BACKGROUND: Evidence suggests that brain-computer interface (BCI)-based rehabilitation strategies show promise in overcoming the limited recovery potential in the chronic phase of stroke. However, the specific mechanisms driving motor function improvements are not fully understood. OBJECTIVE: We aimed at elucidating the potential functional brain connectivity changes induced by BCI training in participants with chronic stroke. METHODS: A longitudinal crossover design was employed with two groups of participants over the span of 4 weeks to allow for within-subject (n = 21) and cross-group comparisons. Group 1 (n = 11) underwent a 6-day motor imagery-based BCI training during the second week, whereas Group 2 (n = 10) received the same training during the third week. Before and after each week, both groups underwent resting state functional MRI scans (4 for Group 1 and 5 for Group 2) to establish a baseline and monitor the effects of BCI training. RESULTS: Following BCI training, an increased functional connectivity was observed between the medial prefrontal cortex of the default mode network (DMN) and motor-related areas, including the premotor cortex, superior parietal cortex, SMA, and precuneus. Moreover, these changes were correlated with the increased motor function as confirmed with upper-extremity Fugl-Meyer assessment scores, measured before and after the training. CONCLUSIONS: Our findings suggest that BCI training can enhance brain connectivity, underlying the observed improvements in motor function. They provide a basis for developing novel rehabilitation approaches using non-invasive brain stimulation for targeting functionally relevant brain regions, thereby augmenting BCI-induced neuroplasticity and enhancing motor recovery.

Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany

Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany; Clinic for Cognitive Neurology University Hospital Leipzig Leipzig Germany; Center for Stroke Research Charité Universitätsmedizin Berlin Berlin Germany

Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany; Department of Neurology Martin Luther University of Halle Wittenberg Halle Germany

Department of Neurophysics Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany

Neural Data Science and Statistical Computing Methods and Development Group Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany; Department of Neurology Charles University 1st Faculty of Medicine and General University Hospital Prague Czech Republic

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