The Neuroinflammatory Response To Exercise In Chronic Multisymptom Illness: A Feasibility Study
Bibliographic record
Abstract
Post-exertional malaise (PEM) is a general worsening of symptoms in response to physical or mental effort and is a characteristic of chronic multisymptom illnesses (CMI) including Gulf War Illness (GWI), Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), and Long COVID. Neuroinflammation has been proposed as a mechanism of PEM but has not been directly tested in humans. Here we test the feasibility of using positron emission tomography (PET) imaging of translocator protein (TSPO) occupancy at rest and following acute, submaximal exercise. PURPOSE: To evaluate the feasibility of measuring neuroinflammation pre- and 24-hours post-exercise in CMI. METHODS: Three individuals with Long COVID (two males, one female), and one female each with ME/CFS, GWI were imaged using the third-generation PET TSPO tracer, 11C-ER176. Scanning occurred pre- and 24 hours post-exercise (30 minutes of cycling at 70% of heart rate reserve). Images were spatially normalized into the Montreal Neurological Institute 152 template space and PET signal was converted into standardized uptake volume (SUV) based on the injected dose and bodyweight. Regional volumes were then normalized to the signal of the whole cerebellar pseudo-reference region, expressed as a ratio (SUVr). Data from the final 20 minutes of the scanning protocol were analyzed to ensure radiotracer equilibrium. Regional and global binding signals were extracted and analyzed using Hedges g effect sizes. RESULTS: All participants tolerated the imaging protocol, completed the prescribed exercise, and returned for the 24-hour post-exercise imaging timepoint. Significant global TSPO occupancy occurred both pre- and post-exercise (SUVr = 8.71, 8.59) and a small decrease in cerebellar SUV (g = -0.03) from pre- to post-exercise. When expressed relative to the cerebellum, there were small decreases for the thalamus, brainstem, hippocampus, and anterior cingulate cortex (SUVr g = -0.25, -0.09, -0.06, -0.32, respectively) following exercise. CONCLUSION: These preliminary findings suggest that PET imaging of acute exercise effects on neuroinflammation is feasible in CMI. Future work will examine voxelwise computation of distribution volume ratios, compare neuroinflammatory responses between CMI and healthy controls, and explore symptom relationships.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".