A Comparison of Facial Emotion Processing in Neurological and Psychiatric Conditions
Bibliographic record
Abstract
Patients suffering from various neurological and psychiatric disorders show different levels of facial emotion recognition (FER) impairment, sometimes from the early phases of the disease. Investigating the relative severity of deficits in FER across different clinical and high-risk populations has potential implications for the diagnosis and treatment of these diseases, and could also allow us to understand the neurobiological mechanisms of emotion perception itself. To investigate the role of the dopaminergic system and of the frontotemporal network in FER, we reanalyzed and compared data from four of our previous studies investigating FER performance in patients with frontotemporal dysfunctions and/or dopaminergic system abnormalities at different stages. The performance of patients was compared to the performance obtained by a specific group of matched healthy controls using Cohen's d effect size. We thus compared emotion and gender recognition in patients with frontotemporal dementia (FTD), amnestic mild cognitive impairment (aMCI), Alzheimer's disease (AD) at the mild dementia stage, major depressive disorder, Parkinson's disease treated by l-DOPA (PD-ON) or not (PD-OFF), remitted schizophrenia (SCZ-rem), first-episode schizophrenia treated by antipsychotic medication (SCZ-ON), and drug-naïve first-episode schizophrenia (SCZ-OFF), as well as in unaffected siblings of patients with schizophrenia (SIB). The analyses revealed a pattern of differential impairment of emotion (but not gender) recognition across pathological conditions. On the one hand, dopaminergic medication seems not to modify the moderate deficits observed in SCZ and PD groups (ON vs. OFF), suggesting that the deficit is independent from the dopaminergic system. On the other hand, the observed increase in effect size of the deficit among the aMCI, AD, and FTD groups (and also among the SIB and SCZ-rem groups) suggests that the deficit is dependent on neurodegeneration of the frontotemporal neural networks. Our transnosographic approach combining clinical and high-risk populations with the impact of medication provides new information on the trajectory of impaired emotion perception in neuropsychiatric conditions, and on the role of the dopaminergic system and the frontotemporal network in emotion perception.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".