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@ARTICLE{Li:1048163,
      author       = {Li, Fali and Wang, Guangying and Genon, Sarah and Eickhoff,
                      Simon B. and He, Runyang and Yi, Chanlin and Dong, Debo and
                      Yao, Dezhong and Jiang, Lin and Wu, Wei and Xu, Peng},
      title        = {{M}apping neurophysiological and molecular profiles of
                      heterogeneity and homogeneity in schizophrenia-bipolar
                      disorder},
      journal      = {Science advances},
      volume       = {11},
      number       = {46},
      issn         = {2375-2548},
      address      = {Washington, DC [u.a.]},
      publisher    = {Assoc.},
      reportid     = {FZJ-2025-04531},
      pages        = {eadz0389},
      year         = {2025},
      abstract     = {The heterogeneity of psychotic disorders leads to
                      instability in subjectively defined diagnoses. This study
                      used a machine learning framework termed common orthogonal
                      basis extraction (COBE) to decompose
                      electroencephalography-based functional connectivity (FC) in
                      patients with psychotic bipolar disorder (PBD),
                      schizophrenia (SCZ), and schizoaffective disorder (SAD) into
                      individualized and shared subspaces. The results
                      demonstrated that individualized FCs captured disease
                      heterogeneity and predicted symptom severity more accurately
                      than raw FCs, while shared FCs revealed diagnosis-specific
                      abnormalities and achieved an accuracy of $79.30\%$ in
                      differentiating PBD, SCZ, and SAD. Furthermore, molecular
                      decoding implicated regionally selective serotonin systems
                      and astrocytes in the neurobiological differences among
                      disorders, suggesting disorder-specific pharmacological
                      targets. Critically, these findings were replicated in an
                      independent cohort, confirming the effectiveness of the COBE
                      framework in mining neurophysiological and molecular
                      profiles of schizophrenia-bipolar disorder. These findings
                      advance mechanistic understanding of psychotic disorders and
                      offer a promising avenue toward objective, clinically
                      relevant tools for psychotic evaluation.},
      cin          = {INM-7},
      ddc          = {500},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5254 - Neuroscientific Data Analytics and AI
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1126/sciadv.adz0389},
      url          = {https://juser.fz-juelich.de/record/1048163},
}