Imaging Microglial Activation in Untreated First-Episode Psychosis: A PET Study With [<sup>18</sup>F]FEPPA
Bibliographic record
Abstract
Objective: Neuroinflammation and abnormal immune responses are increasingly implicated in the pathophysiology of schizophrenia. Previous positron emission tomography (PET) studies targeting the translocator protein 18 kDa (TSPO) have been limited by high nonspecific binding of the first-generation radioligand, low-resolution scanners, small sample sizes, and psychotic patients being on antipsychotics or not being in the first episode of their illness. The present study uses the novel second-generation TSPO PET radioligand [18F]FEPPA to evaluate whether microglial activation is elevated in the dorsolateral prefrontal cortex and hippocampus of untreated patients with first-episode psychosis. Method: Nineteen untreated patients with first-episode psychosis (14 of them antipsychotic naive) and 20 healthy volunteers underwent a high-resolution [18F]FEPPA PET scan and MRI. Dynamic PET data were analyzed using the validated two-tissue compartment model with arterial plasma input function with total volume of distribution (VT) as outcome measure. All analyses were corrected for TSPO rs6971 polymorphism (which is implicated in differential binding affinity). Results: No significant differences were observed between patients and healthy volunteers in microglial activation, as indexed by [18F]FEPPA VT, in either the dorsolateral prefrontal cortex or the hippocampus. There were no significant correlations between [18F]FEPPA VT and duration of illness, clinical presentation, or neuropsychological measures after adjusting for multiple testing. Conclusions: The lack of significant differences in [18F]FEPPA VT between groups suggests that microglial activation is not present in first-episode psychosis.
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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".