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| Hauptseite > Workflowsammlungen > In Bearbeitung > Distinguishing schizophrenia and bipolar disorder through disorder-specific network revealed by contrastive machine learning |
| Hauptseite > Workflowsammlungen > In Bearbeitung > Distinguishing schizophrenia and bipolar disorder through disorder-specific network revealed by contrastive machine learning |
| Journal Article | FZJ-2026-05949 |
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2026
Springer Nature
[London]
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Please use a persistent id in citations: doi:10.1038/s43856-026-01867-1
Abstract: BackgroundSchizophrenia (SCZ) and bipolar disorder (BD) are severe psychotic disorders with overlapping clinical manifestations, leading to high rates of misdiagnosis. This study aims to identify disorder-specific neurophysiological biomarkers using electroencephalography and contrastive machine learning to improve differential diagnosis.MethodsResting-state electroencephalography was recorded from 52 patients with BD, 65 with SCZ, and 75 healthy controls. Temporal variability networks were constructed using sample entropy. Contrastive variational autoencoders decomposed these networks into components shared with healthy controls and components specific to each disorder. Based on disorder-specific components, predictive models for clinical symptoms were constructed. Additionally, spatial pattern network filters were implemented to extract discriminative features for the classification of BD and SCZ patients.ResultsHere we show pronounced differences in disorder-specific network components between SCZ and BD, especially in frontal-central/parietal connectivity, which were not discernible in the original or shared networks. These disorder-specific components correlate significantly with clinical assessment scores and support predictive modeling of symptom severity. By applying spatial pattern network filters to the disorder-specific components, we achieve 96.154% accuracy in distinguishing SCZ from BD, substantially surpassing conventional approaches.ConclusionsThis integrative framework, combining dynamic network analysis with contrastive machine learning, provides a powerful methodology for extracting neurophysiological biomarkers and paves the way for biologically grounded diagnostics in psychotic disorders.This work was supported by the National Natural Science Foundation of China (grant nos. 62501115, 82372084, and W2411084), the China Postdoctoral Science Foundation (grant nos. 2025M782897 and 2026T190764), the Sichuan Science and Technology Program (grant nos. 2024ZDZX0014 and 2024NSFTD0032), the Key R&D projects of the Science & Technology Department of Chengdu (grant no. 2024-YF08-00072-GX), the Key research and development program of Hubei Province (grant no. 2023BCB136).
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