Segregation, integration and balance in resting-state brain functional networks associated with bipolar disorder symptoms

Bipolar disorder (BD) is a serious mental disorder involving widespread abnormal interactions between brain regions, and it is believed to be associated with imbalanced functions in the brain. However, how this brain imbalance underlies distinct BD symptoms remains poorly understood. Here, we used a nested-spectral partition (NSP) method to study the segregation, integration, and balance in resting-state brain functional networks in BD patients and healthy controls (HCs). We first confirmed that there was a high deviation in the brain functional network toward more segregation in BD patients than in HCs and that the limbic system had the largest alteration. Second, we demonstrated a network balance of segregation and integration that corresponded to lower anxiety in BD patients but was not related to other symptoms. Subsequently, based on a machine-learning approach, we identified different system-level mechanisms underlying distinct BD symptoms and found that the features related to the brain network balance could predict BD symptoms better than graph theory analyses. Finally, we studied attention-deficit/hyperactivity disorder (ADHD) symptoms in BD patients and identified specific patterns that distinctly predicted ADHD and BD scores, as well as their shared common domains. Our findings supported an association of brain imbalance with anxiety symptom in BD patients and provided a potential network signature for diagnosing BD. These results contribute to further understanding the neuropathology of BD and to screening ADHD in BD patients.

Automated summary

In this study, the researchers used the nested-spectral partition method to investigate brain functional networks in patients with bipolar disorder (BD) and healthy controls. The results showed that BD patients had a higher degree of segregation in their brain networks, especially in the limbic system. They also found a network balance of segregation and integration that correlated with lower anxiety in BD patients. Additionally, the features related to brain network balance were found to better predict BD symptoms compared to traditional graph theory analyses.