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.
Background: Mental health diagnostic approaches are seeking to identify biological markers to work alongside advanced machine learning approaches. It is difficult to identify a biological marker of disease when the traditional diagnostic labels themselves are not necessarily valid. Methods: We worked with T1 structural magnetic resonance imaging data collected from individuals with mood and psychosis disorders from over 1400 individuals comprising healthy controls, psychosis patients and their unaffected first-degree relatives including 176 bipolar probands, 134 schizoaffective probands, 240 schizophrenia proband, 581 patients relatives and 362 controls. We assumed there might be noise in the diagnostic labeling process. We detected label noise by classifying the data multiple times using a support vector machine classifier, and then we flagged those individuals in which all classifiers unanimously mislabeled those subjects. Next, we assigned a new diagnostic label to these individuals, based on the biological data (MRI), using iterative data cleansing approach. Results: Simulation results showed our method was highly accurate in identifying label noise. Both diagnostic and Biotype categories showed about 65% and 63% respectively of noisy labels with the largest amount of relabeling occurring between the healthy control and bipolar and schizophrenia disorder individuals as well as in the unaffected close relatives. The extraction of imaging features highlighted regional brain changes associated with each group. Conclusions: This approach represents an initial step towards developing strategies that need not assume existing mental health diagnostic categories are always valid, but rather allows us to leverage this information while also acknowledging that there are misassignments.
Graph theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multi-step paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pair-wise relationships. Our results show promise for identifying path-based metrics in neuroimaging data.
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