Imaging Neuroinflammation in Gray and White Matter in Schizophrenia: An In-Vivo PET Study With [18F]-FEPPA
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Bibliographic record
Abstract
Neuroinflammation and abnormal immune responses have been implicated in schizophrenia (SCZ). Past studies using positron emission tomography (PET) that examined neuroinflammation in patients with SCZ in vivo using the translocator protein 18kDa (TSPO) target were limited by the insensitivity of the first-generation imaging agent [(11)C]-PK11195, scanners used, and the small sample sizes studied. Present study uses a novel second-generation TSPO PET radioligand N-acetyl-N-(2-[(18)F]fluoroethoxybenzyl)-2-phenoxy-5-pyridinamine ([(18)F]-FEPPA) to evaluate whether there is increased neuroinflammation in patients with SCZ. A cross-sectional study was performed using [(18)F]-FEPPA and a high-resolution research tomograph (HRRT). Eighteen patients with SCZ with ongoing psychotic symptoms and 27 healthy volunteers (HV) were recruited from a tertiary psychiatric clinical setting and the community, respectively. All participants underwent [(18)F]-FEPPA PET and magnetic resonance imaging, and PET data were analyzed to obtain [(18)F]-FEPPA total volume of distribution (VT) using a 2-tissue compartment model with an arterial plasma input function, as previously validated. All subjects were classified as high-, medium- or low-affinity [(18)F]-FEPPA binders on the basis of rs6971 polymorphism, and genotype information was incorporated into the analyses of imaging outcomes. No significant differences in neuroinflammation indexed as [(18)F]-FEPPA VT were observed between groups in either gray (F(1,39) = 0.179, P = .674) or white matter regions (F(1,38) = 0.597, P = .445). The lack of significant difference in neuroinflammation in treated patients with SCZ in the midst of a psychotic episode and HV suggests that neuroinflammatory processes may take place early in disease progression or are affected by antipsychotic treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it