Capturing Shared and Individual Information in fMRI Data

Cognitive neuroscience seeks to explain the organization of the brain, but typically focuses on aspects that are shared across people rather than those that vary across individuals. Here, we present a new method for analyzing brain imaging data that captures both shared and individual components of brain activity. Inspired by the shared response model (SRM) and the robust principal components analysis, the robust shared response model (RSRM) aligns functional topographies across humans while preserving a component of sparse, individual activity. Experimental results on adult data showed that RSRM performs as well as or better than SRM, while at the same time capturing reliable markers of individual variability. In a test case of participants with extreme variability, we found that RSRM was able to improve the accuracy more than 60% over SRM for the coding of infant fMRI data...


Javier Turek

ML Research Scientist, Brain-Inspired Computing Lab

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Ted Willke

Director, Brain-Inspired Computing Lab

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Cameron T. Ellis

Lena J. Skalaban

Nicholas B. Turk-Browne

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