%0 Journal Article
%A Li, Fali
%A Wang, Guangying
%A Jiang, Lin
%A Yao, Dezhong
%A Xu, Peng
%A Ma, Xuntai
%A Dong, Debo
%A He, Baoming
%T Disease-specific resting-state EEG network variations in schizophrenia revealed by the contrastive machine learning
%J Brain research bulletin
%V 202
%@ 0361-9230
%C Amsterdam [u.a.]
%I Elsevier Science
%M FZJ-2024-03169
%P 110744 -
%D 2023
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ 37591404
%U <Go to ISI:>//WOS:001063361900001
%R 10.1016/j.brainresbull.2023.110744
%U https://juser.fz-juelich.de/record/1025884