Altered brain dynamics of facial emotion processing in schizophrenia: a combined EEG/fMRI study
Bibliographic record
Abstract
Facial stimuli are relevant social cues for humans and essential signals for adequate social interaction. Impairments in face processing are well-documented in schizophrenia and linked to symptomatology, yet the underlying neural dynamics remain unclear. Here, we investigated the processing and underlying neural temporal dynamics of task-irrelevant emotional face stimuli using combined EEG/fMRI in 14 individuals with schizophrenia and 14 matched healthy controls. Specifically, fMRI-informed region-of-interests were subjected to EEG-Dynamic Causal Modeling (DCM) analyses. Among six fMRI-informed EEG-DCM models, alterations in effective connectivity emerged between the primary visual cortex (V1) and the left occipital fusiform gyrus (lOFG). Specifically, individuals with schizophrenia showed enhanced backward connectivity from the lOFG to V1 for stimuli preceded by fearful (but not happy or neutral) faces. Connectivity strength was strongly correlated with self-reported difficulties in comprehending, processing, or articulating emotions (as assessed by the Toronto Alexithymia Scale-20) in individuals with schizophrenia but not in healthy controls. Enhanced backward connectivity from the lOFG to V1 potentially indicates heightened attention towards fearful surroundings and a propensity to assign salience to these stimuli in individuals with schizophrenia. The link to TAS-20 scores indicates that this neural deficit has real-world implications for how individuals with schizophrenia perceive and relate to their emotions and the external world, potentially contributing to the social and cognitive difficulties observed in the disorder.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".