Differential recruitment of the sensorimotor putamen and frontoparietal cortex during motor chunking in humans

Task-Modulated Corticocortical Synchrony in the Cognitive-Motor Network Supporting Handwriting

Both motor and cognitive aspects of behavior depend on dynamic, accurately timed neural processes in large-scale brain networks. Here, we studied synchronous interplay between cortical regions during production of cognitive-motor sequences in humans. Specifically, variants of handwriting that differed in motor variability, linguistic content, and memorization of movement cues were contrasted to unveil functional sensitivity of corticocortical connections. Data-driven magnetoencephalography mapping (n = 10) uncovered modulation of mostly left-hemispheric corticocortical interactions, as quantified by relative changes in phase synchronization. At low frequencies (~2–13 Hz), enhanced frontoparietal synchrony was related to regular handwriting, whereas premotor cortical regions synchronized for simple loop production and temporo-occipital areas for a writing task substituting normal script with loop patterns. At the beta-to-gamma band (~13–45 Hz), enhanced synchrony was observed for regular handwriting in the central and frontoparietal regions, including connections between the sensorimotor and supplementary motor cortices and between the parietal and dorsal premotor/precentral cortices. Interpreted within a modular framework, these modulations of synchrony mainly highlighted interactions of the putative pericentral subsystem of hand coordination and the frontoparietal subsystem mediating working memory operations. As part of cortical dynamics, interregional phase synchrony varies depending on task demands in production of cognitive-motor sequences.

Multi-session Transcranial Direct Current Stimulation Over Primary Motor Cortex Facilitates Sequence Learning, Chunking, and One Year Retention

Transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) can facilitate motor learning, but it has not been established how stimulation to other brain regions impacts online and offline motor sequence learning, as well as long-term retention. Here, we completed three experiments comparing the effects of tDCS and sham stimulation to the prefrontal cortex (PFC), M1, and the supplementary motor area complex to understand the contributions of these brain regions to motor sequence learning. In Experiment 1, we found that both left and right PFC tDCS groups displayed a slowing in learning in both reaction time and number of chunks, whereas stimulation over M1 improved both metrics over the course of three sessions. To better understand the sequence learning impairment of left PFC anodal stimulation, we tested a left PFC cathodal tDCS group in Experiment 2. The cathodal group demonstrated learning impairments similar to the left PFC anodal stimulation group. In Experiment 3, a subset of participants from the left PFC, M1, and sham tDCS groups of Experiment 1 returned to complete a single session without tDCS on the same sequences assigned to them 1 year previously. We found that the M1 tDCS group reduced reaction time at a faster rate relative to the sham and left PFC groups, demonstrating faster relearning after a one-year delay. Thus, our findings suggest that, regardless of the polarity of stimulation, tDCS to PFC impairs sequence learning, whereas stimulation to M1 facilitates learning and relearning, especially in terms of chunk formation.

Microstates-based resting frontal alpha asymmetry approach for understanding affect and approach/withdrawal behavior

The role of resting frontal alpha-asymmetry in explaining neural-mechanisms of affect and approach/withdrawal behavior is still debatable. The present study explores the ability of the quasi-stable resting EEG asymmetry information and the associated neurovascular synchronization/desynchronization in bringing more insight into the understanding of neural-mechanisms of affect and approach/withdrawal behavior. For this purpose, a novel frontal alpha-asymmetry based on microstates, that assess quasi-stable EEG scalp topography information, is proposed and compared against standard frontal-asymmetry. Both proposed and standard frontal alpha-asymmetries were estimated from thirty-nine healthy volunteers resting-EEG simultaneously acquired with resting-fMRI. Further, neurovascular mechanisms of these asymmetry measures were estimated through EEG-informed fMRI. Subsequently, the Hemodynamic Lateralization Index (HLI) of the neural-underpinnings of both asymmetry measures was assessed. Finally, the robust correlation of both asymmetry-measures and their HLI’s with PANAS, BIS/BAS was carried out. The standard resting frontal-asymmetry and its HLI yielded no significant correlation with any psychological-measures. However, the microstate resting frontal-asymmetry correlated significantly with negative affect and its neural underpinning’s HLI significantly correlated with Positive/Negative affect and BIS/BAS measures. Finally, alpha-BOLD desynchronization was observed in neural-underpinning whose HLI correlated significantly with negative affect and BIS. Hence, the proposed resting microstate-frontal asymmetry better assesses the neural-mechanisms of affect, approach/withdrawal behavior.

Modulation of Distinct Intrinsic Resting State Brain Networks by Acute Exercise Bouts of Differing Intensity

Acute exercise bouts alter resting state functional connectivity (rs-FC) within cognitive, sensorimotor, and affective networks, but it remains unknown how these effects are influenced by exercise intensity.

Twenty-five male athletes underwent individual fitness assessments using an incremental treadmill test. On separate days, they performed ‘low’ (35% below lactate threshold) and ‘high’ (20% above lactate threshold) intensity exercise bouts of 30 min. Rs-fMRI and Positive and Negative Affect Scale (PANAS) were acquired before and after each exercise bout. Networks of interest were extracted from twenty-two participants (3 dropouts). Pre-to-post changes and between conditions effects were evaluated using FSL’s randomise by applying repeated measures ANOVA. Results were reported at p < 0.05, corrected for multiple comparisons using threshold free cluster enhancement.

PANAS revealed a significant increase in positive mood after both exercise conditions. Significant effects were observed between conditions in the right affective and reward network (ARN), the right fronto parietal network (FPN) and the sensorimotor network (SMN). Pre-to-post comparisons after ‘low’ exercise intensity revealed a significant increase in rs-FC in the left and right FPN, while after ‘high’-intensity exercise rs-FC decreased in the SMN and the dorsal attention network (DAN) and increased in the left ARN.

Supporting recent findings, this study is the first to report distinct rs-FC alterations driven by exercise intensity: (i) Increased rs-FC in FPN may indicate beneficial functional plasticity for cognitive/attentional processing, (ii) increased rs-FC in ARN may be linked to endogenous opioid-mediated internal affective states. Finally, (iii) decreased rs-FC in the SMN may signify persistent motor fatigue. The distinct effects on rs-FC fit with theories of transient persistent network alterations after acute exercise bouts that are mediated by different exercise intensities and impact differentially on cognitive/attentional or affective responses.

Neural Efficiency and Acquired Motor Skills: An fMRI Study of Expert Athletes

The neural efficiency hypothesis was investigated. Functional magnetic resonance imaging was used to study the differences in brain activity between athletes imagining performing different movements: basketball athletes imagined throwing and volleyball athletes imagined serving. These comparisons of brain activity among athletes imagining movements from their self-sport (e.g., a basketball throw in basketball athletes) versus movements from other sport (e.g., a volleyball serve in basketball athletes) revealed the neural energy consumption each task costs. The results showed better temporal congruence between motor execution and motor imagery and vividness of motor imagery, but lower levels of activation in the left putamen, inferior parietal lobule, supplementary motor area, postcentral gyrus, and the right insula when both groups of athletes imagined movements from their self-sport compared with when they imagined movements from the other-sport. Athletes were more effective in the representation of the motor sequences and the interoception of the motor sequences for their self-sport. The findings of present study suggest that elite athletes achieved superior behavioral performance with minimal neural energy consumption, thus confirming the neural efficiency hypotheses.

The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability

The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brain's modular network organization, which can be assessed quantitatively using computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Although the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability - which would be undetectable using past approaches - is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advance over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints.

Working memory affects anticipatory behavior during implicit pattern learning

We investigated the relation between implicit sequence learning and individual differences in working memory (WM) capacity. Participants performed an oculomotor version of the serial reaction time (SRT) task and three computerized WM tasks. Implicit learning was measured using anticipation measures only, as they represent strong indicators of learning. Our results demonstrate that anticipatory behavior in the SRT task changes as a function of WM capacity, such that it increases with decreased WM capacity. On the other hand, WM capacity did not affect the overall number of correct anticipations in the task. In addition, we report a positive relation between WM capacity and the number of consecutive correct anticipations (or chunks), and a negative relation between WM capacity and the overall number of errors, indicating different learning strategies during implicit sequence learning. The results of the current study are theoretically important, because they demonstrate that individual differences in WM capacity could account for differences in learning processes, and ultimately change individuals' anticipatory behavior, even when learning is implicit, without intention and awareness.

Neural Organization of Hierarchical Motor Sequence Representations in the Human Neocortex

Although it is widely accepted that the brain represents movement sequences hierarchically, the neural implementation of this organization is still poorly understood. To address this issue, we experimentally manipulated how participants represented sequences of finger presses at the levels of individual movements, chunks, and entire sequences. Using representational fMRI analyses, we then examined how this hierarchical structure was reflected in the fine-grained brain activity patterns of the participants while they performed the 8 trained sequences. We found clear evidence of each level of the movement hierarchy at the representational level. However, anatomically, chunk and sequence representations substantially overlapped in the premotor and parietal cortices, whereas individual movements were uniquely represented in the primary motor cortex. The findings challenge the common hypothesis of an orderly anatomical separation of different levels of an action hierarchy and argue for a special status of the distinction between individual movements and sequential context.

Head over heels but I forget why: Disruptive functional connectivity in older adult fallers with mild cognitive impairment

Disrupted functional connectivity has been highlighted as a neural mechanism by which impaired cognitive function and mobility co-exist in older adults with mild cognitive impairment (MCI). The objective of this study was to determine the independent and combined effects of MCI and faller status on functional connectivity of three functional networks: default mode network (DMN), fronto-parietal network (FPN) and sensorimotor network (SMN) between 4 groups of older adults: 1) Healthy; 2) MCI without Falls; 3) Fallers without MCI; and 4) Fallers with MCI.

METHODS

Sixty-six adults aged 70-80 years old were included. Cognition was assessed using: 1) cognitive dual task; 2) Stroop Colour-Word Test; 3) Trail Making Tests (TMT); and 4) Digit Symbol Substitution Test (DSST). Postural sway was assessed with eyes opened and standing on the floor. Functional connectivity was measured using fMRI while performing a finger-tapping task.

RESULTS

Differences in DMN-SMN connectivity were found for Fallers with MCI vs Fallers without MCI (p = .001). Fallers with MCI had significantly greater postural sway than the other groups. Both DMN-SMN connectivity (p = .03) and postural sway (p = .001) increased in a significantly linear fashion from Fallers without MCI, to MCI without Falls, to Fallers with MCI. Participants with MCI performed significantly worse on the DSST (p = .003) and TMT (p = .007) than those without MCI.

CONCLUSION

Aberrant DMN-SMN connectivity may underlie reduced postural stability. Having both impaired cognition and mobility is associated with a greater level of disruptive DMN-SMN connectivity and increased postural sway than singular impairment.

Differences in chunking behavior between young and older adults diminish with extended practice

Previous research found reduced motor chunking behavior in older adults compared to young adults. However, it remains unclear whether older adults are unable to use a chunking strategy or whether they are just slower in developing them. Our goal was to investigate the effect of extended practice on the development of chunking behavior in healthy older adults. A group of young and a group of healthy older adults between 74 and 85 years of age visited the lab on 2 days. A sequence of 3 and a sequence of 6 elements were both practiced 432 times in a discrete sequence production task. We found that age differences in chunking behavior, as measured by the difference between initiation and execution of the sequence, diminish with extended practice. Furthermore, in older, but not in young adults, slow responses that are often interpreted as the first response of a next motor chunk were associated with a finger that was also slow during performance of the random sequences. This finding calls for more attention to biomechanical factors in future theory about aging and sequence learning.

Abnormal Dynamic Functional Connectivity Associated With Subcortical Networks in Parkinson's Disease: A Temporal Variability Perspective

Parkinson's disease (PD) is a neurodegenerative disease characterized by dysfunction in distributed functional brain networks. Previous studies have reported abnormal changes in static functional connectivity using resting-state functional magnetic resonance imaging (fMRI). However, the dynamic characteristics of brain networks in PD is still poorly understood. This study aimed to quantify the characteristics of dynamic functional connectivity in PD patients at nodal, intra- and inter-subnetwork levels. Resting-state fMRI data of a total of 42 PD patients and 40 normal controls (NCs) were investigated from the perspective of the temporal variability on the connectivity profiles across sliding windows. The results revealed that PD patients had greater nodal variability in precentral and postcentral area (in sensorimotor network, SMN), middle occipital gyrus (in visual network), putamen (in subcortical network) and cerebellum, compared with NCs. Furthermore, at the subnetwork level, PD patients had greater intra-network variability for the subcortical network, salience network and visual network, and distributed changes of inter-network variability across several subnetwork pairs. Specifically, the temporal variability within and between subcortical network and other cortical subnetworks involving SMN, visual, ventral and dorsal attention networks as well as cerebellum was positively associated with the severity of clinical symptoms in PD patients. Additionally, the increased inter-network variability of cerebellum-auditory pair was also correlated with clinical severity of symptoms in PD patients. These observations indicate that temporal variability can detect the distributed abnormalities of dynamic functional network of PD patients at nodal, intra- and inter-subnetwork scales, and may provide new insights into understanding PD.

Getting to grips with endoscopy - Learning endoscopic surgical skills induces bi-hemispheric plasticity of the grasping network

Endoscopic surgery requires skilled bimanual use of complex instruments that extend the peri-personal workspace. To delineate brain structures involved in learning such surgical skills, 48 medical students without surgical experience were randomly assigned to five training sessions on a virtual-reality endoscopy simulator or to a non-training group. Brain activity was probed with functional MRI while participants performed endoscopic tasks. Repeated task performance in the scanner was sufficient to enhance task-related activity in left ventral premotor cortex (PMv) and the anterior Intraparietal Sulcus (aIPS). Simulator training induced additional increases in task-related activation in right PMv and aIPS and reduced effective connectivity from left to right PMv. Skill improvement after training scaled with stronger task-related activation of the lateral left primary motor hand area (M1-HAND). The results suggest that a bilateral fronto-parietal grasping network and left M1-HAND are engaged in bimanual learning of tool-based manipulations in an extended peri-personal space.

Explaining the neural activity distribution associated with discrete movement sequences: Evidence for parallel functional systems

To explore the effects of practice we scanned participants with fMRI while they were performing four-key unfamiliar and familiar sequences, and compared the associated activities relative to simple control sequences. On the basis of a recent cognitive model of sequential motor behavior (C-SMB), we propose that the observed neural activity would be associated with three functional networks that can operate in parallel and that allow (a) responding to stimuli in a reaction mode, (b) sequence execution using spatial sequence representations in a central-symbolic mode, and (c) sequence execution using motor chunk representations in a chunking mode. On the basis of this model and findings in the literature, we predicted which neural areas would be active during execution of the unfamiliar and familiar keying sequences. The observed neural activities were largely in line with our predictions, and allowed functions to be attributed to the active brain areas that fit the three above functional systems. The results corroborate C-SMB’s assumption that at advanced skill levels the systems executing motor chunks and translating key-specific stimuli are racing to trigger individual responses. They further support recent behavioral indications that spatial sequence representations continue to be used.

Forget-me-some: General versus special purpose models in a hierarchical probabilistic task

Humans build models of their environments and act according to what they have learnt. In simple experimental environments, such model-based behaviour is often well accounted for as if subjects are ideal Bayesian observers. However, more complex probabilistic tasks require more sophisticated forms of inference that are sufficiently computationally and statistically taxing as to demand approximation. Here, we study properties of two approximation schemes in the context of a serial reaction time task in which stimuli were generated from a hierarchical Markov chain. One, pre-existing, scheme was a generically powerful variational method for hierarchical inference which has recently become popular as an account of psychological and neural data across a wide swathe of probabilistic tasks. A second, novel, scheme was more specifically tailored to the task at hand. We show that the latter model fit significantly better than the former. This suggests that our subjects were sensitive to many of the particular constraints of a complex behavioural task. Further, the tailored model provided a different perspective on the effects of cholinergic manipulations in the task. Neither model fit the behaviour on more complex contingencies that competently. These results illustrate the benefits and challenges that come with the general and special purpose modelling approaches and raise important questions of how they can advance our current understanding of learning mechanisms in the brain.

Modeling and interpreting mesoscale network dynamics

Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development.

Dynamic graph metrics: Tutorial, toolbox, and tale

The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.

Dynamic graph metrics: Tutorial, toolbox, and tale

The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.

Observing Action Sequences Elicits Sequence-Specific Neural Representations in Frontoparietal Brain Regions

Learning new skills by watching others is important for social and motor development throughout the lifespan. Prior research has suggested that observational learning shares common substrates with physical practice at both cognitive and brain levels. In addition, neuroimaging studies have used multivariate analysis techniques to understand neural representations in a variety of domains, including vision, audition, memory, and action, but few studies have investigated neural plasticity in representational space. Therefore, although movement sequences can be learned by observing other people's actions, a largely unanswered question in neuroscience is how experience shapes the representational space of neural systems. Here, across a sample of male and female participants, we combined pretraining and posttraining fMRI sessions with 6 d of observational practice to determine whether the observation of action sequences elicits sequence-specific representations in human frontoparietal brain regions and the extent to which these representations become more distinct with observational practice. Our results showed that observed action sequences are modeled by distinct patterns of activity in frontoparietal cortex and that such representations largely generalize to very similar, but untrained, sequences. These findings advance our understanding of what is modeled during observational learning (sequence-specific information), as well as how it is modeled (reorganization of frontoparietal cortex is similar to that previously shown following physical practice). Therefore, on a more fine-grained neural level than demonstrated previously, our findings reveal how the representational structure of frontoparietal cortex maps visual information onto motor circuits in order to enhance motor performance.

SIGNIFICANCE STATEMENT Learning by watching others is a cornerstone in the development of expertise and skilled behavior. However, it remains unclear how visual signals are mapped onto motor circuits for such learning to occur. Here, we show that observed action sequences are modeled by distinct patterns of activity in frontoparietal cortex and that such representations largely generalize to very similar, but untrained, sequences. These findings advance our understanding of what is modeled during observational learning (sequence-specific information), as well as how it is modeled (reorganization of frontoparietal cortex is similar to that previously shown following physical practice). More generally, these findings demonstrate how motor circuit involvement in the perception of action sequences shows high fidelity to prior work, which focused on physical performance of action sequences.

Interactive reservoir computing for chunking information streams

Chunking is the process by which frequently repeated segments of temporal inputs are concatenated into single units that are easy to process. Such a process is fundamental to time-series analysis in biological and artificial information processing systems. The brain efficiently acquires chunks from various information streams in an unsupervised manner; however, the underlying mechanisms of this process remain elusive. A widely-adopted statistical method for chunking consists of predicting frequently repeated contiguous elements in an input sequence based on unequal transition probabilities over sequence elements. However, recent experimental findings suggest that the brain is unlikely to adopt this method, as human subjects can chunk sequences with uniform transition probabilities. In this study, we propose a novel conceptual framework to overcome this limitation. In this process, neural networks learn to predict dynamical response patterns to sequence input rather than to directly learn transition patterns. Using a mutually supervising pair of reservoir computing modules, we demonstrate how this mechanism works in chunking sequences of letters or visual images with variable regularity and complexity. In addition, we demonstrate that background noise plays a crucial role in correctly learning chunks in this model. In particular, the model can successfully chunk sequences that conventional statistical approaches fail to chunk due to uniform transition probabilities. In addition, the neural responses of the model exhibit an interesting similarity to those of the basal ganglia observed after motor habit formation.

A Feel for Numbers: The Changing Role of Gesture in Manipulating the Mental Representation of an Abacus Among Children at Different Skill Levels

Abacus mental arithmetic involves the skilled acquisition of a set of gestures representing mathematical algorithms to properly manipulate an imaginary abacus. The present study examined how the beneficial effect of abacus co-thought gestures varied at different skill and problem difficulty levels. We compared the mental arithmetic performance of 6- to 8-year-old beginning (N = 57), intermediate (N = 65), and advanced (N = 54) learners under three conditions: a physical abacus, hands-free (spontaneous gesture) mental arithmetic, and hands-restricted mental arithmetic. We adopted a mixed-subject design, with level of difficulty and skill level as the within-subject independent variables and condition as the between-subject independent variable. Our results showed a clear contrast in calculation performance and gesture accuracy among learners at different skill levels. Learners first mastered how to calculate using a physical abacus and later benefitted from using abacus gestures to aid mental arithmetic. Hand movement and gesture accuracy indicated that the beneficial effect of gestures may be related to motor learning. Beginners were proficient with a physical abacus, but performed poorly and had low gesture accuracy during mental arithmetic. Intermediates relied on gestures to do mental arithmetic and had accurate hand movements, but performed more poorly when restricted from gesturing. Advanced learners could perform mental arithmetic with accurate gestures and scored just as well without gesturing. These findings suggest that for intermediate and advanced learners, motor-spatial representation through abacus co-thought gestures may complement visual-spatial representation of a mental abacus to reduce working memory load.

On Curiosity: A Fundamental Aspect of Personality, a Practice of Network Growth

Human personality is reflected in patterns-or networks-of behavior, either in thought or action. Curiosity is an oft-treasured component of one's personality, commonly associated with information-seeking proclivities with distinct neurophysiological correlates. The markers of curiosity can differ substantially across people, suggesting the possibility that personality also determines the architectural style of one's curiosity. Yet progress in defining those styles, and marking their neurophysiological basis, has been hampered by fairly fundamental difficulties in defining curiosity itself. Here, we offer and exercise a definition of the practice of curiosity as knowledge network building, one particular pattern of thought behavior. To unpack this definition and motivate its utility, we begin with a short primer on network science and describe how the mathematical object of a network can be used to map items and relations that are characteristic of bodies of knowledge. Next, we turn to a discussion of how networks grow, how their growth can be modeled, and how the practice of curiosity can be formalized as a process of network growth. We pay particular attention to how individuals may differ in how they build their knowledge networks, and discuss how the sort, manner, and action of building can be modulated by experience. We discuss how this definition of the practice of curiosity motivates new experiments and theory development at the interdisciplinary intersection of network science, personality neuroscience, education, and curiosity studies. We close with a note on the potential of network science to inform studies of other domains of personality, and the patterns of thought- or action-behavior characteristic thereof.

New insights into statistical learning and chunk learning in implicit sequence acquisition

Implicit sequence learning is ubiquitous in our daily life. However, it is unclear whether the initial acquisition of sequences results from learning to chunk items (i.e., chunk learning) or learning the underlying statistical regularities (i.e., statistical learning). By grouping responses with or without a distinct chunk or statistical structure into segments and comparing these responses, previous studies have demonstrated both chunk and statistical learning. However, few studies have considered the response sequence as a whole and examined the temporal dependency of the entire sequence, where the temporal dependencies could disclose the internal representations of chunk and statistical learning. Participants performed a serial reaction time (SRT) task under different stimulus interval conditions. We found that sequence learning reflected by reaction time (RT) rather than motor improvements represented by movement time (MT). The temporal dependency of RT and MT revealed that both RT and MT displayed recursive patterns caused by biomechanical effects of response locations and foot transitions. Chunking was noticeable only in the presence of the recurring RT or MT but vanished after the recursive component was removed, implying that chunk formation may result from biomechanical constraints rather than learning itself. In addition, we observed notable first-order autocorrelations in RT. This trial-to-trial association enhanced as learning progressed regardless of stimulus intervals, reflecting the internal cognitive representation of the first-order stimulus contingencies. Our results suggest that initial acquisition of implicit sequences may arise from first-order statistical learning rather than chunk learning.

Disruption of Boundary Encoding During Sensorimotor Sequence Learning: An MEG Study

Music performance relies on the ability to learn and execute actions and their associated sounds. The process of learning these auditory-motor contingencies depends on the proper encoding of the serial order of the actions and sounds. Among the different serial positions of a behavioral sequence, the first and last (boundary) elements are particularly relevant. Animal and patient studies have demonstrated a specific neural representation for boundary elements in prefrontal cortical regions and in the basal ganglia, highlighting the relevance of their proper encoding. The neural mechanisms underlying the encoding of sequence boundaries in the general human population remain, however, largely unknown. In this study, we examined how alterations of auditory feedback, introduced at different ordinal positions (boundary or within-sequence element), affect the neural and behavioral responses during sensorimotor sequence learning. Analysing the neuromagnetic signals from 20 participants while they performed short piano sequences under the occasional effect of altered feedback (AF), we found that at around 150-200 ms post-keystroke, the neural activities in the dorsolateral prefrontal cortex (DLPFC) and supplementary motor area (SMA) were dissociated for boundary and within-sequence elements. Furthermore, the behavioral data demonstrated that feedback alterations on boundaries led to greater performance costs, such as more errors in the subsequent keystrokes. These findings jointly support the idea that the proper encoding of boundaries is critical in acquiring sensorimotor sequences. They also provide evidence for the involvement of a distinct neural circuitry in humans including prefrontal and higher-order motor areas during the encoding of the different classes of serial order.

Predicting future learning from baseline network architecture

Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain's baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We analyzed functional MRI data from 19 participants prior to six weeks of training on a new motor skill. We found that visual-motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual-motor connectivity at baseline is a relatively stable individual trait. These results suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution.

Connectivity of the ventral visual cortex is necessary for object recognition in patients

The functional profiles of regions in the ventral occipital-temporal cortex (VTC), a critical region for object visual recognition, are associated with the VTC connectivity patterns to nonvisual regions relevant to the corresponding object domain. However, whether and how whole-brain connections affect recognition behavior remains untested. We directly examined the necessity of VTC connectivity in object recognition behavior by testing 82 patients whose lesion spared relevant VTC regions but affected various white matter (WM) tracts and other regions. In these patients, we extracted the whole-brain anatomical connections of two VTC domain-selective (large manmade objects and animals) clusters with probabilistic tractography, and examined whether such connectivity pattern can predict recognition performance of the corresponding domains with support vector regression (SVR) analysis. We found that the whole-brain anatomical connectivity of large manmade object-specific cluster successfully predicted patients' large object recognition performance but not animal recognition or control tasks, even after we excluded connections with early visual regions. The contributing connections to large object recognition included tracts between VTC-large object cluster and distributed regions both within and beyond the visual cortex (e.g., putamen, superior, and middle temporal gyrus). These results provide causal evidence that the VTC whole-brain anatomical connectivity is necessary for at least certain domains of object recognition behavior.

Stuttering treatment and brain research in adults: A still unfolding relationship

PURPOSE

Brain imaging and brain stimulation procedures have now been used for more than two decades to investigate the neural systems that contribute to the occurrence of stuttering in adults, and to identify processes that might enhance recovery from stuttering. The purpose of this paper is to review the extent to which these dual lines of research with adults who stutter have intersected and whether they are contributing towards the alleviation of this impairment.

METHOD

Several areas of research are reviewed in order to determine whether research on the neurology of stuttering is showing any potential for advancing the treatment of this communication disorder: (a) attempts to discover the neurology of stuttering, (b) neural changes associated with treated recovery, and (c) direct neural intervention.

RESULTS AND CONCLUSIONS

Although much has been learned about the neural underpinnings of stuttering, little research in any of the reviewed areas has thus far contributed to the advancement of stuttering treatment. Much of the research on the neurology of stuttering that does have therapy potential has been largely driven by a speech-motor model that is designed to account for the efficacy of fluency-inducing strategies and strategies that have been shown to yield therapy benefits. Investigations on methods that will induce neuroplasticity are overdue. Strategies profitable with other disorders have only occasionally been employed. However, there are signs that investigations on the neurology of adults who have recovered from stuttering are slowly being recognized for their potential in this regard.

A unifying motor control framework for task-specific dystonia

Task-specific dystonia is a movement disorder characterized by a painless loss of dexterity specific to a particular motor skill. This disorder is prevalent among writers, musicians, dancers and athletes. No current treatment is predictably effective, and the disorder generally ends the careers of affected individuals. Traditional disease models of dystonia have a number of limitations with regard to task-specific dystonia. We therefore discuss emerging evidence that the disorder has its origins within normal compensatory mechanisms of a healthy motor system in which the representation and reproduction of motor skill are disrupted. We describe how risk factors for task-specific dystonia can be stratified and translated into mechanisms of dysfunctional motor control. The proposed model aims to define new directions for experimental research and stimulate therapeutic advances for this highly disabling disorder.

Brain state flexibility accompanies motor-skill acquisition

Learning requires the traversal of inherently distinct cognitive states to produce behavioral adaptation. Yet, tools to explicitly measure these states with non-invasive imaging – and to assess their dynamics during learning – remain limited. Here, we describe an approach based on a distinct application of graph theory in which points in time are represented by network nodes, and similarities in brain states between two different time points are represented as network edges. We use a graph-based clustering technique to identify clusters of time points representing canonical brain states, and to assess the manner in which the brain moves from one state to another as learning progresses. We observe the presence of two primary states characterized by either high activation in sensorimotor cortex or high activation in a frontal-subcortical system. Flexible switching among these primary states and other less common states becomes more frequent as learning progresses, and is inversely correlated with individual differences in learning rate. These results are consistent with the notion that the development of automaticity is associated with a greater freedom to use cognitive resources for other processes. Taken together, our work offers new insights into the constrained, low dimensional nature of brain dynamics characteristic of early learning, which give way to less constrained, high-dimensional dynamics in later learning.

The Role of Human Primary Motor Cortex in the Production of Skilled Finger Sequences

Human primary motor cortex (M1) is essential for producing dexterous hand movements. Although distinct subpopulations of neurons are activated during single-finger movements, it remains unknown whether M1 also represents sequences of multiple finger movements. Using novel multivariate functional magnetic resonance imaging (fMRI) analysis techniques and combining evidence from both 3T and 7T fMRI data, we found that after 5 d of intense practice, premotor and parietal areas encoded the different movement sequences. There was little or no evidence for a sequence representation in M1. Instead, activity patterns in M1 could be fully explained by a linear combination of patterns for the constituent individual finger movements, with the strongest weight on the first finger of the sequence. Using passive replay of sequences, we show that this first-finger effect is due to neuronal processes involved in the active execution, rather than to a hemodynamic nonlinearity. These results suggest that M1 receives increased input from areas with sequence representations at the initiation of a sequence, but that M1 activity itself relates to the execution of component finger presses only. These results improve our understanding of the representation of finger sequences in the human neocortex after short-term training and provide important methodological advances for the study of long-term skill development.SIGNIFICANCE STATEMENT There is clear evidence that human primary motor cortex (M1) is essential for producing individuated finger movements, such as pressing a button. Over and above its involvement in movement execution, it is less clear whether M1 also plays a role in learning and controlling sequences of multiple finger movements, such as when playing the piano. Using cutting-edge multivariate fMRI analysis and carefully controlled experiments, we demonstrate here that, while premotor areas clearly show a sequence representation, activity patterns in M1 can be fully explained from the patterns for individual finger movements. The results provide important new insights into the interplay of M1 and premotor cortex for learning of sequential movements.

Resting state functional connectivity of the anterior striatum and prefrontal cortex predicts reading performance in school-age children

The current study investigated the neural basis of reading performance in 60 school-age Spanish-speaking children, aged 6 to 9years. By using a data-driven approach and an automated matching procedure, we identified a left-lateralized resting state network that included typical language regions (Wernicke's and Broca's regions), prefrontal cortex, pre- and post-central gyri, superior and middle temporal gyri, cerebellum, and subcortical regions, and explored its relevance for reading performance (accuracy, comprehension and speed). Functional connectivity of the left frontal and temporal cortices and subcortical regions predicted reading speed. These results extend previous findings on the relationship between functional connectivity and reading competence in children, providing new evidence about such relationships in previously unexplored regions in the resting brain, including the left caudate, putamen and thalamus. This work highlights the relevance of a broad network, functionally synchronized in the resting state, for the acquisition and perfecting of reading abilities in young children.

A corticostriatal deficit promotes temporal distortion of automatic action in ageing

The acquisition of motor skills involves implementing action sequences that increase task efficiency while reducing cognitive loads. This learning capacity depends on specific cortico-basal ganglia circuits that are affected by normal ageing. Here, combining a series of novel behavioural tasks with extensive neuronal mapping and targeted cell manipulations in mice, we explored how ageing of cortico-basal ganglia networks alters the microstructure of action throughout sequence learning. We found that, after extended training, aged mice produced shorter actions and displayed squeezed automatic behaviours characterised by ultrafast oligomeric action chunks that correlated with deficient reorganisation of corticostriatal activity. Chemogenetic disruption of a striatal subcircuit in young mice reproduced age-related within-sequence features, and the introduction of an action-related feedback cue temporarily restored normal sequence structure in aged mice. Our results reveal static properties of aged cortico-basal ganglia networks that introduce temporal limits to action automaticity, something that can compromise procedural learning in ageing.

Learning about learning: Mining human brain sub-network biomarkers from fMRI data

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in "resting state" employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions.

Qualitative differences in offline improvement of procedural memory by daytime napping and overnight sleep: An fMRI study

Daytime napping offers various benefits for healthy adults, including enhancement of motor skill learning. It remains controversial whether napping can provide the same enhancement as overnight sleep, and if so, whether the same neural underpinning is recruited. To investigate this issue, we conducted functional MRI during motor skill learning, before and after a short day-nap, in 13 participants, and compared them with a larger group (n=47) who were tested following regular overnight sleep. Training in a sequential finger-tapping task required participants to press a keyboard in the MRI scanner with their non-dominant left hand as quickly and accurately as possible. The nap group slept for 60min in the scanner after the training run, and the previously trained skill was subsequently re-tested. The whole-night sleep group went home after the training, and was tested the next day. Offline improvement of speed was observed in both groups, whereas accuracy was significantly improved only in the whole-night sleep group. Correspondingly, the offline increment in task-related activation was significant in the putamen of the whole-night group. This finding reveals a qualitative difference in the offline improvement effect between daytime napping and overnight sleep.

The effects of inter-trial interval on implicit learning of sequential visual isometric pinch task

Sequential visual isometric pinch task (SVIPT) has been recently used as a visuomotor sequence task in clinical research. The influence of varying intervals between sequenced trials on the acquisition of implicit sequence learning is not yet determined for SVIPT. The aim of this study was to investigate the effects of inter-trial interval (ITI) on implicit motor sequence learning using SVIPT. A total of 32 healthy participants with mean age 31.3 ± 4.5 years participated in this study. Participants were randomly assigned to one of four ITI groups; (1, 2, 3 and 4 s). They were instructed to control their force on a force transducer to reach a number of targets which appeared on the computer screen by changing the pinch force exerted onto the transducer. In this study, outcome measures were movement time, error rate and skill, which were measured before and after training. Our results indicated that motor sequence learning similarly affected various ITIs. Indeed, all participants exhibited same improvement in implicit learning of SVIPT even though the ITIs varied from 1 to 4 s. Our findings suggest that implicit learning of SVIPT is independent of ITI within this range in healthy individuals.

Striatal Neuropeptides Enhance Selection and Rejection of Sequential Actions

The striatum is the primary input nucleus for the basal ganglia, and receives glutamatergic afferents from the cortex. Under the hypothesis that basal ganglia perform action selection, these cortical afferents encode potential “action requests.” Previous studies have suggested the striatum may utilize a mutually inhibitory network of medium spiny neurons (MSNs) to filter these requests so that only those of high salience are selected. However, the mechanisms enabling the striatum to perform clean, rapid switching between distinct actions that form part of a learned action sequence are still poorly understood. Substance P (SP) and enkephalin are neuropeptides co-released with GABA in MSNs preferentially expressing D₁ or D₂ dopamine receptors respectively. SP has a facilitatory effect on subsequent glutamatergic inputs to target MSNs, while enkephalin has an inhibitory effect. Blocking the action of SP in the striatum is also known to affect behavioral transitions. We constructed phenomenological models of the effects of SP and enkephalin, and integrated these into a hybrid model of basal ganglia comprising a spiking striatal microcircuit and rate–coded populations representing other major structures. We demonstrated that diffuse neuropeptide connectivity enhanced the selection of unordered action requests, and that for true action sequences, where action semantics define a fixed structure, a patterning of the SP connectivity reflecting this ordering enhanced selection of actions presented in the correct sequential order and suppressed incorrect ordering. We also showed that selective pruning of SP connections allowed context–sensitive inhibition of specific undesirable requests that otherwise interfered with selection of an action group. Our model suggests that the interaction of SP and enkephalin enhances the contrast between selection and rejection of action requests, and that patterned SP connectivity in the striatum allows the “chunking” of actions and improves selection of sequences. Efficient execution of action sequences may therefore result from a combination of ordered cortical inputs and patterned neuropeptide connectivity within striatum.

Basal ganglia and autism - a translational perspective

The basal ganglia are a collection of nuclei below the cortical surface that are involved in both motor and non-motor functions, including higher order cognition, social interactions, speech, and repetitive behaviors. Motor development milestones that are delayed in autism such as gross motor, fine motor and walking can aid in early diagnosis of autism. Neuropathology and neuroimaging findings in autism cases revealed volumetric changes and altered cell density in select basal ganglia nuclei. Interestingly, in autism, both the basal ganglia and the cerebellum are impacted both in their motor and non-motor domains and recently, found to be connected via the pons through a short disynaptic pathway. In typically developing individuals, the basal ganglia plays an important role in: eye movement, movement coordination, sensory modulation and processing, eye-hand coordination, action chaining, and inhibition control. Genetic models have proved to be useful toward understanding cellular and molecular changes at the synaptic level in the basal ganglia that may in part contribute to these autism-related behaviors. In autism, basal ganglia functions in motor skill acquisition and development are altered, thus disrupting the normal flow of feedback to the cortex. Taken together, there is an abundance of emerging evidence that the basal ganglia likely plays critical roles in maintaining an inhibitory balance between cortical and subcortical structures, critical for normal motor actions and cognitive functions. In autism, this inhibitory balance is disturbed thus impacting key pathways that affect normal cortical network activity. Autism Res 2017, 10: 1751-1775. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

LAY SUMMARY

Habit learning, action selection and performance are modulated by the basal ganglia, a collection of groups of neurons located below the cerebral cortex in the brain. In autism, there is emerging evidence that parts of the basal ganglia are structurally and functionally altered disrupting normal information flow. The basal ganglia through its interconnected circuits with the cerebral cortex and the cerebellum can potentially impact various motor and cognitive functions in the autism brain.

The Impact of Aerobic Exercise on Fronto-Parietal Network Connectivity and Its Relation to Mobility: An Exploratory Analysis of a 6-Month Randomized Controlled Trial

Impaired mobility is a major concern for older adults and has significant consequences. While the widely accepted belief is that improved physical function underlies the effectiveness of targeted exercise training in improving mobility and reducing falls, recent evidence suggests cognitive and neural benefits gained through exercise may also play an important role in promoting mobility. However, the underlying neural mechanisms of this relationship are currently unclear. Thus, we hypothesize that 6 months of progressive aerobic exercise training would alter frontoparietal network (FPN) connectivity during a motor task among older adults with mild subcortical ischemic vascular cognitive impairment (SIVCI)—and exercise-induced changes in FPN connectivity would correlate with changes in mobility. We focused on the FPN as it is involved in top-down attentional control as well as motor planning and motor execution. Participants were randomized either to usual-care (CON), which included monthly educational materials about VCI and healthy diet; or thrice-weekly aerobic training (AT), which was walking outdoors with progressive intensity. Functional magnetic resonance imaging was acquired at baseline and trial completion, where the participants were instructed to perform bilateral finger tapping task. At trial completion, compared with AT, CON showed significantly increased FPN connectivity strength during right finger tapping (p < 0.05). Across the participants, reduced FPN connectivity was associated with greater cardiovascular capacity (p = 0.05). In the AT group, reduced FPN connectivity was significantly associated with improved mobility performance, as measured by the Timed-Up-and-Go test (r = 0.67, p = 0.02). These results suggest progressive AT may improve mobility in older adults with SIVCI via maintaining intra-network connectivity of the FPN.

Cohesive network reconfiguration accompanies extended training

Human behavior is supported by flexible neurophysiological processes that enable the fine‐scale manipulation of information across distributed neural circuits. Yet, approaches for understanding the dynamics of these circuit interactions have been limited. One promising avenue for quantifying and describing these dynamics lies in multilayer network models. Here, networks are composed of nodes (which represent brain regions) and time‐dependent edges (which represent statistical similarities in activity time series). We use this approach to examine functional connectivity measured by non‐invasive neuroimaging techniques. These multilayer network models facilitate the examination of changes in the pattern of statistical interactions between large‐scale brain regions that might facilitate behavior. In this study, we define and exercise two novel measures of network reconfiguration, and demonstrate their utility in neuroimaging data acquired as healthy adult human subjects learn a new motor skill. In particular, we identify putative functional modules in multilayer networks and characterize the degree to which nodes switch between modules. Next, we define cohesive switches, in which a set of nodes moves between modules together as a group, and we define disjoint switches, in which a single node moves between modules independently from other nodes. Together, these two concepts offer complementary yet distinct insights into the changes in functional connectivity that accompany motor learning. More generally, our work offers statistical tools that other researchers can use to better understand the reconfiguration patterns of functional connectivity over time. Hum Brain Mapp 38:4744–4759, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity

Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.

A network engineering perspective on probing and perturbing cognition with neurofeedback

Network science and engineering provide a flexible and generalizable tool set to describe and manipulate complex systems characterized by heterogeneous interaction patterns among component parts. While classically applied to social systems, these tools have recently proven to be particularly useful in the study of the brain. In this review, we describe the nascent use of these tools to understand human cognition, and we discuss their utility in informing the meaningful and predictable perturbation of cognition in combination with the emerging capabilities of neurofeedback. To blend these disparate strands of research, we build on emerging conceptualizations of how the brain functions (as a complex network) and how we can develop and target interventions or modulations (as a form of network control). We close with an outline of current frontiers that bridge neurofeedback, connectomics, and network control theory to better understand human cognition.

A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior.

Dopamine depletion impairs gait automaticity by altering cortico-striatal and cerebellar processing in Parkinson's disease

Impairments in motor automaticity cause patients with Parkinson's disease to rely on attentional resources during gait, resulting in greater motor variability and a higher risk of falls. Although dopaminergic circuitry is known to play an important role in motor automaticity, little evidence exists on the neural mechanisms underlying the breakdown of locomotor automaticity in Parkinson's disease. This impedes clinical management and is in great part due to mobility restrictions that accompany the neuroimaging of gait. This study therefore utilized a virtual reality gait paradigm in conjunction with functional MRI to investigate the role of dopaminergic medication on lower limb motor automaticity in 23 patients with Parkinson's disease that were measured both on and off dopaminergic medication. Participants either operated foot pedals to navigate a corridor ('walk' condition) or watched the screen while a researcher operated the paradigm from outside the scanner ('watch' condition), a setting that controlled for the non-motor aspects of the task. Step time variability during walk was used as a surrogate measure for motor automaticity (where higher variability equates to reduced automaticity), and patients demonstrated a predicted increase in step time variability during the dopaminergic "off" state. During the "off" state, subjects showed an increased blood oxygen level-dependent response in the bilateral orbitofrontal cortices (walk>watch). To estimate step time variability, a parametric modulator was designed that allowed for the examination of brain regions associated with periods of decreased automaticity. This analysis showed that patients on dopaminergic medication recruited the cerebellum during periods of increasing variability, whereas patients off medication instead relied upon cortical regions implicated in cognitive control. Finally, a task-based functional connectivity analysis was conducted to examine the manner in which dopamine modulates large-scale network interactions during gait. A main effect of medication was found for functional connectivity within an attentional motor network and a significant condition by medication interaction for functional connectivity was found within the striatum. Furthermore, functional connectivity within the striatum correlated strongly with increasing step time variability during walk in the off state (r=0.616, p=0.002), but not in the on state (r=-0.233, p=0.284). Post-hoc analyses revealed that functional connectivity in the dopamine depleted state within an orbitofrontal-striatal limbic circuit was correlated with worse step time variability (r=0.653, p<0.001). Overall, this study demonstrates that dopamine ameliorates gait automaticity in Parkinson's disease by altering striatal, limbic and cerebellar processing, thereby informing future therapeutic avenues for gait and falls prevention.

Parallel basal ganglia circuits for decision making

The basal ganglia control body movements, mainly, based on their values. Critical for this mechanism is dopamine neurons, which sends unpredicted value signals, mainly, to the striatum. This mechanism enables animals to change their behaviors flexibly, eventually choosing a valuable behavior. However, this may not be the best behavior, because the flexible choice is focused on recent, and, therefore, limited, experiences (i.e., short-term memories). Our old and recent studies suggest that the basal ganglia contain separate circuits that process value signals in a completely different manner. They are insensitive to recent changes in value, yet gradually accumulate the value of each behavior (i.e., movement or object choice). These stable circuits eventually encode values of many behaviors and then retain the value signals for a long time (i.e., long-term memories). They are innervated by a separate group of dopamine neurons that retain value signals, even when no reward is predicted. Importantly, the stable circuits can control motor behaviors (e.g., hand or eye) quickly and precisely, which allows animals to automatically acquire valuable outcomes based on historical life experiences. These behaviors would be called 'skills', which are crucial for survival. The stable circuits are localized in the posterior part of the basal ganglia, separately from the flexible circuits located in the anterior part. To summarize, the flexible and stable circuits in the basal ganglia, working together but independently, enable animals (and humans) to reach valuable goals in various contexts.