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.
Keywords: NIBS; Precision medicine; Predictive modeling; Stroke; rTMS; tDCS.
Copyright © 2021. Published by Elsevier Inc.