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An empirical evaluation of functional alignment using inter-subject decoding

Abstract : 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. At present, 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 piecewise Procrustes, searchlight Procrustes, piecewise Optimal Transport, Shared Response Modelling (SRM), and intra-subject alignment; as well as associated methodological choices such as ROI definition. We find that functional alignment generally improves inter-subject decoding accuracy though the best performing method depends on the research context. Specifically, SRM performs best within a region-of-interest while piecewise Optimal Transport performs best at a whole-brain scale. We also benchmark the computational e ciency 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 the methods used.
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Contributor : Thomas Bazeille <>
Submitted on : Tuesday, December 8, 2020 - 5:53:11 PM
Last modification on : Friday, December 11, 2020 - 3:48:07 AM


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  • HAL Id : hal-03044779, version 2



Thomas Bazeille, Elizabeth Dupre, Jean-Baptiste Poline, Bertrand Thirion. An empirical evaluation of functional alignment using inter-subject decoding. 2020. ⟨hal-03044779v2⟩



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