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

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.