RESUMEN
BACKGROUND: Transcranial magnetic stimulation (TMS) interventions could feasibly treat stroke-related motor impairments, but their effects are highly variable. Brain state-dependent TMS approaches are a promising solution to this problem, but inter-individual variation in lesion location and oscillatory dynamics can make translating them to the poststroke brain challenging. Personalized brain state-dependent approaches specifically designed to address these challenges are therefore needed. METHODS: As a first step towards this goal, we tested a novel machine learning-based EEG-TMS system that identifies personalized brain activity patterns reflecting strong and weak corticospinal tract (CST) output (strong and weak CST states) in healthy adults in real-time. Participants completed a single-session study that included the acquisition of a TMS-EEG-EMG training dataset, personalized classifier training, and real-time EEG-informed single pulse TMS during classifier-predicted personalized CST states. RESULTS: MEP amplitudes elicited in real-time during personalized strong CST states were significantly larger than those elicited during personalized weak and random CST states. MEP amplitudes elicited in real-time during personalized strong CST states were also significantly less variable than those elicited during personalized weak CST states. Personalized CST states lasted for ~1-2 seconds at a time and ~1 second elapsed between consecutive similar states. Individual participants exhibited unique differences in spectro-spatial EEG patterns between personalized strong and weak CST states. CONCLUSION: Our results show for the first time that personalized whole-brain EEG activity patterns predict CST activation in real-time in healthy humans. These findings represent a pivotal step towards using personalized brain state-dependent TMS interventions to promote poststroke CST function.
RESUMEN
Motor cortical (M1) transcranial magnetic stimulation (TMS) increases corticospinal output and improves motor learning when delivered during sensorimotor mu rhythm trough but not peak phases, suggesting that mechanisms supporting motor learning may be most active during mu trough phases. If so, learning-related corticospinal plasticity should be most evident during mu trough phases. Healthy adults were assigned to either a sequence or control group. Participants in the sequence group practiced the implicit serial reaction time task (SRTT), which contained an embedded, repeating 12-item sequence. Participants in the control group practiced a version of the SRTT that contained no sequence. We measured mu phase-independent and phase-dependent MEP amplitudes using EEG-informed single-pulse TMS before, immediately, and 30 minutes after the SRTT in both groups. All participants performed a retention test one hour after SRTT acquisition. In both groups, mu phase-independent MEP amplitudes increased following SRTT acquisition, but the pattern of mu phase-dependent MEP amplitude increases after SRTT acquisition differed between groups. MEP amplitude changes from baseline to 30 minutes after SRTT acquisition more strongly differed across phases in the control relative to the sequence group, with the control group showing smaller increases in peak- than trough-specific MEPs. Contrary to our original hypothesis, results revealed that sequence learning recruits peak- rather than trough-specific neurophysiological mechanisms. Overall, these findings suggest that mu peak phases may provide protected time windows for motor memory consolidation and demonstrate the presence of a mu phase-dependent motor learning mechanism in the human brain. Significance statement: Recent work suggests that the neurophysiological mechanisms supporting motor learning may be most active during sensorimotor mu rhythm trough phases. Here, we evaluated this possibility by measuring mu phase-dependent corticospinal plasticity induced by motor sequence learning. Results provide first evidence that motor sequence learning produced corticospinal plasticity that was more pronounced during mu peak than trough phases, demonstrating the presence of a phase-dependent learning mechanism within the human motor system.