White matter alterations related to P300 abnormalities in individuals at high risk for psychosis: an MRI–EEG study
Bibliographic record
Abstract
BACKGROUND: Psychosis onset is characterized by white matter and electrophysiologic abnormalities. The relation between these factors in the development of illness is almost unknown. We studied the relation between white matter volumes and P300 in prodromal psychosis. METHODS: We assessed white matter volume (detected using magnetic resonance imaging) and electrophysiologic response during an oddball task (P300) in healthy controls and individuals at high clinical risk for psychosis (with an "at-risk mental state" [ARMS]). RESULTS: We included 41 controls and 39 patients with an ARMS in our study. A psychotic disorder developed in 26% of the ARMS group within the follow-up period of 2 years. The P300 amplitude was significantly lower in the ARMS group than in the control group. The ARMS group showed reduced volume of white matter underlying the left superior temporal gyrus and the left superior frontal gyrus and increased volume of white matter underlying the right insula and the right angular gyrus compared with controls. Relative to individuals who did not later become psychotic, the subgroup in whom psychosis subsequently developed had a smaller volume of white matter underlying the left precuneus and the right middle temporal gyrus and increased volume in the white matter underlying the right middle frontal gyrus. We observed a significant interaction in the right middle frontal gyrus: white matter volume was negatively associated with P300 amplitude in the ARMS group and positively associated with P300 amplitude in the control group. LIMITATIONS: The voxel-based morphometry method alone cannot determine whether abnormal white matter volumes are due to an altered number of axonal connections or decreased myelination. CONCLUSION: P300 abnormalities precede the onset of psychosis and are directly related to white matter alterations, representing a correlate of an increased vulnerability to disease.
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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".