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Hum Brain Mapp. 2017 May;38(5):2683-2708. doi: 10.1002/hbm.23553. Epub 2017 Mar 10.

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

Author information

The Mind Research Network & LBERI, Albuquerque, New Mexico.
School of Computer & Information Technology, Shanxi University, Taiyuan, China.
Departments of Psychiatry, Yale University, New Haven, Connecticut.
Departments of Neurobiology, Yale University, New Haven, Connecticut.
Olin Neuropsychiatry Research Center, Institute of Living, Hartford, Connecticut.
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.
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico.
Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, Texas.
University of Cincinnati, Cincinnati, Ohio.
Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts.
Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, Georgia.
Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico.


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.


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

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