Training and Neural Plasticity

Running head: TRAINING & PLASTICITY

Training and Neural Plasticity: Evidence for Task-specific Microstructural Neural Reorganization Jonathan Strohl George Mason University

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Abstract Diffusion tensor imaging (DTI) has allowed for further investigation into microstructural organization of white-matter. From the use of DTI and other neurophysiological measures, the plasticity of neural pathways is becoming better understood. Research has provided evidence that fractional anisotropy values of white-matter can change as a result of experience. Whether the task is juggling or a working memory intensive computer task, neural microstructures appear to respond accordingly to experience and behavior. Recent evidence suggests that neural plasticity continues throughout the lifespan and possibly even until death. Future research will explore the extent and perseveration of plasticity as well as the role of genotype in the continual demyelination and myelination process.

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Outline 

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Background and introduction o Review of DTI o Review of Fractional Anisotropy o Review of relevant neurobiology topics  Myelination, axon diameter o Introduction of gray and white-matter and their function in learning Evidence and discussion o Introduction to learning induced neural plasticity o Presentation and discussion of experimental evidence from a visual motor paradigm displaying gray-matter plasticity  Juggling training effects and gray-matter correlates o Presentation and discussion of evidence for a positive correlation between piano training and white-matter plasticity  Piano playing expertise and white-matter correlates o Presentation and discussion of evidence for a positive correlation between complex strategy game training and white-matter plasticity  Expertise of the game “Baduk” and white-matter correlates o Presentation and discussion of experimental evidence from a visual motor paradigm displaying white-matter plasticity  Juggling training effects and white-matter correlates o Presentation and discussion of experimental evidence from a working memory training program and white-matter plasticity  Working memory training effects and white-matter correlates with a younger population o Presentation and discussion of experimental evidence supporting white-matter plasticity through at least the sixth decade  Video game training with older adults Conclusions and future directions o Conclusions on FA measurements o Conclusions on neural plasticity o Discussion of future research on training as an intervention for at risk populations o Discussion of future research on the role of genotype in neural plasticity and training

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Training and Neural Plasticity: Evidence for Task-specific Microstructural Neural Reorganization A plethora of research exists on the robust effects of training in the development of cognitive skills and the enhancement of performance. However, an area of research that has remained elusive in cognitive neuroscience, until recently, has been the attainment of substantial evidence linking neural structural changes to experience. The dearth of research on this topic has mostly been due to the technological limitations of neuroimaging methods. With the development of Diffusion Tensor Imaging (DTI) by Michael Moseley and others in the 1990s, researchers obtained a highly effective method for imaging neural microstructures.

DTI is essentially a measurement of the rate and direction of water diffusivity through neural microstructures. Furthermore, it allows cognitive neuroscientists a method for measuring the integrity of neural tracts, important for a better understanding of the pathways connecting cortical structures. From this technology, measures of anisotropy and diffusivity can be utilized to provide empirical comparisons across individuals.

Fractional Anisotropy (FA) is the most common equation used for reporting anisotropy in a voxel, due to its effectiveness in limiting measurement noise. Axial diffusivity, also known as the longitudinal diffusivity, refers to the diffusivity along the principal axis. Radial diffusivity can then be calculated by averaging the two minor axes. From the data that diffusivity and FA values provide, inferences can be drawn of the integrity of neural connections and corresponding networks.

FA values are considered to be a measurement of the frequency of myelination of an axon as well as an axon’s diameter. This theory is derived from myelin’s known role in the

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facilitation of water diffusion throughout neurons. Myelin, created by oligodendrocytes, act as an insulated coating that facilitates the travel of neural signals over often long distances in the human body. Without sufficient myelin over a given length, the electrical neural signals can lose signal strength. Once axons reach maturation in the myelination process, the changes that these axons can undergo are thought to become more limited. However, the limitation of their plasticity over the human lifespan is not fully understood.

As it relates to neurons, increased mylenation, and consequently faster signal transmission rates, is not always better. In complex tasks, information must be transported across many different neural regions and networks (Fields, 2008). For the execution and precision of these complex cognitive tasks, delays are necessary so transmission speeds can be coordinated appropriately for efficiency. If neural signals reach different sites at the inappropriate time, a decrease in functioning is logically the result. Neurological disorders such as Alzheimer’s disease and schizophrenia are thought to be a result of disrupted connectivity and inefficient neural transmission as a result of an abnormal myelination process.

While FA is the most commonly used measure for detecting cortical health and organization, other measures are also used to assess fu4nctioning. The measurement of graymatter volume is another method for detecting neural structural changes. Unlike white-matter, gray-matter does not contain many myelinated axon tracts, consequently, water diffuses symmetrically. Therefore, FA values are not suitable for detecting signal change. The neurons in gray-matter execute the mental and physical functions, however, white-matter is responsible for the transportation of this information to other neural systems. Research has demonstrated a positive relationship between gray-matter volume and various cognitive functioning.

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6 Evidence & Discussion

Evidence has existed for several years for learning induced gray-matter changes. An often cited paradigm developed by Driemeyer, Boyke, Gaser, Bucel, and May (2008), elucidates the effect of visual motor task training on gray-matter volume. In this study, participants were trained to perform a cascading 3-ball juggling exercise. The researchers scanned participants after 1, 2, and 5 weeks of juggling practice, and then 2 and 4 months after the discontinuation of practice.

Results from the study indicate that gray-matter changes can occur within a week of training (Driemeyer et al., 2008). In their longitudinal analysis, an increase in the middle temporal area of the visual cortex (MT) in the right and left hemisphere was detected at scans 2 through 4 as compared to the baseline scan. After discontinuing juggling practice, gray-matter volume then decreased to pre-training levels in the MT area. The researchers also found increased gray-matter volume after beginning the training, and then correspondingly decreasing gray-matter volume after the cessation of training - bilaterally in the frontal lobe, temporal lobe, and cingulate cortex.

While gray-matter increases were observed while learning the task, these detected changes receded when training stopped (Driemeyer et al., 2008). These results demonstrate that learning has clearly occurred as participants can perform the task if requested to do so despite not actively practicing. Despite the preservation of this acquired skill, the observed change in gray matter has not persisted. When performance results (length of time able to perform the 3-ball cascade) and practice length (number of hours practiced per day) measures were regressed graymatter volumetric changes, no results were found to be significant. Demonstrating that task expertise did not correlate with the gray-matter volumetric changes.

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These results support the theory that neural structural changes, at least for gray-matter, are dependent on the learning of a new task, while continued practice of the task is necessary for these structural changes to persist. The encoding process of new motor and task information seems to result in neural structural changes, while the active maintenance and retrieval of this information supports the preservation of these structural changes. When mental resources need to be redirected from a prior task to a new task, these results suggest that neural structures will respond accordingly in order to most efficiently distribute resources to the actively performed task. This attests to the perseverance of plasticity of the human brain; potentially necessary for humans to adapt and remain efficient, productive, and safe in a changing environment.

One of the earlier studies on white matter specialization was investigated by Bengtsson et al. in 2005. Concert pianists are an interesting and advantageous group for advancing the understanding of neural correlates associated with task specialization. Definitively experts at the task of coordinated fine motor movement, as well as executive functioning, and other higher order cognitive functions. In this investigation of expert pianists, the researches regressed the musicians’ FA values on the number of hours practiced by the pianists during the periods of childhood, adolescence, and adulthood. Childhood and adolescent practice correlated highly in the pianists’ self-reports, so it cannot be ascertained as to which development period contributed more highly to the variance in FA values. Conversely, adult practicing time did not correlate significantly with childhood or adolescent practicing time. Significant correlations between FA and practicing times were found for all development periods. Interestingly, however, FA values from different brain regions correlated with practicing time at different development periods.

Childhood practicing hours correlated with FA in several areas: 1) the bilateral posterior limbs of the internal capsule, 2) the isthmus extending into the upper splenium of the corpus

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callosum, 3) the callosal body, and 4) fiber tracts in the frontal lobe (Bengtsson et al., 2005). The isthmus and upper splenium contain connective fibers from the auditory regions in the superior temporal gyri and parietotemporal area. Superior frontal regions, including the dorsal premotor cortices and the mesial premotor areas, are connected to the body of the corpus callosum. These regions play an important role in bimanual coordination and the learning of complex movement sequences.

The right posterior internal capsule was the only region which pianists had significantly difference FA values from controls (Bengtsson et al., 2005). The primary sensorimotor and premotor cortices have descended fibers which transport information through the corticospinal tracts of the internal capsule. Humans rely on the sensorimotor and premotor areas for complex manual dexterity and independent finger movements - functions continuously utilized during piano playing. This evidence suggests that the area has become strengthened and enhanced in order to meet the demanding task requirements of an expert pianist.

Hours spent practicing piano during adolescence correlated significantly with FA values from 1) the splenium cluster which extends into the white-matter of the occipital lobe, and 2) the body of the corpus callosum (Bengtsson et al., 2005). Since both of these areas also correlated significantly with practicing hours in childhood, it is difficult to deduce the developmental time period for these areas. Hours spent practicing during adulthood correlated with FA in 1) the left anterior limb of the internal capsule, and 2) in a fiber bundle in the right temporoparietal junction. This area connects to the temporal and frontal regions. Continued myelination in these areas during this stage is sensible since the frontal lobe can still be undergoing development in the third decade.

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Bengtsson et al. (2005) conclude that childhood practicing hours significantly correlate with more regions because the brain is more plastic during this time in development. However, it is important to note that the only region of the pianists that differed from controls was the posterior limb of the internal capsule, therefore it is difficult to draw any inferences beyond this finding. Although, this result alone, with this region’s known responsibility for fine finger movement functioning, provides strong evidence for neural specialization based on training and experience.

In a different, yet comparable, study observing FA differences and expertise observationally, Lee et al. (2009) investigated the relationship between expertise and whitematter structures by comparing “Baduk” players to controls. Baduk is a traditional Eastern Asian board game that best compares to chess in its strategy and complexity. The objective of the game is to occupy more territory than your opponent while guarding your own position. The game requires players to utilize spatial positioning and access rule knowledge while executing offensive and defensive maneuvers. Previous fMRI studies have demonstrated that Baduk and chess activate similar regions during gameplay, such as bilateral pre-motor areas in the frontal lobe as well as regions in the parietal and occipital lobes. The authors explain that Baduk activates areas empirically supported to be engaged during attention, spatial perception, imagery, mental rotation, and mnemonic processes. Contrary to the authors’ hypothesis, players of Baduk and chess did not engage the general intelligence area in the frontal lobe associated with computational and logical skills, suggesting these games primarily require spatial reasoning for expertise. In contradiction to chess, Baduk playing facilitated right-hemisphere dominance in their previously conducted fMRI studies.

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In the Lee et al. (2009) study, FA values of Baduk experts were compared to healthy controls while using IQ scores (Korean language version of WAIS) and age as covariates. The study found Baduk experts, compared to controls, exhibited right-hemisphere predominance in the frontal lobe. When compared to controls, Baduk experts exhibited significantly increased FA in anterior cingulate, medial frontal gyrus, middle frontal gyrus, inferior frontal gyrus, as well as in the left medial and middle frontal working memory areas. On the other hand, Baduk experts showed decreased FA in some frontal lobe regions, including the bilateral premotor white-matter regions and right precuneus white-matter regions.

The aforementioned frontal regions, with the increased FA values, are associated with cognitive functions such as attentional control, long-term memory processes, and problem solving, and other executive functions (Lee et al., 2009). All assumed to be essential for expertise in the game. Many connective sites were found to have increased FA values in the frontal region compared to controls, including pericallosal white-matter areas connecting both hemispheres, connective tracts beginning in the right cingulum bundle and extending to the cingulate gyri, as well as several other right subcortical white-matter regions. These results suggest “fronto-cingulo-striatal regulatory structures� are more developed in Baduk players than controls.

Contrary to the FA increases in the frontal lobe, Baduk experts exhibited left-hemisphere predominance in the temporal lobe indicated by increased FA in the left inferior temporal areas, including the fusiform gyrus (Lee et al., 2009). These areas are associated with the perception and manipulation of visual spatial information and attentional shifting in visuospatial attention tasks. In the limbic and subcortical structures, Baduk experts exhibited right hemisphere predominance indicated by increased FA in right peri-lenticular white-matter regions near the

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globus pallidus, putamen, and thalamus. In addition, increased FA values in these inferior temporal lobe areas and “fronto-striato-perithalamic regions� suggest that Baduk mastery requires time-related strategies, such as object identification, in addition to spatial strategies.

Despite these findings for increased FA in many areas, Baduk experts exhibited lower FA in others, including both right and left inferior frontal gyri, bilateral premotor areas, right precuneus, and left inferior parietal lobule (Lee et al., 2009). These results suggest that Baduk players are not dependent on working memory load for mastery of the game. The hypothesis is that Baduk experts rely on the retrieval of chunked information and are therefore not dependent on load-dependent memory capacity.

While the studies investigating expertise in piano playing and Baduk provide support for white-matter specialization, the limitation of these studies is that they are both performed observationally. However, recent studies have investigated white-matter plasticity in more controlled experimental settings. By implementing an experimental approach in repeated studies, greater inferences can be drawn on the effect of experience on white-matter microstructure. From these more experimentally controlled studies, more precise knowledge can be gained on the plasticity of white-matter.

Scholz, Klein, Behrens, and Johansen-Berg (2009) implemented a similar paradigm to the aforementioned Driemeyer et al. study. However, in this investigation, DTI was utilized to allow for FA measures of white-matter. Participants were scanned before training (scan one) and after a six week training period (scan two) on the 3-ball juggling cascade task. After training, participants were able to perform at least two cycles of the 3-ball juggling cascade. Participants then stopped juggling practice and were then scanned again (scan three) approximately four weeks after the cessation of practice. A comparison of the data gathered from scans one and two

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revealed a significant increase in FA within white-matter underlying the intraparietal sulcus (IPS). These elevated FA values persisted and were also found at scan three.

This study also investigated possible changes in associated gray-matter. This explored the hypothesis that learning is associated with co-localized white and gray-matter changes. Using voxel based morphometry, gray-matter was found to have increased density in the medial occipital and parietal lobe in associated and overlying white-matter regions found to have significant FA increase. Comparable to the findings in the study by Driemeyer et al., structural changes were not significantly correlated with juggling performance or progress. These results provide further evidence that structural changes are a result of the novelty of the experience and the length of time spent training and, consequently, reinforcing neural connections.

The aforementioned study presents empirical evidence in an experimental paradigm for microstructural changes as a result of motor training. Despite the cognitive resources, such as attention, necessary for the performance of a juggling task, this study does not provide strong inference for structural changes as a result of a cognitive task independent of physical exercise.

A recent study by Takeuchi et al. (2010) explored the effect of working memory training on white-matter FA. Participants in the study were scanned before training occurred (Takeuchi et al., 2010).

Participants then played a working memory training game for twenty-five minutes per day for two months. The training games consisted of a visuospatial working memory task, an N-back task, and a dual N-back task. Each participant cycled through playing one of the three games each day.

The behavioral performance measures from gameplay demonstrated that participants improved in performance at the end of the training compared to the initial three training sessions

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(Takeuchi et al., 2010). A significant increase in FA was found in 1) a white matter cluster adjacent to the IPS, 2) a cluster that wraps and spreads around the body of the corpus callosum as well as adjacent frontal lobe white-matter regions, and 3) a cluster in the white matter region of the frontal lobe that neighbors the parietal lobe border. The FA values in the first two regions (identified with significant change) also correlated with number of training hours. No areas showed comparatively reduced FA following completion of the training.

These results provide evidence for structural changes in neural connectivity as a result of a solely cognitive task. The IPS and associated white-matter region adjacent to the corpus callosum are both typically associated with working memory functioning. The region around the IPS was shown in a meta-analysis of imaging studies to be involved with executive functions in working memory and plays a key role in working memory functioning (Takeuchi et al., 2010).

This known role of the IPS region in working memory and the results from this study suggest that the white-matter tracts in the connecting IPS region may be responsible for the increased performance in working memory tasks.

The white-matter tracts adjacent to the anterior corpus callosum are known to connect the bilateral dorsolateral prefrontal cortexes (DLPFCs). The DLPFC is also believed to be responsible for working memory executive functioning and a key node in the working memory circuitry. These results suggest that performance increases are a result of an increase of interhemispheric white-matter tracts that connect the two bilateral DLPFCs across the frontal lobe. It is then sensible to suggest that the gameplay from working memory training increased the structural integrity of the white matter tracts, as observed by the increased FA values, through continual repetition and utilization of the tracts for task execution.

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Despite the knowledge that the DLPFC is perhaps the most instrumental in the capacity of the working memory system, the FA changes in this area were minimal compared to the FA changes observed in the IPS. However, this finding may have been a result of the solely visual modality of the tasks used in all three video games. The left DLPFC has been shown to have greater activation during auditory verbal working memory tasks, while the left IPS area has been shown to have greater activation during visual verbal working memory tasks (Takeuchi et al., 2010). The differences in task-specific utilization of neural regions may account for the greater FA changes in the IPS region. Perhaps an fMRI study with participants playing the working memory training games could shed light on the specific areas of activation associated with the tasks and stimuli sets that the researchers used. It is also plausible that the white-matter tracts anterior to the corpus callosum mature at an earlier point in development and are therefore less plastic and susceptible to training intervention than white-matter in the IPS region.

With substantial evidence providing support for task-specific white-matter microstructural changes, the next logical progression for the research was not only furthering understanding of white-matter changes associated with different tasks but the investigation of the status of plasticity later in the lifespan. Research has provided support for neural plasticity into the 20s and even 30s, especially in areas of the frontal lobe where the myelin specialization process is believed to continue late into the development process. However, the extent of neural plasticity perseveration and the time when plasticity terminates (if it all) remains difficult to pinpoint. Perhaps certain areas retain plasticity longer into the lifespan than others. A study by Lovden et al. (2010) attempted to instantiate the idea of the extension of neural plasticity into older age.

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In the study by Lovden et al. (2010), participants trained for 1-hour sessions for approximately one-hundred and one hours on three working memory, three episodic memory, and six perceptual speed tasks. Training took an average of one-hundred and eighty-six hours to complete with scans occurring before and after training. In addition, participants received cognitive computer testing pre and post-training. Four groups were established, 1) a younger intervention group, 2) an older intervention group, 3) a younger control group, and 4) an older control group. The younger participants had a mean age of approximately twenty-five years and the older participants of approximately sixty-nine years.

The younger and older intervention group both displayed mean diffusivity (MD) increases in the white matter tract of the anterior corpus callosum (Lovden et al., 2010). The older intervention group, unlike the three other groups, also displayed significantly increased FA values in the same region of interest. More interestingly, the intervention related effect for FA values for the anterior corpus callosum did not differ significantly between age groups. Also, only changes in radial, but not axial, diffusivity were detected in the region of interest. Cognitive performance increased over time for measures of working memory, episodic memory, and perceptual speed. The younger age group had larger performance increases over time than the older age groups in the perceptual speed and episodic memory tasks.

The results from this study provide indication that neural plasticity continues into at least the sixth decade (Lovden et al., 2010). This also provides evidence that experience dependent white-matter microstructural changes can occur well past the myelination maturation process, suggesting a normal and ongoing dynamic process of demyelination and myelination across the lifespan.

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16 Conclusions and Future Directions

FA measures reflect white matter properties such as axon caliber and myelination. The ruling hypothesis is that experience and behavior can alter neural structure as a result of increased conduction velocity and synchronization of nervous system signals. It is posited that increased neural efficiencies are necessary in order to perform the task and the improvement of the conduction and synchronization facilitates the health and integrity of the cortical microstructures.

A compromise in white-matter microstructural health has been linked to cognitive decline in aging (Lovden et al., 2010). The importance of white-matter as the transportation of signals across brain regions has been substantiated. Strong evidence exists that these microstructures can become altered and often strengthened as a result of experience. This is believed to have evolutionary advantages for adaption to task-specific environmental demands. Not only can these white-matter tracts adapt and change as a result of a task, but they can also become strengthened as a result of continual experience and development of expertise (Lee et al., 2010).

Strong evidence now exists for neural plasticity into at least the sixth decade (Lovden et al., 2010). These findings suggest that plasticity may even continue until death. Further research needs to be conducted to support the hypothesis that continued neural plasticity is possible throughout the lifespan. Additional research is also needed to determine which regions are more plastic than others at set time periods in the lifespan. Evidence suggests that certain areas are more plastic than others at different stages of development (Bengtsson et al., 2005).

Since white-matter persists into at least the sixth decade, future investigations should determine the plausibility of training as an intervention for those at risk for disorders that effect white-matter structures, such as Alzheimer’s disease. Future research should explore the

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effectiveness of varying training programs in combatting white-matter degradation in older populations. The most effective training program may seemingly be low-fidelity but provide the most beneficial outcome. An effective training program as an intervention for those at risk for diseases effecting white-matter will most likely be one that activates areas associated with underutilization in everyday life. By exercising and utilizing these resources, it is plausible that neural microstructures that are declining toward a pathological condition can be strengthened and its progression possibly reversed.

Future research should also explore the role of genotype in neural plasticity. Genotype, especially gene dosage from the APOE4 allele, has been shown to play an important role in cognitive decline and white-matter volume degradation (Espeseth et al., 2006). APOE and other genes likely have an interactive effect on the process of white-matter degradation associated with aging and the effectiveness of cognitive training intervention. Future research will likely shed more light on the influence of genotype on neural plasticity.

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18 References

Bengtsson, S. L., Nag, Z., Skare, S., Forsman, L., Forssberg, H., & Ullén, F. (2005).

Extensive piano playing practicing has regionally specific effects on white matter development. Nature Neuroscience, 8(9), 1148-1150. Doi: 10.1038/nn1516 Driemeyer, J., Boyke, J., Gaser, C., Bucel, C., & May, A. (2008). Changes in gray matter induced by learning--revisited. PLoS ONE, 3(7), 1-5. doi:10.1371/journal.pone.0002669 Espeseth, T. Greenwood, P. M., Reivang, I., Fjell, A. M., Walvhold, K. B., Westlye, E., Lundervold, A., Rootvelt, H., & Parasuraman, R. (2006). Interactive effects of APOE and CHRNA4 on attention and white matter volume in healthy middleaged and older adults. Cognitive, Behavioral, and Affective Neuroscience,6(1), 31-43.

Fields, R. D. (2008, March). White matter matters. Scientific American, 54-61.

Retrieved from http://www.scribd.com/doc/6508146/White-Matter-Matters Lee, B., Park., J., Jung, W. H., Kim, H. S., Oh, J. S., Choi, C., Jang, J. H., Kang, D., & Kwon, J. S. (2010). White matter neuroplastic changes in long-term trained players of the game of “Baduk” (GO): A voxel-based diffusion-tensor imaging study. NeuroImage, 52(1), 9-19. doi: 10.1016/j.neuroimage.2010.04.014 Lovden, M., Bodammer, N. C., Kuhn, S., Kaufmann, J., Schutze, H., Tempelmann, C., Heinze, H., Duzel, E., Schmiedek, & Lindenberger, U. (2010). Experiencedependent plasticity of white-matter microstructure extends into old age.

Neuropsychologia, 48(13), 3878-3883.

Scholz, J., Klein, M. C, Behrens, T. E. J., & Johansen-Berg, H. (2009). Training induces changes in white-matter architecture. Nature neuroscience, 12(11), 1367-1368.

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doi: 10.1038/nn.2412 Takeuchi, H., Sekiguchi, A., Taki, Y., Yokoyama, S., Yomogida, Y., Komuro, N., Yamanouchi, T., Suzuki, S., & Kawashima, R. (2010). Training of working memory impacts structural connectivity. The Journal of Neuroscience, 30(9), 3297-3303. doi: 10.1523/JNEUROSCI.4611-09.2010