EEG Microstates in Early Phase Psychosis: The Effects of Acute Caffeine Consumption
Bibliographic record
Abstract
Individuals with schizophrenia use on average twice as much caffeine than the healthy population, but the underlying cortical effects of caffeine in this population are still not well understood. Using resting electroencephalography (EEG) data, we can determine recurrent configurations of the electric field potential over the cortex. These configurations, referred to as microstates, are reported to be altered in schizophrenia and can give us insight into the functional dynamics of large-scale brain networks. In the current study, we use a placebo-controlled, randomized, double-blind, repeated-measures design to examine the effects of a moderate dose of caffeine (200mg) on microstate classes A, B, C, and D in a sample of individuals within the first five years of psychosis onset compared to healthy controls. The results support the reduction of microstate class C and D, as well as the increase of microstate class A and B in schizophrenia. Further, acute caffeine administration appears to exacerbate these group differences by reducing class D, and increasing occurrences of class A and B states in the patient group only. The current results support the hypothesis of a microstate class D reduction as an endophenotypic marker for psychosis and provide the first descriptive account of how caffeine is affecting these microstate classes in an early phase psychosis sample.
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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 | 0.000 | 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".