Functional connectivity and glutamate levels of the medial prefrontal cortex in schizotypy are related to sensory amplification in a probabilistic reasoning task
Bibliographic record
Abstract
The circular inference (CI) computational model assumes a corruption of sensory data by prior information and vice versa, leading at the extremes to 'see what we expect' (through prior amplification) and/or to 'expect what we see' (through sensory amplification). Although a CI mechanism has been reported in a schizophrenia population, it has not been investigated in individuals experiencing psychosis-like experiences, such as people with high schizotypy traits. Furthermore, the neurobiological basis of CI, such as the link between hierarchical amplifications, excitatory neurotransmission, and resting state functional connectivity (RSFC), remains untested. The participants included in the present study consisted of a subsample of those recruited in a study previously published by our group, Kozhuharova et al. (2021b). We included 36 participants with High (n=18) and Low (n=18) levels of schizotypy who completed a probabilistic reasoning task (the Fisher task) for which individual confidence levels were obtained and fitted to the CI model. Participants also underwent a 1H-Magnetic Resonance Spectroscopy (MRS) scan to measure medial prefrontal cortex (mPFC) glutamate metabolite levels, and a functional Magnetic Resonance Imaging (fMRI) scan to measure RSFC of the medial prefrontal cortex (mPFC). People with high levels of schizotypy exhibited changes in CI parameters, altered cortical excitatory neurotransmission and RSFC that were all associated with sensory amplification. Our findings capture a multimodal signature of CI that is observable in people early in the psychosis spectrum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".