Predicting individuals' learning success from patterns of pre-learning MRI activity

Inspiring a convergent engineering approach to measure and model the tissue microenvironment

Understanding the molecular and physical complexity of the tissue microenvironment (TiME) in the context of its spatiotemporal organization has remained an enduring challenge. Recent advances in engineering and data science are now promising the ability to study the structure, functions, and dynamics of the TiME in unprecedented detail; however, many advances still occur in silos that rarely integrate information to study the TiME in its full detail. This review provides an integrative overview of the engineering principles underlying chemical, optical, electrical, mechanical, and computational science to probe, sense, model, and fabricate the TiME. In individual sections, we first summarize the underlying principles, capabilities, and scope of emerging technologies, the breakthrough discoveries enabled by each technology and recent, promising innovations. We provide perspectives on the potential of these advances in answering critical questions about the TiME and its role in various disease and developmental processes. Finally, we present an integrative view that appreciates the major scientific and educational aspects in the study of the TiME.

Brain tissue iron neurophysiology and its relationship with the cognitive effects of dopaminergic modulation in children with and without ADHD

Children with attention-deficit/hyperactivity disorder (ADHD) exhibit impairments in response inhibition. These impairments are ameliorated by modulating dopamine (DA) via the administration of rewards or stimulant medication like methylphenidate (MPH). It is currently unclear whether intrinsic DA availability impacts these effects of dopaminergic modulation on response inhibition. Thus, we estimated intrinsic DA availability using magnetic resonance-based assessments of basal ganglia and thalamic tissue iron in 36 medication-naïve children with ADHD and 29 typically developing (TD) children (8-12 y) who underwent fMRI scans and completed standard and rewarded go/no-go tasks. Children with ADHD additionally participated in a double-blind, randomized, placebo-controlled, crossover MPH challenge. Using linear regressions covarying for age and sex, we determined there were no group differences in brain tissue iron. We additionally found that higher putamen tissue iron was associated with worse response inhibition performance in all participants. Crucially, we observed that higher putamen and caudate tissue iron was associated with greater responsivity to MPH, as measured by improved task performance, in participants with ADHD. These results begin to clarify the role of subcortical brain tissue iron, a measure associated with intrinsic DA availability, in the cognitive effects of reward- and MPH-related dopaminergic modulation in children with ADHD and TD children.

Association between real-time strategy video game learning outcomes and pre-training brain white matter structure: preliminary study

In recent years the association between video games, cognition, and the brain has been actively investigated. However, it is still unclear how individual predispositions, such as brain structure characteristics, play a role in the process of acquiring new skills, such as video games. The aim of this preliminary study was to investigate whether acquisition of cognitive-motor skills from the real-time strategy video game (StarCraft II) is associated with pre-training measures of brain white matter integrity. Results show that higher white matter integrity in regions (anterior limb of internal capsule, cingulum/hippocampus) and tracts (inferior longitudinal fasciculus) related with motoric functions, set shifting and visual decision making was associated with better Star Craft II performance. The presented findings inline with previous results and suggest that structural brain predispositions of individuals are related to the video game skill acquisition. Our study highlights the importance of neuroimaging studies that focus on white matter in predicting the outcomes of intervention studies and has implications for understanding the neural basis of the skill learning process.

Neuroanatomical predictors of complex skill acquisition during video game training

It is known that the outcomes of complex video game (VG) skill acquisition are correlated with individual differences in demographic and behavioral variables, such as age, intelligence and visual attention. However, empirical studies of the relationship between neuroanatomical features and success in VG training have been few and far between. The present review summarizes existing literature on gray matter (GM) and white matter correlates of complex VG skill acquisition as well as explores its relationship with neuroplasticity. In particular, since age can be an important factor in the acquisition of new cognitive skills, we present studies that compare different age groups (young and old adults). Our review reveals that GM in subcortical brain areas predicts complex VG learning outcomes in young subjects, whereas in older subjects the same is true of cortical frontal areas. This may be linked to age-related compensatory mechanisms in the frontal areas, as proposed by The Scaffolding Theory of Aging and Cognition. In the case of plasticity, there is no such relationship – in the group of younger and older adults there are changes after training in both cortical and subcortical areas. We also summarize best practices in research on predictors of VG training performance and outline promising areas of research in the study of complex video game skill acquisition.

Subcortical brain iron deposition in individuals with schizophrenia

Subcortical structures play a critical role the pathophysiology and treatment of schizophrenia (SZ), yet underlying neurophysiological processes, in vivo, remain largely unexplored. Brain tissue iron, which can be measured with magnetic resonance-based methods, is a crucial component of a variety of neuronal functions including neurotransmitter synthesis. Here we used a proxy measure of tissue iron to examine basal ganglia and thalamic structures in an adult cohort of individuals with chronic SZ. A publicly available dataset of 72 individuals with SZ between ages 18 and 65, and a matched sample of 74 healthy control (HC) participants were included. A novel method that calculated the inverse-normalized T2*-weighted contrast (1/nT2*) was used to estimate brain iron within the basal ganglia and thalamus. Between group, age- and sex-related differences in 1/nT2* were examined, in addition to correlations with measures of psychopathology and cognition. Individuals with SZ showed greater 1/nT2* (iron index) compared to HCs in the thalamus (p < 0.01, FWE corrected). Age-related 1/nT2* accumulation was noted in regions of the basal ganglia, coinciding with prior work, and prominent sex-differences were noted in the caudate and thalamus (p < 0.01, FWE corrected). No significant relationship was observed between 1/nT2* and measures of neurocognition or psychopathology. Overall, our findings characterize a non-invasive proxy measure of tissue iron in SZ and highlight thalamic iron accumulation as a potential marker of illness.

Contributions of dopamine-related basal ganglia neurophysiology to the developmental effects of incentives on inhibitory control

Inhibitory control can be less reliable in adolescence, however, in the presence of rewards, adolescents' performance often improves to adult levels. Dopamine is known to play a role in signaling rewards and supporting cognition, but its role in the enhancing effects of reward on adolescent cognition and inhibitory control remains unknown. Here, we assessed the contribution of basal ganglia dopamine-related neurophysiology using longitudinal MR-based assessments of tissue iron in rewarded inhibitory control, using an antisaccade task. In line with prior work, we show that neutral performance improves with age, and incentives enhance performance in adolescents to that of adults. We find that basal ganglia tissue iron is associated with individual differences in the magnitude of this reward boost, which is strongest in those with high levels of tissue iron, predominantly in adolescence. Our results provide novel evidence that basal ganglia neurophysiology supports developmental effects of rewards on cognition, which can inform neurodevelopmental models of the role of dopamine in reward processing during adolescence.

Linking interindividual variability in brain structure to behaviour

What are the brain structural correlates of interindividual differences in behaviour? More than a decade ago, advances in structural MRI opened promising new avenues to address this question. The initial wave of research then progressively led to substantial conceptual and methodological shifts, and a replication crisis unveiled the limitations of traditional approaches, which involved searching for associations between local measurements of neuroanatomy and behavioural variables in small samples of healthy individuals. Given these methodological issues and growing scepticism regarding the idea of one-to-one mapping of psychological constructs to brain regions, new perspectives emerged. These not only embrace the multivariate nature of brain structure-behaviour relationships and promote generalizability but also embrace the representation of the relationships between brain structure and behavioural data by latent dimensions of interindividual variability. Here, we examine the past and present of the study of brain structure-behaviour associations in healthy populations and address current challenges and open questions for future investigations.

General principles of brain electromagnetic rhythmic oscillations and implications for neuroplasticity

Neuro-plasticity describes the ability of the brain in achieving novel functions, either by transforming its internal connectivity, or by changing the elements of which it is made, meaning that, only those changes, that affect both structural and functional aspects of the system, can be defined as "plastic." The concept of plasticity can be applied to molecular as well as to environmental events that can be recognized as the basic mechanism by which our brain reacts to the internal and external stimuli. When considering brain plasticity within a clinical context-that is the process linked with changes of brain functions following a lesion- the term "reorganization" is somewhat synonymous, referring to the specific types of structural/functional modifications observed as axonal sprouting, long-term synaptic potentiation/inhibition or to the plasticity related genomic responses. Furthermore, brain rewires during maturation, and aging thus maintaining a remarkable learning capacity, allowing it to acquire a wide range of skills, from motor actions to complex abstract reasoning, in a lifelong expression. In this review, the contribution on the "neuroplasticity" topic coming from advanced analysis of EEG rhythms is put forward.

Human Variation in Error-Based and Reinforcement Motor Learning Is Associated With Entorhinal Volume

Error-based and reward-based processes are critical for motor learning and are thought to be mediated via distinct neural pathways. However, recent behavioral work in humans suggests that both learning processes can be bolstered by the use of cognitive strategies, which may mediate individual differences in motor learning ability. It has been speculated that medial temporal lobe regions, which have been shown to support motor sequence learning, also support the use of cognitive strategies in error-based and reinforcement motor learning. However, direct evidence in support of this idea remains sparse. Here we first show that better overall learning during error-based visuomotor adaptation is associated with better overall learning during the reward-based shaping of reaching movements. Given the cognitive contribution to learning in both of these tasks, these results support the notion that strategic processes, associated with better performance, drive intersubject variation in both error-based and reinforcement motor learning. Furthermore, we show that entorhinal cortex volume is larger in better learning individuals-characterized across both motor learning tasks-compared with their poorer learning counterparts. These results suggest that individual differences in learning performance during error and reinforcement learning are related to neuroanatomical differences in entorhinal cortex.

Resting state functional connectivity subtypes predict discrete patterns of cognitive-affective functioning across levels of analysis among patients with treatment-resistant depression

Resting state functional connectivity (RSFC) in ventral affective (VAN), default mode (DMN) and cognitive control (CCN) networks may partially underlie heterogeneity in depression. The current study used data-driven parsing of RSFC to identify subgroups of patients with treatment-resistant depression (TRD; n = 70) and determine if subgroups generalized to transdiagnostic measures of cognitive-affective functioning relevant to depression (indexed across self-report, behavioral, and molecular levels of analysis). RSFC paths within key networks were characterized using Subgroup-Group Iterative Multiple Model Estimation. Three connectivity-based subgroups emerged: Subgroup A, the largest subset and containing the fewest pathways; Subgroup B, containing unique bidirectional VAN/DMN negative feedback; and Subgroup C, containing the most pathways. Compared to other subgroups, subgroup B was characterized by lower self-reported positive affect and subgroup C by higher self-reported positive affect, greater variability in induced positive affect, worse response inhibition, and reduced striatal tissue iron concentration. RSFC-based categorization revealed three TRD subtypes associated with discrete aberrations in transdiagnostic cognitive-affective functioning that were largely unified across levels of analysis and were maintained after accounting for the variability captured by a disorder-specific measure of depressive symptoms. Findings advance understanding of transdiagnostic brain-behavior heterogeneity in TRD and may inform novel treatment targets for this population.

Biobehavioral correlates of an fMRI index of striatal tissue iron in depressed patients

Dopaminergic function is a critical transdiagnostic neurophysiological dimension with broad relevance in psychiatry. Normalized T2*-weighted (nT2*w) imaging has been previously investigated as a method to quantify biological properties of tissue in the striatum (e.g., tissue iron), providing a widely available, in vivo marker with potential relevance to dopaminergic function; but no prior study to our knowledge has examined this neuroimaging marker in clinical depression. In a treatment-seeking, clinically depressed sample (n = 110), we quantified tissue iron (nT2*w) in striatal regions. We assessed test-retest reliability and correlated values with dimensional features across levels of analysis, including demographic/biological (sex, age, Body Mass Index), neuroanatomical (hippocampal atrophy, which was quantified using a recently validated machine-learning algorithm), and performance-based (Affective Go/NoGo task performance) indices with relevance to depressive neurocognition. Across patients, decreased tissue iron concentration (as indexed by higher nT2*w) in striatal regions correlated with indices of decreased cognitive-affective function on the Affective Go/NoGo task. Greater caudate nT2*w also correlated with greater hippocampal atrophy. Striatal tissue iron concentrations were robustly lower in female patients than males but gender differences did not explain relations with other neurocognitive variables. A widely available fMRI index of striatal tissue properties, which exhibited strong psychometric properties and can be readily quantified from most fMRI datasets irrespective of study-specific features such as task design, showed relevance to multiple biobehavioral markers of pathophysiology in the context of moderate-to-severe, treatment-resistant depression. Striatal tissue iron may play a role in dimensional and subgroup-specific features of depression, with implications for future research on depression heterogeneity.

Lenticular nucleus volume predicts performance in real-time strategy game: cross-sectional and training approach using voxel-based morphometry

It is unclear why some people learn faster than others. We performed two independent studies in which we investigated the neural basis of real-time strategy (RTS) gaming and neural predictors of RTS game skill acquisition. In the first (cross-sectional) study, we found that experts in the RTS game StarCraft® II (SC2) had a larger lenticular nucleus volume (LNV) than non-RTS players. We followed a cross-validation procedure where we used the volume of regions identified in the first study to predict the quality of learning a new, complex skill (SC2) in a sample of individuals who were naive to RTS games (a second (training) study). Our findings provide new insights into how the LNV, which is associated with motor as well as cognitive functions, can be utilized to predict successful skill learning and be applied to a much broader context than just video games, such as contributing to optimizing cognitive training interventions.

MRI Predictors of Cognitive Training Outcomes

The prospect of improving or maintaining cognitive functioning has provoked a steadily increasing number of cognitive training interventions over the last years, especially for clinical and elderly populations. However, there are discrepancies between the findings of the studies. One of the reasons behind these heterogeneous findings is that there are vast inter-individual differences in how people benefit from the training and in the extent that training-related gains are transferred to other untrained tasks and domains. In this paper, we address the value of incorporating neural measures to cognitive training studies in order to fully understand the mechanisms leading to inter-individual differences in training gains and their generalizability to other tasks. Our perspective is that it is necessary to collect multimodal neural measures in the pre- and post-training phase, which can enable us to understand the factors contributing to successful training outcomes. More importantly, this understanding can enable us to predict who will benefit from different types of interventions, thereby allowing the development of individually tailored intervention programs.

Brain network modularity predicts cognitive training-related gains in young adults

The brain operates via networked activity in separable groups of regions called modules. The quantification of modularity compares the number of connections within and between modules, with high modularity indicating greater segregation, or dense connections within sub-networks and sparse connections between sub-networks. Previous work has demonstrated that baseline brain network modularity predicts executive function outcomes in older adults and patients with traumatic brain injury after cognitive and exercise interventions. In healthy young adults, however, the functional significance of brain modularity in predicting training-related cognitive improvements is not fully understood. Here, we quantified brain network modularity in young adults who underwent cognitive training with casual video games that engaged working memory and reasoning processes. Network modularity assessed at baseline was positively correlated with training-related improvements on untrained tasks. The relationship between baseline modularity and training gain was especially evident in initially lower performing individuals and was not present in a group of control participants that did not show training-related gains. These results suggest that a more modular brain network organization may allow for greater training responsiveness. On a broader scale, these findings suggest that, particularly in low-performing individuals, global network properties can capture aspects of brain function that are important in understanding individual differences in learning.

Brain Modularity: A Biomarker of Intervention-related Plasticity

Interventions using methods such as cognitive training and aerobic exercise have shown potential to enhance cognitive abilities. However, there is often pronounced individual variability in the magnitude of these gains. Here, we propose that brain network modularity, a measure of brain subnetwork segregation, is a unifying biomarker of intervention-related plasticity. We present work from multiple independent studies demonstrating that individual differences in baseline brain modularity predict gains in cognitive control functions across several populations and interventions, spanning healthy adults to patients with clinical deficits and cognitive training to aerobic exercise. We believe that this predictive framework provides a foundation for developing targeted, personalized interventions to improve cognition.

Caudate nucleus-dependent navigation strategies are associated with increased risk-taking and set-shifting behavior

When people navigate, they use strategies dependent on one of two memory systems. The hippocampus-based spatial strategy consists of using multiple landmarks to create a cognitive map of the environment. In contrast, the caudate nucleus-based response strategy is based on the memorization of a series of turns. Importantly, response learners display more gray matter and functional activity in the caudate nucleus and less gray matter in the hippocampus. In parallel, the caudate nucleus is involved in decision-making by mediating attention toward rewards and in set-shifting by mediating preparatory actions. The present study, therefore, examined the link between navigational strategy use, that are associated with gray matter differences in the caudate nucleus and hippocampus, and decision-making and set-shifting performance. Fifty-three participants completed the 4 on 8 virtual maze, the Iowa Gambling Task (IGT), the Wisconsin Card Sorting Test-64 (WCST-64), and a task-switching test. The results revealed that people who use response strategies displayed increased risk-taking behavior in the IGT compared to the people using hippocampus-dependent spatial strategies. Response strategy was also associated with enhanced set-shifting performance in the WCST-64 and task-switching test. These results confirm that risk-taking and set-shifting behavior, that are differentially impacted by the caudate nucleus and hippocampus memory systems, can be predicted by navigational strategy.

Brain Network Modularity Predicts Exercise-Related Executive Function Gains in Older Adults

Recent work suggests that the brain can be conceptualized as a network comprised of groups of sub-networks or modules. The extent of segregation between modules can be quantified with a modularity metric, where networks with high modularity have dense connections within modules and sparser connections between modules. Previous work has shown that higher modularity predicts greater improvements after cognitive training in patients with traumatic brain injury and in healthy older and young adults. It is not known, however, whether modularity can also predict cognitive gains after a physical exercise intervention. Here, we quantified modularity in older adults (N = 128, mean age = 64.74) who underwent one of the following interventions for 6 months (NCT01472744 on ClinicalTrials.gov): (1) aerobic exercise in the form of brisk walking (Walk), (2) aerobic exercise in the form of brisk walking plus nutritional supplement (Walk+), (3) stretching, strengthening and stability (SSS), or (4) dance instruction. After the intervention, the Walk, Walk+ and SSS groups showed gains in cardiorespiratory fitness (CRF), with larger effects in both walking groups compared to the SSS and Dance groups. The Walk, Walk+ and SSS groups also improved in executive function (EF) as measured by reasoning, working memory, and task-switching tests. In the Walk, Walk+, and SSS groups that improved in EF, higher baseline modularity was positively related to EF gains, even after controlling for age, in-scanner motion and baseline EF. No relationship between modularity and EF gains was observed in the Dance group, which did not show training-related gains in CRF or EF control. These results are consistent with previous studies demonstrating that individuals with a more modular brain network organization are more responsive to cognitive training. These findings suggest that the predictive power of modularity may be generalizable across interventions aimed to enhance aspects of cognition and that, especially in low-performing individuals, global network properties can capture individual differences in neuroplasticity.

Neural Basis of Video Gaming: A Systematic Review

Background: Video gaming is an increasingly popular activity in contemporary society, especially among young people, and video games are increasing in popularity not only as a research tool but also as a field of study. Many studies have focused on the neural and behavioral effects of video games, providing a great deal of video game derived brain correlates in recent decades. There is a great amount of information, obtained through a myriad of methods, providing neural correlates of video games.

Objectives: We aim to understand the relationship between the use of video games and their neural correlates, taking into account the whole variety of cognitive factors that they encompass.

Methods: A systematic review was conducted using standardized search operators that included the presence of video games and neuro-imaging techniques or references to structural or functional brain changes. Separate categories were made for studies featuring Internet Gaming Disorder and studies focused on the violent content of video games.

Results: A total of 116 articles were considered for the final selection. One hundred provided functional data and 22 measured structural brain changes. One-third of the studies covered video game addiction, and 14% focused on video game related violence.

Conclusions: Despite the innate heterogeneity of the field of study, it has been possible to establish a series of links between the neural and cognitive aspects, particularly regarding attention, cognitive control, visuospatial skills, cognitive workload, and reward processing. However, many aspects could be improved. The lack of standardization in the different aspects of video game related research, such as the participants' characteristics, the features of each video game genre and the diverse study goals could contribute to discrepancies in many related studies.

Multivariate Associations of Fluid Intelligence and NAA

Understanding the neural and metabolic correlates of fluid intelligence not only aids scientists in characterizing cognitive processes involved in intelligence, but it also offers insight into intervention methods to improve fluid intelligence. Here we use magnetic resonance spectroscopic imaging (MRSI) to measure N-acetyl aspartate (NAA), a biochemical marker of neural energy production and efficiency. We use principal components analysis (PCA) to examine how the distribution of NAA in the frontal and parietal lobes relates to fluid intelligence. We find that a left lateralized frontal-parietal component predicts fluid intelligence, and it does so independently of brain size, another significant predictor of fluid intelligence. These results suggest that the left motor regions play a key role in the visualization and planning necessary for spatial cognition and reasoning, and we discuss these findings in the context of the Parieto-Frontal Integration Theory of intelligence.

Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults

Cognitive training interventions are a promising approach to mitigate cognitive deficits common in aging and, ultimately, to improve functioning in older adults. Baseline neural factors, such as properties of brain networks, may predict training outcomes and can be used to improve the effectiveness of interventions. Here, we investigated the relationship between baseline brain network modularity, a measure of the segregation of brain sub-networks, and training-related gains in cognition in older adults. We found that older adults with more segregated brain sub-networks (i.e., more modular networks) at baseline exhibited greater training improvements in the ability to synthesize complex information. Further, the relationship between modularity and training-related gains was more pronounced in sub-networks mediating “associative” functions compared with those involved in sensory-motor processing. These results suggest that assessments of brain networks can be used as a biomarker to guide the implementation of cognitive interventions and improve outcomes across individuals. More broadly, these findings also suggest that properties of brain networks may capture individual differences in learning and neuroplasticity.

Trail Registration: ClinicalTrials.gov, NCT#00977418

The sequential structure of brain activation predicts skill

In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation.

Video game training and the reward system

Video games contain elaborate reinforcement and reward schedules that have the potential to maximize motivation. Neuroimaging studies suggest that video games might have an influence on the reward system. However, it is not clear whether reward-related properties represent a precondition, which biases an individual toward playing video games, or if these changes are the result of playing video games. Therefore, we conducted a longitudinal study to explore reward-related functional predictors in relation to video gaming experience as well as functional changes in the brain in response to video game training. Fifty healthy participants were randomly assigned to a video game training (TG) or control group (CG). Before and after training/control period, functional magnetic resonance imaging (fMRI) was conducted using a non-video game related reward task. At pretest, both groups showed strongest activation in ventral striatum (VS) during reward anticipation. At posttest, the TG showed very similar VS activity compared to pretest. In the CG, the VS activity was significantly attenuated. This longitudinal study revealed that video game training may preserve reward responsiveness in the VS in a retest situation over time. We suggest that video games are able to keep striatal responses to reward flexible, a mechanism which might be of critical value for applications such as therapeutic cognitive training.

In vivo evidence of neurophysiological maturation of the human adolescent striatum

Maturation of the striatum has been posited to play a primary role in observed increases in adolescent sensation-seeking. However, evidence of neurophysiological maturation in the human adolescent striatum is limited. We applied T2*-weighted imaging, reflecting indices of tissue-iron concentration, to provide direct in vivo evidence of neurophysiological development of the human adolescent striatum. Multivariate pattern analysis (MVPA) of striatal T2*-weighted signal generated age predictions that accounted for over 60% of the sample variance in 10-25 year olds, using both task-related and resting state fMRI. Dorsal and ventral striatum showed age related increases and decreases respectively of striatal neurophysiology suggesting qualitative differences in the maturation of limbic and executive striatal systems. In particular, the ventral striatum was found to show the greatest developmental differences and contribute most heavily to the multivariate age predictor. The relationship of the T2*-weighted signal to the striatal dopamine system is discussed. Together, results provide evidence for protracted maturation of the striatum through adolescence.

Risky monetary behavior in chronic back pain is associated with altered modular connectivity of the nucleus accumbens

Background

The nucleus accumbens (NAc) has a well established role in reward processing. Yet, there is growing evidence showing that NAc function, and its connections to other parts of the brain, is also critically involved in the emergence of chronic back pain (CBP). Pain patients are known to perform abnormally in reward-related tasks, which suggests an intriguing link between pain, NAc connectivity, and reward behavior. In the present study, we compared performance on a gambling task (indicating willingness to risk losing money) between healthy pain-free controls (CON) and individuals with CBP. We then measured modular connectivity of each participants’ NAc with resting state functional MRI to investigate how connectivity accounts for reward behavior in the presence and absence of pain.

Results

We found gain sensitivity was significantly higher in CBP patients. These scores were significantly correlated to connectivity within the NAc module defined by CON subjects ( which had strong connections to the frontal cortex), but not within that defined by CBP patients ( which was more strongly connected to subcortical areas). An important part of our study was based on the precedence that a range of behaviors, from simple to complex, can be predicted from brain activity during rest. Thus, to corroborate our results we compared them closely to an independent study correlating the same connectivity metric to impulsive behaviors in healthy participants. We found that our CBP patients were highly similarin connectivity to this study’s highly-impulsive healthy subjects, strengthening the notion that there is an important link between the brain systems that support chronic pain and reward processing.

Conclusions

Our results support previous findings that chronic back pain is accompanied by altered connectivity of the NAc. This lends itself to riskier behavior in these patients, a finding which establishes a potential cognitive consequence or co-morbidity of long-term pain and provides a behavioral link to growing research showing that chronic pain is related to abnormal changes in the dopaminergic system.

Resting-state fMRI activity predicts unsupervised learning and memory in an immersive virtual reality environment

In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglia's role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment.

Resting-state cortical connectivity predicts motor skill acquisition

Many studies have examined brain states in an effort to predict individual differences in the capacity for learning, with overall moderate results. The present study investigated how measures of cortical network function acquired at rest using dense-array EEG (256 leads) predict subsequent acquisition of a new motor skill. Brain activity was recorded in 17 healthy young subjects during 3min of wakeful rest prior to a single motor skill training session on a digital version of the pursuit rotor task. Practice was associated with significant gains in task performance (% time on target increased from 24% to 41%, p<0.0001). Using a partial least squares regression (PLS) model, coherence with the region of the left primary motor area (M1) in resting EEG data was a strong predictor of motor skill acquisition (R(2)=0.81 in a leave-one-out cross-validation analysis), exceeding the information provided by baseline behavior and demographics. Within this PLS model, greater skill acquisition was predicted by higher connectivity between M1 and left parietal cortex, possibly reflecting greater capacity for visuomotor integration, and by lower connectivity between M1 and left frontal-premotor areas, possibly reflecting differences in motor planning strategies. EEG coherence, which reflects functional connectivity, predicts individual motor skill acquisition with a level of accuracy that is remarkably high compared to prior reports using EEG or fMRI measures.

Parietal plasticity after training with a complex video game is associated with individual differences in improvements in an untrained working memory task

Researchers have devoted considerable attention and resources to cognitive training, yet there have been few examinations of the relationship between individual differences in patterns of brain activity during the training task and training benefits on untrained tasks (i.e., transfer). While a predominant hypothesis suggests that training will transfer if there is training-induced plasticity in brain regions important for the untrained task, this theory lacks sufficient empirical support. To address this issue we investigated the relationship between individual differences in training-induced changes in brain activity during a cognitive training videogame, and whether those changes explained individual differences in the resulting changes in performance in untrained tasks. Forty-five young adults trained with a videogame that challenges working memory, attention, and motor control for 15 2-h sessions. Before and after training, all subjects received neuropsychological assessments targeting working memory, attention, and procedural learning to assess transfer. Subjects also underwent pre- and post-functional magnetic resonance imaging (fMRI) scans while they played the training videogame to assess how these patterns of brain activity change in response to training. For regions implicated in working memory, such as the superior parietal lobe (SPL), individual differences in the post-minus-pre changes in activation predicted performance changes in an untrained working memory task. These findings suggest that training-induced plasticity in the functional representation of a training task may play a role in individual differences in transfer. Our data support and extend previous literature that has examined the association between training related cognitive changes and associated changes in underlying neural networks. We discuss the role of individual differences in brain function in training generalizability and make suggestions for future cognitive training research.

Complex population response of dorsal putamen neurons predicts the ability to learn

Day-to-day variability in performance is a common experience. We investigated its neural correlate by studying learning behavior of monkeys in a two-alternative forced choice task, the two-armed bandit task. We found substantial session-to-session variability in the monkeys’ learning behavior. Recording the activity of single dorsal putamen neurons we uncovered a dual function of this structure. It has been previously shown that a population of neurons in the DLP exhibits firing activity sensitive to the reward value of chosen actions. Here, we identify putative medium spiny neurons in the dorsal putamen that are cue-selective and whose activity builds up with learning. Remarkably we show that session-to-session changes in the size of this population and in the intensity with which this population encodes cue-selectivity is correlated with session-to-session changes in the ability to learn the task. Moreover, at the population level, dorsal putamen activity in the very beginning of the session is correlated with the performance at the end of the session, thus predicting whether the monkey will have a "good" or "bad" learning day. These results provide important insights on the neural basis of inter-temporal performance variability.

Different slopes for different folks: alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks

We hypothesized that control processes, as measured using electrophysiological (EEG) variables, influence the rate of learning of complex tasks. Specifically, we measured alpha power, event-related spectral perturbations (ERSPs), and event-related brain potentials during early training of the Space Fortress task, and correlated these measures with subsequent learning rate and performance in transfer tasks. Once initial score was partialled out, the best predictors were frontal alpha power and alpha and delta ERSPs, but not P300. By combining these predictors, we could explain about 50% of the learning rate variance and 10%-20% of the variance in transfer to other tasks using only pretraining EEG measures. Thus, control processes, as indexed by alpha and delta EEG oscillations, can predict learning and skill improvements. The results are of potential use to optimize training regimes.

The neural basis of video gaming

Video game playing is a frequent recreational activity. Previous studies have reported an involvement of dopamine-related ventral striatum. However, structural brain correlates of video game playing have not been investigated. On magnetic resonance imaging scans of 154 14-year-olds, we computed voxel-based morphometry to explore differences between frequent and infrequent video game players. Moreover, we assessed the Monetary Incentive Delay (MID) task during functional magnetic resonance imaging and the Cambridge Gambling Task (CGT). We found higher left striatal grey matter volume when comparing frequent against infrequent video game players that was negatively correlated with deliberation time in CGT. Within the same region, we found an activity difference in MID task: frequent compared with infrequent video game players showed enhanced activity during feedback of loss compared with no loss. This activity was likewise negatively correlated with deliberation time. The association of video game playing with higher left ventral striatum volume could reflect altered reward processing and represent adaptive neural plasticity.

Brain regions engaged by part- and whole-task performance in a video game: a model-based test of the decomposition hypothesis

Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model's predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits.