The Publication Database hosted by SPL
|
All Publications
Upload
Advanced Search
Gallery View
Download Statistics
Help
Import
Log in
Joint Modeling of Anatomical and Functional Connectivity for Population Studies
|
|
Institution: |
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. 2Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, MA, USA. 3Laboratory of Mathematical Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. |
Publisher: |
IEEE Engineering in Medicine and Biology Society |
Publication Date: |
Feb-2012 |
Journal: |
IEEE Trans Med Imaging |
Volume Number: |
31 |
Issue Number: |
2 |
Pages: |
164-82 |
Citation: |
IEEE Trans Med Imaging. 2012 Feb;31(2):164-82. |
PubMed ID: |
21878411 |
Keywords: |
Projects:GenerativeBrainConnectivity |
Appears in Collections: |
NAC, LMI, NA-MIC, PNL, SPL |
Sponsors: |
P41 RR13218 (RR) funded by NCRR NIH HHS R01 MH074794 (MH) funded by NIMH NIH HHS U54 EB005149 (EB) funded by NIBIB NIH HHS |
Generated Citation: |
Venkataraman A., Rathi Y., Kubicki M., Westin C-F., Golland P. Joint Modeling of Anatomical and Functional Connectivity for Population Studies. IEEE Trans Med Imaging. 2012 Feb;31(2):164-82. PMID: 21878411. |
| Export citation: |
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.
Additional Material
1 File (186.289kB)
Venkataraman-IEEE-TMI2012-fig10.jpg (186.289kB)