期刊
NEUROIMAGE
卷 245, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2021.118683
关键词
fMRI; Functional alignment; Predictive modeling; Inter-subject variability
资金
- European Union [945539]
- Digiteo French program
- National Institutes of Health (NIH) [NIH-NIMH R01 MH083320, NIH-NIBIB P41 EB019936, NIH RF1 MH120021]
- National Institute Of Mental Health [R01MH096906]
- Canada First Research Excellence Fund
- Health Canada
This study benchmarks five functional alignment methods and finds that functional alignment generally improves inter-subject decoding accuracy, with the best performing methods being dependent 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.
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.
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