TY - JOUR T1 - Extracting orthogonal subject- and behavior-specific signatures from fMRI data using whole-brain effective connectivity JF - bioRxiv DO - 10.1101/201624 SP - 201624 AU - Vicente Pallarés AU - Andrea Insabato AU - Ana Sanjuán AU - Simone Kühn AU - Dante Mantini AU - Gustavo Deco AU - Matthieu Gilson Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/02/12/201624.abstract N2 - 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. In particular, condition-specific signature should not be contaminated by subject-related information. 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 and movie viewing, we show that the two signatures related to subjects and tasks correspond to distinct subnetworks, which are thus topologically orthogonal. Our results set solid foundations for applications tailored to individual subjects, such as clinical diagnostic. ER -