Distinct neural alterations in schizophrenia and autism: A meta-analysis of social cognition and emotion processing
Bibliographic record
Abstract
Abstract Objective Two conditions alter socio-communicative behaviors in humans: autism and schizophrenia. However, it is not well-known if these disorders share the same neural alterations during socio-emotional tasks. The main objective was to examine neural alterations in autism and schizophrenia during emotional and social cognition tasks. Our second objective was to determine if these alterations were common or distinct between disorders. Methods Functional neuroimaging studies using an emotional or a social cognition paradigm in schizophrenia or autism were queried from three databases. We selected articles if they reported whole brain coordinates of different activations between autism/schizophrenia participants and non-clinical controls. Using SDM, we analyzed the coordinates of brain activity differences between case and control groups, categorized by diagnosis and paradigm. Results The meta-analysis aggregated 104 studies in schizophrenia and 80 studies in autism spectrum disorder. During emotional tasks, individuals with autism showed reduced activity in the left amygdala, while those with schizophrenia showed reduced activity in the right inferior frontal gyrus and the median cingulate gyrus. During social cognition tasks, alterations in both conditions did not survive corrected statistical thresholds. No spatial conjunction was observed between the alterations seen in each disorder during both emotional/social cognition tasks at both corrected and uncorrected thresholds. Conclusions These results suggest that the emotional processing in autism and schizophrenia in adulthood are characterized by alterations of bottom-up and top-down mechanisms of the emotional network, respectively. It should encourage the pursuit of functional neuroimaging studies on emotion processing using machine learning to differentiate these two conditions.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.016 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".