Abnormal brain network community structure related to psychological stress in schizophrenia

Castro, Mariana N.; Bocaccio, Hernán; Pino, Gabriela; Sánchez, Stella M.; Wainsztein, Agustina; Drucaroff, Lucas; Costanzo, Elsa Y.; Crossley, Nicolás; Villarreal, Mirta F.; Guinjoan, Salvador M.

doi:10.1016/j.schres.2023.02.007

Cited by 3 publications

(1 citation statement)

References 108 publications

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“…The connection strength of the frontal lobe nodes and central region of the SZ patients is considerably weaker than that of the HC. Additionally, it is evident that there is significantly less connection between the two hemispheres, with the occipital lobe being the only area of significant retention, which is consistent with the research finding of Mariana et al [45].…”

Section: Crossover Frequency Experiments Performancesupporting

confidence: 89%

Automated Diagnosis of Schizophrenia based on EEG and Spatial–temporal Residual Graph Convolutional Network

Xu,

Zhu,

et al. 2023

Preprint

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Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required. In this study, we provide a classification approach for SZ patients based on a spatial-temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single channel temporal convolution respectively and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial features across brain regions. The spatial frequency features are then obtained by single channel temporal convolution, and the processed matrix is flattened after multiple convolutions and input to the classification model. We conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best performance in the classification results in the sub-bands.

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Section: Crossover Frequency Experiments Performancesupporting

confidence: 89%

Automated Diagnosis of Schizophrenia based on EEG and Spatial–temporal Residual Graph Convolutional Network

Xu,

Zhu,

et al. 2023

Preprint

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Evolving brain network dynamics in early childhood: Insights from modular graph metrics

Song,

Jiang,

Zhang

et al. 2024

NeuroImage

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Dynamicity of brain network organization and their community architecture as characterizing features for classification of common mental disorders from the whole-brain connectome

Sastry

Banerjee

2023

Preprint

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The urgency of addressing common mental disorders (bipolar disorder, ADHD, and schizophrenia) arises from their significant societal impact. Developing strategies to support psychiatrists is crucial. Previous studies focused on the relationship between these disorders and changes in the resting-state functional connectome's modularity, often using static functional connectivity (sFC) estimation. However, understanding the dynamic reconfiguration of resting-state brain networks with rich temporal structure is essential for comprehending neural activity and addressing mental health disorders. This study proposes an unsupervised approach combining spatial and temporal characterization of brain networks to classify common mental disorders using fMRI timeseries data from two cohorts (N=408 participants). We employ the weighted stochastic block model to uncover mesoscale community architecture differences, providing insights into neural organization. Our approach overcomes sFC limitations and biases in community detection algorithms by modelling the functional connectome's temporal dynamics as a landscape, quantifying temporal stability at whole-brain and network levels. Findings reveal individuals with schizophrenia exhibit less assortative community structure and participate in multiple motif classes, indicating less specialized neural organization. Patients with schizophrenia and ADHD demonstrate significantly reduced temporal stability compared to healthy controls. This study offers insights into functional connectivity (FC) patterns' spatiotemporal organization and their alterations in common mental disorders, highlighting the potential of temporal stability as a biomarker.

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Abnormal brain network community structure related to psychological stress in schizophrenia

Cited by 3 publications

References 108 publications

Automated Diagnosis of Schizophrenia based on EEG and Spatial–temporal Residual Graph Convolutional Network

Automated Diagnosis of Schizophrenia based on EEG and Spatial–temporal Residual Graph Convolutional Network

Evolving brain network dynamics in early childhood: Insights from modular graph metrics

Dynamicity of brain network organization and their community architecture as characterizing features for classification of common mental disorders from the whole-brain connectome

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