Predictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and pitfalls.

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.