An empirical evaluation of functional alignment using inter-subject decoding

NeuroImage

Elizabeth DuPre¹  Jean-Baptiste Poline¹  Hugo Richard²  Bertrand Thirion²  Thomas Bazeille² 

[1] Montréal Neurological Institute, McGill University, Montréal, Canada;Université Paris-Saclay, Inria, CEA, Palaiseau 91120, France;

关键词: fMRI;    Functional alignment;    Predictive modeling;    Inter-subject variability;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown