Structural and Functional Connectivity of Visual Cortex in Schizophrenia and Bipolar Disorder: A Graph-Theoretic Analysis

Reavis, Eric A.; Lee, Jung‐Hee; Altshuler, Lori L.; Cohen, Mark S.; Engel, Stephen A.; Glahn, David C.; Jimenez, Amy M.; Narr, Katherine L.; Nuechterlein, Keith H.; Riedel, Philipp; Wynn, Jonathan K.; Green, Michael F.

doi:10.1093/schizbullopen/sgaa056

Cited by 15 publications

(7 citation statements)

References 53 publications

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“…For the BP disease, secondary visual cortex and medial to superior temporal gyrus are selected as salient ROIs. This observation is in line with existing studies that visual processing abnormalities have been characterized in bipolar disorder patients [28,30], which is also confirmed in Fig. 2(c)(d).…”

Section: Interpretation Analysissupporting

confidence: 93%

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Cui¹,

Dai²,

Zhu³

et al. 2022

Preprint

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Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit for neuroscience research and clinical disorder therapy. Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged as a potential method for modeling complex network data. Deep models, on the other hand, have low interpretability, which prevents their usage in decision-critical contexts like healthcare. To bridge this gap, we propose an interpretable framework to analyze disorder-specific Regions of Interest (ROIs) and prominent connections. The proposed framework consists of two modules: a brain-network-oriented backbone model for disease prediction and a globally shared explanation generator that highlights disorder-specific biomarkers including salient ROIs and important connections. We conduct experiments on three real-world datasets of brain disorders. The results verify that our framework can obtain outstanding performance and also identify meaningful biomarkers. All code for this work is available at https://github.com/HennyJie/IBGNN.git.

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Section: Interpretation Analysissupporting

confidence: 93%

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Cui¹,

Dai²,

Zhu³

et al. 2022

Preprint

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show abstract

“…Overall, our work supports the hypothesis that BD may be associated with impaired visual processing (Reavis et al, 2020) and provides evidence of structural abnormalities of the visual system in BD-I similar to other disorders of the psychosis spectrum (Reavis et al, 2020). Furthermore, the association between structural changes and impairments in cognitive and social-cognitive domains may contribute to functional deficits and clinical outcomes in BD-I.…”

Section: Discussionsupporting

confidence: 82%

Gray matter volume covariance networks are associated with altered emotional processing in bipolar disorder: a source-based morphometry study

Miola

Trevisan

Merola

et al. 2021

Brain Imaging and Behavior

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Widespread regional gray matter volume (GMV) alterations have been reported in bipolar disorder (BD). Structural networks, which are thought to better reflect the complex multivariate organization of the brain, and their clinical and psychological function have not been investigated yet in BD. 24 patients with BD type-I (BD-I), and 30 with BD type-II (BD-II), and 45 controls underwent MRI scan. Voxel-based morphometry and source-based morphometry (SBM) were performed to extract structural covariation patterns of GMV. SBM components associated with morphometric differences were compared among diagnoses. Executive function and emotional processing correlated with morphometric characteristics. Compared to controls, BD-I showed reduced GMV in the temporo-insular-parieto-occipital cortex and in the culmen. An SBM component spanning the prefrontal-temporal-occipital network exhibited significantly lower GMV in BD-I compared to controls, but not between the other groups. The structural network covariance in BD-I was associated with the number of previous manic episodes and with worse executive performance. Compared to BD-II, BD-I showed a loss of GMV in the temporal-occipital regions, and this was correlated with impaired emotional processing. Altered prefrontal-temporal-occipital network structure could reflect a neural signature associated with visuospatial processing and problem-solving impairments as well as emotional processing and illness severity in BD-I.

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“…For example, BD patients spend the majority of their lives suffering from depression, and they are frequently misdiagnosed as UD (Phillips & Kupfer, 2013 ; Rai et al, 2021 ). Identifying robust and reliable neural biomarkers for BD is a promising approach for better understanding the underlying neurobiology and improving patient treatment outcomes (Ching et al, 2022 ; Reavis et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Identifying robust and reliable neural biomarkers for BD is a promising approach for better understanding the underlying neurobiology and improving patient treatment outcomes (Ching et al, 2022;Reavis et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

Chang

Wang

et al. 2022

Human Brain Mapping

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Bipolar disorder (BD) is a serious mental disorder involving widespread abnormal interactions between brain regions, and it is believed to be associated with imbalanced functions in the brain. However, how this brain imbalance underlies distinct BD symptoms remains poorly understood. Here, we used a nested‐spectral partition (NSP) method to study the segregation, integration, and balance in resting‐state brain functional networks in BD patients and healthy controls (HCs). We first confirmed that there was a high deviation in the brain functional network toward more segregation in BD patients than in HCs and that the limbic system had the largest alteration. Second, we demonstrated a network balance of segregation and integration that corresponded to lower anxiety in BD patients but was not related to other symptoms. Subsequently, based on a machine‐learning approach, we identified different system‐level mechanisms underlying distinct BD symptoms and found that the features related to the brain network balance could predict BD symptoms better than graph theory analyses. Finally, we studied attention‐deficit/hyperactivity disorder (ADHD) symptoms in BD patients and identified specific patterns that distinctly predicted ADHD and BD scores, as well as their shared common domains. Our findings supported an association of brain imbalance with anxiety symptom in BD patients and provided a potential network signature for diagnosing BD. These results contribute to further understanding the neuropathology of BD and to screening ADHD in BD patients.

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Structural and Functional Connectivity of Visual Cortex in Schizophrenia and Bipolar Disorder: A Graph-Theoretic Analysis

Cited by 15 publications

References 53 publications

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Gray matter volume covariance networks are associated with altered emotional processing in bipolar disorder: a source-based morphometry study

Segregation, integration and balance in resting‐state brain functional networks associated with bipolar disorder symptoms

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