Personality traits in psychosis and psychosis risk linked to TSPO expression: a neuroimmune marker
Bibliographic record
Abstract
Abstract Personality has been correlated with differences in cytokine expression, an indicator of peripheral inflammation; however, the associations between personality and central markers of inflammation have never been investigated in vivo in humans. Microglia are the resident macrophages of the central nervous system, and the first responders to tissue damage and brain insult. Microglial activation is associated with elevated expression of translocator protein 18kDa (TSPO), which can be imaged with positron emission tomography (PET) to quantify immune activation in the human brain. This study aimed to investigate the association between personality and TSPO expression across the psychosis spectrum. A total of 61 high-resolution [18F]FEPPA PET scans were conducted in 28 individuals at clinical high risk (CHR) for psychosis, 19 First-Episode Psychosis (FEP), and 14 healthy volunteers (HVs), and analyzed using a two-tissue compartment model and plasma input function to obtain a total volume of distribution (VT) as an index of brain TSPO expression (controlling for the rs6971 TSPO polymorphism). Personality was assessed using the Revised NEO Personality Inventory (NEO-PI-R). We found TSPO expression to be specifically associated with neuroticism. A positive association between TSPO expression and neuroticism was found in HVs, in contrast to a nonsignificant, negative association in CHR and significant negative association in FEP. The TSPO-associated neuroticism trait indicates an unexplored connection between neuroimmune activation and personality that varies across the psychosis spectrum.
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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) | 0.002 | 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".