Objective: Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ). Methods: We start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method. Results: Our results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components. Conclusion: We identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality. Significance: The proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE Transactions on Biomedical Engineering|
|State||Published - Sep 2020|
Bibliographical noteFunding Information:
Manuscript received November 26, 2019; accepted December 28, 2019. Date of publication January 7, 2020; date of current version August 20, 2020. This work was supported in part by NIH under Grants R01EB020407, R01EB006841, P20GM103472, P30GM122734 and NSF under Grant 1539067. (Corresponding author: Haleh Falak-shahi.) H. Falakshahi is with the Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250 and also with the Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA 30300 USA (e-mail: email@example.com).
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- covariance matrix
- data fusion
- default mode network
- graphical model
- joint estimation
- partial correlation
- precision matrix