and a DOWN state will exist. Thus, τi will not affect the position of these states. However, changing τi affects the direction of the unstable manifolds as well as the stability of the UP state. These outcomes are based on where τi lies in the two parameter diagram of τi vs. aee in relation to the homoclinic trajectory and the hopf bifurcation (Figure 5).

The neurons used in the fitted model were placed into a network of 200 neuron populations, with σ=1, q=1 (conv-noise-fit).

The first 100 populations were set initially to the DOWN state, and the rest to the UP state. aee was varied manually, and it was found that for aee>3.8, the entire network would linearly propagate into the UP state. Conversely, for aee<3.8, the network would reach the DOWN state. The array of 200 excitatory neurons (U0-199) propagating DOWN, remaining unchanged, and propagating UP respectively for different values of aee. Thus, for lower values of aee, it is possible for the network to self propagate to the DOWN state without the help of any stimuli (Figure 7). The ability of the network to self propagate UP was previously seen (Figure 2) with higher values of aee.

We also varied aee to determine the minimum number of neuron populations required to initially be in the UP state that would still allow for the entire network to propagate to the UP state. The phase planes for V(inhibitory) vs. U(excitatory) for σ=1, q=1 with A

aee=3.5, 4.5, and 5.5 respectively. For aee=3.5, the network had a maximum of 3 populations initially in the DOWN state for it to propagate to the UP state are shown (Figure 8). For aee=4.5, the network had a maximum of 189 populations initially in the DOWN state for it to propagate. For aee=5.5, the network had a maximum of 192 populations initially in the DOWN state for it to propagate.

Thus, as aee was increased, the limit for the number of populations initially in the DOWN state required to allow for propagation to the UP state was also increased.

A

B

C

D

B

Figure 4: Array of 200 coupled excitatory neuron populations vs. time (ms) using the square-root function with white noise and no stimulus added. All populations were initially set to be in the DOWN state The blue represents the original DOWN state and the green represents the UP state. 4A: White noise added with σe = 33, σi =0.01. 4B: White noise added with σe = 40, σi =0.01. ©2008 Cornell Synapse | www.cusn.org

Figure 5: Two parameter diagram, τi vs. aee, of a two neuron population model using the noise-fitted square-root function with σ=1, q=1. The vertical line has equation aee =3.193. The lower curve is the homoclinic trajectory and the upper curve is the hopf bifurcation.

Figure 6: Phase plane, V(inhibitory) vs. U(excitatory), of a two neuron population model using the noise-fitted square-root function with σ =1, q=1. The red (blue) and green (purple) lines represent the excitatory and inhibitory nullclines respectively. The blue (red) and yellow (dark blue) lines represent the stable and unstable manifolds, respectively, which extend from the unstable point. 6A: aee= 3.1 and ¬τi = 13, with the only equilibrium point occurring at U=0 and V=0 (the DOWN state).

6B: aee= 15 and ¬τi = 13, with two stable equilibrium points existing. The DOWN state occurs at U=0.009 and V=0 and the UP state at U=1.4433 and V=0.77101.

The unstable equilibrium point between these two states occurs at U=0.32317 and V=0. 6C: aee= 5.5 and ¬τi = 23, with the three equilibrium points having the same values as in Figure 6B. The open circle represents the stable UP state. 6D: aee= 5.5 and ¬τi = 24, with the three equilibrium points having the same values as in Figure 6B. The open square represents the now unstable UP state.

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A second spiking model was created (spiking) to simulate the cortical neurons described by Shu et al. and the neurons were set into a network of 100 neuron populations (udnetnzs) each with sparse coupling (p=0.1). Both excitatory (ce) and inhibitory (ci) stimuli were added to observe their affects. Figure 10A shows the network initially set to the DOWN state with a stimulus added at ton=50 and ce=5.0, causing the network to jump to the UP state.

It was manually found that for ce>2.0, the network would reach the UP state. However, if ce>10.0, the network will go back to the original DOWN state after the stimulus has been added, which C reflects the elliptical plane of attraction of the UP state (as defined by the stable manifold surrounding it). The network returning to the DOWN state for ce= 11 is also shown (Figure 10).

The network was also initially set to the UP state and an excitatory stimulus added. It was manually found that for ce>5.4 (and ci=0), the network would be stimulated towards the DOWN state. The network being stimulated and remaining in the UP state for ce=5.0 (Figure 11A). In contrast, the network jumping to the DOWN state for ce=6.0. An inhibitory stimulus was also added and the excitatory stimulus turned off (ce=0). It was manually Figure 7: Array of 200 coupled excitatory neuron populations (U0-199) vs. time found that for ci>2.9, the network would once again jump to the (ms) using the noise-fitted square-root function with σ =1, q=1. The first 100 popuDOWN state. An inhibitory stimulus being added to the network lations had initial values of U=0 and V=0 (DOWN state), while the rest had initial with ci=3.0 and the network reaching the DOWN state as a result values of U=1.1294, V=0.20359 (attraction plane of UP state). The blue represents (Figure 11B). the DOWN state and the yellow represents the UP state. aee was varied while all other parameters remained as in Figure 1A. 7A: aee = 3.5, resulting in a propagation The network was also initially set to have the first 50 poputo the DOWN state. 7B: aee = 3.8, with no propagation. 7C: aee = 4.3, resulting in a lations (half) in the DOWN state, and the rest in the UP state. propagation to the UP state.

Without any stimulus, the network self propaA B C gates towards the UP state (as seen in Figure 2 with the square-root model). However, if a strong enough excitatory stimulus was added (ce>5.9), it was possible to bring the entire network back to the DOWN state, for ci=0.

Similarly, a strong enough inhibitory stimulus (ci>2.1) was also capable of doing this, for ce=0. Figure 13b shows an inhibitory stimulus with ci=2.2 bringing the network to the DOWN state. Thus, if more neurons are initially placed Figure 8: Array of 200 coupled excitatory neuron populations (U0-199) vs. time (ms) using the noise-fitin the UP state, the threshold for the excitated square-root function σ =1, q=1. aee =3.5 and all other parameters are set as described in Figure 1A. The blue represents the DOWN state and the orange represents the UP state. 8A: Phase plane, V(inhibitory) vs. tory stimulus (ce) required to bring the network U(excitatory) with DOWN state at U=0, V=0 and UP state occurring at U=1.047, V=0.11926. 8B: U0-U2 back to the DOWN state is lowered. Likewise, had initial values in the DOWN state, while the rest (U3-U199) were initially in the UP state. The network if more neuron populations are initially set in propagates to the UP state. 8C: U0-U3 had initial values in the DOWN state, while the rest (U4-U199) the DOWN state, the threshold for the inhibiwere initially in the UP state. The network propagates to the DOWN state. tory stimulus (ci) required to stimulate the netA B C work to the DOWN state is lowered.

Discussion

Figure 9: Array of 200 coupled excitatory neuron populations (U0-199) vs. time (ms) using the noisefitted square-root function σ=1, q=1. aee=4.5 and all other parameters are set as described in Figure 1A. The blue represents the DOWN state and the yellow represents the UP state. 9A: Phase plane, V(inhibitory) vs. U(excitatory) with DOWN state at U=0, V=0 and UP state occurring at U=1.2543, V=0.4143. 9B: U0-U188 had initial values in the DOWN state, while the rest (U189-U199) were initially in the UP state.

The network propagates to the UP state. 9C: U0-U189 had initial values in the DOWN state, while the rest (U190-U199) were initially in the UP state. The network propagates to the DOWN state. ©2008 Cornell Synapse | www.cusn.org

The phase plane diagram of the two neuron population square-root model shows the stability of the UP and DOWN states and their plane of attraction as defined by the stable manifolds extending from a third unstable equilibrium point. Decreasing τi (the strength of the inhibitory connection) causes a change in the unstable manifold and the desired basins of attraction of the UP and DOWN states. The addition of excitatory stimuli into the two neuron population model is capable of transitioning 33

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Figure 10: Array of 100 sparsely coupled excitatory neuron populations (SE0-99) vs. time (ms) using the spiking model with stimulus and noise added. All populations were initially set to be in the DOWN state (SE=0, SI=0) and a stimulus was added at ton =50. For all stimuli in this model, the simulations have amp=1, xx=100, wid=10 and the blue represents the original DOWN state. 10A: Excitatory stimulus added with ce = 5.0 and ci = 0. 10B: Excitatory stimulus added with ce = 11.0 and ci = 0.

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C

Figure 11: Array of 100 coupled excitatory neuron populations (SE0-99) vs. time (ms) using the spiking model with stimulus and noise added. All populations were initially set to be in the UP state (SE=1.6149, SI=1.0747), and a stimulus was added at ton = 50. 11A: Excitatory stimulus added with ce = 5.0 and ci = 0. 11B: Excitatory stimulus added with ce = 6.0 and ci = 0. 11C: Inhibitory stimulus added with ce = 0.0 and ci =3.0.

the two populations from the UP state to the DOWN state, as well as from the DOWN state to the UP state. Furthermore, stimuli with strength over a threshold value can bring the populations from the DOWN state back to the DOWN state. By increasing aee, the transition between the two states becomes more feasible, resulting in a reduced amount of time in any one state before the network transitions to the other state.

A full network of both excitatory and inhibitory neuron populations show their ability to self propagate towards the UP state if a minimum number of populations in the network are initially in the UP state, as demonstrated experimentally by Luczak et al.12. By adding an excitatory stimulus, it is possible to stimulate the entire network which was originally in the DOWN state to the UP state, provided the strength of the stimulus is above a threshold value.

Similarly, white noise over a threshold value is also capable of propagating the network to the UP state. Thus, the network’s selfpropagation, an external stimulus, and white noise are all possible ways of taking the entire network to a stable UP state and maintaining it in that position.

Bifurcation diagrams for the noise-fitted model show that there is a specific range of values for aee (the strength of the excitatory connection to itself) for which two stable states will occur. Twoparameter diagrams for the same model demonstrate that for a fixed value of aee, the amount of white noise (σ) must remain below a certain limit to maintain these two states. Similarly, τi must be below a certain limit in order to maintain the ideal direction of ©2008 Cornell Synapse | www.cusn.org

the unstable manifold and the stability of the UP state, for legitimate values of aee (as given by the bifurcation diagram). By placing this model into a network, it was observed that changing the values of aee affects the direction in which the network propagates (initially set to half in the DOWN state, half in the UP state). For values of aee above a certain threshold, the network propagates to the UP state. Furthermore, as aee is increased, the speed of this upward propagation also increases. By increasing aee, it is also easier for the network to propagate towards the UP state. As a result, the network does not require as many populations to initially be in the UP state.

Finally, the spiking model demonstrates the ability of an external stimulus to shift the state of the network from its original condition. An excitatory stimulus (ce) over a threshold value is capable of bringing the network from the DOWN state to the UP state. However, if ce is increased, it can ultimately bring the network back to the DOWN state. This was also observed by placing the network in the UP state and adding a large excitatory stimulus, which brought it back down again. Similarly, an inhibitory stimulus added to a network in the UP state is also capable of bringing it to the DOWN state. These findings reflect the phase plane diagram observed in the first model, as well as the properties of the in vitro cortical neurons described by Shu et al.11.

The close fit between our computational models and experimental data by Shu et. al and Luczak et. al. reinforce their findings that local cortical circuits are capable of recurrent activity through a precise combination of excitatatory and inhibitory inputs11,12.

References 1. K. D. Harris, Nat Rev Neurosci. 6, 399–407 (2005).

2. M. I. Garrido et al., PNAS 104, 20961-20966 (2007).

3. M. Steriade, A. Nunez, F. Amzica, J Neurosci. 13, 3252–3265 (1993).

4. D. Durstewitz, J. K. Seamans, T. J. Sejnowski, Nature Neurosci.

3, 1184–1191 (2000).

5. N. Brunel, X. J. Wang, J. Comput. Neurosci. 11, 63–85 (2001).

6. R. H. R. Hahnloser, R. J. Douglas, K. Hepp, Neural Comput. 14, 1669–1689 (2002).

7. F. S. Chance, L. F. Abbott, A. D. Reyes, Neuron 35, 773–782 (2002).

8. M. Steriade, A. Nunez, F. Amzica, J. Neurosci. 13, 3252–3265 (1993).

9. M. Steriade, I. Timofeev, F. Grenier, J. Neurophysiol. 85, 1969– 1985 (2001).

10. M. V. Sanchez-Vives, D. A. McCormick, Nature Neurosci. 3, 1027–1034 (2000).

11. Y. Shu, A. Hasenstaub, D. A. McCormick, D. A. Nature 423, 288-292 (2003).

12. A. Luczak, P. Bartho, S. L. Marguet, G. Buzsaki, K. D. Harris, PNAS 104, 347-352 (2007).

13. A. M. Sillito, J. Physiol. 250, 305–329 (1975).

14. Z. F. Mainen, T. J. Sejnowski, Nature 382, 363–366 (1996).

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PROPOSAL Disruption to reinstatement of nicotine self-administration after induced lesions and glutamatergic receptor antagonism in the insular cortex of the rat brain Steven B. Sachs

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

Introduction and Specific Aims In a recent retrospective study of stroke victims, Naqvi et al. discovered that smokers who suffered ischemic damage to the insular cortex were more prone to easily, immediately, and permanently quit smoking without sustained addictive urges than were smokers who experienced lesioning in other regions1. This finding corroborates numerous fMRI studies that have associated the sensation of craving with metabolic fluctuations in the insula.

Vorel et al., however, elucidate a significant limitation to these findings: “the non-selectivity of brain lesions”2. To address this concern, I propose a prospective study to determine whether a selective lesion of the rat insular cortex interferes with the animal’s nicotine craving. I hypothesize that the insula is fundamental to the neurological pathways of craving. Thus, if specific electrolytic lesions are performed on the insula of nicotine self-administering rats, these animals should demonstrate decreased reinstatement of nicotine self-administration following presentation of nicotinepaired stimuli.

I also plan to examine the role of glutamatergic innervation of the insula in mediating nicotine craving. Among ethanol, cocaine, and amphetamine self-administering rats, intraperitoneal (i.p.) injection with alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA)/kainate antagonists consistently attenuated second order reinstatement; similar results were found with Nmethyl-D-aspartic acid (NMDA) receptor antagonism, albeit with less consistency3,4, and mGlu1 receptor antagonists among nicotine self-administering rats5. Since the insular cortex is densely innervated with NMDA and AMPA/kainate receptors6,7, I hypothesize that enhanced ionotropic, glutamatergic activity in the insula correlates with nicotine cue-induced reinstatement of lever pressing. Therefore, specific injection of glutamate (Glu) ionotropic receptor antagonists into the insula should interfere with second order lever pressing. This would implicate glutamatergic activity of the insula as a pivotal mechanism in nicotine craving.

Significance Although it is widely known that cigarette smoking is a primary risk factor for heart disease and lung cancer, only a small fraction of smokers successfully quit each year. Data suggests that approximately 70% of all smokers desire to quit, but only 4.7% achieve success. For most addicts, relapse occurs rapidly, during ©2008 Cornell Synapse | www.cusn.org

the initial 5-10 days following a cessation attempt8. However, the threat of relapse persists for years9.

Most treatments to promote smoking cessation are thought to combat the aversive symptoms of withdrawal8. However, research indicates that craving, rather than initial negative affect, is responsible for the majority of relapses. Intense craving generally results when former addicts are presented drug-related cues10. Thus, drugs that interfere with nicotine craving would prove clinically beneficial in promoting smoking cessation.

To examine the brain substrates mediating this craving sensation, researchers have conducted neuroimaging studies. Both fMRI and PET scans of heavy smokers reveal increased glucose metabolism in the anterior insula, perigenual anterior cingulate gyrus, obitofrontal cortex, and dorsolateral prefrontal cortex upon exposure to cigarette-related cues11. Similarly, presentation of drug-related cues resulted in insular activity among cocaine addicts and alcoholics10,12. The recurrence of insular activation suggests that it is a fundamental substrate in the neurophysiology of craving.

Located in the telencephalon, underneath the lateral sulcus covered by the operculum, the insula corresponds to the fifth lobe of the brain13. The central insular sulcus splits the insula into anterior and posterior segments14. In primates, widespread connections have been discovered between the insular lobe and cortical and limbic regions: the “(1) cerebral cortex, (2) basal nuclei (basal ganglia), (3) amygdaloid body, (4) other limbic areas, and (5) the dorsal thalamus”13. Owing to these dense interconnections with the amygdala and basal ganglia, it is likely that the insula serves as a “limbic integrating cortex with ongoing behavior and emotion”15.

It is not entirely surprising, therefore, that Naqvi et al. uncovered an association between brain damage to the insula and interference with smoking addiction. According to their retrospective study, ischemic lesions in only the right and left insulae correlated with facility in quitting smoking, without craving or threat of relapse. No association was discovered between lesioning of the orbitofrontal cortex, also activated during intense craving, and interference with nicotine addiction1. In a subsequent study, Contreras et al. reported that temporary inactivation of the rat insular cortex with lidocaine, a competitive Na+ channel antagonist, interfered with amphetamine conditioned place preference, corroborating the insula’s central role in craving16.

To assess craving and relapse of drug use in addicted animals, 35

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a generally accepted model is the reinstatement of drug self-administration. In this behavioral assay, animals are re-exposed to a drug-associated conditioned stimulus after a period of extinction, and the increase in stimulus response frequency is recorded.

Previous research indicates that rats do exhibit reinstated response after presentation of nicotine-paired stimuli17.

Further studies will be essential to more precisely elucidate the role of the insular cortex in mediating nicotine craving. The findings by Naqvi et al. are limited by the retrospective nature of their study: high probability of recall bias and non-selectivity of brain lesions2. Therefore, I propose a specific electrolytic lesion of the rat insular cortex to confirm the finding that lesions to the insula attenuate nicotine craving, as measured by cue-induced reinstatement of lever pressing.

Evidence suggests that glutamatergic neurotransmission plays a primary role in regulating drug relapse. Among cocaine-seeking rats, for example, injection of AMPA and NMDA Glu receptor agonists into the nucleus accumbens promoted reinstatement of cocaine lever pressing18. In contrast, i.p. administration of the NMDA/glycine antagonist L-701324, AMPA/kainate antagonists CNQX and NBQX, and the mGluR5 antagonist MPEP attenuated reinstatement following presentation of the cocaine-related stimuli, while the NMDA receptor antagonist CGP39551 did not interfere with cue-induced reinstatement3. Similarly, among ethanol-seeking rats, i.p. administration of L-701324 and CNQX attenuated the ethanol cue-induced reinstatement of lever pressing.

However, CGP39551 and the noncompetitive NMDA antagonist MK-801 did not decrease the number of lever presses compared to control levels4. CNQX has been shown to interfere with amphetamine conditioned place preference as well19. While AMPA/kainate receptor antagonism consistently interferes with cue-induced reinstatement, NMDA receptor antagonism has not produced consistent results.

Furthermore, among nicotine self-administering rats, i.p. administration of mGlu1 receptor antagonist EMQMCM has been shown to reduce reinstatement of nicotine-seeking behavior. Evidence suggests that group I mGluR activation augments the excitatory effects of Glu by moderating NMDA receptor-linked ion channel function5.

To date, however, no studies have examined the role of Glu neurotransmission in the insula and its potential modulation of nicotine craving. Immunocytochemistry of the rat insular cortex reveals dense glutamatergic innervation. The level of staining for the NMDAR1 subunit of the NMDA receptor is relatively high in the insula compared to other cortical regions, receiving a score of 2 on a scale from 0-47. Furthermore, staining for AMPA receptor subunits revealed even higher density in the insular cortex, receiving a score of 3.5 out of 4 for the GluR1-3 subunits6. Autoradiography reveals only moderate metabotropic glutamate receptor localization in the cortex20. Thus, to demonstrate that activation of the iontropic Glu receptors in the insula is central to nicotine craving, I plan to directly inject both NMDA and AMPA/kainate antagonists. I expect to measure decreased cue-induced reinstatement of lever pressing compared to vehicle-treated rats.

Experimental Design Procedures in the experimental design are adapted from LeS©2008 Cornell Synapse | www.cusn.org

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age et al., 2004 and Dravolina et al., 20075,17.

Subjects Naive male Holtzmann rats weighing 300-400g will be acquired for testing. The rats will receive a restricted diet of 20g/day rat chow to stabilize weight. All procedures will abide by the U.S. NIH Principles of Laboratory Animal Care.

Apparatus Animals will be tested in operant-conditioning chambers with two response levers (inactive and active) each on the front wall and 10cm above the bottom of the chamber as well as stimulus lights 2cm above each lever. The entire apparatus will be placed inside a noise-damping cubicle to mask background sounds. Infusion pumps will be located outside the cubicle; tubing will extend from the pumps to a fluid swivel above the chamber and, ultimately, to a guide cannula in a harness atop each animal’s back. Chambers will be connected to a computer with MED-PC IV software. The computer software will record all lever presses.

Drugs Nicotine barbitrate will be dissolved in saline, buffered at pH=7.4. Competitive AMPA/kainate antagonist CNQX and NMDA/glycine site antagonist L-701,324 will be dissolved in distilled water and propylene glycol, Tween-80, and saline (ratio 1:1:18) respectively3,5.

Catheter Implantation Surgery After anesthesia is induced with ketamine and xylazine, intravenous catheters will be implanted into the rats. The proximal end of the catheter will pass through the right jugular vein and terminate at the convergence of the vena cava and right atrium21.

Catheters will be flushed twice a day with metamisole, a non-steroidal analgesic and anti-inflammatory, as well as heparin and the antibiotic gentamycine sulfate to hinder infection. The tubing will be flushed with streptokinase daily to confirm and promote catheter patency.

Rats with clogged intravenous catheters will be rejected from the study.

Experiment 1: Effects of electrolytic lesion of insula on cueinduced reinstatement of nicotine self-administration (NSA) Self-administration training Receiving nicotine doses of 0.03mg/kg/infusion, 30 rats will be trained to self-administer nicotine during 1-hour sessions. Sessions start with activation of the stimulus light atop the active (right) response lever. After the rat presses the active lever a sufficient number of times, the stimulus light turns off, the rat receives a nicotine infusion, and a 15-s “timeout” ensues (i.e. lever pressing not reinforced). Inactive lever pressing has no consequence. The stimulus light will again be lit after the timeout period to indicate nicotine availability. If rats fail to demonstrate lever-pressing after five trials, they will be baited to the active lever with food for 2-3 sessions. Training will begin on a fixed-ratio 1 (FR-1) schedule (i.e. each lever press is reinforced). Once the rats successfully obtain at least 8 infusions per period, the administration schedule will be slowly augmented to FR-5 (i.e. five active lever presses correspond to one nicotine injection). Once rats earn at least eight 36

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injections per session and the ratio of active to inactive lever presses exceeds 2:1, the rat is said to have acquired NSA. Extinction Phase This phase will be similar to the acquisition phase with several exceptions. First, the stimulus light remains off. Second, nicotine will be unavailable, so saline is instead administered after active lever presses. Extinction is achieved when “active” lever pressing decreases by at least 50% for three consecutive periods.

Electrolytic Lesion of Insula Under anesthesia, half the rats will receive bilateral electrolytic lesions of the insular cortex. An electrode will be stereotaxically inserted into the insula using lesion coordinates from Paxinos and Watson22, and a 2mA anodal current will be passed through the electrode for 28s. Among control animals, the skull will be opened and dura penetrated, but no electrolytic lesions will be generated.

Animals will be treated with the antibiotic Gentamicin sulfate and given 10 days to recuperate23.

Reinstatement Phase Two reinstatement conditions will be examined during two 1-hour sessions in both groups of rats: (1) constant stimulus light illumination, and (2) stimulus light activation and inactivation as in the FR-5 schedule during training. Inactive and active lever presses will be recorded. Before each reinstatement condition is tested, extinction criteria must be met.

Forebrain Histology The rats will be sacrificed under heavy anesthesia and their brains excised. 50μm slices will be cut along the transverse plane through the insula, and the slices will be desiccated and stained with Cresyl violet. The extent of lesioning will be observed with light microscopy to confirm that electrolytic lesions are confined to the insula23.

Experiment 2: Effects of NMDA receptor antagonism on cueinduced reinstatement of NSA Procedures will be identical to those in experiment 1 with several exceptions. First, after the catheter is inserted, guide cannulae will be stereotaxically and bilaterally inserted into the insula according to coordinates outlined by Paxinos and Watson22. Three stainless steel screws into the skull will stabilize the cannulae. Second, instead of generating an electrolytic lesion, 3nmol L-701,324 will be injected into each side of the insular cortex via the guide cannulae; previous studies have deemed this dosage behaviorally active. Control rats will receive a vehicle injection of 0.9% sterile saline, with 0.5μl on each side. Third, after experimentation, brain slices will similarly be stained and viewed with a light microscope to ensure proper localization of guide cannulae18.

Experiment 3: Effects of AMPA/Kainate receptor antagonism on cue-induced reinstatement of NSA Procedures will be identical to those in experiment 2 with several exceptions. 1nmol CNQX, instead of L-701,324, will be injected into each side of the insular cortex18.

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Data Analysis and Expected Results The primary data collected will be the number of active and inactive lever presses during the reinstatement phase of all three experiments. I will consider the mean number of active-lever presses during the final five periods prior to extinction as the baseline response level. At α=0.5, one way ANOVAs will be used to compare mean active lever responses of each control group to its corresponding experimental groups receiving an electrolytic lesion, L-701,324, or CNQX. If my hypothesis that the insula is fundamental to the neurological pathways of nicotine craving is correct, then the mean number of second-order active lever presses among the rats with electrolytically lesioned insulae should be significantly less than among control rats. Similarly, if activation of the insular AMPA/kainate or NMDA glutamate receptors is central to the insular function of mediating craving, then antagonism of these ionotropic Glu receptors should significantly decrease active lever pressing compared to rats receiving vehicle injections. Since NMDA receptor antagonism has produced inconsistent results in the past, and the NMDA receptors are less dense than AMPA receptors in the insula, I suspect that AMPA receptor antagonism will result in a greater decrease in lever pressing from baseline.

To confirm that potential decreases in active lever pressing do not result from nonspecific effects of the drugs (i.e. motor impairment or decreased locomotion), I will also compare the numbers of inactive lever responses after Glu receptor antagonism to baseline levels using a one way ANOVA. The number of inactive lever presses should not be significantly different. Furthermore, no delayed reinstatement of lever pressing should be observed3.

References 1. Naqvi NH, Rudrauf D, Damasio H, Bechara A. Damage to the Insula Disrupts Addiction to Cigarette Smoking. Science 2007; 315: 531-534.

2. Vorel SR, Bisaga A, McKhann G, Kleber HD. Insula Damage and Quitting Smoking. Science 2007; 317: 318-320.

3. Bäckström P, Hyytiä P. Ionotropic and Metabotropic Glutamate Receptor Antagonism Attenuates Cue-Induced Cocaine Seeking.

Neuropsychopharmacology 2006; 31: 778-786.

4. Bäckström P, Hyytiä P. Ionotropic Glutamate Receptor Antagonists Modulate Cue-Induced Reinstatement of Ethanol-Seeking Behavior. Alcoholism: Clinical and Experimental Reserearch 2004; 28.4: 558-565.

5. Dravolina OA, Zakharova ES, Shekunova EV, Zvartau EE, Danysz W, Bespalov AY. mGlu1 receptor blockade attenuates cue-and nicotine-induced reinstatement of extinguished nicotine self-administration behavior in rats. Neuropharmacology 2007; 52: 263-269.

6. Petralia RS, Wenthold RJ. Light and Electron Immunocytochemical Localization of AMPA-Selective Glutamate Receptors in the Rat Brain. The Journal of Comparative Neurology 1992; 318: 329-354.

7. Petralia RS, Yokotani N, Wenthold RJ. Light and Electron Microscope Distribution of the NMDA Receptor Subunit NMDAR1 in the Rat Nerous System Using a Selective Anti-Peptide Antibody.

The Journal of Neuroscience 1994; 14.2: 667-696.

8. Piasecki TM. Relapse to Smoking. Clinical Psychology Review 37

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2006; 26: 196-215.

9. Hays JT, Hurt RD, Rigotti NA, Gonzalez NR, Durcan MJ, Sachs DPL, Wolter TD, Bulst AS, Johnston JA, White JD. “SustainedRelease Bupropion for Pharmacologic Relapse Prevention after Smoking Cessation.” Annals of Internal Medicine 2001; 135: 423433.

10. Olbrich HM. Valerius G, Paris C, Hagenbuch F, Ebert D, Juengling FD. Brain activation during craving for alcohol measured by positron emission tomography. Australian and New Zealand Journal of Psychiatry 2006; 40: 171-178.

11. Brody AL, Mandelkern MA, London ED, Childress AR, Lee GS, Bota RG, Ho ML, Saxena S, Baxter LR, Madsen D, Jarvik ME. Brain Metabolic Changes During Cigarette Craving.

Archives of General Psychiatry 2002; 59: 1162-1172.

12. Kilts CD, Schweitzer JB, Quinn CK, Gross RE, Faber TL, Muhammad F, Ely TD, Hoffman JM, Drexler KPG. Neural Activity Related to Drug Craving in Cocaine Addiction. Archives of General Psychiatry 2001; 58: 334-341.

13. Augustine JR. Circuitry and functional aspects of the insular lobe in primates including humans. Brain Research Reviews 1996; 22: 229-244.

14. Augustine JR. The insular lobe in primates including humans.

Neurological Research 1985; 7.1: 2-10.

15. Dupont S, Bouilleret V, Hasboun D, Semah F, Baulac M. Functional Anatomy of the Insula: New Insights from Imaging. Surgical and Radiologic Anatomy 2003; 25: 113-119.

16. Contreras M, Ceric F, Torrealba F. Inactivation of the Interoceptive Insula Disrupts Drug Craving and Malaise Induced by Lithium. Science 2007; 318: 655-658.

17. LeSage MG, Burroughs D, Dufek M, Keyler DE, Pentel PR. Reinstatement of nicotine self-administration in rats by presentation of nicotine-paired stimuli, but not nicotine priming. Pharmacology, Biochemistry, and Behavior 2004; 79: 507-513.

18. Cornish JL, Duffy P, Kalivas PW. A Role for Nucleus Accumbens Glutamate Transmission in the Relapse to Cocaine-Seeking Behavior. Neuroscience 1999; 93.4: 3359-1367.

19. Mead AN, Stephens DN. CNQX but not NBQX Prevents Expression of Amphetamine-Induced Place Preference Conditioning: A Role for the Glycine Site of the NMDA Receptor, but not AMPA Receptors. The Journal of Pharmacology and Experimental Therapeutics 1999; 290.1: 9-15.

20. Lavreysen H, Pereira SN, Leysen JE, Langlois X, Lesage ASJ. Metabotropic glutamate 1 receptor distribution and occupancy in the rat brain: a quantitative autoradiographic study using [3H] R214127. Neuropharmacology 2004; 46: 609-619.

21. Caine SB, Lintz R, Koob GF. Intravenous drug self-administration techniques in animals. In: Sahgal A (ed). Behavioral Neuroscience: A Practical Approach 1993. University Press: Oxford.

117-143.

22. Paxinos G, Watson C. The Rat Brain in Stereotaxic Coordinates, Fourth Edition 1998. Academic Press: New York.

23. Cubero I, Thiele TE, Bernstein IL. Insular cortex lesions and taste aversion learning: effects of conditioning method and timing of lesion. Brain Research 1999; 839: 323-330.

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PROPOSAL Role of PINK1 in adolescent and adult substantia nigra pars compacta dopaminergic pacemaking neurons Julianna G. Marwell

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

Introduction Parkinson’s disease (PD) is an age-related neurodegenerative disease. It affects approximately 1% of the people over 60 years of age1. PD is characterized by the degeneration of dopaminergic neurons and the formation of Lewey bodies in the substantia nigra pars compacta (SNc)1. First dopamine neuronal axons die; this is followed by the death of the cell bodies. PD symptoms, such as bradykinesia and rigidity, correlate to the decrease in striatal dopamine levels2.

There are many genes that have a role in familial PD; as well as are other genes that predispose a person to PD3. Several genetic mutations, like those that affect PINK1, OMI/HTRA2, DJ-1, and LRRK2, are all found in a subset of people suffering from PD. For example, the PINK1 (PTEN-induced putative kinase I) gene is linked to 8-15% of the cases of autosomal recessive PD, in people from ages 18-514. These genetic mutations affect the mitochondria and prevent them from functioning normally however the details of how mitochondria function is disrupted by genetic mutations like PINK1 are still being worked out5.

Among many functions, mitochondria play an extremely important role in a cell’s ability to respond to stressful conditions.

When a cell is stressed, the intracellular concentration of calcium increases dramatically; mitochondria buffer these high calcium levels to prevent the cell from dying6. If the mitochondria are damaged, they cannot respond as effectively to stress, and the cells are more likely to die. In cells that die, the intracellular concentrations of calcium are as high as 1,400nM6.

Functioning calcium channels and controlled cellular calcium concentrations are necessary for tonic firing of neurons. In particular, neurons in the adult substantia nigra (SN) depend on the L-type voltage-gated calcium channels for their tonic activity and autonomous pace-making properties7. These channels, specifically Cav1.2 and Cav1.3, open at membrane potentials lower than the threshold for firing action potentials5. Chan et al. recently showed that juvenile dopaminergic pacemaking substantia nigra neurons rely on hyperpolarization-activated and cyclic nucleotide gated cation (HCN) channels, in conjunction with sodium channels, while adult cells rely on voltage gated calcium channels to pacemake5.

A PINK1 mutation could thus adversely affect mitochondria stress-response abilities in a variety of cell-types, including SN neurons. This proposal seeks to explore the hypothesis that a mutation in PINK1 will affect SN neuron recovery under stress induced ©2008 Cornell Synapse | www.cusn.org

by high calcium concentrations as well as an inhibition of calcium channel function; this will ultimately impair their pace-making abilities and cause cell death.

Significance Neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease, are very prevalent in the United States. As many as one million Americans suffer from Parkinson’s disease. While approximately 15 percent of people with Parkinson’s are diagnosed before the age of 50, incidence increases with age8. The causes of these neurodegenerative diseases are still unknown, which makes them difficult to treat effectively.

By looking at how PINK1 mutations correlate with PD, researchers will be one step closer to understanding the disease.

PD sufferers will also ultimately benefit most from this study.

People who have a family history of PD will also find this study useful because it will help them understand the extent to which a change in PINK1 function could affect their brain’s function.

Specific Aims This study will focus on the L-type voltage gated calcium channels within mouse neurons. I hypothesize that the PINK1 mutation in Parkinsonian mice will negatively affect the pacemaking ability of their dissociated dopaminergic SN neurons, in the presence of ZD 7288 (a hyperpolarization activated cation current blocker), nimodipine (a L-type calcium channel blocker), and increased intracellular calcium levels.

In this experiment, the tonic firing properties of substantia nigra dopaminergic neurons will be examined. In addition, the pacemaking mechanism of dissociated dopaminergic neurons from the PINK1 mutated mice by blocking the hyperpolarization activated cation current (Ih) with ZD 7288 (30μM) and the L-type calcium current with nimodipine (20μM). I will also monitor the neurons’ response to a high extracellular calcium concentration (1,100nM) which is found in extremely stressed cells6.

Methods Mice To generate the PINK1 mutant mice, a stop codon will be inserted in exon 9 of the amino acid sequence of the PINK1 gene in male C57BL/6J mice1.

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To generate Parkinsonian symptoms in both wild-type and PINK1 mice, the mice will be given chronic subcutaneous injections of 25mg/kg 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). MPTP is a neurotoxin used for inducing irreversible parkinsonian-like syndrome because it depletes dopamine in the cells/animals into which it is injected2. Mice would receive MPTP injections every 2.5 days.

Mice will have access to food and water ad libitum. Wild-type (control) mice will be separated from mutant (experimental) mice.

The room in which the mice will be raised will be kept at a constant temperature and humidity level. They will be kept on a 12h light/dark cycle.

creased calcium levels.

Whole Cell Patch Clamp Recording Mice will be divided into two groups: one group will be euthanized at 14 days old, and the second group at 30 days old. Once the mice are euthanized, their brains will be quickly removed. Following the method used by Puopolo et al. (2007) the dopaminergic neurons will be separated from the SNc9. Whole-cell recordings will be used to determine the neuron’s response to the drugs by using a patch clamp to observe the changes in current.

The substantia nigra pars compacta dopaminergic neurons will be put in a recording chamber. According to Chan et al. (2007), neurons in the ventral tier of the SNc should be used because they are known to rely on voltage gated calcium channels the most for pacemaking in vivo and in vitro5. Following the methods outlined in Chan et al., conventional tight seal whole-cell patch-clamp will be made on the SN dopaminergic neurons5. The neuron’s firing pattern will be monitored for one minute before the drug or excess calcium is added to the pipette. The patch clamp’s pipette will have an internal solution will be equivalent to the neuron’s intracellular fluid, except when the three treatments are added to the pipette.

The pipette’s solution will be heated to 37˚C, roughly equivalent to the body temperature of the mice. This is to make sure the change in pacemaking that will be recorded is only in response to the drugs, not to an intracellular temperature change.

Euthanize juvenile Euthanize adult Euthanize juvenile Euthanize adult mice day 14 (N=40) mice day 30 (N=40) mice day 14 (N=40) mice day 30 (N=40) Remove SN DA Remove SN DA Remove SN DA Remove SN DA neurons neurons neurons neurons

Mouse Schedule There will be 80 mutant and 40 control (120 total) mice in the experiment. 40, 14 day old experimental mice will be divided into four groups of 10, where each group will receive one of the four treatments. The 40 30 day old mice will be divided into the same treatment groups. The control mice will be divided into similar groups except that there will be 20 14 day old mice and 20 thirty day old mice. This means that there will be 5 mice in each treatment group.

All of the mice in the experiment will receive MPTP treatment.

Half of the experimental and control mice will be euthanized when the mice are 14 days old. These neurons will show how adolescent neurons respond to stress. Both the experimental and control 14 day old euthanized mice will be divided into four groups of equal number, where each group will receive one of the following treatments: no treatment, ZD 7288, nimodipine, and increased calcium levels. These remaining mice will be euthanized at 30 days of age to represent the adult neurons’ response to stress. The 30 day old experimental and control euthanized mice will be divided into four groups of equal number, where each group will receive one of the following treatments: no treatment, ZD 7288, nimodipine, and in©2008 Cornell Synapse | www.cusn.org

Predicted Outcomes According to Puopolo et al. (2007), it is expected that the adult wild-type SN dopaminergic neurons’ pacemaking will slow and the adolescent neurons’ pacemaking will drastically slow (if not stop) in the presence of the Ih current blocker ZD 7288, when comPINK1 mutant mice with chronic MPTP treatment (N=80)

No Treatment M: N=10 C: N=5

ZD 7288 M: N=10 C: N=5

Control mice (no PINK1 mutation) with chronic MPTP treatment (N=80)

Nimodipine M: N=10 C: N=5

High [Ca2+] M: N=10 C: N=5

Whole cell patch-clamp recording

Figure 1: Flow chart depicting experimental design. “M” refers to PINK1 mutant mice, while “C” refers to control mice lacking a PINK1 mutation.

pared to the wild-type parkinsonian neurons that are not treated9.

All neurons have a hyperpolarizing current during action potentials, which the ZD 7288 blocks. While ZD 7822 should slow both adult and juvenile pacemaking neurons, the younger neurons will be affected more because they rely on hyperpolarization-activated and cyclic nucleotide gated cation (HCN) channels5. When the hyperpolarization activated cation current is blocked, the cells will have an extremely hard time spontaneously firing.

It is also expected that in the presence of nimodipine the adult wild-type SN dopaminergic neurons’ pacemaking will immediately stop. After an extended period of time, the adult wild-type neurons should recover pacemaking ability5. According to Chan et al. (2007), adult SN dopaminergic pacemaking neurons rely on Ltype voltage gated calcium channels5. If these channels are blocked pacemaking will stop, but slowly the neurons will pacemake again through using HCN channels which drove juvenile pacemaking5.

The adolescent neurons’ pacemaking will respond to nimodipine similar to that of the untreated juvenile neurons.

Finally, one would expect that the dramatically increased intracellular calcium concentrations will greatly affect the wild-type adult and young dopamine neurons. Both adult and young neurons will show a decreased action potential firing rate, until they eventually recover from the stress or die. Under these conditions, it is predicted that more of the adult neurons will die.

The mutation in PINK1 will affect the mitochondria’s ability to respond to external stress, therefore the mutated neurons should not be able to recover from the stress of the drugs and pacemak40

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ing will stop. These neurons should die extremely quickly. On the other hand, wild-type neurons should recover from such stress due to normal mitochondria function.

Summary PINK1 correlates with the death/impaired functioning of dopaminergic neurons in the SNc. This result would be an important finding because then more research could be done on how this specific gene directly affects mitochondrial function. However, if the predicted hypothesis is incorrect it could be possible that the homeostatic mechanism of the SNc dopaminergic neurons found a way to counteract the mitochondrial mutation, and helped the neuron respond to stress. For example, the mutant neurons could have had higher levels of cadmodulin, a protein that helps cells respond to stress, than the control neurons.

We do not know the exact role PINK1 plays in mitochondrial import and mitochondrial localization1. It would be important for future studies to further explore the exact mechanism through which the PINK1 protein affects the mitochondria. Furthermore, it would be important to examine other mutations and their role in affecting the dopamine SNc pacemaking neurons’ response to stresses. Answering these questions will bring us one step closer to understanding the cause of this debilitating neurodegenerative disease.

References 1. Abou-Sleiman P, Muquit M, Wood N. Expanding insights of mitochondrial dysfunction in Parkinson’s disease. Nature 7(3): 207-219. (2006).

2. Novikova L, Garris B, Garris D, Lau Y. Early signs of neuronal apoptosis in the substancia nigra pars compacta of the progressive neurodegenerative mouse 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine/probenecid model of parkinson’s disease. Neuroscience 140: 67-76. (2006).

3. Sulzer, D. Multiple hit hypothesis for dopamine neuron loss in Parkinson’s disease. Trends in Neuroscience 30 (5): 244. (2007).

4. Tan J, Dawson T. Parkin Blushed by PINK1. Neuron 50 (4):527529. (2006).

5. Chan C, Guzman J, Ilijic Em Mercer J, Rick C, Tkatch T, Meredith G, Surmier D. ‘Rejuvenation’ protects neurons in mouse models of Parkinson’s disease. Nature 447: 1081-1089. (2007).

6. Radoskevic K, Grooth B, Greve J. Changes in intracellular calcium concentration and pH of target cells during the cytotoxic process: a quantitative study at the single cell level. Cytometry 20(4):281-289. (2005).

7. Bean B. Stressful Pacemaking. Nature 227: 1059-1060. (2007).

8. Parkinsons Disease: An Overview. Retrieved May 18, 2008, from Parkinson’s Disease Foundation Website: http://www.pdf. org/AboutPD/index.cfm 9. Puopolo M, Raviola E, Bean B. Roles of Subthreshold Calcium Current and Sodium Current in Spontaneous Firing of Mouse Midbrain Dopamine Neurons. The Journal of Neuroscience 27(3):645656. (2007).

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PROPOSAL DHEA mediation of acute and chronic cortisol effects on CA1 subset hippocampal neurons Nishant J. Trivedi

Cornell University, Department of Neurobiology and Behavior, Ithaca, NY 14853

Introduction and Specific Aims Cortisol is a corticosteroid hormone synthesized from cholesterol in the zona fasciculate of the adrenal cortex. It is one of the most abundant glucocorticoid in humans. Among other roles, it plays a critical function in mediating the hypothalamic-pituitaryadrenal (HPA) axis, which is the final common pathway in the stress response1. Cortisol elevates blood pressure, blood sugar levels, and has an immunosuppressive effect. Corticosteroids also regulate gene expression of a variety of membrane receptors and channels through mineralocorticoid (MR) and glucocorticoid (GR) receptors, which are highly concentrated in the hippocampus.

The effects of corticosteroids are complex and vary dramatically with concentration. In excess, they can lead to neuronal degeneration, disruption of calcium homeostasis, and reduced neurogenesis2. Corticosteroid therapy for days to weeks has been associated with mania or hypomania whereas longer therapies (those lasting months or years) are correlated with depression. The development of virtually all psychiatric diseases begins with stress; elevated cortisol levels have been observed in pathologies such as Alzheimer’s disease as well as major depression3.

Dihydroxyepiandosterone (DHEA), also synthesized in the adrenal cortex, is an abundant, enigmatic hormone that is hypothesized to have anti-glucocorticoid properties. Preliminary research shows that DHEA exerts a neuro-protective force in the hippocampus, while improving memory, and stimulating neural differentiation4. The mechanism by which DHEA exerts its neuroprotective effects is not understood, but it is well known that its concentrations are very high in the developing human, and that its levels decrease progressively with old age. The decline of DHEA has been implicated as a cause of many age-related illnesses including memory loss and Alzheimer’s5.

Research of acute and chronic exposure of cortisol in hippocampal neurons is underway; however, there are many holes that need to be filled in the current knowledge base. The mechanism via which chronic cortisol exposure leads to transcriptional activation of G-protein inwardly-rectifying potassium channels (GIRK) is unknown. The profiles and the ratio of burst-firing to non-burstfiring in hippocampal neurons are also altered by chronic hypercortisolemia via unknown mechanisms6.

DHEA is presumed to have anti-glucocorticoid properties with respect to its ability to alleviate N-methyl-D-aspartate (NMDA) ionotropic receptor (NMDA-R) mediated excitotoxicity in hippoc©2008 Cornell Synapse | www.cusn.org

ampal neurons7. These data do not provide insight as to whether DHEA can alleviate cortisol induced NMDA-R-mediated excitotoxicity; profiles of NMDA-R-mediated Ca2+ elevation differ in the presence and absence of cortisol. DHEA administration has demonstrated improved mood and memory, while leading to reduced evening cortisol levels, however; it is unknown whether DHEA can offer any form of immunoprotection against chronic cortisol-mediated neuronal atrophy.

Through our series of proposed experiments, we wish to determine how variable levels of DHEA on CA1 hippocampal neurons, in vitro, can affect acute cortisol inducement of NMDA-R-mediated Ca2+ excitotoxicity. Additionally, we hope to gain insight on whether DHEA can offer in vivo immunoprotection against chronic hypercortisolemia in CA1 subset neurons.

Experimental Design and Aims Effects of variable [DHEA] on cortisol-induced, NMDA-Rmediated Ca2+ elevation This experiment seeks to investigate NMDA-R in CA1 hippocampal neurons. Specifically, we wish to investigate the hypothesis that in vitro treatment of CA1 hippocampal neurons with DHEA before, during, and after cortisol administration will transiently reduce cortisol-induced NMDA-R mediated Ca2+ elevation. Our null hypothesis is that DHEA operates via an intracellular, genetranscription pathway to emolliate prolonged Ca2+ elevation.

The first experimental aim is to characterize cortisol induced NMDA-R mediated Ca2+ elevation in the presence and absence of DHEA in rat CA1 hippocampal neurons. Two different scenarios will be examined. For the first scenario, CA1 neurons will be subjected to an acute exposure to cortisol, followed by continuous NMDA perfusion, and Ca2+ elevation will be characterized. This setup will serve as a control.

For the second scenario, CA1 neurons will be exposed to DHEA before and during an acute administration of cortisol.

NMDA, DHEA, and cortisol will be perfused and the resulting Ca2+ elevation will be characterized.

To evaluate the validity of the null hypothesis, our investigation of these scenarios will be executed in the presence of an intracellular glucocorticoid receptor (GR) antagonist (RU38486) and a protein synthesis blocker (cycloheximide) as demonstrated by Takahashi et al. (2002)8.

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In vivo neuroprotective effects of DHEA against chronic hypercortisolemia in CA1 subset hippocampal neurons DHEA Treatment

NMDA Perfused Cortisol Added

10 min DHEA 15 min DHEA 5 min perfusion of NMDA, DHEA, pre-incubation + Cortisol Cortisol (+ Modulators) (+ Modulators) co-incubation (+ Modulators) Figure 1: Kinetics of DHEA, cortisol, and NMDA treatments. Fura-2AM CA1 neurons will be pre-incubated from t=0 min to t=10 min. minutes with DHEA. Cortisol will be added to the incubation from t=10min to t=25min. NMDA will then be perfused at 1.5mL/min for from t=25 min to t=30 min.

The goal of this experiment is to observe whether in vivo administration of DHEA can exert a neuroprotective effect against the neural atrophy characteristic of hypercortisolemia, chronic elevated cortisol concentration in CA1 hippocampal neurons. Hippocampi, specifically the CA1 subset of neurons, will be excised from rats after the experimental procedures. A Terminal uridine deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay performed on the CA1 subset to identify the extent of neuronal death. This assay measures cell death in tissue based on levels of DNA fragmentation9.

Methods

of the cortisol-induced, NMDA-R mediated Ca2+ elevation. The duration and intensity of Ca2+-sensitive cytosolic fluorescence will be measured. Any changes in Ca2+ elevation caused by protein synthesis blockers or intracellular GR antagonists will provide insight as to whether DHEA operates via intracellular paths.

Effects of variable [DHEA] on cortisol-induced, NMDA-Rmediated Ca2+ elevation CA1 hippocampal neurons, taken from 59 day old Wistar rats (procedure outlined by Takahashi et al., 2002) will be treated with 5µM fura-2AM and washed with BSS for five minutes at 37ºC8.

Cytosolic Fura-2AM fluorescence intensity correlates with concentration of cytosolic Ca2+. Stock solutions of NMDA (100µM), cortisol (10µM), DHEA (10µM & 50µM), cycloheximide (10µM),

Preincubation t=0 to t=10 min

Co-Incubation t=10 min to t=25 min

Perfusion t=25 min to t=30 min

No DHEA (BSS sub.)

(0 µM DHEA Control)

No Cortisol (BSS sub.)

100µM NMDA

No DHEA (BSS sub.)

10µM Cortisol

100µM NMDA 10µM Cortisol

10µM Cortisol

100µM NMDA 10µM Cortisol 10µM DHEA

10µM DHEA (10µM DHEA Control) 10µM DHEA 10µM Cycloheximide Fura 2-AM-treated CA1 neurons

10µM DHEA 10µM RU38486

Monitor Fura-2-AM fluorescence during 5 min perfusion

50µM DHEA (50 µM DHEA Control)

50µM DHEA 10µM Cycloheximide 50µM DHEA 10µM RU38486 Figure 2: Flow chart of experimental design for testing effects of variable [DHEA] on cortisol-induced, NMDA-R-mediated Ca2+ elevation. ©2008 Cornell Synapse | www.cusn.org

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and RU38486 (10µM) will be prepared with BSS, with the final concentration of DMSO being less than 0.01% in each case (to minimalize membrane permeability)8.

Phase One Fura-2AM treated CA1 neurons will be pre-incubated in either 10µM or 50µM DHEA for 10 minutes at 37ºC. After pre-incubation with DHEA, 10µM cortisol will be added to the incubation for 15 minutes (without washing DHEA) for a total incubation time of 25 minutes. 100µM NMDA, 10µM cortisol and 10 or 50µM DHEA will be perfused at a rate of 1.5mL/min and real-time Ca2+ induced fluorescence will be measured for a length of 300s.

Phase Two To test the null hypothesis, modulators such as 10µM cycloheximide, and in a subsequent procedure, 10µM RU38486, will be added for the entire length of the 25 minute incubation to determine whether intracellular GR receptor activation or protein synthesis is responsible for any effects mediated by DHEA (Fig. 1).

In vivo neuroprotective effects of DHEA against chronic hypercortisolemia in CA1 subset hippocampal neurons Five experimental groups, 1 SHAM surgery (simulated adrenalectomy to control for surgical trauma) group and 1 naïve group will be studied. Each group will have 10 rats.

All groups except for naïve and SHAM will receive adrenalectomies to remove circulating cortisol. Group One will be adrenalectomized with no further treatment. Group 2, after adrenalectomy, will be implanted with controlled-release, cortisol-infused pellets (120-150mg); plasma corticosterone levels using the procedure outlined by Muma et al. (1999) have been measured to remain constant at 32 ug/dl for 14 days10. Group Three will receive 100% controlled-release DHEA pellets (120-150mg) in addition to cortisol-infused pellets. A fourth group will receive 50% DHEA /50% cholesterol pellets (120-150mg) and cortisol-infused pellets.

Stable plasma cortisol and DHEA levels will be monitored closely.

A fifth group will receive an equal mass of paraffin pellets containing neither cortisol nor DHEA to control for any effect of the presence of pellets in the brain. Surgeries will be performed under halothane/N2O anesthesia as outlined by Kimonides et al. (1998).

(Fig. 3)7.

Fourteen days after treatment, brains will be rapidly removed and rinsed. Under microscopic guidance, CA1 subset will be isolated and frozen at -70°C until TUNEL assay is performed.

Predicted Outcomes Effects of variable [DHEA] on cortisol-induced, NMDA-Rmediated Ca2+ elevation We expect that 10 µM cortisol will result in prolonged in Ca2+ elevation in the absence of DHEA, and that 10 µM DHEA preincubation followed by co-incubation of 10 µM cortisol with 10 µM DHEA will result in a less prolonged NMDA-R mediated Ca2+ elevation compared to control (cortisol treatment alone). Increasing DHEA concentration to 50µM will serve to further reduce prolongation of Ca2+ elevation. Furthermore, we expect that cycloheximide and RU38486 will not affect DHEA modulation of ©2008 Cornell Synapse | www.cusn.org

CA1 Hippocampal Neurons

Naïve

SHAM Surgery

Adrenalectomized

Monitor [Cort] and [DHEA]

Excise CA1, perform TUNEL

Pellets

No Cort No NMDA

Cort No NMDA

Cort 100% NMDA

Cort 1:1 NMDA/ cholesterol

Monitor [Cort] and [DHEA]

Excise CA1, perform TUNEL Figure 3: Flow chart depicting experimental design for examining the effects of DHEA against chronic hypercortisolemia in CA1 subset hippocampal neurons.

Corticosterone and DHEA levels are monitored for 14 days.

Ca2+ elevation. An unexpected result would be that 10 µM preincubation followed by co-incubation with 10 µM cortisol will result in no change of NMDA-R mediated Ca2+ elevation compared to 10 µM cortisol administration alone. In this case, contrary to our hypothesis, a 50 µM DHEA pre-incubation, followed by coincubation with 10 µM cortisol will remain ineffective in altering the time length of Ca2+ elevation.

If DHEA treatment serves to reduce the time length of a cortisol-induced NMDA-R mediated Ca2+ elevation, it can be concluded that DHEA has anti-glucocorticoid properties, at least against acute cortisol administration. If increasing the concentration of DHEA demonstrates even further reduction in the time length of Ca2+ elevation, then it can be concluded that the effect of DHEA is dose dependent.

If neither the 10µM nor 50µM DHEA concentrations alter cortisol induced Ca2+ elevation, it is possible that the concentrations used were too small to affect any change10. The experimental procedure should be adjusted to take into account larger concentrations of DHEA. If DHEA continues to remain ineffective at higher concentrations, it is possible that DHEA does not have anti-glucocorticoid properties against acute cortisol induced NMDA-R-mediated Ca2+ elevation. It can still be possible for DHEA to exhibit anti-glucocorticoid properties via intracellular, genomic pathways on a longer time scale.

If DHEA treatment increases the cortisol-induced prolongation of Ca2+ elevation, a variety of possibilities need to be considered.

Many modulators have different physiological effects at different concentrations. It is possible that DHEA can only exhibit anti-glucocorticoid properties at a narrow range of concentrations. Recent research also demonstrates that the anti-glucocorticoid properties of DHEA may not be as dependent upon the concentration of DHEA as it is upon the ratio of DHEA/cortisol11.

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modulation of cortisol-induced Ca2+ elevation, DHEA is at least partially exerting a physiological effect via gene transcriptional activation or other intracellular pathways, respectively. Reducing incubation time of DHEA, or conjugating DHEA to BSA (introduce membrane impermeability) will permit more accurate characterization of transient DHEA modulation6.

models of Alzheimer’s disease can be treated with DHEA at different stages and concentrations to evaluate whether the progression of this illness can be halted, or impeded.

If the results from our experiment support the favored hypothesis, we will have taken a critical step in finding a therapy to counteract the debilitating effects of elevated neuronal cortisol levels.

In vivo neuroprotective effects of DHEA against chronic hypercortisolemia in CA1 subset hippocampal neurons The outcome most consistent with our hypothesis is that the TUNEL assay will reflect that CA1 neurons exposed to cortisol and DHEA had less neuronal atrophy than those exposed to cortisol alone. Furthermore, we would expect that a higher concentration of DHEA (100% DHEA pellets) will have a greater neuroprotective effect than lesser concentration (50% DHEA/50% cholesterol).

Two alternative outcomes would be that the TUNEL assay would either show no difference in neuronal death between DHEA-treated CA1 neurons and those chronically exposed to cortisol alone, or demonstrate that DHEA + cortisol receiving CA1 neurons had a greater degree of neuronal death compared to the cortisol-treated controls. These outcomes would be inconsistent with our hypothesis and would suggest that increasing DHEA concentration would have no neuroprotective effect at best, and, at worst, would actually cause further neuronal atrophy.

If results are consistent with favored hypothesis, then we have shown that in vivo controlled-release of DHEA can prevent the neuronal atrophy characteristic of chronic exposure to cortisol in CA1 hippocampal neurons. If TUNEL assays suggest that DHEA had no appreciable effect in decreasing cortisol induced neuronal atrophy, then the experiment will be repeated using different concentrations of DHEA. Other means of exposing the CA1 subset to DHEA and cortisol will also be evaluated (osmotic pumps)12. If DHEA is shown to exacerbate neuronal atrophy, once again, other concentrations of DHEA will be explored. The anti-glucocorticoid properties of DHEA may function at very specific concentrations, or ratios to cortisol.

References

Discussion Should DHEA demonstrate anti-glucocorticoid properties in both the in vitro and in vivo portions of this experiment, there is great potential for DHEA to be used therapeutically against cortisol-induced neurodegeneration. However, subsequent experiments will need to elucidate the dose-dependency and DHEA:cortisol dependency to determine the maximal effectiveness of DHEA. Also, the mechanism by which DHEA mediates its transient effects on the neuron is unknown. It is hypothesized that cortisol mediates its acute effects on neurons by activating CORT membrane receptors which subsequently interact with NMDA-R via a protein kinasedependent pathway. We predict a similar mechanism for the action of DHEA, however; further experimentation is necessary to clarify the exact pathways involved. Answering these questions would solidify the spotty knowledge we have on the relationship between cortisol and DHEA. Those suffering from alzheimer’s disease, a neurodegenerative pathology characterized by increased cortisol levels, may benefit from the neuroprotective effects of DHEA. For this reason, animal ©2008 Cornell Synapse | www.cusn.org

1. Swaab, D. F., A. Bao and P. Lucassen: The stress system in the human brain in depression and neurodegeneration. Aging Research 2005; 4:141-194.

2. Li, X. and P. B. DePetrillo: Corticosterone increases serotonin type-3 receptormRNA in rat pheochromocytoma-12 cells. Neuroscience 2002; 331:143-145.

3. Brown, E., L. Beard, A. Frol and A. Rush: Effect of two prednisone exposures on mood and declarative memory. Neurobiology of Learning and Memory 2005; 86:28-34.

4. Gubba, E. M., J. W. Facett and J. Herbert: The effects of corticosterone and dehydroepiandrosterone on neurotrophic factor mRNA expression in primaryhippocampal and astrocyte cultures.

Molecular Brain Research 2004; 127:48-59.

5. Alhaj, H.A., A. E. Massey and R. Williams: Effects of DHEA administration on episodic memory, cortisol and mood in healthy young men: a double-blind, placebo-controlled study. Psychopharmacology 2006; 188:541-551.

6. Okuhara, D. and S. Beck: Corticosteroids Influence the Action Potential Firing Pattern of Hippocampal Subfield CA3 Pyramidal Cells. Neuroendocrinology 1998; 67:58-66.

7. Kimonides, V. G., N. H. Khatibi, C. N. Svendsen, M. V. Sofroniew and J. Herbert: Dehydroepiandrosterone (DHEA) and DHEA-sulfate (DHEAS) protect hippocampal neurons against excitatory amino acid-induced neurotoxicity. Proc. Natl. Acad. Sci. 1998; 95:1852-1857.

8. Takahashi, T. and T. Kimoto: Corticosterone acutely prolonged N-methyl-D-aspartate receptor-mediated Ca2+ elevation in cultured rat hippocampal neurons. J. of Neurochemistry 2002; 83:14411451.

9. Torres, C., F. Munell, I. Ferrer, J. Reventos and A. Macaya: Identification of necrotic cell death by the TUNEL assay in thehypoxicischemic neonatal rat brain. Neuroscience 1997; 230: 1-4.

10. Muma, N. A. and S. G. Beck: Corticosteroids alter G protein inwardly rectifying potassium channels protein levels in hippocampal subfields. Brain Research 1999; 839:331-335.

11. Li, G. and M. Cherrier: Salivary cortisol and memory function in human aging. Neurobiology of Aging 2006; 27:1705-1714.

12. Howell, M. P. and L. J. Muglia: Effects of genetically altered brain glucocorticoid receptor action on behavior and adrenal axis regulation in mice. Frontiers in Neuroendocrinology 2006; 27:275284.

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ACKNOWLEDGEMENTS The publication of this and previous volumes of the Cornell Synapse were made possible with generous contributions, guidance, and encouragement from the following: Dr. Kraig Adler

Professor, Department of Neurobiology and Behavior

Dr. Andrew H. Bass

Professor, Department of Neurobiology and Behavior

Dr. Ronald Booker

Professor, Department of Neurobiology and Behavior

Ms. Bonnie Comella

Director of Undergraduate Advising, Office of Undergraduate Biology

Dr. Robin L. Davisson

Professor, Department of Biomedical Sciences and Department of Cell and Developmental Biology

Dr. Thomas Eisner

Professor, Department of Neurobiology and Behavior

Dr. Gerald W. Feigenson

Professor, Department of Biochemistry, Molecular and Cell Biology

Dr. Joseph R. Fetcho

Professor, Department of Neurobiology and Behavior

Dr. Carl D. Hopkins

Professor, Department of Neurobiology and Behavior

Dr. David P. McCobb

Associate Professor, Department of Neurobiology and Behavior

Dr. Michele Moody-Adams

Vice Provost of Undergraduate Education and Professor, Department of Ethics and Public Life

Dr. Thomas D. Seeley

Chairman and Professor, Department of Neurobiology and Behavior

Dr. David J. Skorton

President, Cornell University

Dr. Laurel Southard

Director of Undergraduate Research and Outreach, Office of Undergraduate Biology

Cornell Student Assembly Finance Commission (SAFC) If you are interested in contributing your support (financial or otherwise) to the Cornell Synapse and the Cornell Undergraduate Society for Neuroscience, please email us at synapse@cusn.org Cover design by Adrian Humphrey (adrian@ember.co.nz). Original image can be accessed at http://www.ember.co.nz/gallery/neuron/. Š2008 Cornell Synapse | www.cusn.org

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