Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated with Psychopathology Across Independent Cohorts
Bibliographic record
Abstract
Background: Early Psychosis patients (EP, within 3 years after psychosis onset) show significant variability, making outcome predictions challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, limiting the development of early interventions. Methods: A data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders. Results: In both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use. Conclusions: This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".