MétaCan
Menu
Back to cohort
Record W2009951468 · doi:10.1159/000318086

Neural Substrates of Affective Face Recognition in Alexithymia: A Functional Magnetic Resonance Imaging Study

2010· article· en· W2009951468 on OpenAlexaboutno aff
Byeong-Taek Lee, Hwa-Young Lee, Sae-Ah Park, Jin-Young Lim, Woo‐Suk Tae, Min-Soo Lee, Sook-Haeng Joe, In‐Kwa Jung, Byung‐Joo Ham

Bibliographic record

VenueNeuropsychobiology · 2010
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaFunctional magnetic resonance imagingMagnetic resonance imagingPsychologyFunctional imagingNeuroscienceNeuroimagingMedicinePsychiatryRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Alexithymia is a condition characterized by deficits in cognitive processing and the regulation of emotions. Several theories have been proposed for the underlying neurobiology, but the etiology of alexithymia remains unclear. METHODS: Using functional magnetic resonance imaging, we investigated brain activation measured on the scale of alexithymia in 38 individuals who were presented with neutral, sad, or angry affective facial stimuli. RESULTS: We found significant inverse correlations between the degree of alexithymia represented by the Korean version of the Toronto Alexithymia Scale (TAS-20K) and the intensity of the neural response to angry facial stimuli over neutral facial stimuli in the right caudate. This result was mainly due to the activations in factor 2 (difficulty describing feelings) in TAS-20K scale. CONCLUSIONS: The results suggest that functional impairments in the caudate of the fronto-striatal circuitry may play important roles in the pathophysiology of alexithymia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.055
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.273
Teacher spread0.256 · 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 teacher head, 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

Citations26
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueNeuropsychobiologySame topicPsychosomatic Disorders and Their TreatmentsFrench-language works237,207