GABA levels and TSPO expression in people at clinical high risk for psychosis and healthy volunteers: a PET-MRS study
Bibliographic record
Abstract
Background: γ-Aminobutyric acidergic (GABAergic) dysfunction and immune activation have been implicated in the pathophysiology of schizophrenia. Preclinical evidence suggests that inflammation-related abnormalities may contribute to GABAergic alterations in the brain, but this has never been investigated in vivo in humans. In this multimodal imaging study, we quantified cerebral GABA plus macromolecule (GABA+) levels in antipsychotic-naive people at clinical high risk for psychosis and in healthy volunteers. We investigated for the first time the association between GABA+ levels and expression of translocator protein 18 kDa (TSPO; a marker of microglial activation) using positron emission tomography (PET). Methods: Thirty-five people at clinical high risk for psychosis and 18 healthy volunteers underwent 3 T proton magnetic resonance spectroscopy to obtain GABA+ levels in the medial prefrontal cortex (mPFC). A subset (29 people at clinical high risk for psychosis and 15 healthy volunteers) also underwent a high-resolution [ 18 F]FEPPA PET scan to quantify TSPO expression. Each participant was genotyped for the TSPO rs6971 polymorphism. Results: We found that GABA+ levels were significantly associated with TSPO expression in the mPFC ( F 1,40 = 10.45, p = 0.002). We found no significant differences in GABA+ levels in the mPFC ( F 1,51 = 0.00, p > 0.99) between people at clinical high risk for psychosis and healthy volunteers. We found no significant correlations between GABA+ levels or residuals of the association with TSPO expression and the severity of prodromal symptoms or cognition. Limitations Given the cross-sectional nature of this study, we could determine no cause-and-effect relationships for GABA alterations and TSPO expression. Conclusion: Our findings suggest that TSPO expression is negatively associated with GABA+ levels in the prefrontal cortex, independent of disease status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".