Temporal imprecision and its dynamics in schizophrenia
Bibliographic record
Abstract
Schizophrenia is a complex mental disorder whose pathophysiological mechanisms remain yet unclear. Various lines of evidence converge on a temporal disorder with temporal imprecision occurring in the millisecond range of the ongoing phase cycles. However, the intertrial phase coherence (ITPC) often used to index such temporal imprecision in EEG, is by itself not able to capture temporal irregularities in the range of around 10 milliseconds. This is due to its static calculation with the averaging over trials. To obtain a more dynamic measures in the millisecond range, we introduce 1. The precision index (PI) as temporally more precise measure, and 2. a novel more dynamic method to calculate the ITPC in temporally resolved way, i.e., dITPC. We show that schizophrenia subjects show decreased PI during deviant tones in an auditory oddball task which shows strong but not one to one correlation with the ITPC. Moreover, we demonstrate that schizophrenia subjects showed higher latencies and frequencies over the course of time in the dITPC. Finally, employing multiple regression models, we show that the latency of the dITPC, as calculated dynamically across both standard and deviant tones, predicts the PI deficits in the deviant tones. Together, our findings demonstrate temporal alterations in the phase dynamics of schizophrenia with temporal irregularities in the dynamic background predicting temporal imprecision in the lower millisecond range in the more cognitive foreground.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".