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Record W2585979806 · doi:10.1093/scan/nsx008

Trait anxiety mediates the link between inferior frontal cortex volume and negative affective bias in healthy adults

2017· article· en· W2585979806 on OpenAlexfundno aff
Yifan Hu, Sanda Dolcos

Bibliographic record

VenueSocial Cognitive and Affective Neuroscience · 2017
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignUniversity of AlbertaCanadian Psychiatric Research FoundationNational Alliance for Research on Schizophrenia and Depression
KeywordsPsychologyTrait anxietyAnxietyTraitPrefrontal cortexDevelopmental psychologyFunctional magnetic resonance imagingCognitionClinical psychologyAudiologyNeurosciencePsychiatryMedicine

Abstract

fetched live from OpenAlex

Anxious individuals tend to show a negative affective bias in attention that likely reflects reduced executive control, a cognitive function associated with the inferior frontal cortex (IFC), particularly its posterior segment, pars opercularis. Here, we investigated the relations among gray matter volume in the pars opercularis of IFC, trait anxiety, and negative biases in attention, in healthy participants. Sixty-two adults underwent structural magnetic resonance imaging scanning, completed a trait anxiety measure, and performed an Affective Go/No-Go (AGN) task. IFC volumes were extracted using Freesurfer, and negative bias scores were calculated from AGN performance. Trait anxiety correlated negatively with left IFC volume, and positively with the negative bias in reaction time. Furthermore, trait anxiety mediated the negative relation between the IFC volume and the negative bias measure. Overall, the present findings extend previous understanding of the IFC involvement in anxiety at the structural level, and may inform the development of intervention programs targeting anxiety.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.003
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.366
Teacher spread0.309 · 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; both teacher heads agree on what is shown here.

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

Citations43
Published2017
Admission routes1
Has abstractyes

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