MétaCan
Menu
Back to cohort
Record W2114751061 · doi:10.3389/fpsyg.2015.01480

Frontal EEG alpha activity and obsessive-compulsive behaviors in non-clinical young adults: a pilot study

2015· article· en· W2114751061 on OpenAlexafffund
Michael Wong, Erik Z. Woody, Louis A. Schmidt, Michael Van Ameringen, Noam Soreni, Henry Szechtman

Bibliographic record

VenueFrontiers in Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsUniversity of WaterlooMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHypoactivityPsychologyElectroencephalographyAlpha (finance)ShynessFrontal lobeAudiologyObsessive compulsiveBrain activity and meditationClinical psychologyAnxietyPsychometricsPsychiatryNeuroscienceInternal consistencyMedicine

Abstract

fetched live from OpenAlex

Previous studies have shown that the resting electroencephalogram (EEG) alpha patterns of non-clinical participants who score high on measures of negative affect, such as depression and shyness, are different from those who score low. However, we know relatively little about patterns of resting EEG alpha patterns in a non-clinical sample of individuals with high levels of obsessive-compulsive behaviors indicative of obsessive-compulsive disorder (OCD). Here we measured resting EEG alpha activity in frontal and parietal regions of non-clinical participants who scored high and low on the Padua-R, a measure of the severity of OCD-related behaviors. We found that participants who scored high on the Padua-R exhibited decreased overall activity in frontal regions relative to individuals who scored low on the measure. We speculate that frontal hypoactivity may be a possible marker and/or index of risk for OCD.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.031
GPT teacher head0.368
Teacher spread0.337 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in PsychologySame topicObsessive-Compulsive Spectrum DisordersFrench-language works237,207