Characteristics of brain network connectome and connectome-based efficacy predictive model in bipolar depression.
Authors
Affiliations (12)
Affiliations (12)
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Nanhu Brain-computer Interface institute, Hangzhou, China.
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China. [email protected].
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China. [email protected].
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. [email protected].
- Nanhu Brain-computer Interface institute, Hangzhou, China. [email protected].
- The Zhejiang Key Laboratory of Precision Psychiatry, Hangzhou, China. [email protected].
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of Medicine, Hangzhou, China. [email protected].
- Brain Research Institute of Zhejiang University, Hangzhou, China. [email protected].
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China. [email protected].
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China. [email protected].
Abstract
Aberrant functional connectivity (FC) between brain networks has been indicated closely associated with bipolar disorder (BD). However, the previous findings of specific brain network connectivity patterns have been inconsistent, and the clinical utility of FCs for predicting treatment outcomes in bipolar depression was underexplored. To identify robust neuro-biomarkers of bipolar depression, a connectome-based analysis was conducted on resting-state functional MRI (rs-fMRI) data of 580 bipolar depression patients and 116 healthy controls (HCs). A subsample of 148 patients underwent a 4-week quetiapine treatment with post-treatment clinical assessment. Adopting machine learning, a predictive model based on pre-treatment brain connectome was then constructed to predict treatment response and identify the efficacy-specific networks. Distinct brain network connectivity patterns were observed in bipolar depression compared to HCs. Elevated intra-network connectivity was identified within the default mode network (DMN), sensorimotor network (SMN), and subcortical network (SC); and as to the inter-network connectivity, increased FCs were between the DMN, SMN and frontoparietal (FPN), ventral attention network (VAN), and decreased FCs were between the SC and cortical networks, especially the DMN and FPN. And the global network topology analyses revealed decreased global efficiency and increased characteristic path length in BD compared to HC. Further, the support vector regression model successfully predicted the efficacy of quetiapine treatment, as indicated by a high correspondence between predicted and actual HAMD reduction ratio values (r<sub>(df=147)</sub>=0.4493, p = 2*10<sup>-4</sup>). The identified efficacy-specific networks primarily encompassed FCs between the SMN and SC, and between the FPN, DMN, and VAN. These identified networks further predicted treatment response with r = 0.3940 in the subsequent validation with an independent cohort (n = 43). These findings presented the characteristic aberrant patterns of brain network connectome in bipolar depression and demonstrated the predictive potential of pre-treatment network connectome for quetiapine response. Promisingly, the identified connectivity networks may serve as functional targets for future precise treatments for bipolar depression.