Disease-specific resting-state EEG network variations in schizophrenia revealed by the contrastive machine learning
Disease-specific resting-state EEG network variations in schizophrenia revealed by the contrastive machine learning
Given a multitude of genetic and environmental factors, when investigating the variability in schizophrenia (SCZ) and the first-degree relatives (R-SCZ), latent disease-specific variation is usually hidden. To reliably investigate the mechanism underlying the brain deficits from the aspect of functi...
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Personal Name(s): | Li, Fali |
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Wang, Guangying / Jiang, Lin / Yao, Dezhong / Xu, Peng / Ma, Xuntai / Dong, Debo / He, Baoming (Corresponding author) | |
Contributing Institute: |
Gehirn & Verhalten; INM-7 |
Published in: | Brain research bulletin, 202 (2023) S. 110744 - |
Imprint: |
Amsterdam [u.a.]
Elsevier Science
2023
|
DOI: |
10.34734/FZJ-2024-03169 |
DOI: |
10.1016/j.brainresbull.2023.110744 |
Document Type: |
Journal Article |
Research Program: |
Multilevel Brain Organization and Variability |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1016/j.brainresbull.2023.110744 in citations.
Given a multitude of genetic and environmental factors, when investigating the variability in schizophrenia (SCZ) and the first-degree relatives (R-SCZ), latent disease-specific variation is usually hidden. To reliably investigate the mechanism underlying the brain deficits from the aspect of functional networks, we newly iterated a framework of contrastive variational autoencoders (cVAEs) applied in the contrasts among three groups, to disentangle the latent resting-state network patterns specified for the SCZ and R-SCZ. We demonstrated that the comparison in reconstructed resting-state networks among SCZ, R-SCZ, and healthy controls (HC) revealed network distortions of the inner-frontal hypoconnectivity and frontal-occipital hyperconnectivity, while the original ones illustrated no differences. And only the classification by adopting the reconstructed network metrics achieved satisfying performances, as the highest accuracy of 96.80% ± 2.87%, along with the precision of 95.05% ± 4.28%, recall of 98.18% ± 3.83%, and F1-score of 96.51% ± 2.83%, was obtained. These findings consistently verified the validity of the newly proposed framework for the contrasts among the three groups and provided related resting-state network evidence for illustrating the pathological mechanism underlying the brain deficits in SCZ, as well as facilitating the diagnosis of SCZ. |