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Record W2622344751

Medial prefrontal GABA in generalized social anxiety disorder (GSAD)

2009· article· en· W2622344751 on OpenAlexaff
Rachel Grills, Daniel Li, Changho Choi, Peter Seres, Paramjit P. Bhardwaj, Jeff Armstrong, Nick Coupland

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeneralized anxiety disorderSocial anxietyPrefrontal cortexPsychologyNeuroscienceGABAergicAnxietyPsychiatryCognitionInhibitory postsynaptic potential
DOInot available

Abstract

fetched live from OpenAlex

Objectives: GSAD is associated with altered function of medial prefrontal cortex (mPFC), which plays a key role in social cognition and GSAD is responsive to treatments that augment GABA neurotransmission. The aim was to compare mPFC GABA concentrations in GSAD and controls. Methods: 21 GSAD and 61 healthy age-, sex- and education-matched controls were compared using single voxel magnetic resonance spectroscopy at 3T. Absolute quantification of GABA was derived from a double quantum filter sequence with selective J-refocusing and tissue water measurements. Results: Liebowitz Social Anxiety Scale scores were 77 ± 17 in GSAD and 13 ± 10 in controls. mPFC GABA was lower (t = 2.8; df = 81; p = .006) in GSAD (0.96 ± .25 mmol) than controls (1.12 ± .22 mmol) independently of a history of major depressive episodes. Discussion: The study provides the first evidence for reduced mPFC GABA concentrations in GSAD. The results show that such changes are not specific to major depressive disorder. The data suggest a possible rationale for the efficacy of GABAergic treatments, but it remains to be investigated whether such measures may predict treament response.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.026
GPT teacher head0.343
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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