A systematic review of the neural correlates of multisensory integration in schizophrenia
Bibliographic record
Abstract
Multisensory integration (MSI), in which sensory signals from different modalities are unified, is necessary for our comprehensive perception of and effective adaptation to the objects and events around us. However, individuals with schizophrenia suffer from impairments in MSI, which could explain typical symptoms like hallucination and reality distortion. Because the neural correlates of aberrant MSI in schizophrenia help us understand the physiognomy of this psychiatric disorder, we performed a systematic review of the current research on this subject. The literature search concerned investigated MSI in diagnosed schizophrenia patients compared to healthy controls using brain imaging. Seventeen of 317 identified studies were finally included. To assess risk of bias, the Newcastle-Ottawa quality assessment was used, and the review was written according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). The results indicated that multisensory processes in schizophrenia are associated with aberrant, mainly reduced, neural activity in several brain regions, as measured by event-related potentials, oscillations, activity and connectivity. The conclusion is that a fronto-temporal region, comprising the frontal inferior gyrus, middle temporal gyrus and superior temporal gyrus/sulcus, along with the fusiform gyrus and dorsal visual stream in the occipital-parietal lobe are possible key regions of deficient MSI in schizophrenia.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".