Resting-state cortical connectivity predicts motor skill acquisition

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

Adolescent brain development

Adolescence starts with puberty and ends when individuals attain an independent role in society. Cognitive neuroscience research in the last two decades has improved our understanding of adolescent brain development. The evidence indicates a prolonged structural maturation of grey matter and white matter tracts supporting higher cognitive functions such as cognitive control and social cognition. These changes are associated with a greater strengthening and separation of brain networks, both in terms of structure and function, as well as improved cognitive skills. Adolescent-specific sub-cortical reactivity to emotions and rewards, contrasted with their developing self-control skills, are thought to account for their greater sensitivity to the socio-affective context. The present review examines these findings and their implications for training interventions and education

Neural Signatures of Motor Skill in the Resting Brain

Stroke-induced disturbances of large-scale cortical networks are known to be associated with the extent of motor deficits. We argue that identifying brain networks representative of motor behavior in the resting brain would provide significant insights for current neurorehabilitation approaches. Particularly, we aim to investigate the global configuration of brain rhythms and their relation to motor skill, instead of learning performance as broadly studied. We empirically approach this problem by conducting a three-dimensional physical space visuomotor learning experiment during electroencephalographic (EEG) data recordings with thirty-seven healthy participants. We demonstrate that across-subjects variations in average movement smoothness as the quantified measure of subjects' motor skills can be predicted from the global configuration of resting-state EEG alpha-rhythms (8-14 Hz) recorded prior to the experiment. Importantly, this neural signature of motor skill was found to be orthogonal to (independent of) task -- as well as to learning-related changes in alpha-rhythms, which we interpret as an organizing principle of the brain. We argue that disturbances of such configurations in the brain may contribute to motor deficits in stroke, and that reconfiguring stroke patients' brain rhythms by neurofeedback may enhance post-stroke neurorehabilitation.Comment: 2019 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2019

Análisis objetivo de la evolución de la descarga de peso mediante el registro de parámetros biomecánicos y electrofisiológicos

En el ámbito de la rehabilitación motora la descarga del peso corporal durante el apoyo bipodal es crucial en el periodo de recuperación de pacientes ortopédicos que presentan distintas patologías en los miembros inferiores. Durante el proceso de rehabilitación se producen cambios en el sistema osteoartromuscular y en el sistema nervioso central, ya que es este último el que tiene que reentrenar o reaprender las funcionalidades que se perdieron o comprometieron.&nbsp; Las técnicas para la medición de la descarga de peso que se utilizan en la actualidad en el ámbito clínico de la región son muy subjetivas y las alternativas del ámbito de investigación son costosas. Esta situación es contemplada por este proyecto en el que se desarrolló un prototipo de herramienta que registra en forma síncrona y confiable, a través de una interface de control, señales biomecánicas utilizando la plataforma Wii de Nintendo y señales electrofisiológicas empleando el amplificador de biopotenciales BioAmp. Además, posee una interfaz de procesamiento y visualización para obtener parámetros relevantes de las señales registradas. La herramienta fue testeada en sujetos sanos y está siendo utilizada en pacientes amputados y con fibromialgia, gracias a la colaboración de distintos actores del ámbito académico y social

Predicting Gains With Visuospatial Training After Stroke Using an EEG Measure of Frontoparietal Circuit Function

The heterogeneity of stroke prompts the need for predictors of individual treatment response to rehabilitation therapies. We previously studied healthy subjects with EEG and identified a frontoparietal circuit in which activity predicted training-related gains in visuomotor tracking. Here we asked whether activity in this same frontoparietal circuit also predicts training-related gains in visuomotor tracking in patients with chronic hemiparetic stroke. Subjects (n = 12) underwent dense-array EEG recording at rest, then received 8 sessions of visuomotor tracking training delivered via home-based telehealth methods. Subjects showed significant training-related gains in the primary behavioral endpoint, Success Rate score on a standardized test of visuomotor tracking, increasing an average of 24.2 ± 21.9% (p = 0.003). Activity in the circuit of interest, measured as coherence (20–30Hz) between leads overlying ipsilesional frontal (motor cortex) and parietal lobe, significantly predicted training-related gains in visuomotor tracking change, measured as change in Success Rate score (r = 0.61, p = 0.037), supporting the main study hypothesis. Results were specific to the hypothesized ipsilesional motor-parietal circuit, as coherence within other circuits did not predict training-related gains. Analyses were repeated after removing the four subjects with injury to motor or parietal areas; this increased the strength of the association between activity in the circuit of interest and training-related gains. The current study found that (1) Eight sessions of training can significantly improve performance on a visuomotor task in patients with chronic stroke, (2) this improvement can be realized using home-based telehealth methods, (3) an EEG-based measure of frontoparietal circuit function predicts training-related behavioral gains arising from that circuit, as hypothesized and with specificity, and (4) incorporating measures of both neural function and neural injury improves prediction of stroke rehabilitation therapy effects

tDCS effects on pointing task learning in young and old adults

Skill increase in motor performance can be defined as explicitly measuring task success but also via more implicit measures of movement kinematics. Even though these measures are often related, there is evidence that they represent distinct concepts of learning. In the present study, the effect of multiple tDCS-sessions on both explicit and implicit measures of learning are investigated in a pointing task in 30 young adults (YA) between 27.07 ± 3.8 years and 30 old adults (OA) between 67.97 years ± 5.3 years. We hypothesized, that OA would show slower explicit skill learning indicated by higher movement times/lower accuracy and slower implicit learning indicated by higher spatial variability but profit more from anodal tDCS compared with YA. We found age-related differences in movement time but not in accuracy or spatial variability. TDCS did not skill learning facilitate learning neither in explicit nor implicit parameters. However, contrary to our hypotheses, we found tDCS-associated higher accuracy only in YA but not in spatial variability. Taken together, our data shows limited overlapping of tDCS effects in explicit and implicit skill parameters. Furthermore, it supports the assumption that tDCS is capable of producing a performance-enhancing brain state at least for explicit skill acquisition

Multivariate patterns and long‐range temporal correlations of alpha oscillations are associated with flexible manipulation of visual working memory representations

Data Availability Statement: Data and resources will be provided from the corresponding author upon request..The ability to flexibly manipulate memory representations is embedded in visual working memory (VWM) and can be tested using paradigms with retrospective cues. Although valid retrospective cues often facilitate memory recall, invalid ones may or may not result in performance costs. We investigated individual differences in utilising retrospective cues and evaluated how these individual differences are associated with brain oscillatory activity at rest. At the behavioural level, we operationalised flexibility as the ability to make effective use of retrospective cues or disregard them if required. At the neural level, we tested whether individual differences in such flexibility were associated with properties of resting‐state alpha oscillatory activity (8–12 Hz). To capture distinct aspects of these brain oscillations, we evaluated their power spectral density and temporal dynamics using long‐range temporal correlations (LRTCs). In addition, we performed multivariate patterns analysis (MVPA) to classify individuals' level of behavioural flexibility based on these neural measures. We observed that alpha power alone (magnitude) at rest was not associated with flexibility. However, we found that the participants' ability to manipulate VWM representations was correlated with alpha LRTC and could be decoded using MVPA on patterns of alpha power. Our findings suggest that alpha LRTC and multivariate patterns of alpha power at rest may underlie some of the individual differences in using retrospective cues in working memory tasks.Graduate School Goldsmiths, University of London; National Research University Higher School of Economics (Russian Federation); Fundação Bial; British Academy

Resting-state functional connectivity predicts the ability to adapt to robot-mediated force fields

Motor deficits are common outcomes of neurological conditions such as stroke. In order to design personalised motor rehabilitation programmes such as robot-assisted therapy, it would be advantageous to predict how a patient might respond to such treatment. Spontaneous neural activity has been observed to predict differences in the ability to learn a new motor behaviour in both healthy and stroke populations. This study investigated whether spontaneous resting-state functional connectivity could predict the degree of motor adaptation of right (dominant) upper limb reaching in response to a robot-mediated force field. Spontaneous neural activity was measured using resting-state electroencephalography (EEG) in healthy adults before a single session of motor adaptation. The degree of beta frequency (β; 15–25 Hz) resting-state functional connectivity between contralateral electrodes overlying the left primary motor cortex (M1) and the anterior prefrontal cortex (aPFC) could predict the subsequent degree of motor adaptation. This result provides novel evidence for the functional significance of resting-state synchronization dynamics in predicting the degree of motor adaptation in a healthy sample. This study constitutes a promising first step towards the identification of patients who will likely gain most from using robot-mediated upper limb rehabilitation training based on simple measures of spontaneous neural activity

Motor Learning Based on Oscillatory Brain Activity Using Transcranial Alternating Current Stimulation: A Review

Developing effective tools and strategies to promote motor learning is a high-priority scientific and clinical goal. In particular, motor-related areas have been investigated as potential targets to facilitate motor learning by noninvasive brain stimulation (NIBS). In addition to shedding light on the relationship between motor function and oscillatory brain activity, transcranial alternating current stimulation (tACS), which can noninvasively entrain oscillatory brain activity and modulate oscillatory brain communication, has attracted attention as a possible technique to promote motor learning. This review focuses on the use of tACS to enhance motor learning through the manipulation of oscillatory brain activity and its potential clinical applications. We discuss a potential tACS-based approach to ameliorate motor deficits by correcting abnormal oscillatory brain activity and promoting appropriate oscillatory communication in patients after stroke or with Parkinson\u27s disease. Interpersonal tACS approaches to manipulate intra- and inter-brain communication may result in pro-social effects and could promote the teaching-learning process during rehabilitation sessions with a therapist. The approach of re-establishing oscillatory brain communication through tACS could be effective for motor recovery and might eventually drive the design of new neurorehabilitation approaches based on motor learning

Neurobehavioral Strategies of Skill Acquisition in Left and Right Hand Dominant Individuals

The brain consists of vast networks of connected pathways communicating through synchronized electrochemical activity propagated along fiber tracts. The current understanding is that the brain has a modular organization where regions of specialized processes are dynamically coupled through long-range projections of dense axonal networks connecting spatially distinct regions enabling signal transfer necessary for all complex thought and behavior, including regulation of movement. The central objective of the dissertation was to understand how sensorimotor information is integrated, allowing for adaptable motor behavior and skill acquisition in the left-and right-hand dominant populations. To this end participants, of both left- and right-hand dominance, repeatedly completed a visually guided, force matching task while neurobiological and neurobehavioral outcome measurements were continuously recorded via EEG and EMG. Functional connectivity and graph theoretical measurements were derived from EEG. Cortico-cortical coherence patterns were used to infer neurostrategic discrepancies employed in the execution of a motor task for each population. EEG activity was also correlated with neuromuscular activity from EMG to calculate cortico-muscular connectivity. Neurological patterns and corresponding behavioral changes were used to express how hand dominance influenced the developing motor plan, thereby increasing understanding of the sensorimotor integration process. The cumulative findings indicated fundamental differences in how left- and right-hand dominant populations interact with the world. The right-hand dominant group was found to rely on visual information to inform motor behavior where the left-hand dominant group used visual information to update motor behavior. The left-hand group was found to have a more versatile motor plan, adaptable to both dominant, nondominant, and bimanual tasks. Compared to the right-hand group it might be said that they were more successful in encoding the task, however behaviorally they performed the same. The implications of the findings are relevant to both clinical and performance applications providing insight as to potential alternative methods of information integration. The inclusion of the left-hand dominant population in the growing conceptualization of the brain will generate a more complete, stable, and accurate understanding of our complex biology