% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Li:1025884,
      author       = {Li, Fali and Wang, Guangying and Jiang, Lin and Yao,
                      Dezhong and Xu, Peng and Ma, Xuntai and Dong, Debo and He,
                      Baoming},
      title        = {{D}isease-specific resting-state {EEG} network variations
                      in schizophrenia revealed by the contrastive machine
                      learning},
      journal      = {Brain research bulletin},
      volume       = {202},
      issn         = {0361-9230},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-03169},
      pages        = {110744 -},
      year         = {2023},
      abstract     = {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.},
      cin          = {INM-7},
      ddc          = {150},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37591404},
      UT           = {WOS:001063361900001},
      doi          = {10.1016/j.brainresbull.2023.110744},
      url          = {https://juser.fz-juelich.de/record/1025884},
}