Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia

Abstract

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.

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

“…The abnormalities in brain regions between SZ patients and NCs were primarily in the bilateral prefrontal cortex, right limbic system, left temporal cortex and left motor strip. While the findings on the prefrontal cortex ( Janousova et al, 2015 ; Ou et al, 2015 ; Zhuang et al, 2019 ; Webler et al, 2020 ), limbic system ( Shon et al, 2018 ; Abdolalizadeh et al, 2020 ; Falakshahi et al, 2020 ), and temporal cortex ( Shu et al, 2012 ; Ehrlich et al, 2014 ; Schnack et al, 2014 ; Koch et al, 2018 ; Chatterjee et al, 2020 ) are aligned with previous studies, the discovery of differences in the motor strip has rarely been reported. The motor strip is imperative as the neural hub that participates in perception, action and anticipation in relation to the environment ( Schroeder et al, 1994 ).…”

Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification

“…The blood‐oxygenation‐level‐dependent (BOLD) signals measured via fMRI imaging techniques became important tools to quantify and visualize brain activities. fMRI features have been used for studying Schizophrenia, Alzheimer, and many different human brain disorders (Abrol et al, 2019 ; Falakshahi et al, 2022 ; Falakshahi, Rokham, et al, 2020 ; Falakshahi, Vergara, et al, 2020 ). Among resting‐state fMRI features, functional alteration of the brain is obtained via functional connectivity (FC), widely used to detect abnormalities within the brain and identify biomarkers for brain disorders (Rashid & Calhoun, 2020 ; Yin et al, 2022 ).…”

Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data

“…We estimate time-varying and static graphs of the control and SZ group and apply our proposed method for comprehensive path analysis. A preliminary version of this work has been reported in Falakshahi, Vergara, et al (2020) .…”