Role of beta-band resting-state functional connectivity as a predictor of motor learning ability

Resting-state functional connectivity involved in tactile orientation processing

BACKGROUND

Grating orientation discrimination (GOD) is commonly used to assess somatosensory spatial processing. It allows discrimination between parallel and orthogonal orientations of tactile stimuli applied to the fingertip. Despite its widespread application, the underlying mechanisms of GOD, particularly the role of cortico-cortical interactions and local brain activity in this process, remain elusive. Therefore, we aimed to investigate how a specific cortico-cortical network and inhibitory circuits within the primary somatosensory cortex (S1) and secondary somatosensory cortex (S2) contribute to GOD.

METHODS

In total, 51 healthy young adults were included in our study. We recorded resting-state magnetoencephalography (MEG) and somatosensory-evoked magnetic field (SEF) in participants with open eyes. We converted the data into a source space based on individual structural magnetic resonance imaging. Next, we estimated S1- and S2-seed resting-state functional connectivity (rs-FC) at the alpha and beta bands through resting-state MEG using the amplitude envelope correlation method across the entire brain (i.e., S1/S2-seeds × 15,000 vertices × two frequencies). We assessed the inhibitory response in the S1 and S2 from SEFs using a paired-pulse paradigm. We automatically measured the GOD task in parallel and orthogonal orientations to the index finger, applying various groove widths with a custom-made device.

RESULTS

We observed a specific association between the GOD threshold (all P < 0.048) and the alpha rs-FC in the S1-superior parietal lobule and S1-adjacent to the parieto-occipital sulcus (i.e., lower rs-FC values corresponded to higher performance). In contrast, no association was observed between the local responses and the threshold.

DISCUSSION

The results of this study underpin the significance of specific cortico-cortical networks in recognizing variations in tactile stimuli.

Rhythm Facilitates Auditory Working Memory via Beta-Band Encoding and Theta-Band Maintenance

Rhythm, as a prominent characteristic of auditory experiences such as speech and music, is known to facilitate attention, yet its contribution to working memory (WM) remains unclear. Here, human participants temporarily retained a 12-tone sequence presented rhythmically or arrhythmically in WM and performed a pitch change-detection task. Behaviorally, while having comparable accuracy, rhythmic tone sequences showed a faster response time and lower response boundaries in decision-making. Electroencephalographic recordings revealed that rhythmic sequences elicited enhanced non-phase-locked beta-band (16 Hz-33 Hz) and theta-band (3 Hz-5 Hz) neural oscillations during sensory encoding and WM retention periods, respectively. Importantly, the two-stage neural signatures were correlated with each other and contributed to behavior. As beta-band and theta-band oscillations denote the engagement of motor systems and WM maintenance, respectively, our findings imply that rhythm facilitates auditory WM through intricate oscillation-based interactions between the motor and auditory systems that facilitate predictive attention to auditory sequences.

Pupillometry reveals resting state alpha power correlates with individual differences in adult auditory language comprehension

Although individual differences in adult language processing are well-documented, the neural basis of this variability remains largely unexplored. The current study addressed this gap in the literature by examining the relationship between resting state alpha activity and individual differences in auditory language comprehension. Alpha oscillations modulate cortical excitability, facilitating efficient information processing in the brain. While resting state alpha oscillations have been tied to individual differences in cognitive performance, their association with auditory language comprehension is less clear. Participants in the study were 80 healthy adults with a mean age of 25.8 years (SD = 7.2 years). Resting state alpha activity was acquired using electroencephalography while participants looked at a benign stimulus for 3 min. Participants then completed a language comprehension task that involved listening to 'syntactically simple' subject-relative clause sentences and 'syntactically complex' object-relative clause sentences. Pupillometry measured real-time processing demand changes, with larger pupil dilation indicating increased processing loads. Replicating past research, comprehending object relative clauses, compared to subject relative clauses, was associated with lower accuracy, slower reaction times, and larger pupil dilation. Resting state alpha power was found to be positively correlated with the pupillometry data. That is, participants with higher resting state alpha activity evidenced larger dilation during sentence comprehension. This effect was more pronounced for the 'complex' object sentences compared to the 'simple' subject sentences. These findings suggest the brain's capacity to generate a robust resting alpha rhythm contributes to variability in processing demands associated with auditory language comprehension, especially when faced with challenging syntactic structures. More generally, the study demonstrates that the intrinsic functional architecture of the brain likely influences individual differences in language comprehension.

Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals

Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.

Resting network architecture of theta oscillations reflects hyper-learning of sensorimotor information in Gilles de la Tourette syndrome

Gilles de la Tourette syndrome is a neurodevelopmental disorder characterized by motor and vocal tics. It is associated with enhanced processing of stimulus-response associations, including a higher propensity to learn probabilistic stimulus-response contingencies (i.e. statistical learning), the nature of which is still elusive. In this study, we investigated the hypothesis that resting-state theta network organization is a key for the understanding of superior statistical learning in these patients. We investigated the graph-theoretical network architecture of theta oscillations in adult patients with Gilles de la Tourette syndrome and healthy controls during a statistical learning task and in resting states both before and after learning. We found that patients with Gilles de la Tourette syndrome showed a higher statistical learning score than healthy controls, as well as a more optimal (small-world-like) theta network before the task. Thus, patients with Gilles de la Tourette syndrome had a superior facility to integrate and evaluate novel information as a trait-like characteristic. Additionally, the theta network architecture in Gilles de la Tourette syndrome adapted more to the statistical information during the task than in HC. We suggest that hyper-learning in patients with Gilles de la Tourette syndrome is likely a consequence of increased sensitivity to perceive and integrate sensorimotor information leveraged through theta oscillation-based resting-state dynamics. The study delineates the neural basis of a higher propensity in patients with Gilles de la Tourette syndrome to pick up statistical contingencies in their environment. Moreover, the study emphasizes pathophysiologically endowed abilities in patients with Gilles de la Tourette syndrome, which are often not taken into account in the perception of this common disorder but could play an important role in destigmatization.

Functional connectivity via the dorsolateral prefrontal cortex in the late phase of rest periods predicts offline learning

The relationship between offline learning gains and functional connectivity (FC) has been investigated in several studies. They have focused on average motor task performance and resting-state FC across subjects. Generally, individual differences are seen in both offline learning gain and neurophysiological profiles in resting-state FC. However, few studies have focused on the relationship between individual differences in offline learning gain and temporal characteristics of resting-state FC. The present study aimed to clarify this relationship between the two profiles. Thirty-four healthy right-handed participants performed a force-controlled motor task. Electroencephalography was performed during the 15-minute wakeful rest period between tasks. The results revealed a significant correlation between offline learning gain and FC between the contralateral dorsolateral prefrontal cortex (DLPFC) and contralateral primary motor cortex (M1), and ipsilateral primary somatosensory cortex (S1) during late phase of the rest interval. These results are consistent with the findings of previous studies showing the FC between M1, which is necessary for awake offline learning, and DLPFC, which is related to motor control. Additionally, sensory feedback related to force control may be caused by the interaction between contralateral DLPFC and ipsilateral S1. Our study shed light on the temporal profiles of resting-state FC associated with individual differences in offline learning.

Neurophysiological underpinnings of an intensive protocol for upper limb motor recovery in subacute and chronic stroke patients

BACKGROUND

Upper limb (UL) motor impairment following stroke is a leading cause of functional limitations in activities of daily living. Robot-assisted therapy supports rehabilitation, but how its efficacy and the underlying neural mechanisms depend on the time after stroke is yet to be assessed.

AIM

We investigated the response to an intensive protocol of robot-assisted rehabilitation in sub-acute and chronic stroke patients, by analyzing the underlying changes in clinical scores, electroencephalography (EEG) and end-effector kinematics. We aimed at identifying neural correlates of the participants' upper limb motor function recovery, following an intensive 2-week rehabilitation protocol.

DESIGN

Prospective cohort study.

SETTING

Inpatients and outpatients from the Neurorehabilitation Unit of Pisa University Hospital, Italy.

POPULATION

Sub-acute and chronic stroke survivors.

METHODS

Thirty-one stroke survivors (14 sub-acute, 17 chronic) with mild-to-moderate UL paresis were enrolled. All participants underwent ten rehabilitative sessions of task-oriented exercises with a planar end-effector robotic device. All patients were evaluated with the Fugl-Meyer Assessment Scale and the Wolf Motor Function Test, at recruitment (T0), end-of-treatment (T1), and one-month follow-up (T2). Along with clinical scales, kinematic parameters and quantitative EEG were collected for each patient. Kinematics metrics were related to velocity, acceleration and smoothness of the movement. Relative power in four frequency bands was extracted from the EEG signals. The evolution over time of kinematic and EEG features was analyzed, in correlation with motor recovery.

RESULTS

Both groups displayed significant gains in motility after treatment. Sub-acute patients displayed more pronounced clinical improvements, significant changes in kinematic parameters, and a larger increase in Beta-band in the motor area of the affected hemisphere. In both groups these improvements were associated to a decrease in the Delta-band of both hemispheres. Improvements were retained at T2.

CONCLUSIONS

The intensive two-week rehabilitation protocol was effective in both chronic and sub-acute patients, and improvements in the two groups shared similar dynamics. However, stronger cortical and behavioral changes were observed in sub-acute patients suggesting different reorganizational patterns.

CLINICAL REHABILITATION IMPACT

This study paves the way to personalized approaches to UL motor rehabilitation after stroke, as highlighted by different neurophysiological modifications following recovery in subacute and chronic stroke patients.

Beta resting-state functional connectivity predicts tactile spatial acuity

Tactile perception is a complex phenomenon that is processed by multiple cortical regions via the primary somatosensory cortex (S1). Although somatosensory gating in the S1 using paired-pulse stimulation can predict tactile performance, the functional relevance of cortico-cortical connections to tactile perception remains unclear. We investigated the mechanisms by which corticocortical and local networks predict tactile spatial acuity in 42 adults using magnetoencephalography (MEG). Resting-state MEG was recorded with the eyes open, whereas evoked responses were assessed using single- and paired-pulse electrical stimulation. Source data were used to estimate the S1-seed resting-state functional connectivity (rs-FC) in the whole brain and the evoked response in the S1. Two-point discrimination threshold was assessed using a custom-made device. The beta rs-FC revealed a negative correlation between the discrimination threshold and S1-superior parietal lobule, S1-inferior parietal lobule, and S1-superior temporal gyrus connection (all P < 0.049); strong connectivity was associated with better performance. Somatosensory gating of N20m was also negatively correlated with the discrimination threshold (P = 0.015), with weak gating associated with better performance. This is the first study to demonstrate that specific beta corticocortical networks functionally support tactile spatial acuity as well as the local inhibitory network.

Resting state electroencephalography power correlates with individual differences in implicit sequence learning

Neuroimaging resting state paradigms have revealed synchronised oscillatory activity is present even in the absence of completing a task or mental operation. One function of this neural activity is likely to optimise the brain's sensitivity to forthcoming information that, in turn, likely promotes subsequent learning and memory outcomes. The current study investigated whether this extends to implicit forms of learning. A total of 85 healthy adults participated in the study. Resting state electroencephalography was first acquired from participants before they completed a serial reaction time task. On this task, participants implicitly learnt a visuospatial-motor sequence. Permutation testing revealed a negative correlation between implicit sequence learning and resting state power in the upper theta band (6-7 Hz). That is, lower levels of resting state power in this frequency range were associated with superior levels of implicit sequence learning. This association was observed at midline-frontal, right-frontal and left-posterior electrodes. Oscillatory activity in the upper theta band supports a range of top-down processes including attention, inhibitory control and working memory, perhaps just for visuospatial information. Our results may be indicating that disengaging theta-supported top-down attentional processes improves implicit learning of visuospatial-motor information that is embedded in sensory input. This may occur because the brain's sensitivity to this type of information is optimally achieved when learning is driven by bottom-up processes. Moreover, the results of this study further demonstrate that resting state synchronised brain activity influences subsequent learning and memory.

The correlation between upper body grip strength and resting-state EEG network

Current research in the field of neuroscience primarily focuses on the analysis of electroencephalogram (EEG) activities associated with movement within the central nervous system. However, there is a dearth of studies investigating the impact of prolonged individual strength training on the resting state of the brain. Therefore, it is crucial to examine the correlation between upper body grip strength and resting-state EEG networks. In this study, coherence analysis was utilized to construct resting-state EEG networks using the available datasets. A multiple linear regression model was established to examine the correlation between the brain network properties of individuals and their maximum voluntary contraction (MVC) during gripping tasks. The model was used to predict individual MVC. The beta and gamma frequency bands showed significant correlation between RSN connectivity and MVC (p < 0.05), particularly in left hemisphere frontoparietal and fronto-occipital connectivity. RSN properties were consistently correlated with MVC in both bands, with correlation coefficients greater than 0.60 (p < 0.01). Additionally, predicted MVC positively correlated with actual MVC, with a coefficient of 0.70 and root mean square error of 5.67 (p < 0.01). The results show that the resting-state EEG network is closely related to upper body grip strength, which can indirectly reflect an individual's muscle strength through the resting brain network.

Predicting individual differences in motor learning: a critical review

The ability to predict individual differences in motor learning has significant implications from both theoretical and applied perspectives. However, there is high variability in the methodological and analytical strategies employed as evidence for such predictions. Here, we critically examine the evidence for predictions of individual differences in motor learning by reviewing the literature from a 20-year period (2000-2020). Specifically, we examined four factors: (i) the predictor and predicted variables used, (ii) the strength of the prediction and associated sample size, (iii) the timescale over which the prediction was made, and (iv) the type of motor task used. Overall, the results highlight several issues that raise concerns about the quality of the evidence for such predictions. First, there was a large variation in both predictor and predicted variables, suggesting the presence of a large number of researcher degrees of freedom. Second, sample sizes tended to be small, and the strength of the correlation showed an inverse relation with sample size. Third, the timescale of most predictions was very short, mostly constrained to a single day. Last, most studies were largely restricted to two experimental paradigms - adaptation and sequence learning. Based on these issues, we highlight recommendations for future studies to improve the quality of evidence for predicting individual differences in motor learning.

Paired associative stimulation applied to the cortex can increase resting-state functional connectivity: A proof of principle study

INTRODUCTION

There is emerging evidence that high Beta coherence (hBc) between prefrontal and motor corticies, measured with resting-state electroencephalography (rs-EEG), can be an accurate predictor of motor skill learning and stroke recovery. However, it remains unknown whether and how intracortical connectivity may be influenced using neuromodulation. Therefore, a cortico-cortico PAS (ccPAS) paradigm may be used to increase resting-state intracortical connectivity (rs-IC) within a targeted neural circuit.

PURPOSE

Our purpose is to demonstrate proof of principle that ccPAS can be used to increase rs-IC between a prefrontal and motor cortical region.

METHODS

Eleven non-disabled adults were recruited (mean age 26.4, sd 5.6, 5 female). Each participant underwent a double baseline measurement, followed by a real and control ccPAS condition, counter-balanced for order. Control and ccPAS conditions were performed over electrodes of the right prefrontal and motor cortex. Both ccPAS conditions were identical apart from the inter-stimulus interval (i.e ISI 5 ms: real ccPAS and 500 ms: control ccPAS). Whole brain rs-EEG of high Beta coherence (hBc) was acquired before and after each ccPAS condition and then analyzed for changes in rs-IC along the targeted circuit.

RESULTS

Compared to ccPAS500 and baseline, ccPAS5 induced a significant increase in rs-IC, measured as coherence between electrodes over right prefrontal and motor cortex, (p <.05).

CONCLUSION

These findings demonstrate proof of principle that ccPAS with an STDP derived ISI, can effectively increase hBc along a targeted circuit.

Connectivity in Large-Scale Resting-State Brain Networks Is Related to Motor Learning: A High-Density EEG Study

Previous research has shown that resting-state functional connectivity (rsFC) between different brain regions (seeds) is related to motor learning and motor memory consolidation. Using high-density electroencephalography (hdEEG), we addressed this question from a brain network perspective. Specifically, we examined frequency-dependent functional connectivity in resting-state networks from twenty-nine young healthy participants before and after they were trained on a motor sequence learning task. Consolidation was assessed with an overnight retest on the motor task. Our results showed training-related decreases in gamma-band connectivity within the motor network, and between the motor and functionally distinct resting-state networks including the attentional network. Brain-behavior correlation analyses revealed that baseline beta, delta, and theta rsFC were related to subsequent motor learning and memory consolidation such that lower connectivity within the motor network and between the motor and several distinct resting-state networks was correlated with better learning and overnight consolidation. Lastly, training-related increases in beta-band connectivity between the motor and the visual networks were related to greater consolidation. Altogether, our results indicate that connectivity in large-scale resting-state brain networks is related to—and modulated by—motor learning and memory consolidation processes. These finding corroborate previous seed-based connectivity research and provide evidence that frequency-dependent functional connectivity in resting-state networks is critically linked to motor learning and memory consolidation.

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network

Large-scale functional connectivity is an important indicator of the brain’s normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the functional connectivity assessment based on artificial intelligence to reveal age-related changes in neural response in a simple motor execution task. Twenty subjects of two age groups performed repetitive motor tasks on command, while the whole-scalp EEG was recorded. We applied the model based on the feed-forward multilayer perceptron to detect functional relationships between five groups of sensors located over the frontal, parietal, left, right, and middle motor cortex. Functional dependence was evaluated with the predicted and original time series coefficient of determination. Then, we applied statistical analysis to highlight the significant features of the functional connectivity network assessed by our model. Our findings revealed the connectivity pattern is consistent with modern ideas of the healthy aging effect on neural activation. Elderly adults demonstrate a pronounced activation of the whole-brain theta-band network and decreased activation of frontal–parietal and motor areas of the mu-band. Between-subject analysis revealed a strengthening of inter-areal task-relevant links in elderly adults. These findings can be interpreted as an increased cognitive demand in elderly adults to perform simple motor tasks with the dominant hand, induced by age-related working memory decline.

Resting-State Beta-Band Recovery Network Related to Cognitive Improvement After Stroke

Background

Stroke is the second leading cause of death worldwide and it causes important long-term cognitive and physical deficits that hamper patients' daily activity. Neuropsychological rehabilitation (NR) has increasingly become more important to recover from cognitive disability and to improve the functionality and quality of life of these patients. Since in most stroke cases, restoration of functional connectivity (FC) precedes or accompanies cognitive and behavioral recovery, understanding the electrophysiological signatures underlying stroke recovery mechanisms is a crucial scientific and clinical goal.

Methods

For this purpose, a longitudinal study was carried out with a sample of 10 stroke patients, who underwent two neuropsychological assessments and two resting-state magnetoencephalographic (MEG) recordings, before and after undergoing a NR program. Moreover, to understand the degree of cognitive and neurophysiological impairment after stroke and the mechanisms of recovery after cognitive rehabilitation, stroke patients were compared to 10 healthy controls matched for age, sex, and educational level.

Findings

After intra and inter group comparisons, we found the following results: (1) Within the stroke group who received cognitive rehabilitation, almost all cognitive domains improved relatively or totally; (2) They exhibit a pattern of widespread increased in FC within the beta band that was related to the recovery process (there were no significant differences between patients who underwent rehabilitation and controls); (3) These FC recovery changes were related with the enhanced of cognitive performance. Furthermore, we explored the capacity of the neuropsychological scores before rehabilitation, to predict the FC changes in the brain network. Significant correlations were found in global indexes from the WAIS-III: Performance IQ (PIQ) and Perceptual Organization index (POI) (i.e., Picture Completion, Matrix Reasoning, and Block Design).

Sports-Related Motor Processing at Different Rates of Force Development

How does our brain manage to process vast quantities of sensory information that define movement performance? By extracting the required movement parameters for which brain dynamics are, inter alia, assumed to be functionally related to, we used electroencephalography to investigate motor-related brain oscillations. Visually guided movement (i.e., motor) tasks at explosive, medium and slow rates of force development (RFD) revealed increased broad-band activity at explosive RFD, whereas decreasing activity could be observed during both intermediate and slow RFD. Moreover, a continuously decreasing activity pattern from faster to slower RFD and a return to baseline activity after full muscle relaxation was found. We suggest oscillatory activity to desynchronize in sensorimotor demanding tasks, whereas task-specific synchronization mirrors movement acceleration. The pre/post-stimulus activity steady state may indicate an inhibitory baseline that provides attentional focus and timing.

The Effect of Music Listening on EEG Functional Connectivity of Brain: A Short-Duration and Long-Duration Study

Music is considered a powerful brain stimulus, as listening to it can activate several brain networks. Music of different kinds and genres may have a different effect on the human brain. The goal of this study is to investigate the change in the brain’s functional connectivity (FC) when music is used as a stimulus. Secondly, the effect of listening to the subject’s favorite music is compared with listening to specifically formulated relaxing music with alpha binaural beats. Finally, the effect of the duration of music listening is studied. Subjects’ electroencephalographic (EEG) signals were captured as they listened to favorite and relaxing music. After preprocessing and artifact removal, the EEG recordings were decomposed into the delta, theta, alpha, and beta frequency bands, and the grand-averaged connectivity matrices were generated using Inter-Site Phase Clustering (ISPC) for each frequency band and each type of music. Furthermore, each lobe of the brain was analyzed separately to understand the effect of music on specific regions of the brain. EEG-FC among different channels was accessed by using graph theory and Network-based Statistics (NBS). To determine the significance of the changes in brain networks after listening to music, statistical analysis was conducted using Analysis of Variance (ANOVA) and t-test. The study of listening to music for a short duration verifies that either favorite or preferred music can affect the FC of the subject and induce a relaxation state. The short duration study also verifies a significant (ANOVA and t-test: p < 0.05) effectiveness of relaxing music over favorite music to induce relaxation and alertness in the subject. In the study of long duration, it is concluded that listening to relaxing music can increase functional connectivity and connections strength in the frontal lobe of the subject. A significant increase (ANOVA and t-test: p < 0.05) in FC in alpha and theta band and a significant decrease (ANOVA and t-test: p < 0.05) in FC in beta band in the frontal and parietal lobe of the brain verifies the hypothesis that the relaxing music can help the subject to achieve relaxation, activeness, and alertness.

The neural correlates of intermanual transfer

Intermanual transfer of motor learning is a form of learning generalization that leads to behavioral advantages in various tasks of daily life. It might also be useful for rehabilitation of patients with unilateral motor deficits. Little is known about neural structures and cognitive processes that mediate intermanual transfer. Previous studies have suggested a role for primary motor cortex (M1) and the supplementary motor area (SMA). Here, we investigated the functional neuroanatomy of intermanual transfer with a special emphasis on functional connectivity within the motor network and between motor regions and attentional networks, including the fronto-parietal executive control network and visual attention networks. We designed a finger tapping task, in which young, heathy subjects trained the non-dominant left hand in the MRI scanner. Behaviorally, transfer of sequence learning was observed in most cases, independently of the trained hand's performance. Pre- and post-training functional connectivity patterns of cortical motor seeds were investigated using generalized psychophysiological interaction analyses. Transfer was correlated with the strength of connectivity between the left premotor cortex and structures within the dorsal attention network (superior parietal cortex, left middle temporal gyrus) and executive control network (right prefrontal regions) during pre-training, relative to post-training. Changes in connectivity within the motor network, and more particularly between trained and untrained M1, as well as between the SMA and untrained M1, correlated with transfer after training. Together, these results suggest that the interplay between attentional, executive and motor networks may support processes leading to transfer, whereas, following training, transfer translates into increased connectivity within the motor network.

The development of brain rhythms at rest and its impact on vocabulary acquisition

A long-standing question in developmental science is how the neurodevelopment of the brain influences cognitive functions. Here, we examined the developmental change of resting EEG power and its links to vocabulary acquisition in school-age children. We further explored what mechanisms may mediate the relation between brain rhythm maturation and vocabulary knowledge. Eyes-opened resting-state EEG data were recorded from 53 typically-developing Chinese children every 2 years between the ages of 7 and 11. Our results showed first that delta, theta, and gamma power decreased over time, whereas alpha and beta power increased over time. Second, after controlling for general cognitive abilities, age, home literacy environment, and phonological skills, theta decreases explained 6.9% and 14.4% of unique variance in expressive vocabulary at ages 9 and 11, respectively. We also found that beta increase from age 7 to 9 significantly predicted receptive vocabulary at age 11. Finally, theta decrease predicted expressive vocabulary through the effects of phoneme deletion at age 9 and tone discrimination at age 11. These results substantiate the important role of brain oscillations at rest, especially theta rhythm, in language development. The developmental change of brain rhythms could serve as sensitive biomarkers for vocabulary development in school-age children, which would be of great value in identifying children at risk of language impairment.

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.

Distinct connectivity profiles predict different in-time processes of motor skill learning

Learning through intensive practice has been largely observed in motor, sensory and higher-order cognitive processing. Neuroimaging studies have shown that learning phases are associated with different patterns of functional and structural neural plasticity in spatially distributed brain systems. Yet, it is unknown whether distinct neural signatures before practice can foster different subsequent learning stages over time. Here, we employed a bimanual implicit sequence reaction time task (SRTT) to investigate whether the rates of early (one day after practice) and late (one month after practice) post-training motor skill learning were predicted by distinct patterns of pre-training resting state functional connectivity (rs-FC), recorded with functional MRI. We observed that both motor learning descriptors were positively correlated with the strength of rs-FC among pairs of regions within a SRTT-relevant network comprising cerebellar as well as cortical and subcortical motor areas. Crucially, we detected a double dissociation such that early post-training learning was significantly associated with the functional connections within cerebellar regions, whereas late post-training learning was significantly related to the functional connections between cortical and subcortical motor areas. These findings indicate that spontaneous brain activity prospectively carries out behaviorally relevant information to perform experience-dependent cognitive operations far distant in time.

Age-dependent modulation of motor network connectivity for skill acquisition, consolidation and interlimb transfer after motor practice

OBJECTIVE

Age-related differences in neural strategies for motor learning are not fully understood. We determined the effects of age on the relationship between motor network connectivity and motor skill acquisition, consolidation, and interlimb transfer using dynamic imaging of coherent sources.

METHODS

Healthy younger (n = 24, 18-24 y) and older (n = 24, 65-87 y) adults unilaterally practiced a visuomotor task and resting-state electroencephalographic data was acquired before and after practice as well as at retention.

RESULTS

The results showed that right-hand skill acquisition and consolidation did not differ between age groups. However, age affected the ability to transfer the newly acquired motor skill to the non-practiced limb. Moreover, strengthened left- and right-primary motor cortex-related beta connectivity was negatively and positively associated with right-hand skill acquisition and left-hand skill consolidation in older adults, respectively.

CONCLUSION

Age-dependent modulations of bilateral resting-state motor network connectivity indicate age-specific strategies for the acquisition, consolidation, and interlimb transfer of novel motor tasks.

SIGNIFICANCE

The present results provide insights into the mechanisms underlying motor learning that are important for the development of interventions for patients with unilateral injuries.

Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators

Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing's neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability.

Multiparametric and multilevel characterization of morphological alterations in patients with transient ischemic attack

Transient ischemic attack (TIA), an important risk factor for stroke, is associated with widespread disruptions of functional brain architecture. However, TIA-related structural alterations are not well established. By analyzing structural MRI data from 50 TIA patients versus 40 healthy controls (HCs), here we systematically investigated TIA-related morphological alterations in multiple cortical surface-based indices (cortical thickness [CT], fractal dimension [FD], gyrification index [GI], and sulcal depth [SD]) at multiple levels (local topography, interregional connectivity and whole-brain network topology). For the observed alterations, their associations with clinical risk factors and abilities as diagnostic and prognostic biomarkers were further examined. We found that compared with the HCs, the TIA patients showed widespread morphological alterations and the alterations depended on choices of morphological index and analytical level. Specifically, the patients exhibited: (a) regional CT decreases in the transverse temporal gyrus and lateral sulcus; (b) impaired FD- and GI-based connectivity mainly involving visual, somatomotor and ventral attention networks and interhemispheric connections; and (c) altered GI-based whole-brain network efficiency and decreased FD-based nodal centrality in the middle frontal gyrus. Moreover, the impaired morphological connectivity showed high sensitivities and specificities for distinguishing the patients from HCs. Altogether, these findings demonstrate the emergence of morphological index-dependent and analytical level-specific alterations in TIA, which provide novel insights into neurobiological mechanisms underlying TIA and may serve as potential biomarkers to help diagnosis of the disease. Meanwhile, our findings highlight the necessity of using multiparametric and multilevel approaches for a complete mapping of cerebral morphology in health and disease.

Off-line effects of alpha-frequency transcranial alternating current stimulation on a visuomotor learning task

INTRODUCTION

It has been suggested that transcranial alternating current stimulation (tACS) at both alpha and beta frequencies promotes motor function as well as motor learning. However, limited information exists on the aftereffects of tACS on motor learning and neurophysiological profiles such as entrainment and neural plasticity in parallel. Therefore, in the present study, we examined the effect of tACS on motor learning and neurophysiological profiles using an off-line tACS condition.

METHODS

Thirty-three healthy participants were randomly assigned to 10 Hz, 20 Hz, or the sham group. Participants performed visuomotor learning tasks consisting of a baseline task (preadaptation task) and training task (adaptation task) to reach a target with a lever-type controller. Electroencephalography was recorded from eight locations during the learning tasks. tACS was performed between the preadaptation task and adaptation task over the left primary motor cortex for 10 min at 1 mA.

RESULTS

As a result, 10 Hz tACS was shown to be effective for initial angular error correction in the visuomotor learning tasks. However, there were no significant differences in neural oscillatory activities among the three groups.

CONCLUSION

These results suggest that initial motor learning can be facilitated even when 10 Hz tACS is applied under off-line conditions. However, neurophysiological aftereffects were recently demonstrated to be induced by tACS at individual alpha frequencies rather than fixed alpha tACS, which suggests that the neurophysiological aftereffects by fixed frequency stimulation in the present study may have been insufficient to generate changes in oscillatory neural activity.