Joint Modeling of Anatomical and Functional Connectivity for Population Studies

A. Venkataraman, Y. Rathi, M. Kubicki, C.F. Westin, P. Golland
IEEE Trans Medical Imaging
Volume 31, Pages 164 - 182


We propose a novel probabilistic framework to merge information from diffusion weighted imaging tractography and resting-state functional magnetic resonance imaging correlations to identify connectivity patterns in the brain. In particular, we model the interaction between latent anatomical and functional connectivity and present an intuitive extension to population studies. We employ the EM algorithm to estimate the model parameters by maximizing the data likelihood. The method simultaneously infers the templates of latent connectivity for each population and the differences in connectivity between the groups. We demonstrate our method on a schizophrenia study. Our model identifies significant increases in functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe in schizophrenia. We further establish that our model learns predictive differences between the control and clinical populations, and that combining the two modalities yields better results than considering each one in isolation.


Venkataraman A, Rathi Y, Kubicki M, Westin C, Golland P. Joint modeling of anatomical and functional connectivity for population studies. IEEE Trans Medical Imaging 2012;31:164 - 182.


P41 RR13218, R01 MH074794, U54 EB005149

Research areas

fmri, dti
© 2013 Psychiatry Neuroimaging Laboratory | Last updated 04.15.2013