Investigation of Methodologies for Extracting Individual Brain Oscillations: Comparisons & Insights.

There is increasing evidence that the effects of non-invasive brain stimulation can be maximized when the applied intervention matches internal brain oscillations. Extracting individual brain oscillations is thus a necessary step for implementing personalized brain stimulation. In this context, different methods have been proposed for obtaining subject-specific spectral peaks from electrophysiological recordings. However, comparing the results obtained using different approaches is still lacking. Therefore, in the present work, we examined the following methodologies in terms of obtaining individual motor-related EEG spectral peaks: fast Fourier Transform analysis, power spectrum density analysis, wavelet analysis, and a principal component based time-frequency analysis. We used EEG data obtained when performing two different motor tasks - a hand grip task and a hand opening- and-closing task. Our results showed that both the motor task type and the specific method for performing the analysis had considerable impact on the extraction of subject-specific oscillation spectral peaks.Clinical Relevance-This exploratory study provides insights into the potential effects of using different methods to extract individual brain oscillations, which is important for designing personalized brain-machine-interfaces.

Deep learning prediction of motor performance in stroke individuals using neuroimaging data.

The degree of motor impairment and profile of recovery after stroke are difficult to predict for each individual. Measures obtained from clinical assessments, as well as neurophysiological and neuroimaging techniques have been used as potential biomarkers of motor recovery, with limited accuracy up to date. To address this, the present study aimed to develop a deep learning model based on structural brain images obtained from stroke participants and healthy volunteers. The following inputs were used in a multi-channel 3D convolutional neural network (CNN) model: fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity maps obtained from Diffusion Tensor Imaging (DTI) images, white and gray matter intensity values obtained from Magnetic Resonance Imaging, as well as demographic data (e.g., age, gender). Upper limb motor function was classified into "Poor" and "Good" categories. To assess the performance of the DL model, we compared it to more standard machine learning (ML) classifiers including k-nearest neighbor, support vector machines (SVM), Decision Trees, Random Forests, Ada Boosting, and Naïve Bayes, whereby the inputs of these classifiers were the features taken from the fully connected layer of the CNN model. The highest accuracy and area under the curve values were 0.92 and 0.92 for the 3D-CNN and 0.91 and 0.91 for the SVM, respectively. The multi-channel 3D-CNN with residual blocks and SVM supported by DL was more accurate than traditional ML methods to classify upper limb motor impairment in the stroke population. These results suggest that combining volumetric DTI maps and measures of white and gray matter integrity can improve the prediction of the degree of motor impairment after stroke. Identifying the potential of recovery early on after a stroke could promote the allocation of resources to optimize the functional independence of these individuals and their quality of life.

Effect of a tailored upper extremity strength training intervention combined with direct current stimulation in chronic stroke survivors: A Randomized Controlled Trial.

Strengthening exercises are recommended for managing persisting upper limb (UL) weakness following a stroke. Yet, strengthening exercises often lead to variable gains because of their generic nature. For this randomized controlled trial (RCT), we aimed to determine whether tailoring strengthening exercises using a biomarker of corticospinal integrity, as reflected in the amplitude of motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS), could optimize training effects in the affected UL. A secondary aim was to determine whether applying anodal transcranial direct current stimulation (tDCS) could enhance exercise-induced training effects. For this multisite RCT, 90 adults at the chronic stage after stroke (>6 months) were recruited. Before training, participants underwent TMS to detect the presence of MEPs in the affected hand. The MEP amplitude was used to stratify participants into three training groups: (1) low-intensity, MEP <50 µV, (2) moderate-intensity, 50 µV < MEP < 120 µV, and (3) high-intensity, MEP>120 µV. Each group trained at a specific intensity based on the one-repetition maximum (1 RM): low-intensity, 35-50% 1RM; moderate-intensity, 50-65% 1RM; high-intensity, 70-85% 1RM. The strength training targeted the affected UL and was delivered 3X/week for four consecutive weeks. In each training group, participants were randomly assigned to receive either real or sham anodal tDCS (2 mA, 20 min) over the primary motor area of the affected hemisphere. Pre-/post-intervention, participants underwent a clinical evaluation of their UL to evaluate motor impairments (Fugl-Meyer Assessment), manual dexterity (Box and Blocks test) and grip strength. Post-intervention, all groups exhibited similar gains in terms of reduced impairments, improved dexterity, and grip strength, which was confirmed by multivariate and univariate analyses. However, no effect of interaction was found for tDCS or training group, indicating that tDCS had no significant impact on outcomes post-intervention. Collectively, these results indicate that adjusting training intensity based on the size of MEPs in the affected extremity provides a useful approach to optimize responses to strengthening exercises in chronic stroke survivors. Also, the lack of add-on effects of tDCS applied to the lesioned hemisphere on exercise-induced improvements in the affected UL raises questions about the relevance of combining such interventions in stroke. Clinical trial registry number: NCT02915185. https://www.clinicaltrials.gov/ct2/show/NCT02915185.

Long COVID symptoms in a population-based sample of persons discharged home from hospital.

OBJECTIVES: The impact of long COVID among persons hospitalized and discharged home is unknown. We aimed to (1) report the prevalence of long COVID in persons hospitalized for COVID-19 and discharged home; (2) estimate the prevalence of physical, sensory, and psychological/mental health impairments; and (3) explore associated factors. METHODS: We conducted a telephone survey of adult residents in Laval, Quebec, who were discharged home ≥ 2 months post-hospitalization for COVID-19. Participants responded to a standard questionnaire regarding persistent symptoms. We calculated the prevalence of long COVID and of persistent types of symptoms and evaluated associated factors using bivariate analysis and multivariable logistic regression. RESULTS: In our sample (n = 398), 70% reported physical symptoms, 58% psychological problems, and 16% sensory impairments. 31.5% reported being troubled by persistent symptoms (long COVID). Factors associated with long COVID were a greater number of symptoms (odds ratio (OR) = 1.97, 95% confidence interval (CI) = 1.69-2.28) and increased hospital stay (OR = 1.03, 95% CI = 1.01-1.06). Other factors associated with physical and psychological symptoms were female sex (OR = 2.17, 95% CI = 1.27-3.71 and OR = 2.06, 95% CI = 1.25-3.39; respectively), higher education level (OR = 2.10, 95% CI = 1.20-3.68 and OR = 2.43, 95% CI = 1.44-4.14; respectively), and obesity (OR = 1.95, 95% CI = 1.15-3.34 and OR = 1.70, 95% CI = 1.05-2.77; respectively). CONCLUSION: In this population-based study of persons hospitalized for COVID-19 and discharged home, nearly one third were troubled by symptoms for 2 months or more post-discharge. There was a high proportion with persistent physical and psychological/mental health symptoms. Further research will assess the specific needs of these patients to inform health policy makers on service requirements for these persons.

RéSUMé: OBJECTIFS: L'impact de la présence de la COVID longue chez les personnes hospitalisées et lors de leur congé de l'hôpital est inconnu. Dans le cadre de cette étude, nous visions à 1) rapporter la prévalence de la présence de la COVID longue chez les personnes hospitalisées en raison de la COVID-19 et lors de leur congé à la maison; 2) estimer la prévalence des déficiences physiques, sensorielles et psychologiques/cognitives; et 3) explorer les facteurs associés. MéTHODES: Nous avons mené une enquête téléphonique auprès des résidents adultes de Laval, au Québec, qui ont reçu leur congé de l'hôpital plus de deux mois après avoir été hospitalisés en raison de la COVID-19. Les participants ont répondu à un questionnaire standard concernant leurs symptômes résiduels. Nous avons calculé la prévalence de la COVID longue et le type de symptômes résiduels et nous avons évalué les facteurs associés en utilisant une analyse bivariée et une régression logistique multivariable. RéSULTATS: Dans notre échantillon (n=398), 70 % ont déclaré des symptômes physiques, 58 % des problèmes psychologiques et 16 % des déficiences sensorielles. 31,5 % ont déclaré être perturbés par des symptômes résiduels (COVID longue). Les facteurs associés à la COVID longue étaient un plus grand nombre de symptômes (Rapport de cotes (OR)=1,97, intervalle de confiance à 95% (IC)=1,69-2,28) et une durée d'hospitalisation plus longue (OR=1,03, IC 95%=1,01-1,06). Les autres facteurs associés aux symptômes physiques et psychologiques étaient le sexe féminin (OR=2,17, IC 95%=1,27-3,71 et OR=2,06, IC 95%=1,25-3,39; respectivement), un niveau d'éducation plus élevé (OR=2,10, IC 95%=1,20-3,68 et OR=2,43, IC 95%=1,44-4,14; respectivement) et l'obésité (OR=1,95, IC 95%=1,15-3,34 et OR=1,70, IC 95%=1,05-2,77; respectivement). CONCLUSION: Dans cette étude effectuée sur une population de personnes hospitalisées pour la COVID-19 et lors de leur congé de l'hôpital, près d'un tiers ont été perturbées par la présence de symptômes résiduels présents pendant 2 mois ou plus après leur congé. Une forte proportion d'entre elles présentait des symptômes physiques et psychologiques/enjeux de santé mentale persistants. Des recherches futures permettront d'évaluer les besoins spécifiques de ces individus afin d'informer les décideurs politiques en santé de leurs besoins afin d'offrir des services adaptés à leur condition.

Modeling the Hemodynamic Response Function Using EEG-fMRI Data During Eyes-Open Resting-State Conditions and Motor Task Execution.

Being able to accurately quantify the hemodynamic response function (HRF) that links the blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) signal to the underlying neural activity is important both for elucidating neurovascular coupling mechanisms and improving the accuracy of fMRI-based functional connectivity analyses. In particular, HRF estimation using BOLD-fMRI is challenging particularly in the case of resting-state data, due to the absence of information about the underlying neuronal dynamics. To this end, using simultaneously recorded electroencephalography (EEG) and fMRI data is a promising approach, as EEG provides a more direct measure of neural activations. In the present work, we employ simultaneous EEG-fMRI to investigate the regional characteristics of the HRF using measurements acquired during resting conditions. We propose a novel methodological approach based on combining distributed EEG source space reconstruction, which improves the spatial resolution of HRF estimation and using block-structured linear and nonlinear models, which enables us to simultaneously obtain HRF estimates and the contribution of different EEG frequency bands. Our results suggest that the dynamics of the resting-state BOLD signal can be sufficiently described using linear models and that the contribution of each band is region specific. Specifically, it was found that sensory-motor cortices exhibit positive HRF shapes, whereas the lateral occipital cortex and areas in the parietal cortex, such as the inferior and superior parietal lobule exhibit negative HRF shapes. To validate the proposed method, we repeated the analysis using simultaneous EEG-fMRI measurements acquired during execution of a unimanual hand-grip task. Our results reveal significant associations between BOLD signal variations and electrophysiological power fluctuations in the ipsilateral primary motor cortex, particularly for the EEG beta band, in agreement with previous studies in the literature.

Removal of Transcranial Alternating Current Stimulation EEG Artifacts Using Blind Source Separation and Wavelets.

GOAL: Transcranial alternating current stimulation (tACS) is a non-invasive technology for modulating brain activity, with significant potential for improving motor and cognitive functions. To investigate the effects of tACS, many studies have used electroencephalographic (EEG) data recorded during brain stimulation. However, the large artifacts induced by tACS make the analysis of tACS-EEG recordings challenging, which in turn has prevented the implementation of closed-loop brain stimulation schemes. Here, we propose a novel combination of blind source separation (BSS) and wavelets to achieve removal of tACS-EEG artifacts with improved performance. METHODS: We examined the performance of several BSS methods both applied individually, as well as combined with the empirical wavelet transform (EWT) in terms of denoising realistic simulated and experimental tACS-EEG data. RESULTS: EWT combined with BSS yielded considerably improved performance compared to BSS alone for both simulated and experimental data. Overall, independent vector analysis (IVA) combined with EWT yielded the best performance. SIGNIFICANCE: The proposed method yields promise for quantifying the effects of tACS on simultaneously recorded EEG data, which can in turn contribute towards understanding the effects of tACS on brain activity, as well as extracting reliable biomarkers that may be used to develop closed-loop tACS strategies for modulating the underlying brain activity in real time.

Identification of Beta Oscillatory Patterns During a Hand Grip Motor Task: A Comparative Analysis pre- and post-Exercise.

Electroencephalography (EEG) based Movement-Related Beta Band Desynchronization (MRBD) within the beta frequency band (13 - 30Hz) is commonly observed during motor task execution, and it has been associated with motor task performance. More recently, transient burst-like events termed beta bursts have been identified as another potential biomarker of motor function. Previous studies have reported decreased MRBD magnitude induced by exercise. However, little is known in terms of the effects of high-intensity exercise on beta burst patterns. In the present work, we investigated the modulatory effects of exercise on different beta burst features prior to, during and post motor task execution. We found that exercise mainly affected burst duration and burst rate within the left motor cortex area (M1) that is contralateral to the moving hand. Meanwhile, burst amplitude in the contralateral M1 area was affected differently by exercise, with smaller burst amplitude values observed during the movement preparation phase and larger magnitude during as well as post motor task execution. Since MRBD and beta burst patterns are closely associated with motor task performance, results from the present study can promote understanding about the association between exercise induced neural plasticity changes and motor performance, which can be further used for designing a subject-specific training therapy for improving motor function.

Neurophysiological Changes Induced by Music-Supported Therapy for Recovering Upper Extremity Function after Stroke: A Case Series.

Music-supported therapy (MST) follows the best practice principles of stroke rehabilitation and has been proven to instigate meaningful enhancements in motor recovery post-stroke. The existing literature has established that the efficacy and specificity of MST relies on the reinforcement of auditory-motor functional connectivity in related brain networks. However, to date, no study has attempted to evaluate the underlying cortical network nodes that are key to the efficacy of MST post-stroke. In this case series, we evaluated changes in connectivity within the auditory-motor network and changes in upper extremity function following a 3-week intensive piano training in two stroke survivors presenting different levels of motor impairment. Connectivity was assessed pre- and post-training in the α- and the ß-bands within the auditory-motor network using magnetoencephalography while participants were passively listening to a standardized melody. Changes in manual dexterity, grip strength, movement coordination, and use of the upper extremity were also documented in both stroke survivors. After training, an increase in the clinical measures was accompanied by enhancements in connectivity between the auditory and motor network nodes for both the α- and the ß-bands, especially in the affected hemisphere. These neurophysiological changes associated with the positive effects of post-stroke MST on motor outcomes delineate a path for a larger scale clinical trial.

Yoga Interventions Used for the Rehabilitation of Stroke, Parkinson's Disease, and Multiple Sclerosis: A Scoping Review of Clinical Research.

Objectives: The current body of literature was reviewed to compile and describe yoga interventions that have been applied in clinical research and neurologic rehabilitation settings with patients affected by stroke, Parkinson's disease (PD), and multiple sclerosis (MS). Design: Available literature on yoga therapy (YT) was mapped following a five-stage framework to identify key concepts, knowledge gaps, and evidence to inform practice. Publications were identified through Medline, CINAHL, EMBASE, and PsycINFO. Selected studies required subjects with a clinical diagnosis of stroke, PD, and MS to participate in a yoga intervention and have physical, cognitive, and/or psychosocial outcome measures assessed. Results: A total of 50 studies were included in this review. Study characteristics, patient demographics, description of the yoga intervention, reported outcome measures and the main findings were extracted from the studies. Conclusion: Implementing YT in neurorehabilitation can help health care professionals integrate a more holistic approach that addresses the fundamental physical and psychological challenges of living with a chronic and debilitating neurologic disorder. The included studies described yogic interventions consisting of group or individual therapy sessions lasting 60-75 min that were carried out one to three times per week for 8-12 consecutive weeks across all three conditions. All studies described in this scoping review used different yoga protocols confirming the lack of specific interventional parameters available for implementing yoga into the rehabilitation of individuals affected by stroke, PD, or MS.

Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques.

Brain age prediction studies aim at reliably estimating the difference between the chronological age of an individual and their predicted age based on neuroimaging data, which has been proposed as an informative measure of disease and cognitive decline. As most previous studies relied exclusively on magnetic resonance imaging (MRI) data, we hereby investigate whether combining structural MRI with functional magnetoencephalography (MEG) information improves age prediction using a large cohort of healthy subjects (N = 613, age 18-88 years) from the Cam-CAN repository. To this end, we examined the performance of dimensionality reduction and multivariate associative techniques, namely Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA), to tackle the high dimensionality of neuroimaging data. Using MEG features (mean absolute error (MAE) of 9.60 years) yielded worse performance when compared to using MRI features (MAE of 5.33 years), but a stacking model combining both feature sets improved age prediction performance (MAE of 4.88 years). Furthermore, we found that PCA resulted in inferior performance, whereas CCA in conjunction with Gaussian process regression models yielded the best prediction performance. Notably, CCA allowed us to visualize the features that significantly contributed to brain age prediction. We found that MRI features from subcortical structures were more reliable age predictors than cortical features, and that spectral MEG measures were more reliable than connectivity metrics. Our results provide an insight into the underlying processes that are reflective of brain aging, yielding promise for the identification of reliable biomarkers of neurodegenerative diseases that emerge later during the lifespan.

Artifact Removal in tACS-EEG Recordings: A Combined Methodology Based on the Empirical Wavelet Transform.

Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that modulates brain activity, which yields promise for achieving desired behavioral outcomes in different contexts. Combining tACS with electroencephalography (EEG) allows for the monitoring of the real-time effects of stimulation. However, the EEG signal recorded with simultaneous tACS is largely contaminated by stimulation-induced artifacts. In this work, we examine the combination of the empirical wavelet transform (EWT) with three blind source separation (BSS) methods: principal component analysis (PCA), multiset canonical correlation analysis (MCCA) and independent vector analysis (IVA), aiming to remove artifacts in tACS-contaminated EEG recordings. Using simulated data, we show that EWT followed by IVA achieves the best performance. Using experimental data, we show that BSS combined with EWT performs better compared to standard BSS methodology in terms of preserving useful information while eliminating artifacts.

Unfolding the Effects of Acute Cardiovascular Exercise on Neural Correlates of Motor Learning Using Convolutional Neural Networks.

Cardiovascular exercise is known to promote the consolidation of newly acquired motor skills. Previous studies seeking to understand the neural correlates underlying motor memory consolidation that is modulated by exercise, have relied so far on using traditional statistical approaches for a priori selected features from neuroimaging data, including EEG. With recent advances in machine learning, data-driven techniques such as deep learning have shown great potential for EEG data decoding for brain-computer interfaces, but have not been explored in the context of exercise. Here, we present a novel Convolutional Neural Network (CNN)-based pipeline for analysis of EEG data to study the brain areas and spectral EEG measures modulated by exercise. To the best of our knowledge, this work is the first one to demonstrate the ability of CNNs to be trained in a limited sample size setting. Our approach revealed discriminative spectral features within a refined frequency band (27-29 Hz) as compared to the wider beta bandwidth (15-30 Hz), which is commonly used in data analyses, as well as corresponding brain regions that were modulated by exercise. These results indicate the presence of finer EEG spectral features that could have been overlooked using conventional hypothesis-driven statistical approaches. Our study thus demonstrates the feasibility of using deep network architectures for neuroimaging analysis, even in small-scale studies, to identify robust brain biomarkers and investigate neuroscience-based questions.

Older adults exhibit a more pronounced modulation of beta oscillations when performing sustained and dynamic handgrips.

Muscle contractions are associated with a decrease in beta oscillatory activity, known as movement-related beta desynchronization (MRBD). Older adults exhibit a MRBD of greater amplitude compared to their younger counterparts, even though their beta power remains higher both at rest and during muscle contractions. Further, a modulation in MRBD has been observed during sustained and dynamic pinch contractions, whereby beta activity during periods of steady contraction following a dynamic contraction is elevated. However, how the modulation of MRBD is affected by aging has remained an open question. In the present work, we investigated the effect of aging on the modulation of beta oscillations and their putative link with motor performance. We collected magnetoencephalography (MEG) data from younger and older adults during a resting-state period and motor handgrip paradigms, which included sustained and dynamic contractions, to quantify spontaneous and motor-related beta oscillatory activity. Beta power at rest was found to be significantly increased in the motor cortex of older adults. During dynamic hand contractions, MRBD was more pronounced in older participants in frontal, premotor and motor brain regions. These brain areas also exhibited age-related decreases in cortical thickness; however, the magnitude of MRBD and cortical thickness were not found to be associated after controlling for age. During sustained hand contractions, MRBD exhibited a decrease in magnitude compared to dynamic contraction periods in both groups and did not show age-related differences. This suggests that the amplitude change in MRBD between dynamic and sustained contractions is larger in older compared to younger adults. We further probed for a relationship between beta oscillations and motor behaviour and found that greater MRBD in primary motor cortices was related to degraded motor performance beyond age, but our results suggested that age-related differences in beta oscillations were not predictive of motor performance.

What are the known effects of yoga on the brain in relation to motor performances, body awareness and pain? A narrative review.

OBJECTIVE: The current body of literature was reviewed to evaluate the effects of yoga on the brain in relation to motor performance, body awareness and pain. BACKGROUND: Yoga has been increasingly popular in the Western countries especially for its unique integration of the mind and body. Yoga has been studied more intensely in the last decade. Although it has been shown to improve cognitive functions, few studies have looked into the effects of yoga on improving motor performance, body awareness or pain and the possible underlying brain mechanisms associated with them. METHODS: A search of the current literature was made using keywords such as: "yoga brain motor", "yoga brain pain", "effects yoga brain" and "effects yoga brain motor performance". The findings were then discussed in relation to motor performance, body awareness and pain and their reported mechanisms of action on the brain. RESULTS: A total of 61 articles were selected, out of which 29 were excluded because they did not meet our criteria. A total of thirty-two articles were included in this review, which we further subdivided by focus: motor performance (n = 10), body awareness (n = 14) and pain (n = 8). DISCUSSION: Our review shows that yoga has a positive effect on learning rate, speed and accuracy of a motor task by increasing attention and decreasing stress through a better control of sensorimotor rhythms. Yoga also seems to improve sensory awareness and interoception, regulate autonomic input, increase parasympathetic activity and promote self-regulation. Yoga was also shown to reduce the threat signal, increase pain tolerance, decrease pain unpleasantness and decrease the anxiety and distress associated with pain. Those changes are associated with the recruitment of specific brain areas such as the insula, the amygdala and the hippocampus. CONCLUSION: Based on the studies reviewed in this report, we found that the practice of yoga seems to facilitate motor learning, to increase body awareness and to decrease pain. These are associated with a wide variety of changes in terms of brain activity and structure. Further studies are necessary to reveal its precise mechanism of action on the brain and to validate its wider application in clinical settings.

Effects of a tailored strength training program of the upper limb combined with transcranial direct current stimulation (tDCS) in chronic stroke patients: study protocol for a randomised, double-blind, controlled trial.

BACKGROUND: A significant proportion of individuals are left with poor residual functioning of the affected arm after a stroke. This has a great impact on the quality of life and the ability for stroke survivors to live independently. While strengthening exercises have been recommended to improve arm function, their benefits are generally far from optimal due to the lack of appropriate dosing in terms of intensity. One way to address this problem is to develop better tools that could predict an individual's potential for recovery and then adjust the intensity of exercise accordingly. In this study, we aim at determining whether an individualized strengthening program based on the integrity of the corticospinal tract, as reflected in the amplitude of motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS), in conjunction with transcranial direct current stimulation (tDCS), could lead to more optimal outcomes in terms of arm function in chronic stroke patients. METHODS: This multicentre, double-blinded, randomised controlled trial will aim to recruit 84 chronic stroke patients. Before and after training, participants will undergo a clinical evaluation, assessing motor recovery of the affected arm (Fugl-Meyer Stroke Assessment-FMA) and a TMS evaluation to assess the integrity of the corticospinal tract, as reflected in MEP amplitude. Based on their baseline MEPs amplitude, participants will be stratified into three groups of training intensity levels determined by the one-repetition maximum (1RM); 1) low: 35-50% 1 RM (MEPs < 50 µV); 2) moderate: 50-65% 1RM (MEPs 50-120 µV); and 3) high: 70-80% 1RM (MEPs > 120 µV). Training will target the affected arm (3 times/week for 4 weeks). In addition, participants will be randomly allocated into two tDCS groups (real vs. sham) and tDCS will be applied in an anodal montage during the exercise. DISCUSSION: This study will determine whether an individualized strength training intervention in chronic stroke survivors can lead to improved arm function. In addition, we will also determine whether combining anodal tDCS over the lesioned hemisphere with strength training can lead to further improvement in arm function, when compared to sham tDCS. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02915185. Registered September 21 2016.

Functional and effective reorganization of the aging brain during unimanual and bimanual hand movements.

Motor performance decline observed during aging is linked to changes in brain structure and function, however, the precise neural reorganization associated with these changes remains largely unknown. We investigated the neurophysiological correlates of this reorganization by quantifying functional and effective brain network connectivity in elderly individuals (n = 11; mean age = 67.5 years), compared to young adults (n = 12; mean age = 23.7 years), while they performed visually-guided unimanual and bimanual handgrips inside the magnetoencephalography (MEG) scanner. Through a combination of principal component analysis and Granger causality, we observed age-related increases in functional and effective connectivity in whole-brain, task-related motor networks. Specifically, elderly individuals demonstrated (i) greater information flow from contralateral parietal and ipsilateral secondary motor regions to the left primary motor cortex during the unimanual task and (ii) decreased interhemispheric temporo-frontal communication during the bimanual task. Maintenance of motor performance and task accuracy in elderly was achieved by hyperactivation of the task-specific motor networks, reflecting a possible mechanism by which the aging brain recruits additional resources to counteract known myelo- and cytoarchitectural changes. Furthermore, resting-state sessions acquired before and after each motor task revealed that both older and younger adults maintain the capacity to adapt to task demands via network-wide increases in functional connectivity. Collectively, our study consolidates functional connectivity and directionality of information flow in systems-level cortical networks during aging and furthers our understanding of neuronal flexibility in motor processes.

Identification of Time-Varying Cortico-cortical and Cortico-Muscular Coherence during Motor Tasks with Multivariate Autoregressive Models.

Neural populations coordinate at fast subsecond time-scales during rest and task execution. As a result, functional brain connectivity assessed with different neuroimaging modalities (EEG, MEG, fMRI) may also change over different time scales. In addition to the more commonly used sliding window techniques, the General Linear Kalman Filter (GLFK) approach has been proposed to estimate time-varying brain connectivity. In the present work, we propose a modification of the GLFK approach to model timevarying connectivity. We also propose a systematic method to select the hyper-parameters of the model. We evaluate the performance of the method using MEG and EMG data collected from 12 young subjects performing two motor tasks (unimanual and bimanual hand grips), by quantifying time-varying cortico-cortical and corticomuscular coherence (CCC and CMC). The CMC results revealed patterns in accordance with earlier findings, as well as an improvement in both time and frequency resolution compared to sliding window approaches. These results suggest that the proposed methodology is able to unveil accurate time-varying connectivity patterns with an excellent time resolution.

Disrupted functional network integrity and flexibility after stroke: Relation to motor impairments.

Previous studies investigating brain activation present during upper limb movement after stroke have greatly detailed activity alterations in the ipsi- and contralesional primary motor cortices (M1). Despite considerable interest in M1, investigations into the integration and coordination of large-scale functional networks subserving motor, sensory, and cognitive control after stroke remain scarce. The purpose of this study was to assess non-static functional connectivity within whole-brain networks involved in the production of isometric, visually-paced hand grips. Seventeen stroke patients and 24 healthy controls underwent functional MRI while performing a series of 50 isometric hand grips with their affected hand (stroke patients) or dominant hand (control subjects). We used task-based multivariate functional connectivity to derive spatial and temporal information of whole-brain networks specifically underlying hand movement. This technique has the advantage of extracting within-network commonalities across groups and identifying connectivity differences between these groups. We further used a nonparametric statistical approach to identify group differences in regional activity within these task-specific networks and assess whether such alterations were related to the degree of motor impairment in stroke patients. Our whole-brain multivariate analysis revealed group differences in two networks: (1) a motor network, including pre- and postcentral gyri, dorsal and ventral premotor cortices, as well as supplementary motor area, in which stroke patients showed reduced task-related activation compared to controls, and (2) a default-mode network (DMN), including the posterior cingulate cortex, precuneus, and medial prefrontal cortex, in which patients showed less deactivation than controls. Within-network group differences revealed decreased activity in ipsilesional primary sensorimotor cortex in stroke patients, which also positively correlated with lower levels of motor impairment. Moreover, the temporal information extracted from the functional networks revealed that stroke patients did not show a reciprocal DMN deactivation peak following activation of their motor network. This finding suggests that allocation of functional resources to motor areas during hand movement may impair their ability to efficiently switch from one network to another. Taken together, our study expands our understanding of functional reorganization during motor recovery after a stroke, and suggests that modulation of ipsilesional sensorimotor activity may increase the integrity of a whole-brain motor network, contribute to better motor performance, and optimize network flexibility.

Acute cardiovascular exercise promotes functional changes in cortico-motor networks during the early stages of motor memory consolidation.

A single bout of cardiovascular exercise performed immediately after practicing a visuo-motor tracking task has been shown to improve the long-term retention of this motor skill through an optimization of the memory consolidation process. The mechanisms underlying the time-dependent effects of acute cardiovascular exercise on motor memory consolidation, however, remain poorly understood. In this study, we sought to determine the impact of a single bout of cardiovascular exercise performed immediately after motor skill practice on those mechanisms using electroencephalography (EEG) and electromyography (EMG). Specifically, we assessed exercise-induced changes in the activity and connectivity of cortico-motor networks during early consolidation and the impact of these changes on skill retention. Participants practiced a visuo-motor tracking task followed by either a short bout of intense exercise or a rest period. EEG along with EMG data of hand muscles were collected during the production of low-force isometric contractions. Event-related desynchronization, functional connectivity and corticomuscular coherence were measured at baseline, 30, 60 and 90 min after the bout of exercise or the rest period. Improvements in motor memory were inferred via retention tests of the motor skill performed 8 and 24 h after motor practice. We found that participants who performed the single bout of exercise showed better motor skill retention 24 h after motor practice. This improvement in skill retention in the exercise group was associated with significant decreases in beta-band event-related desynchronization in EEG electrodes located over the left sensorimotor areas. We also found that after exercise, alpha-, and even more significantly, beta-band functional connectivity, increased between EEG electrodes located over left and right sensorimotor areas. The exercise group also showed greater beta-band corticomuscular coherence but only in a small number of electrodes. Neither functional connectivity nor corticomuscular coherence measures correlated with skill retention scores. This is the first study exploring brain mechanisms underlying the summative effects of motor learning and cardiovascular exercise on motor memory consolidation. We have identified potential neural substrates through which a single bout of acute exercise, when performed in close temporal proximity to motor practice, strengthens motor memories. Our findings provide new mechanistic insights into a better understanding of the complex temporal relationship existing between cardiovascular exercise and motor memory consolidation.

Transcranial direct current stimulation over multiple days enhances motor performance of a grip task.

BACKGROUND: Recovery of handgrip is critical after stroke since it is positively related to upper limb function. To boost motor recovery, transcranial direct current stimulation (tDCS) is a promising, non-invasive brain stimulation technique for the rehabilitation of persons with stroke. When applied over the primary motor cortex (M1), tDCS has been shown to modulate neural processes involved in motor learning. However, no studies have looked at the impact of tDCS on the learning of a grip task in both stroke and healthy individuals. OBJECTIVE: To assess the use of tDCS over multiple days to promote motor learning of a grip task using a learning paradigm involving a speed-accuracy tradeoff in healthy individuals. METHODS: In a double-blinded experiment, 30 right-handed subjects (mean age: 22.1±3.3 years) participated in the study and were randomly assigned to an anodal (n=15) or sham (n=15) stimulation group. First, subjects performed the grip task with their dominant hand while following the pace of a metronome. Afterwards, subjects trained on the task, at their own pace, over 5 consecutive days while receiving sham or anodal tDCS over M1. After training, subjects performed de novo the metronome-assisted task. The change in performance between the pre and post metronome-assisted task was used to assess the impact of the grip task and tDCS on learning. RESULTS: Anodal tDCS over M1 had a significant effect on the speed-accuracy tradeoff function. The anodal tDCS group showed significantly greater improvement in performance (39.28±15.92%) than the sham tDCS group (24.06±16.35%) on the metronome-assisted task, t(28)=2.583, P=0.015 (effect size d=0.94). CONCLUSIONS: Anodal tDCS is effective in promoting grip motor learning in healthy individuals. Further studies are warranted to test its potential use for the rehabilitation of fine motor skills in stroke patients.