An empirical evaluation of functional alignment using inter-subject decoding

Inter-individual variability in the functional organization of the brain presents a major obstacle to identifying generalizable neural coding principles. Functional alignment—a class of methods that matches subjects’ neural signals based on their functional similarity—is a promising strategy for add...

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Autores principales:	Thomas Bazeille, Elizabeth DuPre, Hugo Richard, Jean-Baptiste Poline, Bertrand Thirion
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	fMRI Functional alignment Predictive modeling Inter-subject variability Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/c899f442dfeb4946b50ad52bbb019798
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spelling	oai:doaj.org-article:c899f442dfeb4946b50ad52bbb0197982021-11-06T04:21:21ZAn empirical evaluation of functional alignment using inter-subject decoding1095-957210.1016/j.neuroimage.2021.118683https://doaj.org/article/c899f442dfeb4946b50ad52bbb0197982021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1053811921009563https://doaj.org/toc/1095-9572Inter-individual variability in the functional organization of the brain presents a major obstacle to identifying generalizable neural coding principles. Functional alignment—a class of methods that matches subjects’ neural signals based on their functional similarity—is a promising strategy for addressing this variability. To date, however, a range of functional alignment methods have been proposed and their relative performance is still unclear. In this work, we benchmark five functional alignment methods for inter-subject decoding on four publicly available datasets. Specifically, we consider three existing methods: piecewise Procrustes, searchlight Procrustes, and piecewise Optimal Transport. We also introduce and benchmark two new extensions of functional alignment methods: piecewise Shared Response Modelling (SRM), and intra-subject alignment. We find that functional alignment generally improves inter-subject decoding accuracy though the best performing method depends on the research context. Specifically, SRM and Optimal Transport perform well at both the region-of-interest level of analysis as well as at the whole-brain scale when aggregated through a piecewise scheme. We also benchmark the computational efficiency of each of the surveyed methods, providing insight into their usability and scalability. Taking inter-subject decoding accuracy as a quantification of inter-subject similarity, our results support the use of functional alignment to improve inter-subject comparisons in the face of variable structure-function organization. We provide open implementations of all methods used.Thomas BazeilleElizabeth DuPreHugo RichardJean-Baptiste PolineBertrand ThirionElsevierarticlefMRIFunctional alignmentPredictive modelingInter-subject variabilityNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeuroImage, Vol 245, Iss , Pp 118683- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fMRI Functional alignment Predictive modeling Inter-subject variability Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	fMRI Functional alignment Predictive modeling Inter-subject variability Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Thomas Bazeille Elizabeth DuPre Hugo Richard Jean-Baptiste Poline Bertrand Thirion An empirical evaluation of functional alignment using inter-subject decoding
description	Inter-individual variability in the functional organization of the brain presents a major obstacle to identifying generalizable neural coding principles. Functional alignment—a class of methods that matches subjects’ neural signals based on their functional similarity—is a promising strategy for addressing this variability. To date, however, a range of functional alignment methods have been proposed and their relative performance is still unclear. In this work, we benchmark five functional alignment methods for inter-subject decoding on four publicly available datasets. Specifically, we consider three existing methods: piecewise Procrustes, searchlight Procrustes, and piecewise Optimal Transport. We also introduce and benchmark two new extensions of functional alignment methods: piecewise Shared Response Modelling (SRM), and intra-subject alignment. We find that functional alignment generally improves inter-subject decoding accuracy though the best performing method depends on the research context. Specifically, SRM and Optimal Transport perform well at both the region-of-interest level of analysis as well as at the whole-brain scale when aggregated through a piecewise scheme. We also benchmark the computational efficiency of each of the surveyed methods, providing insight into their usability and scalability. Taking inter-subject decoding accuracy as a quantification of inter-subject similarity, our results support the use of functional alignment to improve inter-subject comparisons in the face of variable structure-function organization. We provide open implementations of all methods used.
format	article
author	Thomas Bazeille Elizabeth DuPre Hugo Richard Jean-Baptiste Poline Bertrand Thirion
author_facet	Thomas Bazeille Elizabeth DuPre Hugo Richard Jean-Baptiste Poline Bertrand Thirion
author_sort	Thomas Bazeille
title	An empirical evaluation of functional alignment using inter-subject decoding
title_short	An empirical evaluation of functional alignment using inter-subject decoding
title_full	An empirical evaluation of functional alignment using inter-subject decoding
title_fullStr	An empirical evaluation of functional alignment using inter-subject decoding
title_full_unstemmed	An empirical evaluation of functional alignment using inter-subject decoding
title_sort	empirical evaluation of functional alignment using inter-subject decoding
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/c899f442dfeb4946b50ad52bbb019798
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An empirical evaluation of functional alignment using inter-subject decoding

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