Graph network analysis of immediate motor-learning induced changes in resting state BOLD

Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep

Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated neural network characteristics during the boost but not the silent window. Re-exposure-related induction effects were observed on the next day, to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at the behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.

Brain connectome correlates of short-term motor learning in healthy older subjects

The motor learning process entails plastic changes in the brain, especially in brain network reconfigurations. In the current study, we sought to characterize motor learning by determining changes in the coupling behaviour between the brain functional and structural connectomes on a short timescale. 39 older subjects (age: mean (SD) = 69.7 (4.7) years, men:women = 15:24) were trained on a visually guided sequential hand grip learning task. The brain structural and functional connectomes were constructed from diffusion-weighted MRI and resting-state functional MRI, respectively. The association of motor learning ability with changes in network topology of the brain functional connectome and changes in the correspondence between the brain structural and functional connectomes were assessed. Motor learning ability was related to decreased efficiency and increased modularity in the visual, somatomotor, and frontoparietal networks of the brain functional connectome. Between the brain structural and functional connectomes, reduced correspondence in the visual, ventral attention, and frontoparietal networks as well as the whole-brain network was related to motor learning ability. In addition, structure-function correspondence in the dorsal attention, ventral attention, and frontoparietal networks before motor learning was predictive of motor learning ability. These findings indicate that, in the view of brain connectome changes, short-term motor learning is represented by a detachment of the brain functional from the brain structural connectome. The structure-function uncoupling accompanied by the enhanced segregation into modular structures over the core functional networks involved in the learning process may suggest that facilitation of functional flexibility is associated with successful motor learning.

Motor cortical functional connectivity changes due to short-term immobilization of upper limb: an fNIRS case report

Introduction

A short-term immobilization of one hand affects musculoskeletal functions, and the associated brain network adapts to the alterations happening to the body due to injuries. It was hypothesized that the injury-associated temporary disuse of the upper limb would alter the functional interactions of the motor cortical processes and will produce long-term changes throughout the immobilization and post-immobilization period.

Methods

The case participant (male, 12 years old, right arm immobilized for clavicle fracture) was scanned using optical imaging technology of fNIRS over immobilization and post-immobilization. Pre-task data was collected for 3 min for RSFC analysis, processed, and analyzed using the Brain AnalyzIR toolbox. Connectivity was measured using Pearson correlation coefficients (R) from NIRS Toolbox's connectivity module.

Results

The non-affected hand task presented an increased ipsilateral response during the immobilization period, which then decreased over the follow-up visits. The right-hand task showed a bilateral activation pattern following immobilization, but the contralateral activation pattern was restored during the 1-year follow-up visit. Significant differences in the average connection strength over the study period were observed. The average Connection strength decreased from the third week of immobilization and continued to be lower than the baseline value. Global network efficiency decreased in weeks two and three, while the network settled into a higher efficient state during the follow-up periods after post-immobilization.

Discussion

Short-term immobilization of the upper limb is shown to have cortical changes in terms of activations of brain regions as well as connectivity. The short-term dis-use of the upper limb has shifted the unilateral activation pattern to the bilateral coactivation of the motor cortex from both hemispheres. Resting-state data reveals a disruption in the motor cortical network during the immobilization phase, and the network is reorganized into an efficient network over 1 year after the injury. Understanding such cortical reorganization could be informative for studying the recovery from neurological disorders affecting motor control in the future.

Changes in Resting State Functional Connectivity Associated with Dynamic Adaptation of Wrist Movements

Dynamic adaptation is an error-driven process of adjusting planned motor actions to changes in task dynamics (Shadmehr, 2017). Adapted motor plans are consolidated into memories that contribute to better performance on re-exposure. Consolidation begins within 15 min following training (Criscimagna-Hemminger and Shadmehr, 2008), and can be measured via changes in resting state functional connectivity (rsFC). For dynamic adaptation, rsFC has not been quantified on this timescale, nor has its relationship to adaptative behavior been established. We used a functional magnetic resonance imaging (fMRI)-compatible robot, the MR-SoftWrist (Erwin et al., 2017), to quantify rsFC specific to dynamic adaptation of wrist movements and subsequent memory formation in a mixed-sex cohort of human participants. We acquired fMRI during a motor execution and a dynamic adaptation task to localize brain networks of interest, and quantified rsFC within these networks in three 10-min windows occurring immediately before and after each task. The next day, we assessed behavioral retention. We used a mixed model of rsFC measured in each time window to identify changes in rsFC with task performance, and linear regression to identify the relationship between rsFC and behavior. Following the dynamic adaptation task, rsFC increased within the cortico-cerebellar network and decreased interhemispherically within the cortical sensorimotor network. Increases within the cortico-cerebellar network were specific to dynamic adaptation, as they were associated with behavioral measures of adaptation and retention, indicating that this network has a functional role in consolidation. Instead, decreases in rsFC within the cortical sensorimotor network were associated with motor control processes independent from adaptation and retention.SIGNIFICANCE STATEMENT Motor memory consolidation processes have been studied via functional magnetic resonance imaging (fMRI) by analyzing changes in resting state functional connectivity (rsFC) occurring more than 30 min after adaptation. However, it is unknown whether consolidation processes are detectable immediately (<15 min) following dynamic adaptation. We used an fMRI-compatible wrist robot to localize brain regions involved in dynamic adaptation in the cortico-thalamic-cerebellar (CTC) and cortical sensorimotor networks and quantified changes in rsFC within each network immediately after adaptation. Different patterns of change in rsFC were observed compared with studies conducted at longer latencies. Increases in rsFC in the cortico-cerebellar network were specific to adaptation and retention, while interhemispheric decreases in the cortical sensorimotor network were associated with alternate motor control processes but not with memory formation.

Human motor sequence learning drives transient changes in network topology and hippocampal connectivity early during memory consolidation

In the last decade, the exclusive role of the hippocampus in human declarative learning has been challenged. Recently, we have shown that gains in performance observed in motor sequence learning (MSL) during the quiet rest periods interleaved with practice are associated with increased hippocampal activity, suggesting a role of this structure in motor memory reactivation. Yet, skill also develops offline as memory stabilizes after training and overnight. To examine whether the hippocampus contributes to motor sequence memory consolidation, here we used a network neuroscience strategy to track its functional connectivity offline 30 min and 24 h post learning using resting-state functional magnetic resonance imaging. Using a graph-analytical approach we found that MSL transiently increased network modularity, reflected in an increment in local information processing at 30 min that returned to baseline at 24 h. Within the same time window, MSL decreased the connectivity of a hippocampal-sensorimotor network, and increased the connectivity of a striatal-premotor network in an antagonistic manner. Finally, a supervised classification identified a low-dimensional pattern of hippocampal connectivity that discriminated between control and MSL data with high accuracy. The fact that changes in hippocampal connectivity were detected shortly after training supports a relevant role of the hippocampus in early stages of motor memory consolidation.

Reduced functional connectivity supports statistical learning of temporally distributed regularities

Statistical learning is a powerful ability that extracts regularities from our environment and makes predictions about future events. Using functional magnetic resonance imaging, we aimed to probe how a wide range of brain areas are intertwined to support statistical learning, characterising its architecture in the whole-brain functional connectivity (FC). Participants performed a statistical learning task of temporally distributed regularities. We used refined behavioural learning scores to associate individuals' learning performances with the FC changed by statistical learning. As a result, the learning performance was mediated by the activation strength in the lateral occipital cortex, angular gyrus, precuneus, anterior cingulate cortex, and superior frontal gyrus. Through a group independent component analysis, activations of the superior frontal network showed the largest correlation with the statistical learning performances. Seed-to-voxel whole-brain and seed-to-ROI FC analyses revealed that the FC between the superior frontal gyrus and the salience, language, and dorsal attention networks were reduced during statistical learning. We suggest that the weakened functional connections between the superior frontal gyrus and brain regions involved in top-down control processes serve a pivotal role in statistical learning, supporting better processing of novel information such as the extraction of new patterns from the environment.

Resting state fast brain dynamics predict interindividual variability in motor performance

Motor learning features rapid enhancement during practice then offline post-practice gains with the reorganization of related brain networks. We hypothesised that fast transient, sub-second variations in magnetoencephalographic (MEG) network activity during the resting-state (RS) reflect early learning-related plasticity mechanisms and/or interindividual motor variability in performance. MEG RS activity was recorded before and 20 min after motor learning. Hidden Markov modelling (HMM) of MEG power envelope signals highlighted 8 recurrent topographical states. For two states, motor performance levels were associated with HMM temporal parameters both in pre- and post-learning resting-state sessions. However, no association emerged with offline changes in performance. These results suggest a trait-like relationship between spontaneous transient neural dynamics at rest and interindividual variations in motor abilities. On the other hand, transient RS dynamics seem not to be state-dependent, i.e., modulated by learning experience and reflect neural plasticity, at least on the short timescale.

Coherent theta oscillations in the cerebellum and supplementary motor area mediate visuomotor adaptation

The cerebellum and its interaction with cortical areas play a key role in our ability to flexibly adapt a motor program in response to sensory input. Current knowledge about specific neural mechanisms underlying the process of visuomotor adaptation is however lacking. Using a novel placement of EEG electrodes to record electric activity from the cerebellum, we studied local cerebellar activity, as well as its coupling with neocortical activity to obtain direct neurophysiological markers of visuomotor adaptation in humans. We found increased theta (4-8Hz) power in "cerebellar" as well as cortical electrodes, when subjects first encountered a visual manipulation. Theta power decreased as subjects adapted to the perturbation, and rebounded when the manipulation was suddenly removed. This effect was observed in two distinct locations: a cerebellar cluster and a central cluster, which were localized in left cerebellar crus I (lCB) and right supplementary motor area (rSMA) using linear constrained minimum variance beamforming. Importantly, we found that better adaptation was associated with increased theta power in left cerebellar electrodes and a right sensorimotor cortex electrode. Finally, increased rSMA -> lCB connectivity was significantly decreased with adaptation. These results demonstrate that: (1) cerebellar theta power is markedly modulated over the course of visuomotor adaptation and (2) theta oscillations could serve as a key mechanism for communication within a cortico-cerebellar loop.

Motor sequences; separating the sequence from the motor. A longitudinal rsfMRI study

In motor learning, sequence specificity, i.e. the learning of specific sequential associations, has predominantly been studied using task-based fMRI paradigms. However, offline changes in resting state functional connectivity after sequence-specific motor learning are less well understood. Previous research has established that plastic changes following motor learning can be divided into stages including fast learning, slow learning and retention. A description of how resting state functional connectivity after sequence-specific motor sequence learning (MSL) develops across these stages is missing. This study aimed to identify plastic alterations in whole-brain functional connectivity after learning a complex motor sequence by contrasting an active group who learned a complex sequence with a control group who performed a control task matched for motor execution. Resting state fMRI and behavioural performance were collected in both groups over the course of 5 consecutive training days and at follow-up after 12 days to encompass fast learning, slow learning, overall learning and retention. Between-group interaction analyses showed sequence-specific decreases in functional connectivity during overall learning in the right supplementary motor area (SMA). We found that connectivity changes in a key region of the motor network, the superior parietal cortex (SPC) were not a result of sequence-specific learning but were instead linked to motor execution. Our study confirms the sequence-specific role of SMA that has previously been identified in online task-based learning studies, and extends it to resting state network changes after sequence-specific MSL.

Resting-state functional brain connectivity is related to subsequent procedural learning skills in school-aged children

This magnetoencephalography (MEG) study investigates how procedural sequence learning performance is related to prior brain resting-state functional connectivity (rsFC), and to what extent sequence learning induces rapid changes in brain rsFC in school-aged children. Procedural learning was assessed in 30 typically developing children (mean age ± SD: 9.99 years ± 1.35) using a serial reaction time task (SRTT). During SRTT, participants touched as quickly and accurately as possible a stimulus sequentially or randomly appearing in one of the quadrants of a touchscreen. Band-limited power envelope correlation (brain rsFC) was applied to MEG data acquired at rest pre- and post-learning. Correlation analyses were performed between brain rsFC and sequence-specific learning or response time indices. Stronger pre-learning interhemispheric rsFC between inferior parietal and primary somatosensory/motor areas correlated with better subsequent sequence learning performance and faster visuomotor response time. Faster response time was associated with post-learning decreased rsFC within the dorsal extra-striate visual stream and increased rsFC between temporo-cerebellar regions. In school-aged children, variations in functional brain architecture at rest within the sensorimotor network account for interindividual differences in sequence learning and visuomotor performance. After learning, rapid adjustments in functional brain architecture are associated with visuomotor performance but not sequence learning skills.

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

Accurate identification of brain function is necessary to understand neurocognitive aging, and thereby promote health and well-being. Many studies of neurocognitive aging have investigated brain function with the blood-oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is, therefore, essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable, and robust means to calibrate vascular responsivity, as an alternative to breath-holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age-related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health (CVH) and cerebrovascular function, are insufficient when used alone to fully explain age-related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and cerebrovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population-based, lifespan Cam-CAN cohort. After controlling for cardiovascular and cerebrovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and cerebrovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age differences in RSFA, after controlling for CVH. The results were consistent between voxel-level analysis and independent component analysis. Our findings indicate that cardiovascular and cerebrovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterize neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging.

BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks

Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the α band was paralleled by a decrease of the integration of visual processing and working memory areas in the β band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the α2 band: positively in somatosensory and decision-making related areas and negatively in associative areas. Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.

Separating vascular and neuronal effects of age on fMRI BOLD signals

Accurate identification of brain function is necessary to understand the neurobiology of cognitive ageing, and thereby promote well-being across the lifespan. A common tool used to investigate neurocognitive ageing is functional magnetic resonance imaging (fMRI). However, although fMRI data are often interpreted in terms of neuronal activity, the blood oxygenation level-dependent (BOLD) signal measured by fMRI includes contributions of both vascular and neuronal factors, which change differentially with age. While some studies investigate vascular ageing factors, the results of these studies are not well known within the field of neurocognitive ageing and therefore vascular confounds in neurocognitive fMRI studies are common. Despite over 10 000 BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct for vascular effects. However, neurovascular ageing is not only a confound in fMRI, but an important feature in its own right, to be assessed alongside measures of neuronal ageing. We review current approaches to dissociate neuronal and vascular components of BOLD-fMRI of regional activity and functional connectivity. We highlight emerging evidence that vascular mechanisms in the brain do not simply control blood flow to support the metabolic needs of neurons, but form complex neurovascular interactions that influence neuronal function in health and disease. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Awake Reactivation of Prior Experiences Consolidates Memories and Biases Cognition

After experiences are encoded into memory, post-encoding reactivation mechanisms have been proposed to mediate long-term memory stabilization and transformation. Spontaneous reactivation of hippocampal representations, together with hippocampal-cortical interactions, are leading candidate mechanisms for promoting systems-level memory strengthening and reorganization. While the replay of spatial representations has been extensively studied in rodents, here we review recent fMRI work that provides evidence for spontaneous reactivation of nonspatial, episodic event representations in the human hippocampus and cortex, as well as for experience-dependent alterations in systems-level hippocampal connectivity. We focus on reactivation during awake post-encoding periods, relationships between reactivation and subsequent behavior, how reactivation is modulated by factors that influence consolidation, and the implications of persistent reactivation for biasing ongoing perception and cognition.

Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning

Successful human behaviour depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive corticostriatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.

Differential impact of reward and punishment on functional connectivity after skill learning

Reward and punishment shape behavior, but the mechanisms underlying their effect on skill learning are not well understood. Here, we tested whether the functional connectivity of premotor cortex (PMC), a region known to be critical for learning of sequencing skills, is altered after training when reward or punishment is given during training. Resting-state fMRI was collected in two experiments before and after participants trained on either a serial reaction time task (SRTT; n = 36) or force-tracking task (FTT; n = 36) with reward, punishment, or control feedback. In each experiment, training-related change in PMC functional connectivity was compared across feedback groups. In both tasks, we found that reward and punishment differentially affected PMC functional connectivity. On the SRTT, participants trained with reward showed an increase in functional connectivity between PMC and cerebellum as well as PMC and striatum, while participants trained with punishment showed an increase in functional connectivity between PMC and medial temporal lobe connectivity. After training on the FTT, subjects trained with control and reward showed increases in PMC connectivity with parietal and temporal cortices after training, while subjects trained with punishment showed increased PMC connectivity with ventral striatum. While the results from the two experiments overlapped in some areas, including ventral pallidum, temporal lobe, and cerebellum, these regions showed diverging patterns of results across the two tasks for the different feedback conditions. These findings suggest that reward and punishment strongly influence spontaneous brain activity after training, and that the regions implicated depend on the task learned.

Finding the baby in the bath water - evidence for task-specific changes in resting state functional connectivity evoked by training

Resting-state functional connectivity (rsFC) between brain regions has been used for studying training-related changes in brain function during the offline period of skill learning. However, it is difficult to infer whether the observed training-related changes in rsFC measured between two scans occur as a consequence of task performance, whether they are specific to a given task, or whether they reflect confounding factors such as diurnal fluctuations in brain physiology that impact the MRI signal. Here, we sought to elucidate whether task-specific changes in rsFC are dissociable from time-of-day related changes by evaluating rsFC changes after participants were provided training in either a visuospatial task or a motor sequence task compared to a non-training condition. Given the nature of the tasks, we focused on changes in rsFC of the hippocampal and sensorimotor cortices after short-term training, while controlling for the effect of time-of-day. We also related the change in rsFC of task-relevant brain regions to performance improvement in each task. Our results demonstrate that, even in the absence of any experimental manipulation, significant changes in rsFC can be detected between two resting state functional MRI scans performed just a few hours apart, suggesting time-of-day has a significant impact on rsFC. However, by estimating the magnitude of the time-of-day effect, our findings also suggest that task-specific changes in rsFC can be dissociated from the changes attributed to time-of-day. Taken together, our results show that rsFC can provide insights about training-related changes in brain function during the offline period of skill learning. However, demonstrating the specificity of the changes in rsFC to a given task requires a rigorous experimental design that includes multiple active and passive control conditions, and robust behavioral measures.

Visual search task immediate training effects on task-related functional connectivity

Brain plasticity occurs over the course of the human lifetime. Learning and training modify our neuronal synapses and adapt our brain activity, from priming effects in modal areas to higher-order changes in the association cortex. The current state of the art suggests that learning and training effects might induce large-scale brain connectivity changes. Here, we used task-fMRI data and graph-based approaches to study the immediate brain changes in functional connections associated with training on a visual search task, and the individual differences in learning were studied by means of brain-behavior correlations. In a previous work, we found that trained participants improved their response speed on a visual search task by 31%, whereas the control group hardly changed. In the present study, we showed that trained individuals changed regional connections (local links) in cortical areas devoted to the specific visual search processes and to areas that support information integration, and largely modified distributed connections (distant links) linking primary visual areas to specific attentional and cognitive control areas. In addition, we found that the individuals with the most enhanced connectivity in the dorsolateral prefrontal cortex performed the task faster after training. The observed behavioral and brain connectivity findings expand our understanding of large-scale dynamic readjustment of the human brain after learning experiences.

Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity

In cognitive neuroscience, focus is commonly placed on associating brain function with changes in objectively measured external stimuli or with actively generated cognitive processes. In everyday life, however, many forms of cognitive processes are initiated spontaneously, without an individual's active effort and without explicit manipulation of behavioral state. Recently, there has been increased emphasis, especially in functional neuroimaging research, on spontaneous correlated activity among spatially segregated brain regions (intrinsic functional connectivity) and, more specifically, on intraindividual fluctuations of such correlated activity on various time scales (time-varying functional connectivity). In this Perspective, we propose that certain subtypes of spontaneous cognitive processes are detectable in time-varying functional connectivity measurements. We define these subtypes of spontaneous cognitive processes and review evidence of their representations in time-varying functional connectivity from studies of attentional fluctuations, memory reactivation, and effects of baseline states on subsequent perception. Moreover, we describe how these studies are critical to validating the use of neuroimaging tools (e.g., fMRI) for assessing ongoing brain network dynamics. We conclude that continued investigation of the behavioral relevance of time-varying functional connectivity will be beneficial both in the development of comprehensive neural models of cognition, and in informing on best practices for studying brain network dynamics.

Resting-state brain network features associated with short-term skill learning ability in humans and the influence of N-methyl-d-aspartate receptor antagonism

Graph theoretical functional magnetic resonance imaging (fMRI) studies have demonstrated that brain networks reorganize significantly during motor skill acquisition, yet the associations between motor learning ability, brain network features, and the underlying biological mechanisms remain unclear. In the current study, we applied a visually guided sequential pinch force learning task and graph theoretical analyses to investigate the associations between short-term motor learning ability and resting-state brain network metrics in 60 healthy subjects. We further probed the test-retest reliability (n = 26) and potential effects of the N-methyl-d-aspartate (NMDA) antagonist ketamine (n = 19) in independent healthy volunteers. Our results show that the improvement of motor performance after short-term training was positively correlated with small-worldness (p = 0.032) and global efficiency (p = 0.025), whereas negatively correlated with characteristic path length (p = 0.014) and transitivity (p = 0.025). In addition, using network-based statistics (NBS), we identified a learning ability–associated (p = 0.037) and ketamine-susceptible (p = 0.027) cerebellar-cortical network with fair to good reliability (intraclass correlation coefficient [ICC] > 0.7) and higher functional connectivity in better learners. Our results provide new evidence for the association of intrinsic brain network features with motor learning and suggest a role of NMDA-related glutamatergic processes in learning-associated subnetworks.

Learning a new motor skill prompts immediate reconfigurations of distributed brain networks followed by adaptive changes in intrinsic brain circuits related to synaptic plasticity. Here, we identify global brain network properties and a cerebellar-cortical functional subnetwork that are both significantly associated with motor learning ability in a previously trained visuomotor task in humans. We further show that the associated functional subnetwork connectivity but not the global brain network properties are susceptible to ketamine. Our findings suggest a distinct functional role for learning-related global versus local network metrics and support the idea of a preferential susceptibility of learning-associated subnetworks to N-methyl-d-aspartate antagonist and plasticity-related consolidation effects.

Musical training induces functional and structural auditory-motor network plasticity in young adults

Playing music requires a strong coupling of perception and action mediated by multimodal integration of brain regions, which can be described as network connections measured by anatomical and functional correlations between regions. However, the structural and functional connectivities within and between the auditory and sensorimotor networks after long-term musical training remain largely uninvestigated. Here, we compared the structural connectivity (SC) and resting-state functional connectivity (rs-FC) within and between the two networks in 29 novice healthy young adults before and after musical training (piano) with those of another 27 novice participants who were evaluated longitudinally but with no intervention. In addition, a correlation analysis was performed between the changes in FC or SC with practice time in the training group. As expected, participants in the training group showed increased FC within the sensorimotor network and increased FC and SC of the auditory-motor network after musical training. Interestingly, we further found that the changes in FC within the sensorimotor network and SC of the auditory-motor network were positively correlated with practice time. Our results indicate that musical training could induce enhanced local interaction and global integration between musical performance-related regions, which provides insights into the mechanism of brain plasticity in young adults.

Age differences in brain signal variability are robust to multiple vascular controls

A host of studies support that younger, better performing adults express greater moment-to-moment blood oxygen level-dependent (BOLD) signal variability (SD_BOLD) in various cortical regions, supporting an emerging view that the aging brain may undergo a generalized reduction in dynamic range. However, the exact physiological nature of age differences in SD_BOLD remains understudied. In a sample of 29 younger and 45 older adults, we examined the contribution of vascular factors to age group differences in fixation-based SD_BOLD using (1) a dual-echo BOLD/pseudo-continuous arterial spin labeling (pCASL) sequence, and (2) hypercapnia via a computer-controlled gas delivery system. We tested the hypothesis that, although SD_BOLD may relate to individual differences in absolute cerebral blood flow (CBF), BOLD cerebrovascular reactivity (CVR), or maximum BOLD signal change (M), robust age differences in SD_BOLD would remain after multiple statistical controls for these vascular factors. As expected, our results demonstrated that brain regions in which younger adults expressed higher SD_BOLD persisted after comprehensive control of vascular effects. Our findings thus further establish BOLD signal variability as an important marker of the aging brain.

Repeatability of graph theoretical metrics derived from resting-state functional networks in paediatric epilepsy patients

OBJECTIVE

To measure the repeatability of metrics that quantify brain network architecture derived from resting-state functional MRI in a cohort of paediatric patients with epilepsy.

METHODS

We identified patients with: (1) epilepsy; (2) brain MRI at 3 T; (3) two identical resting-state functional MRI acquisitions performed on the same day. Undirected, weighted networks were constructed based on the resting-state time series using a range of processing parameters including parcellation size and graph threshold. The following topological properties were calculated: degree, strength, characteristic path length, global efficiency, clustering coefficient, modularity and small worldness. Based on repeated measures, we then calculated: (1) Pearson correlation coefficient; (2) intraclass correlation coefficient; (3) root-mean-square coefficient of variation; (4) repeatability coefficient; and (5) 95% confidence limits for change.

RESULTS

26 patients were included (age range: 4-21 years). Correlation coefficients demonstrated a highly consistent relationship between repeated observations for all metrics, and the intraclass correlation coefficients were generally in the excellent range. Repeatability in the data set was not significantly influenced by parcellation size. However, trends towards decreased repeatability were observed at higher graph thresholds.

CONCLUSION

These findings demonstrate the reliability of network metrics in a cohort of paediatric patients with epilepsy. Advances in knowledge: Our results point to the potential for graph theoretical analyses of resting-state data to provide reliable markers of network architecture in children with epilepsy. At the level of an individual patient, change over time greater than the repeatability coefficient or 95% confidence limits for change is unlikely to be related to intrinsic variability of the method.

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.

An Increase in Postural Load Facilitates an Anterior Shift of Processing Resources to Frontal Executive Function in a Postural-Suprapostural Task

Increase in postural-demand resources does not necessarily degrade a concurrent motor task, according to the adaptive resource-sharing hypothesis of postural-suprapostural dual-tasking. This study investigated how brain networks are organized to optimize a suprapostural motor task when the postural load increases and shifts postural control into a less automatic process. Fourteen volunteers executed a designated force-matching task from a level surface (a relative automatic process in posture) and from a stabilometer board while maintaining balance at a target angle (a relatively controlled process in posture). Task performance of the postural and suprapostural tasks, synchronization likelihood (SL) of scalp EEG, and graph-theoretical metrics were assessed. Behavioral results showed that the accuracy and reaction time of force-matching from a stabilometer board were not affected, despite a significant increase in postural sway. However, force-matching in the stabilometer condition showed greater local and global efficiencies of the brain networks than force-matching in the level-surface condition. Force-matching from a stabilometer board was also associated with greater frontal cluster coefficients, greater mean SL of the frontal and sensorimotor areas, and smaller mean SL of the parietal-occipital cortex than force-matching from a level surface. The contrast of supra-threshold links in the upper alpha and beta bands between the two stance conditions validated load-induced facilitation of inter-regional connections between the frontal and sensorimotor areas, but that contrast also indicated connection suppression between the right frontal-temporal and the parietal-occipital areas for the stabilometer stance condition. In conclusion, an increase in stance difficulty alters the neurocognitive processes in executing a postural-suprapostural task. Suprapostural performance is not degraded by increase in postural load, due to (1) increased effectiveness of information transfer, (2) an anterior shift of processing resources toward frontal executive function, and (3) cortical dissociation of control hubs in the parietal-occipital cortex for neural economy.

Remote Effects of Non-Invasive Cerebellar Stimulation on Error Processing in Motor Re-Learning

BACKGROUND

While concurrent transcranial direct current stimulation (tDCS) affects motor memory acquisition and long-term retention, it is unclear how behavioral interference modulates long-term tDCS effects. Behavioral interference can be introduced through a secondary task learned in-between motor memory acquisition and later recall of the original task.

OBJECTIVE/HYPOTHESIS

The cerebellum is important for the processing of errors if movements should be adapted to external perturbations (motor memory acquisition). We hypothesized that concurrent cerebellar tDCS during adaptation influences both memory acquisition and re-acquisition if motor errors are enlarged due to behavioral interference.

METHODS

In a sham-controlled and double-blinded study, we applied anodal and cathodal tDCS to the ipsilateral cerebellum while subjects adapted reaching movements to an external, clockwise force field perturbation (acquisition task A) with their dominant right arm. Behavioral interference by an oppositely oriented, counter-clockwise perturbation (secondary task B) was introduced in between the acquisition and re-acquisition (24 h later) sessions.

RESULTS

Learning task B disrupted memory retention of A and re-increased motor errors in the re-acquisition session. Anodal but not sham or cathodal tDCS impaired motor memory acquisition and, additionally, increased motor errors during re-acquisition of the original motor memory.

CONCLUSION(S)

Behavioral interference disrupted motor memory retention but tDCS delivered online during memory acquisition induced lasting and robust effects on re-acquisition performance one day later. Our data also suggest different error-processing mechanisms at work during motor memory acquisition and re-acquisition.

Explore Interregional EEG Correlations Changed by Sport Training Using Feature Selection

This paper investigated the interregional correlation changed by sport training through electroencephalography (EEG) signals using the techniques of classification and feature selection. The EEG data are obtained from students with long-time professional sport training and normal students without sport training as baseline. Every channel of the 19-channel EEG signals is considered as a node in the brain network and Pearson Correlation Coefficients are calculated between every two nodes as the new features of EEG signals. Then, the Partial Least Square (PLS) is used to select the top 10 most varied features and Pearson Correlation Coefficients of selected features are compared to show the difference of two groups. Result shows that the classification accuracy of two groups is improved from 88.13% by the method using measurement of EEG overall energy to 97.19% by the method using EEG correlation measurement. Furthermore, the features selected reveal that the most important interregional EEG correlation changed by training is the correlation between left inferior frontal and left middle temporal with a decreased value.

Changes in functional connectivity related to direct training and generalization effects of a word finding treatment in chronic aphasia

The neural mechanisms that underlie generalization of treatment-induced improvements in word finding in persons with aphasia (PWA) are currently poorly understood. This study aimed to shed light on changes in functional network connectivity underlying generalization in aphasia. To this end, we used fMRI and graph theoretic analyses to examine changes in functional connectivity after a theoretically-based word-finding treatment in which abstract words were used as training items with the goal of promoting generalization to concrete words. Ten right-handed native English-speaking PWA (7 male, 3 female) ranging in age from 47 to 75 (mean=59) participated in this study. Direct training effects coincided with increased functional connectivity for regions involved in abstract word processing. Generalization effects coincided with increased functional connectivity for regions involved in concrete word processing. Importantly, similarities between training and generalization effects were noted as were differences between participants who generalized and those who did not.

Functional connectivity in the resting-state motor networks influences the kinematic processes during motor sequence learning

Neuroimaging studies support the involvement of the cerebello-cortical and striato-cortical motor loops in motor sequence learning. Here, we investigated whether the gain of motor sequence learning could depend on a-priori resting-state functional connectivity (rsFC) between motor areas and structures belonging to these circuits. Fourteen healthy subjects underwent a resting-state functional magnetic resonance imaging session. Afterward, they were asked to reproduce a verbally-learned sequence of finger opposition movements as fast and as accurately as possible. All subjects increased their movement rate with practice, by reducing the touch duration and/or intertapping interval. The rsFC analysis showed that, at rest, the left and right primary motor cortex (M1) and left and right supplementary motor area (SMA) were mainly connected with other motor areas. The covariate analysis taking into account the different kinematic parameters indicated that the subjects achieving greater movement rate increase were those showing stronger rsFC of the left M1 and SMA with the right lobule VIII of the cerebellum. Notably, the subjects with greater intertapping interval reduction showed stronger rsFC of the left M1 and SMA with the association nuclei of the thalamus. Conversely, the regression analysis with the right M1 and SMA seeds showed only a few significant clusters for the different covariates not located in the cerebellum and thalamus. No common clusters were found between the right M1 and SMA. All of these findings indicated important functional connections at rest of those neural circuits responsible for motor learning improvement, involving the motor areas related to the hemisphere directly controlling the finger movements, the thalamus and cerebellum.

Resting-State fMRI

Although brain plasticity is greatest in the first few years of life, the brain continues to be shaped by experience throughout adulthood. Advances in fMRI have enabled us to examine the plasticity of large-scale networks using blood oxygen level–dependent (BOLD) correlations measured at rest. Resting-state functional connectivity analysis makes it possible to measure task-independent changes in brain function and therefore could provide unique insights into experience-dependent brain plasticity in humans. Here, we evaluate the hypothesis that resting-state functional connectivity reflects the repeated history of co-activation between brain regions. To this end, we review resting-state fMRI studies in the sensory, motor, and cognitive learning literature. This body of research provides evidence that the brain’s resting-state functional architecture displays dynamic properties in young adulthood.

Strengthening connections: functional connectivity and brain plasticity

The ascendancy of functional neuroimaging has facilitated the addition of network-based approaches to the neuropsychologist's toolbox for evaluating the sequelae of brain insult. In particular, intrinsic functional connectivity (iFC) mapping of resting state fMRI (R-fMRI) data constitutes an ideal approach to measuring macro-scale networks in the human brain. Beyond the value of iFC mapping for charting how the functional topography of the brain is altered by insult and injury, iFC analyses can provide insights into experience-dependent plasticity at the macro level of large-scale functional networks. Such insights are foundational to the design of training and remediation interventions that will best facilitate recovery of function. In this review, we consider what is currently known about the origin and function of iFC in the brain, and how this knowledge is informative in neuropsychological settings. We then summarize studies that have examined experience-driven plasticity of iFC in healthy control participants, and frame these findings in terms of a schema that may aid in the interpretation of results and the generation of hypotheses for rehabilitative studies. Finally, we outline some caveats to the R-fMRI approach, as well as some current developments that are likely to bolster the utility of the iFC paradigm for neuropsychology.