Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features

【Author】

Sugai Liang;Roberto Vega;Xiangzhen Kong;Wei Deng;Qiang Wang;Xiaohong Ma;Mingli Li;Xun Hu;Andrew J.Greenshaw;Russell Greiner;Tao Li;Mental Health Centre, West China Hospital, Sichuan University;Huaxi Brain Research Centre, West China Hospital, Sichuan University;Department of Computing Science, University of Alberta;Language and Genetics Department, Max Planck Institute for Psycholinguistics;Huaxi Biobank, West China Hospital, Sichuan University;Department of Psychiatry, University of Alberta;

【Institution】

Mental Health Centre, West China Hospital, Sichuan University;Huaxi Brain Research Centre, West China Hospital, Sichuan University;Department of Computing Science, University of Alberta;Language and Genetics Department, Max Planck Institute for Psycholinguistics;Huaxi Biobank, West China Hospital, Sichuan University;Department of Psychiatry, University of Alberta;

【Abstract】

Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder(MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia(FES), 125 with MDD, and 237 demographically-matched healthy controls(HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with aone-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD.Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.

【Keywords】

Schizophrenia;;Major depressive disorder;;Neurocognition;;Neurocognitive graph;;Graphical LASSO

References

To explore the background and basis of the node document

Springer Journals Database

Total: 46 articles

  • [1] Kai Zhang;Yunqi Zhu;Yuankai Zhu;Shuang Wu;Hao Liu;Wei Zhang;Caiyun Xu;Hong Zhang;Takuya Hayashi;Mei Tian;Department of Nuclear Medicine,The Second Affiliated Hospital of Zhejiang University School of Medicine;Zhejiang University Medical PET Center;Institute of Nuclear Medicine and Molecular Imaging,Zhejiang University;Key Laboratory of Medical Molecular Imaging of Zhejiang Province;Department of Orthopedics,The Second Affiliated Hospital of Zhejiang University School of Medicine;Functional Architecture Imaging Unit,RIKEN Center for Life Science Technologies;, Molecular,Functional,and Structural Imaging of Major Depressive Disorder, Neuroscience Bulletin,
  • [2] Mao-Lin Hu;Xiao-Fen Zong;J.John Mann;Jun-Jie Zheng;Yan-Hui Liao;Zong-Chang Li;Ying He;Xiao-Gang Chen;Jin-Song Tang;Department of Psychiatry,The Second Xiangya Hospital,Central South University;Division of Molecular Imaging and Neuropathology,New York State Psychiatric Institute and Departments of Psychiatry and Radiology,Columbia University;Mental Health Institute of Central South University,China National Clinical Research Center on Mental Disorders(Xiangya),China National Technology Institute on Mental Disorders,Hunan Technology Institute of Psychiatry Hunan Key Laboratory of Psychiatry and Mental Health,The Second Xiangya Hospital of Central South University;Key Laboratory for Neuro Information of the Ministry of Education,School of Life Science and Technology and Center for Information in Bio Medicine,University of Electronic Science and Technology of China;Department of Psychiatry and Biobehavioral Sciences,UCLA Semel Institute for Neuroscience,David Geffen School of Medicine;, A Review of the Functional and Anatomical Default Mode Network in Schizophrenia, Neuroscience Bulletin,
  • [3] Steven L. Bressler;;Vinod Menon, Large-scale brain networks in cognition: emerging methods and principles, Trends in Cognitive Sciences,
  • [4] Working memory dysfunction in schizophrenia compared to healthy controls and patients with depression: Evidence from event-related fMRI, Neuroimage,

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