Transcriptional and neurochemical signatures of cerebral blood flow alterations in schizophrenia and the clinical high-risk state for psychosis
Bibliographic record
Abstract
ABSTRACT The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behaviour, and understanding the relationships between these layers may be fundamental to advancing our understanding of how the brain works in health and disease. Recent neuroimaging research has shown that alterations in brain function that are associated with schizophrenia spectrum disorders (SSD) are already present in young adults at clinical high-risk for psychosis (CHR-P), yet the cellular and molecular determinants of these alterations are not well understood. Here, combining regional cerebral blood flow (rCBF) data with existing transcriptomic and neurotransmitter data, we show that cell-types involved in stress response and inflammation, as well as the dopamine, acetylcholine, GABAA and NMDA receptor systems, align as shared and distinct cellular and neurochemical signatures of rCBF phenotypes in people with SSD and those at CHR-P. Decoding the biological pathways involved in neuroimaging-based psychosis phenotypes may provide a basis for the development of novel interventions.
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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 | 0.000 |
| 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) | 0.002 | 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".