Systematic review and meta-analyses of neural structural and functional differences in generalized anxiety disorder and healthy controls using magnetic resonance imaging
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.023 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".