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Developing personalized brain state-dependent TMS to target residual corticospinal connections after stroke
Abstract   Open access   Peer reviewed

Developing personalized brain state-dependent TMS to target residual corticospinal connections after stroke

Uttara Khatri, Tharan Suresh, Muskan Manesiya, Valeria Marquez, José del Millán, Michael Borich and Sara Hussain
Brain stimulation, Vol.18(1), p.272
01/2025
DOI: 10.1016/j.brs.2024.12.174
url
https://doi.org/10.1016/j.brs.2024.12.174View
Published (Version of record) Open Access

Abstract

The corticospinal tract (CST) is the chief contributor to voluntary hand movement, and many stroke survivors have residual CST connections. Brain state-dependent transcranial magnetic stimulation (BSD-TMS) interventions could promote residual CST function and improve poststroke hand motor recovery. Given that individual stroke survivors are heterogenous in their lesion patterns, magnitude of recovery-related adaptive plasticity, and motor impairments, poststroke BSD-TMS interventions should be delivered during personalized brain states reflecting strong CST activation. In this study, we developed and tested a novel real-time machine learning-driven EEG-TMS system that accurately identifies and targets personalized EEG activity patterns during which TMS elicits large (i.e., strong states) or small MEPs (i.e., weak states). In neurotypical adults (N=19), our system accurately identified and targeted the desired state 83-95% of the time (p<0.001). At 120% RMT, MEPs elicited in real-time during strong states were significantly larger than those elicited during both weak and random states (p<0.04). At 110% RMT, MEPs did not differ between states (p>0.33). MEPs were overall less variable during personalized strong than weak states (p=0.04). Although participants exhibited unique patterns of pre-stimulus EEG power differences between states, right centroparietal alpha power and whole-scalp theta power were typically higher during strong than weak states. Preliminary analysis of data acquired from stroke survivors (N=3) showed that our system accurately identified and targeted the desired state 86-95% of the time. MEPs elicited in real-time during strong states were on average larger than those elicited during weak and random states, and stroke survivors with more severe upper extremity motor impairments showed smaller state-dependent MEP amplitude variation. Results demonstrate that real-time personalized BSD-TMS is both feasible and accurate in neurotypical adults and stroke survivors. Overall, our findings represent a key step towards using personalized BSD-TMS interventions to improve residual CST transmission and promote poststroke hand motor recovery.

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