TY  - JOUR
AU  - Li, Fali
AU  - Wang, Guangying
AU  - Jiang, Lin
AU  - Yao, Dezhong
AU  - Xu, Peng
AU  - Ma, Xuntai
AU  - Dong, Debo
AU  - He, Baoming
TI  - Disease-specific resting-state EEG network variations in schizophrenia revealed by the contrastive machine learning
JO  - Brain research bulletin
VL  - 202
SN  - 0361-9230
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - FZJ-2024-03169
SP  - 110744 -
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 37591404
UR  - <Go to ISI:>//WOS:001063361900001
DO  - DOI:10.1016/j.brainresbull.2023.110744
UR  - https://juser.fz-juelich.de/record/1025884
ER  -