From resting-state functional hippocampal centrality to functional outcome: An extended neurocognitive model of psychosis
Bibliographic record
Abstract
BACKGROUND: We previously proposed a neurocognitive model of psychosis in which reduced morphometric hippocampal-cortical connectivity precedes impaired episodic memory, social cognition, negative symptoms, and functional outcome. We provided support for this model in a patient subtype, and aimed to extend these findings to resting-state functional MRI to potentially explain the progression for a broader range of patients. METHODS: We used a subsample of our previous analysis consisting of 54 patients with first-episode psychosis and 52 controls and applied the machine-learning algorithm Subtype and Stage Inference, which combines clustering and disease progression modeling, to the patient data for rs-functional hippocampal connectivity, episodic memory, social cognition, negative symptoms and functioning. RESULTS: We identified three subtypes, with Subtype 0 being unimpaired on the markers, Subtype 1 showing impaired hippocampal connectivity and episodic memory, and Subtype 2 showing impaired memory and a trend for impaired functioning. We identified similar progression patterns to our previously published morphometric results in functional MRI data (hippocampal dysconnectivity preceded cognition, symptoms, and functioning in one subtype and followed these alterations in another subtype). We further show that the impairments in our previously published and current findings across modalities do not necessarily overlap in patients, hinting towards an additive effect of morphometric and resting-state connectivity in explaining the neurocognitive underpinnings of this model. CONCLUSION: Our results provide an extension of our previous work and build the foundation for a multimodal neurocognitive model of psychosis, potentially elucidating this aspect of illness progression in psychosis for a broader range of patients.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".