Quantitative imaging of neuroinflammation in human white matter: A positron emission tomography study with translocator protein 18 kDa radioligand, [<sup>18</sup>F]‐FEPPA
Bibliographic record
Abstract
The ability to quantify translocator protein 18 kDa (TSPO) in white matter (WM) is important to understand the role of neuroinflammation in neurological disorders with WM involvement. This article aims to extend the utility of TSPO imaging in WM using a second-generation radioligand, [18F]-FEPPA, and high-resolution research tomograph (HRRT) positron emission tomography (PET) camera system. Four WM regions of interests (WM-ROI), relevant to the study of aging and neuroinflammatory diseases, were examined. The corpus callosum, cingulum bundle, superior longitudinal fasciculus, and posterior limb of internal capsule were delineated automatically onto subject's T1 -weighted magnetic resonance image using a diffusion tensor imaging-based WM template. The TSPO polymorphism (rs6971) stratified individuals to three genetic groups: high-affinity binders (HAB), mixed-affinity binders (MAB), and low-affinity binders. [18F]-FEPPA PET scans were acquired on 32 healthy subjects and analyzed using a full kinetic compartment analysis. The two-tissue compartment model showed moderate identifiability (coefficient of variation 15-19%) for [18F]-FEPPA total volume distribution (VT ) in WM-ROIs. Noise affects VT variability, although its effect on bias was small (6%). In a worst-case scenario, ≤6% of simulated data did not fit reliably. A simulation of increased TSPO density exposed minimal effect on variability and identifiability of [18F]-FEPPA VT in WM-ROIs. We found no association between age and [18F]-FEPPA VT in WM-ROIs. The VT values were 15% higher in HAB than in MAB, although the difference was not statistically significant. This study provides evidence for the utility and limitations of [18F]-FEPPA PET to measure TSPO expression in WM.
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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 | 0.001 | 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) | 0.001 | 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".