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Updated: Oct 19, 2025
Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke
Published on: February 22, 2020
Predictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and
Maximilian J Wessel1, Philip Egger1, Friedhelm C Hummel2
1Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology Lausanne (EPFL), Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL Valais), Sion, Switzerland.
Background:
Noninvasive brain stimulation has been successfully applied to improve stroke-related impairments in different behavioral domains. Yet, clinical translation is limited by heterogenous outcomes within and across studies. It has been proposed to develop and apply noninvasive brain stimulation in a patient-tailored, precision medicine-guided fashion to maximize response rates and effect magnitude. An important prerequisite for this task is the ability to accurately predict the expected response of the individual patient.
Objective:
This review aims to discuss current approaches studying noninvasive brain stimulation in stroke and challenges associated with the development of predictive models of responsiveness to noninvasive brain stimulation.
Methods:
Narrative review.
Results:
Currently, the field largely relies on in-sample associational studies to assess the impact of different influencing factors. However, the associational approach is not valid for making claims of prediction, which generalize out-of-sample. We will discuss crucial requirements for valid predictive modeling in particular the presence of sufficiently large sample sizes.
Conclusion:
Modern predictive models are powerful tools that must be wielded with great care. Open science, including data sharing across research units to obtain sufficiently large and unbiased samples, could provide a solid framework for addressing the task of building robust predictive models for noninvasive brain stimulation responsiveness.
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