Psychotic‐Like Experiences and White Matter Microstructure: A Fixel‐Based Analysis Approach With Robust Replication Across Two Cohorts
Bibliographic record
Abstract
Structural deficits in white matter fibre have been linked to psychosis. However, it remains unclear whether these aberrations are present in individuals that experience non-clinical psychotic-like experiences, predating illness onset. While previous research demonstrates that alterations in white matter in schizotypy are consistent with those in clinical psychosis, these studies often dichotomise healthy samples into high and low schizotypy, which may reduce statistical sensitivity. Previous research is also confounded by the investigation of diffusion MRI parameters that fail to account for complex crossing fibre populations. In this work, we treat psychotic-like experiences as a continuous variable, and applied Fixel-Based Analysis (FBA), a framework for investigating microstructural and morphological effects in brain white matter using diffusion-weighted imaging data. Across two independent cohorts of healthy participants with varied psychotic-like experiences including data from the IMAGEN consortium (Study 1 n = 41; Study 2 n = 1098), we hypothesized that greater psychotic-like experiences would be associated with FBA metrics sensitive to microstructural fibre density and/or cross-sectional morphological effects. Contrary to our hypothesis, we did not find significant correlations between psychotic-like experiences and FBA metrics across either dataset (FWE p < 0.05). Bayesian analysis of tract-aggregated data showed substantial evidence of no association (Bayes factor < 1/3) between psychotic-like experiences and fibre density, nor cross-sectional morphology, across several white matter tracts of interest, pre-defined from prior neuroimaging literature. These findings suggest that the relationship between non-clinical psychotic-like experiences and white matter microstructure may not be as robust as previously thought. This raises the possibility that white matter alterations across the psychosis spectrum echo clinical diagnostic thresholding, with observable effects in clinical but not sub-clinical presentations. Our findings show no association between whole-brain fibre-specific properties of white matter microstructure and sub-clinical psychotic-like experiences. Further, we show evidence for the lack of an association within tract-aggregated fibre-specific metrics. Future research should integrate longitudinal designs to explore whether fibre-specific white matter attributes provide clinically meaningful insight into the risk of psychosis onset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".