10 minute read
Breakthrough Research
by UCICNLM
BREAKTHROUGH
RESEARCH
A mechanism for how our senses and thoughts come together
Our ability to think, decide, remember recent events and more, comes from our brain’s neocortex. A team led by CNLM Fellow Gyorgy Lur and graduate student Daniel Rindner recently discovered key aspects of the mechanisms behind these functions. The neocortex integrates feedforward and feedback information streams, a back-and-forth communication that allows the brain to pay attention, retain short-term memories, and make decisions. Think about crossing a road at a busy intersection. There are trees, people, moving vehicles, traffic signals, signs and more. Your higher-level neocortex tells your sensory system which things in the environment merit attention for deciding when to go across. How neurons in the brain participate in these complex processes has been a mystery until now. The team discovered that feedforward and feedback signals converge onto single neurons in the parietal region of the neocortex. The researchers also found that distinct types of cortical neurons merge the two information streams on markedly different time scales and identified the cellular and circuit architecture underpinning these differences. Understanding these processes is critical for developing future treatments for neuropsychiatric ailments like sensory-processing disorders, schizophrenia and ADHD, as well as for strokes and other injuries to the neocortex. The paper was published in Neuron.
Learn more at: https://cnlm.uci.edu/lur-ids
Sex differences in memory encoding triggered by puberty
New research has highlighted a femalespecific memory mechanism leading to differences in learning between males and females, which are triggered by biological events occurring during puberty. The research, led by CNLM Fellow Christine Gall, further found that pharmacological intervention could restore these key memory mechanisms—in females—to levels observed before puberty. The study demonstrated that female rodents had enhanced spatial learning and long-term potentiation (a form of cellular memory) relative to males. This situation, however, reverses after puberty. Gall and colleagues show that the threshold for memory encoding increases in females and decreases in males after puberty. In females, this is linked to increased inhibition by a specific type of GABA receptor that undergoes changes during puberty. Suppressing this receptor pharmacologically restored learning to before puberty levels in females. Clearly, this shows that males and females have different mechanisms underlying their learning and memory and emphasizes the need to develop new approaches to develop sexually differentiated approaches for the development of therapeutic treatments and their applications at different life stages. This work was published in Nature Neuroscience with Aliza Le, alumna of the CNLM’s Howard Schneiderman Training Program in Learning and Memory as first author.
Learn more at: https://cnlm.uci.edu/memory-encoding
How a Parkinson’s drug may improve then diminish quality of life
A team of researchers led by CNLM Fellow Amal Alachkar has discovered a possible reason why L-dopa, the front-line drug for treating Parkinson’s disease, loses efficacy and causes dyskinesia—involuntary, erratic muscle movements of the patient’s face, arms, legs and torso—as treatment progresses. The team studied L-dopa using a technology called surface plasmon resonance to measure interactions between the drug and target proteins. They discovered that L-dopa interacts with proteins creating a complex that causes inflammation which may trigger dyskinesia. As Parkinson’s progresses, higher doses of L-dopa are required to alleviate disease symptoms and negative side effects occur with even lower doses, resulting in a narrow therapeutic window. It’s possible that L-dopa forming complexes with other proteins reduces the amount of circulating L-dopa available for dopamine synthesis in the brain. Future studies aim to determine whether such complexes can be detected in the blood of Parkinson’s patients, serving as a new biomarker and target for novel disease treatments. Findings from the study were recently published in ACS Chemical Neuroscience.
UCI team uncovers key brain mechanisms for organizing memories in time
Combining electrophysiological recording techniques in rodents with a statistical machine learning analysis of huge troves of data, a new study led by CNLM Fellow Norbert Fortin and statistician Babak Shahbaba uncovered evidence that the hippocampus encodes and preserves progressions of experiences to plan future behavior. Findings could have implications for our understanding of how this type of memory gets worse as we get older and is severely impacted by a variety of neurological conditions. The project, which took more than three years to complete, involved monitoring the firing of neurons in rats’ brains as they completed sequence odor memory tasks and using machine learning techniques to “decode” how these neurons captured this sequential information. Interestingly, the team used convolutional neural networks, a type of deep learning network that is used widely in other applications like face recognition. The tools and methodologies developed during this project can be applied to a wide range of problems and to other brain regions. The work appears in Nature Communications.
Learn more at: https://cnlm.uci.edu/memories-in-time
Solving algorithm ‘amnesia’ reveals clues to how we learn
A discovery about how algorithms can learn and retain information more efficiently offers potential insight into the brain’s ability to absorb new knowledge. A team led by CNLM Fellow Bruce McNaughton and graduate student Rajat Saxena tested a new hypothesis about learning using artificial neural networks. Researchers have long theorized that our ability to learn new concepts stems from the interplay between the brain’s hippocampus and the neocortex. The hippocampus captures fresh information and replays it during rest and sleep. The neocortex grabs the new material and reviews its existing knowledge so it can interleave, or layer, the fresh material into similar categories developed from the past. However, how the brain can sort through the whole trove of information it has gathered during a lifetime remains a mystery. The team discovered that when the network interleaved a much smaller subset of old information, including mainly items that were similar to the new knowledge they were acquiring, they learned it without forgetting what they already knew. These findings suggest a brain mechanism for why experts at something can learn new things in that area much faster than nonexperts. If the brain already has a cognitive framework related to the new information, the new material can be absorbed more quickly because changes are only needed in the part of the brain’s network that encodes the expert knowledge. The findings offer possibilities as well for making algorithms in machines such as medical diagnostic equipment, autonomous cars and many others more precise and efficient. The study appears in Proceedings of the National Academy of Sciences.
Learn more at: https://cnlm.uci.edu/algorithm-amnesia
Learn more at: https://cnlm.uci.edu/smarter-ai
A hybrid human-machine framework for building smarter AI
From chatbots that answer tax questions to algorithms that drive autonomous vehicles and dish out medical diagnoses, artificial intelligence undergirds many aspects of daily life. Creating smarter, more accurate systems requires a hybrid human-machine approach, according to CNLM Fellow Mark Steyvers, computer scientist Padharic Smyth and their colleagues who developed a new mathematical model that can improve performance by combining human and algorithmic predictions and confidence scores. To test the framework, the team conducted an image classification experiment in which human participants and computer algorithms worked separately to correctly identify distorted pictures of animals and everyday items – chairs, bottles, bicycles, trucks, and ranked their confidence. The results showed large differences in confidence between humans and AI algorithms across images. When predictions and confidence scores from both were combined using the team’s new Bayesian framework, the hybrid model led to better performance than either human or machine predictions achieved alone. This work points to new and improved approaches to human-AI collaboration. The study was published in Proceedings of the National Academy of Sciences.
Who benefits from brain training, and why?
If you are skilled at playing puzzles on your smartphone or tablet, what does it say about how fast you learn new puzzles, or more broadly, how well can you focus in school or at work? In the language of psychologists, does “near transfer” predict “far transfer”? A new study in the labs of CNLM Fellow Susanne Jaeggi and UC Riverside psychologist Aaron Seitz is investigating these questions. The team conducted three randomized control trials involving nearly 500 participants and discovered that the extent to which people improve on near transfer tasks determines whether far transfer to an abstract reasoning task is successful. By analogy, if a person running on a treadmill in the gym proceeds to be able to run faster outdoors (near transfer), then this improvement predicts whether this person would be better prepared to engage in other physical activities such as cycling or playing a sport (far transfer). Why is this relevant? People are constantly being sold brain-training games. Some studies claim these games work; other studies claim the opposite, making it difficult to interpret the results of interventions. Studies also lump together people who show near transfer with people who show no near transfer. The new paper clarifies some of this confusion. Jaeggi cautions that the claims of companies promising that their games improve core cognitive functions need to be carefully evaluated. The study was published in Nature Human Behavior.
Learn more at: https://cnlm.uci.edu/brain-training
UCI-led study is first to find that long- and short-term memory vie for brain space
- SARA MEDNICK, PHD
PROFESSOR, COGNITIVE SCIENCE
The brain is a battlefield where cognitive domains vie for limited resources, and this appears to be particularly true during sleep. A UCI-led research team has discovered that long-term memory consolidation and working memory processes that happen during rest do so at a cost to one another. The study, published in Proceedings of the National Academy of Sciences, is the first to illuminate this critical trade-off in the human brain.
Research has found that both working memory and long-term memory rely on offline periods that include sleep to effectively accomplish their tasks. These two very different types of memory processes happen during the same cycle of slow-wave sleep, which occurs during the first three to four hours of slumber.
In this new study, participants who ingested zolpidem (Ambien’s generic equivalent) performed better on the longterm memory tasks but worse on working memory tasks when compared with those who had taken the placebo.
Learn more at: https://cnlm.uci.edu/memory-mednick
Brain inflammation may link sleep disturbance with risk for Alzheimer’s disease
Brain inflammation, sleep disturbance and disrupted brain waves during sleep have all been associated with Alzheimer’s disease, but the interactions among them have not been investigated until now. A multisite team led by CNLM Fellows Bryce Mander and Ruth Benca discovered that brain inflammation may link Alzheimer’s disease risk with sleep disturbance. The study included adults in their 50s and 60s who had genetic risk for Alzheimer’s disease. Sleep was recorded overnight using high-density electroencephalography to map brain wave expression, and overnight memory retention was assessed. Participants also underwent a lumbar puncture so that cerebrospinal fluid biomarkers of brain inflammation, beta-amyloid and tau proteins, and neuronal integrity could be examined. The team discovered that brain inflammation had downstream effects on tau proteins and neuronal synaptic integrity. This resulted in deficits in the brain’s capacity to generate fast sleep spindles, which contribute to age-related memory impairment in older adults. These results may aid early detection and prevention efforts by identifying novel treatment targets at preclinical stages. The study was published in the journal Sleep.
Learn more at: https://cnlm.uci.edu/brain-inflamation
