Neurocomputational modelling for characterizing neurocognitive pathophysiology in clinical high risk for psychosis
Bibliographic record
Abstract
Abstract Background The N400 semantic-priming event-related potential (ERP) is attenuated in schizophrenia and in youth at clinical high risk for psychosis (CHRP); however, the circuit mechanisms linking this abnormality to functional outcome remain unclear. Methods We recorded 32-channel EEG while 46 CHRP outpatients and 38 demographically matched healthy controls (HC) performed a word-pair priming task (80 related, 80 unrelated pairs; prime-to-target stimulus-onset asynchronies [SOA]=300ms or 750ms). Twenty-six CHRP participants were reassessed after one year. N400 difference waves (unrelated–related) were fit with a connectome-constrained Jansen–Rit neural-mass model in 200 cortical parcels. Local gains, synaptic time constants and effective connectivity parameters were optimised with the WhoBPyt framework, and principal-component trajectories of the inferred excitatory–inhibitory | E − I | balance were analysed with partial least squares. Results Under the long-SOA (750ms)/unrelated condition, CHRP showed a sharply elevated early | E − I | peak at 70–100ms relative to HC (p=0.0004), driven by greater pyramidal excitatory gain (parameter A), stronger excitatory-to-pyramidal coupling and faster inhibitory decay, indicating cortical disinhibition. The amplitude of this early peak predicted poorer social functioning at one-year follow-up (r=–0.56,p=0.003). Conversely, in the short-SOA (300ms)/related condition CHRP exhibited a larger N400-window | E − I | peak (350ms) associated with enhanced inhibitory-to-pyramidal feedback (parameter C3) and lengthened inhibitory decay (parameter b); this putative compensatory inhibition correlated with better functional outcome (r=0.60,p=0.001). Network-level analyses revealed an amplified early sensory-network burst and attenuated default-mode and salience-network responses, consistent with a systems-wide shift toward disinhibition in CHRP. Conclusions Computational modeling demonstrates that N400 abnormalities in CHRP arise from temporally specific E–I imbalances: early cortical disinhibition that forecasts functional decline and a later inhibitory reinforcement that may confer resilience. These time-resolved E–I metrics constitute low-burden, mechanistically interpretable biomarkers for stratifying psychosis risk and guiding early intervention.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".