Identifying brain dynamic network states via GIG-ICA: Application to schizophrenia, bipolar and schizoaffective disorders

Yuhui Du, Godfrey D. Pearlson, Hao He, Lei Wu, Jiayu Chen, Vince Daniel Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There has been an increasing interest in brain dynamic functional networks revealed by resting-state fMRI data. We hypothesized that dynamic functional networks could offer important information for detecting subtle differences in symptom-related mental diseases. Schizophrenia (SZ), bipolar disorder (BP), and schizoaffective disorder (SAD) have similar symptoms, and there is still controversy about the SAD category. In this paper, we applied a novel method, group information guided ICA (GIG-ICA), to extract functional connectivity states and their fluctuations from dynamic functional network. Using the proposed approach, we analyzed fMRI data of healthy controls, SZ patients, BP patients and two symptom-defined subsets of SAD patients. Results demonstrate that, measured by the dominant functional connectivity state, different groups have a similar pattern, while the two subsets of SAD patients were most correlated to each other, supporting SAD's status as an independent category. The significant difference in the dominant functional connectivity state among these disorders involved cerebellum-related functional connectivity.

Original languageEnglish (US)
Title of host publicationProceedings - International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages478-481
Number of pages4
Volume2015-July
ISBN (Print)9781479923748
DOIs
StatePublished - Jul 21 2015
Externally publishedYes
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

Fingerprint

Independent component analysis
Bipolar Disorder
Psychotic Disorders
Brain
Schizophrenia
Magnetic Resonance Imaging
Cerebellum

Keywords

  • bipolar disorder
  • brain dynamic functional network
  • independent component analysis
  • schizoaffective disorder
  • schizophrenia

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Du, Y., Pearlson, G. D., He, H., Wu, L., Chen, J., & Calhoun, V. D. (2015). Identifying brain dynamic network states via GIG-ICA: Application to schizophrenia, bipolar and schizoaffective disorders. In Proceedings - International Symposium on Biomedical Imaging (Vol. 2015-July, pp. 478-481). [7163915] IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7163915

Identifying brain dynamic network states via GIG-ICA : Application to schizophrenia, bipolar and schizoaffective disorders. / Du, Yuhui; Pearlson, Godfrey D.; He, Hao; Wu, Lei; Chen, Jiayu; Calhoun, Vince Daniel.

Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July IEEE Computer Society, 2015. p. 478-481 7163915.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Du, Y, Pearlson, GD, He, H, Wu, L, Chen, J & Calhoun, VD 2015, Identifying brain dynamic network states via GIG-ICA: Application to schizophrenia, bipolar and schizoaffective disorders. in Proceedings - International Symposium on Biomedical Imaging. vol. 2015-July, 7163915, IEEE Computer Society, pp. 478-481, 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 4/16/15. https://doi.org/10.1109/ISBI.2015.7163915
Du Y, Pearlson GD, He H, Wu L, Chen J, Calhoun VD. Identifying brain dynamic network states via GIG-ICA: Application to schizophrenia, bipolar and schizoaffective disorders. In Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July. IEEE Computer Society. 2015. p. 478-481. 7163915 https://doi.org/10.1109/ISBI.2015.7163915
Du, Yuhui ; Pearlson, Godfrey D. ; He, Hao ; Wu, Lei ; Chen, Jiayu ; Calhoun, Vince Daniel. / Identifying brain dynamic network states via GIG-ICA : Application to schizophrenia, bipolar and schizoaffective disorders. Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July IEEE Computer Society, 2015. pp. 478-481
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