Systematic review and meta-analyses of neural structural and functional differences in generalized anxiety disorder and healthy controls using magnetic resonance imaging

利用磁共振成像技术对广泛性焦虑症患者和健康对照组的神经结构和功能差异进行系统评价和荟萃分析

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Abstract

OBJECTIVE: To compare structure, functional connectivity (FC) and task-based neural differences in subjects with generalized anxiety disorder (GAD) compared to healthy controls (HC). METHODS: The Embase, Ovid Medline, PsycINFO, Scopus, and Web of Science databases were searched from inception until March 12, 2018. Two reviewers independently screened titles, abstracts, and full-text articles. Data were extracted from records directly contrasting GAD and HC that included structure (connectivity and local indices such as volume, etc.), FC, or task-based magnetic resonance imaging data. Meta-analyses were conducted, as applicable, using AES-SDM software. RESULTS: The literature search produced 4,645 total records, of which 85 met the inclusion criteria for the systematic review. Records included structural (n = 35), FC (n = 33), and task-based (n = 42) findings. Meta-analyses were conducted on voxel-based morphometry and task-based results. DISCUSSION: The systematic review confirms and extends findings from previous reviews. Although few whole-brain resting state studies were conducted, key nodes of resting state networks have altered physiology: the hippocampus (default network), ACC and amygdala (salience network), have reduced volume, and the dlPFC (central executive network) and ACC have reduced FC with the amygdala in GAD. Nodes in the sensorimotor network are also altered with greater pre- and postcentral volume, reduced supplementary motor area volume, and reduced FC in anterior and increased FC in posterior cerebellum. CONCLUSIONS: Despite limitations due to sample size, the meta-analyses highly agree with the systematic review and provide evidence of widely distributed neural differences in subjects with GAD, compared to HC. Further research optimized for meta-analyses would greatly improve large-scale comparisons.

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