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001025884 1001_ $$00000-0002-2450-4591$$aLi, Fali$$b0
001025884 245__ $$aDisease-specific resting-state EEG network variations in schizophrenia revealed by the contrastive machine learning
001025884 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2023
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001025884 520__ $$aGiven 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.
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001025884 7001_ $$aWang, Guangying$$b1
001025884 7001_ $$aJiang, Lin$$b2
001025884 7001_ $$aYao, Dezhong$$b3
001025884 7001_ $$aXu, Peng$$b4
001025884 7001_ $$aMa, Xuntai$$b5
001025884 7001_ $$0P:(DE-Juel1)190904$$aDong, Debo$$b6$$ufzj
001025884 7001_ $$0P:(DE-HGF)0$$aHe, Baoming$$b7$$eCorresponding author
001025884 773__ $$0PERI:(DE-600)2004068-4$$a10.1016/j.brainresbull.2023.110744$$gVol. 202, p. 110744 -$$p110744 -$$tBrain research bulletin$$v202$$x0361-9230$$y2023
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