@article {4567, title = {IpsiHand Brain-Computer Interface Therapy Induces Broad Upper Extremity Motor Rehabilitation in Chronic Stroke.}, journal = {Neurorehabil Neural Repair}, volume = {39}, year = {2025}, month = {2025 Jan}, pages = {74-86}, abstract = {

BACKGROUND: Chronic hemiparetic stroke patients have very limited benefits from current therapies. Brain-computer interface (BCI) engaging the unaffected hemisphere has emerged as a promising novel therapeutic approach for chronic stroke rehabilitation.

OBJECTIVES: This study investigated the effectiveness of contralesionally-controlled BCI therapy in chronic stroke patients with impaired upper extremity motor function. We further explored neurophysiological features of motor recovery driven by BCI. We hypothesized that BCI therapy would induce a broad motor recovery in the upper extremity, and there would be corresponding changes in baseline theta and gamma oscillations, which have been shown to be associated with motor recovery.

METHODS: Twenty-six prospectively enrolled chronic hemiparetic stroke patients performed a therapeutic BCI task for 12 weeks. Motor function assessment data and resting state electroencephalogram signals were acquired before initiating BCI therapy and across BCI therapy sessions. The Upper Extremity Fugl-Meyer assessment served as a primary motor outcome assessment tool. Theta-gamma cross-frequency coupling (CFC) was computed and correlated with motor recovery.

RESULTS: Chronic stroke patients achieved significant motor improvement in both proximal and distal upper extremity with BCI therapy. Motor function improvement was independent of Botox application. Theta-gamma CFC enhanced bilaterally over the C3/C4 motor electrodes and positively correlated with motor recovery across BCI therapy sessions.

CONCLUSIONS: BCI therapy resulted in significant motor function improvement across the proximal and distal upper extremities of patients, which significantly correlated with theta-gamma CFC increases in the motor regions. This may represent rhythm-specific cortical oscillatory mechanism for BCI-driven rehabilitation in chronic stroke patients.

TRIAL REGISTRATION: Advarra Study: https://classic.clinicaltrials.gov/ct2/show/NCT04338971 and Washington University Study: https://classic.clinicaltrials.gov/ct2/show/NCT03611855.

}, keywords = {Adult, Aged, brain-computer interfaces, Chronic Disease, Electroencephalography, Female, Gamma Rhythm, Humans, Male, Middle Aged, Motor Activity, Paresis, Prospective Studies, Recovery of Function, Stroke, Stroke Rehabilitation, Theta Rhythm, Upper Extremity}, issn = {1552-6844}, doi = {10.1177/15459683241287731}, author = {Rustamov, Nabi and Souders, Lauren and Sheehan, Lauren and Carter, Alexandre and Leuthardt, Eric C} } @article {4598, title = {Multidimensional feature analysis shows stratification in robotic-motor-training gains based on the level of pre-training motor impairment in stroke.}, journal = {Annu Int Conf IEEE Eng Med Biol Soc}, volume = {2024}, year = {2024}, month = {2024 Jul}, pages = {1-5}, abstract = {

Stroke involves heterogeneity in injury and ongoing endogenous recovery, which are seldom stratified before testing post-stroke robot assisted motor training (RAMT). Pretraining variations, especially sensory-motor differences may also affect the gains achieved from the RAMT. Moreover, one assessment test may not effectively characterize the baseline sensory-motor status or the RAMT gains. Pre-therapy stratification may help personalize therapy and increase therapy gains. Towards this goal, we propose a data-driven approach to assess multiple functional scores with t-distributed stochastic neighbor embedding and affinity propagation clustering, both for pre-therapy and RAMT gains. Data included behavioral scores from 27 people with chronic stroke who underwent RAMT for finger movement. Three clusters were observed at start-of-therapy (SoT), concurrent with the overall impairment level. Four clusters were observed for the RAMT gains, indicating specific improvements. The SoT clusters showed agreement with the RAMT gain clusters, suggesting that the pre-therapy state, assessed across multiple domains, could be useful in guiding RAMT interventions to improve outcomes.

}, keywords = {Aged, Cluster Analysis, Female, Humans, Male, Middle Aged, Robotics, Stroke, Stroke Rehabilitation}, issn = {2694-0604}, doi = {10.1109/EMBC53108.2024.10781784}, author = {Rueda-Parra, Sebastian and Perry, Joel C and Wolbrecht, Eric T and Reinkensmeyer, David J and Gupta, Disha} }