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Abstract #5538

Individual Resting-State Brain Networks using Multivariate Conditional Mutual Information

Padmavathi Sundaram1, Martin Luessi2, Marta Bianciardi1, Steven Stufflebeam1, Matti Hamalainen1, and Victor Solo1,3

1Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, 2BrainFPV LLC, Boston, MA, United States, 3Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia

Current methods of functional brain connectivity from resting-state fMRI data such as linear correlation have limitations, which result in connectivity maps affected by indirect connections and information loss. To address these problems, we propose to use a multivariate conditional mutual information (mvCMI) measure. mvCMI is a multivariate association method, which does not discard information and eliminates indirect connections. We tested mvCMI for single-subject fMRI-connectivity analysis in 10 healthy subjects. mvCMI was able to generate single-subject maps of functional connectivity showing mostly direct connections; mvCMI-based connectivity-maps were more closely related to diffusion-tensor-imaging-based structural connectivity-maps than linear-correlation-based connectivity-maps.

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