ArticleJournal of psychiatry & neuroscience : JPN2020
Neurobiological commonalities and distinctions among 3 major psychiatric disorders: a graph theoretical analysis of the structural connectome
Article in Journal of psychiatry & neuroscience : JPN, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 28 citations in OpenAlex.
- Characteristic volume, texture, and functional connectivity alterations in hypothalamic subunits of adult primary hypothyroidism: a multiparametric MRI analysis.Journal of endocrinological investigation · 2025Article
- Common and disease-specific patterns of functional connectivity and topology alterations across unipolar and bipolar disorder during depressive episodes: a transdiagnostic study.Translational psychiatry · 2025Article
- Functional network characteristics in adolescent psychotic mood disorder: associations with symptom severity and treatment effects.European child & adolescent psychiatry · 2024Article
- Graph theory-based analysis reveals neural anatomical network alterations in chronic post-traumatic stress disorder.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Abnormal Structural Network Communication Reflects Cognitive Deficits in Schizophrenia.Brain topography · 2023Article
- Negative symptoms are associated with modularity and thalamic connectivity in schizophrenia.European archives of psychiatry and clinical neuroscience · 2023Article
- Intrinsic Organization of Occipital Hubs Predicts Depression: A Resting-State fNIRS Study.Brain sciences · 2022Article
- Genetics and neurobiology of eating disorders.Nature neuroscience · 2022Review
- Shared and distinct functional connectivity of hippocampal subregions in schizophrenia, bipolar disorder, and major depressive disorder.Frontiers in psychiatry · 2022Article
- Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction.Molecular psychiatry · 2022Review
- Altered Topological Properties of Grey Matter Structural Covariance Networks in Complete Thoracic Spinal Cord Injury Patients: A Graph Theoretical Network Analysis.Neural plasticity · 2021Article
- State- and trait-related dysfunctions in bipolar disorder across different mood states: a graph theory study.Journal of psychiatry & neuroscience : JPNArticle
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: White matter network alterations have increasingly been implicated in major depressive disorder, bipolar disorder and schizophrenia. The aim of this study was to identify shared and distinct white matter network alterations among the 3 disorders. Methods: We used analysis of covariance, with age and gender as covariates, to investigate white matter network alterations in 123 patients with schizophrenia, 123 with bipolar disorder, 124 with major depressive disorder and 209 healthy controls. Results: We found significant group differences in global network efficiency (F = 3.386, p = 0.018), nodal efficiency (F = 8.015, p < 0.001 corrected for false discovery rate [FDR]) and nodal degree (F = 5.971, pFDR < 0.001) in the left middle occipital gyrus, as well as nodal efficiency (F = 6.930, pFDR < 0.001) and nodal degree (F = 5.884, pFDR < 0.001) in the left postcentral gyrus. We found no significant alterations in patients with major depressive disorder. Post hoc analyses revealed that compared with healthy controls, patients in the schizophrenia and bipolar disorder groups showed decreased global network efficiency, nodal efficiency and nodal degree in the left middle occipital gyrus. Furthermore, patients in the schizophrenia group showed decreased nodal efficiency and nodal degree in the left postcentral gyrus compared with healthy controls. Limitations: Our findings could have been confounded in part by treatment differences. Conclusion: Our findings implicate graded white matter network alterations across the 3 disorders, enhancing our understanding of shared and distinct pathophysiological mechanisms across diagnoses and providing vital insights into neuroimaging-based methods for diagnosis and research.
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