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 stimulat...

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Autores principales:	Maximilian J. Wessel, Philip Egger, Friedhelm C. Hummel
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Stroke NIBS tDCS rTMS Predictive modeling Precision medicine Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/878e136341c841d4bacf8a9e92360d27
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spelling	oai:doaj.org-article:878e136341c841d4bacf8a9e92360d272021-11-20T04:58:28ZPredictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and pitfalls1935-861X10.1016/j.brs.2021.09.006https://doaj.org/article/878e136341c841d4bacf8a9e92360d272021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1935861X21002369https://doaj.org/toc/1935-861XBackground: 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.Maximilian J. WesselPhilip EggerFriedhelm C. HummelElsevierarticleStrokeNIBStDCSrTMSPredictive modelingPrecision medicineNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENBrain Stimulation, Vol 14, Iss 6, Pp 1456-1466 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Stroke NIBS tDCS rTMS Predictive modeling Precision medicine Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Stroke NIBS tDCS rTMS Predictive modeling Precision medicine Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Maximilian J. Wessel Philip Egger Friedhelm C. Hummel Predictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and pitfalls
description	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.
format	article
author	Maximilian J. Wessel Philip Egger Friedhelm C. Hummel
author_facet	Maximilian J. Wessel Philip Egger Friedhelm C. Hummel
author_sort	Maximilian J. Wessel
title	Predictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and pitfalls
title_short	Predictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and pitfalls
title_full	Predictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and pitfalls
title_fullStr	Predictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and pitfalls
title_full_unstemmed	Predictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and pitfalls
title_sort	predictive models for response to non-invasive brain stimulation in stroke: a critical review of opportunities and pitfalls
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/878e136341c841d4bacf8a9e92360d27
work_keys_str_mv	AT maximilianjwessel predictivemodelsforresponsetononinvasivebrainstimulationinstrokeacriticalreviewofopportunitiesandpitfalls AT philipegger predictivemodelsforresponsetononinvasivebrainstimulationinstrokeacriticalreviewofopportunitiesandpitfalls AT friedhelmchummel predictivemodelsforresponsetononinvasivebrainstimulationinstrokeacriticalreviewofopportunitiesandpitfalls
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Predictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and pitfalls

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