Evidence map›Paper›PMID 29375339›Full record

ArticleFrontiers in human neuroscience2017

Aberrant Functional Network Connectivity as a Biomarker of Generalized Anxiety Disorder.

Jianping Qiao, Anning Li, Chongfeng Cao, Zhishun Wang, Jiande Sun, Guangrun Xu

Abstract read
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Article in Frontiers in human neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Jianping QiaoSchool of Physics and Electronics, Shandong Normal University, Jinan, China.
Anning LiDepartment of Radiology, Qilu Hospital of Shandong University, Jinan, China.
Chongfeng CaoDepartment of Emergency, Jinan Central Hospital Affiliated to Shandong University, Jinan, China.
Zhishun WangDepartment of Psychiatry, Columbia University, New York, NY, United States.
Jiande SunInstitute of Data Science and Technology, Shandong Normal University, Jinan, China.
Guangrun XuDepartment of Neurology, Qilu Hospital of Shandong University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neural disruptions during emotion regulation are common of generalized anxiety disorder (GAD). Identifying distinct functional and effective connectivity patterns in GAD may provide biomarkers for their diagnoses. This study aims to investigate the differences of features of brain network connectivity between GAD patients and healthy controls (HC), and to assess whether those differences can serve as biomarkers to distinguish GAD from controls. Independent component analysis (ICA) with hierarchical partner matching (HPM-ICA) was conducted on resting-state functional magnetic resonance imaging data collected from 20 GAD patients with medicine-free and 20 matched HC, identifying nine highly reproducible and significantly different functional brain connectivity patterns across diagnostic groups. We then utilized Granger causality (GC) to study the effective connectivity between the regions that identified by HPM-ICA. The linear discriminant analysis was finally used to distinguish GAD from controls with these measures of neural connectivity. The GAD patients showed stronger functional connectivity in amygdala, insula, putamen, thalamus, and posterior cingulate cortex, but weaker in frontal and temporal cortex compared with controls. Besides, the effective connectivity in GAD was decreased from the cortex to amygdala and basal ganglia. Applying the ICA and GC features to the classifier led to a classification accuracy of 87.5%, with a sensitivity of 90.0% and a specificity of 85.0%. These findings suggest that the presence of emotion dysregulation circuits may contribute to the pathophysiology of GAD, and these aberrant brain features may serve as robust brain biomarkers for GAD.

Indexed as

brain connectivityclassificationgeneralized anxiety disorderGranger causalityindependent component analysis

Identifiers

PMID29375339
PMCPMC5770732

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.