Event-Related Potentials in the Clinical High-Risk (CHR) State for Psychosis: A Systematic Review
Bibliographic record
Abstract
There is emerging evidence that identification and treatment of individuals in the prodromal or clinical high-risk (CHR) state for psychosis can reduce the probability that they will develop a psychotic disorder. Event-related brain potentials (ERPs) are a noninvasive neurophysiological technique that holds promise for improving our understanding of neurocognitive processes underlying the CHR state. We aimed to systematically review the current literature on cognitive ERP studies of the CHR population, in order to summarize and synthesize the results, and their implications for our understanding of the CHR state. Across studies, amplitudes of the auditory P300 and duration mismatch negativity (MMN) ERPs appear reliably reduced in CHR individuals, suggesting that underlying impairments in detecting changes in auditory stimuli are a sensitive early marker of the psychotic disease process. There are more limited data indicating that an earlier-latency auditory ERP response, the N100, is also reduced in amplitude, and in the degree to which it is modulated by stimulus characteristics, in the CHR population. There is also evidence that a number of auditory ERP measures (including P300, MMN and N100 amplitudes, and N100 gating in response to repeated stimuli) can further refine our ability to detect which CHR individuals are most at risk for developing psychosis. Thus, further research is warranted to optimize the predictive power of algorithms incorporating these measures, which could help efforts to target psychosis prevention interventions toward those most in need.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".