Extracting orthogonal subject- and condition-specific signatures from fMRI data using whole-brain effective connectivity

NeuroImage
Vicente PallarésMatthieu Gilson

Abstract

The study of brain communication based on fMRI data is often limited because such measurements are a mixture of session-to-session variability with subject- and condition-related information. Disentangling these contributions is crucial for real-life applications, in particular when only a few recording sessions are available. The present study aims to define a reliable standard for the extraction of multiple signatures from fMRI data, while verifying that they do not mix information about the different modalities (e.g., subjects and conditions such as tasks performed by them). In particular, condition-specific signatures should not be contaminated by subject-related information, since they aim to generalize over subjects. Practically, signatures correspond to subnetworks of directed interactions between brain regions (typically 100 covering the whole brain) supporting the subject and condition identification for single fMRI sessions. The key for robust prediction is using effective connectivity instead of functional connectivity. Our method demonstrates excellent generalization capabilities for subject identification in two datasets, using only a few sessions per subject as reference. Using another dataset with resting state a...Continue Reading

Citations

Aug 6, 2019·Cerebral Cortex·Edmund T RollsJianfeng Feng
Apr 15, 2020·Proceedings of the National Academy of Sciences of the United States of America·Morten L KringelbachGustavo Deco
Jun 17, 2020·Network Neuroscience·Matthieu GilsonAndrea Insabato
Sep 5, 2020·Network Neuroscience·Meenusree RajapandianJoaquín Goñi
Jul 24, 2020·NeuroImage·Kausar AbbasJoaquín Goñi
Mar 30, 2021·Brain Topography·Katharina GlombBenedetta Franceschiello
May 22, 2021·Brain Connectivity·Benjamin ChiêmJoaquín Goñi

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