Early and late contingent negative variation (CNV) reflect different aspects of deficits in schizophrenia
Bibliographic record
Abstract
Abnormal reward processing and psychomotor slowing are well-known in schizophrenia (SZ). As a slow frontocentral potential, contingent negative variation (CNV) is associated with anticipatory attention, motivation and motor planning. The present study aims to evaluate the early and late amplitude and latencies of CNV in patients with SZ compared to healthy controls during a reward processing task and to show its association with clinical symptoms. We recruited 21 patients with SZ and 22 healthy controls to compare early and late CNV amplitude and latency values during a Monetary Incentive Delay (MID) Task between groups. Patients' symptom severity, levels of negative symptoms and depressive symptoms were assessed. Clinical features of the patients were further examined for their relation with CNV components. In conclusion, we found decreased early CNV amplitudes in SZ during the reward condition. They also displayed diminished and shortened late CNV responses for incentive cues, specifically at the central location. Furthermore, early CNV amplitudes exhibited a significant correlation with positive symptoms. Both CNV latencies were linked with medication dosage and the behavioural outcomes of the MID task. We revealed that early and late CNV exhibit different functions in neurophysiology and correspond to various facets of the deficits observed in patients. Our findings also emphasized that slow cortical potentials are indicative of deficient motivational processes as well as impaired reaction preparation in SZ. To gain a deeper understanding of the cognitive and motor impairments associated with psychosis, future studies must compare the effects of CNV in the early and late phases.
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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 | 0.001 |
| 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 | 0.000 |
| 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".