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Abstract #0858

Predicting Resting-State Functional Connectivity from Structural Connectivity

Chris J. Honey1, Olaf Sporns1, Leila Cammoun2, Xavier Gigandet2, Jean-Philippe Thiran2, Reto Meuli3, Patric Hagmann2,3

11 Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA; 2Signal Processing Laboratory 5, Ecole Polytechnique Fdrale de Lausanne, Lausanne, VD, Switzerland; 3Department of Radiology, University Hospital Center and University of Lausanne (CHUV), Lausanne, VD, Switzerland

The patterns of functional connectivity across the brain are presumed to reflect its underlying structural (anatomical) architecture. In the present study, we measured resting state functional connection patterns (using fMRI) and structural connection patterns (using DSI) in the same individuals. Structural connectivity then provided the couplings for a model of macroscopic linear and non-linear cortical dynamics. In both models, (i) we where able to infer functional connectivity from structural data with strong accuracy (ii) the correlations between simulated and empirical rsFC were highest for many regions located in the default mode network.