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Record W2148116126 · doi:10.1192/bjp.bp.106.034728

Cortical activation in alexithymia as a response to emotional stimuli

2008· article· en· W2148116126 on OpenAlexaboutno aff
Hasse Karlsson, Petri Näätänen, Hanna Stenman

Bibliographic record

VenueThe British Journal of Psychiatry · 2008
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
FundersSigne ja Ane Gyllenbergin Säätiö
KeywordsAlexithymiaToronto Alexithymia ScalePsychologySadnessInsulaAnterior cingulate cortexFeelingAudiologyClinical psychologyNeuroscienceMedicineAngerCognition

Abstract

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BACKGROUND: Alexithymia has been shown to be related to many psychiatric and somatic illnesses. Aberrant emotion processing in the brain may underlie several psychiatric disorders. However, little is known about the neurobiological underpinnings of alexithymia. AIMS: To determine the way in which the brain processes emotion in alexithymia. METHOD: The participants were 10 healthy women with alexithymia and 11 healthy women without this condition, recruited into the study on the basis of their scores on the 20-item Toronto Alexithymia Scale. Four films were projected on a video screen to induce each of three emotional conditions (neutral, amusement, sadness). The brain areas activated during emotional stimuli in the alexithymia group were compared with those activated in the non-alexithymia group. Scans of the distribution of [(15)O]H(2)O were acquired using a positron emission tomography (PET) scanner operated in three-dimensional mode. RESULTS: In response to emotional stimuli participants with alexithymia activated more parts of their sensory and motor cortices and insula, especially on the left side, and less of their anterior cingulate, compared with the control group. CONCLUSIONS: Women with alexithymia seem to over-activate their 'bodily' brain regions, implying a different mode of emotion processing. This may be related to their tendency to experience physical symptoms.

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 score0.188
Threshold uncertainty score0.276

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 head0.019
GPT teacher head0.293
Teacher spread0.274 · 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

Citations106
Published2008
Admission routes1
Has abstractyes

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