Bibliographic record
Abstract
Introduction: The role of facial emotion recognition as a risk-marker for illness is better studied in schizophrenia than in Bipolar Disorder (BD).No fNIRS studies have looked at the same.The present study investigated the prefrontal cortical activation using fNIRS during emotion processing tasks, in an attempt to identify a risk marker for BD. Methods:In this cross sectional case control study, 15 adult righthanded patients with the ICD-10 diagnosis of BD/first episode mania in remission, 15 siblings of BD patients (HR) and 15 Healthy controls (HC) formed the three study groups.Subjects underwent fNIRS while performing facial emotional recognition (ER) task.Results: HR group had the most activation in fNIRS during emotion processing of disgust, fear and anger.There were no significant between-group differences on the performance of ER task.There was no correlation between the fNIRS patterns and the ER task performance in the HR and BD groups (all P > 0.17).Conclusions: The prefrontal activation seems to indicate an increased risk for BD.Lack of similar findings in the BD group might be because of normalizing effects of medications.It is likely that the fNIRS is picking up differences in prefrontal functions during emotion recognition which are not being manifest in the task performance early in the course of the disease.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.695 | 0.412 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".