Citation

Venkataraman A, Rathi Y, Kubicki M, Westin C, Golland P. IEEE Trans. Med. Imaging 2012; 31(2): 164-182.

Copyright

(Copyright © 2012, Institute of Electrical and Electronics Engineers)

DOI

10.1109/TMI.2011.2166083

PMID

21878411

Abstract

We propose a novel probabilistic framework to merge information from DWI tractography and resting-state fMRI 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.

Language: en