Large-scale network abnormality in bipolar disorder: A multimodal meta-analysis of resting-state functional and structural magnetic resonance imaging studies

Jiaying Gong; Junjing Wang; Pan Chen; Zhangzhang Qi; Zhenye Luo; Jurong Wang; Li Huang; Ying Wang

doi:10.1016/j.jad.2021.05.052

Large-scale network abnormality in bipolar disorder: A multimodal meta-analysis of resting-state functional and structural magnetic resonance imaging studies

J Affect Disord. 2021 Sep 1:292:9-20. doi: 10.1016/j.jad.2021.05.052. Epub 2021 May 27.

Authors

Jiaying Gong¹, Junjing Wang², Pan Chen³, Zhangzhang Qi³, Zhenye Luo³, Jurong Wang³, Li Huang³, Ying Wang⁴

Affiliations

¹ Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, China.
² Department of Applied Psychology, Guangdong University of Foreign Studies, Guangzhou 510006, China.
³ Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China.
⁴ Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China. Electronic address: johneil@vip.sina.com.

PMID: 34087634
DOI: 10.1016/j.jad.2021.05.052

Abstract

Background: Bipolar disorder (BD) has been linked to abnormalities in the communication and gray matter volume (GMV) of large-scale brain networks, as reflected by impaired resting-state functional connectivity (rs-FC) and aberrant voxel-based morphometry (VBM). However, identifying patterns of large-scale network abnormality in BD has been elusive.

Methods: Whole-brain seed-based rs-FC and VBM studies comparing individuals with BD and healthy controls (HCs) were retrieved from multiple databases. Multilevel kernel density analysis was used to identify brain networks in which BD was linked to hyper-connectivity or hypo-connectivity with each prior network and the overlap between dysconnectivity and GMV changes.

Results: Thirty-six seed-based rs-FC publications (1526 individuals with BD and 1578 HCs) and 70 VBM publications (2715 BD and 3044 HCs) were included in the meta-analysis. Our results showed that BD was characterized by hypo-connectivity within the default network (DN), hyper-connectivity within the affective network (AN), and ventral attention network (VAN) and hypo- and hyper-connectivity within the frontoparietal network (FN). Hyper-connectivity between-network of AN-DN, AN-FN, AN-VAN, AN-thalamus network (TN), VAN-TN, VAN-DN, VAN-FN, and TN-sensorimotor network were found. Hypo-connectivity between-network of FN and DN was observed. Decreased GMV was found in the insula, inferior frontal gyrus, and anterior cingulate cortex.

Limitations: Differential weights in the number of included studies and sample size of FC and VBM might have a disproportionate influence on the meta-analytic results.

Conclusions: These results suggest that BD is characterized by both structural and functional abnormalities of large-scale neurocognitive networks, especially in the DN, AN, VAN, FN, and TN.

Keywords: Bipolar disorder; Functional connectivity; Functional magnetic resonance imaging; Gray matter volume; Meta-analysis.

Publication types

Meta-Analysis
Research Support, Non-U.S. Gov't
Review

MeSH terms

Bipolar Disorder* / diagnostic imaging
Brain / diagnostic imaging
Gray Matter / diagnostic imaging
Humans
Magnetic Resonance Imaging
Prefrontal Cortex