Falakshahi H, Vergara VM, Liu J, Mathalon DH, Ford JM, Voyvodic J, Mueller BA, Belger A, McEwen S, Potkin SG, Preda A, Rokham H, Sui J, Turner JA, Plis S, Calhoun VD. Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia.
IEEE Trans Biomed Eng 2020;
67:2572-2584. [PMID:
31944934 PMCID:
PMC7538162 DOI:
10.1109/tbme.2020.2964724]
[Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
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.
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