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      Identifying dynamic functional connectivity biomarkers using GIG‐ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder

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          Abstract

          Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically‐related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group‐level and subject‐specific connectivity states from DFC. Using resting‐state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole‐brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis‐related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post‐central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD‐unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683–2708, 2017. © 2017 Wiley Periodicals, Inc.

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          Author and article information

          Contributors
          ydu@mrn.org
          Journal
          Hum Brain Mapp
          Hum Brain Mapp
          10.1002/(ISSN)1097-0193
          HBM
          Human Brain Mapping
          John Wiley and Sons Inc. (Hoboken )
          1065-9471
          1097-0193
          10 March 2017
          May 2017
          : 38
          : 5 ( doiID: 10.1002/hbm.v38.5 )
          : 2683-2708
          Affiliations
          [ 1 ] The Mind Research Network & LBERI Albuquerque New Mexico
          [ 2 ] School of Computer & Information Technology Shanxi University Taiyuan China
          [ 3 ] Departments of Psychiatry Yale University New Haven Connecticut
          [ 4 ] Departments of Neurobiology Yale University New Haven Connecticut
          [ 5 ] Olin Neuropsychiatry Research Center, Institute of Living Hartford Connecticut
          [ 6 ] Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences Beijing China
          [ 7 ] Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico
          [ 8 ] Department of Psychiatry University of Texas Southwestern Medical School Dallas Texas
          [ 9 ] University of Cincinnati Cincinnati Ohio
          [ 10 ] Department of Psychiatry Beth Israel Deaconess Medical Center and Harvard Medical School Boston Massachusetts
          [ 11 ] Departments of Psychology and Neuroscience BioImaging Research Center, University of Georgia Athens Georgia
          [ 12 ] Department of Psychiatry University of New Mexico Albuquerque New Mexico
          Author notes
          [*] [* ]Correspondence to: Yuhui Du, Ph.D., The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87131. E‐mail: ydu@ 123456mrn.org
          Author information
          http://orcid.org/0000-0002-0079-8177
          Article
          PMC5399898 PMC5399898 5399898 HBM23553
          10.1002/hbm.23553
          5399898
          28294459
          4842e451-d79a-4709-8a17-f0e83c397683
          © 2017 Wiley Periodicals, Inc.
          History
          : 30 September 2016
          : 15 February 2017
          : 17 February 2017
          Page count
          Figures: 16, Tables: 1, Pages: 26, Words: 12265
          Funding
          Funded by: National Science Foundation , open-funder-registry 10.13039/100000001;
          Award ID: 1539067
          Award ID: 1016619
          Funded by: National Institutes of Health , open-funder-registry 10.13039/100000002;
          Award ID: R01EB006841 (to V.C.)
          Award ID: R01EB020407 (to V.C.)
          Award ID: P20RR021938/P20GM103472 (to V.C.)
          Award ID: R01MH43775 (to G.P.)
          Award ID: R01MH077945 (to G.P.)
          Award ID: R01MH077851 (to C.T.)
          Award ID: R01MH078113 (to M.K.)
          Award ID: R01MH077852 (to G.T.)
          Award ID: R01MH077862 (to J.S.)
          Funded by: Natural Science Foundation of Shanxi
          Award ID: 2016021077 (to Y.D.)
          Funded by: The Chinese National Science Foundation grant
          Award ID: 81471367
          Funded by: The National High‐Tech Development Plan
          Award ID: 2015AA020513
          Funded by: The Strategic Priority Research Program of the Chinese Academy of Sciences
          Award ID: XDB02060005
          Categories
          Research Article
          Research Articles
          Custom metadata
          2.0
          May 2017
          Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.2 mode:remove_FC converted:15.11.2019

          functional magnetic resonance imaging,dynamic functional connectivity,independent component analysis,schizophrenia,schizoaffective disorder,bipolar disorder

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