Longitudinal Inference of Multiscale Markers in Psychosis: From Hippocampal Centrality to Functional Outcome
Bibliographic record
Abstract
Psychosis represents a heterogeneous collection of biological and behavioural alterations that evolve over time. We propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impaired episodic memory and social cognition, worsening negative symptoms and lowering functional outcome. In two cross-sectional datasets of first- and multi-episode psychosis (163 patients; 117 controls), we applied a recently developed machine-learning algorithm, SuStaIn, which uniquely integrates clustering and disease progression modeling. SuStaIn identified three patient subtypes, with Subtype 0 showing normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity. Subtype 1 deteriorated from (social) cognition to symptoms, functioning and hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from hippocampal-cortical dysconnectivity to (social) cognition, functioning and symptoms. This first application of SuStaIn in a multiscale model of psychiatry provides distinguishable disease trajectories of hippocampal-cortical connectivity, which might drive heterogeneous behavioural alterations in psychosis.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".