Magnetic resonance spectroscopy shows associations between neurometabolite levels and perivascular space volume in Parkinson’s disease: a pilot and feasibility study
Bibliographic record
Abstract
OBJECTIVE: Higher volume fraction of perivascular space (PVS) has recently been reported in Parkinson's disease (PD) and related disorders. Both elevated PVS and altered levels of neurometabolites, assayed by proton magnetic resonance spectroscopy (MRS), are suspected indicators of neuroinflammation, but no published reports have concurrently examined PVS and MRS neurometabolites. METHODS: In an exploratory pilot study, we acquired multivoxel 3-T MRS using a semi-Localization by Adiabatic SElective Refocusing (sLASER) pulse-sequence (repetition time/echo time = 2810/60 ms, voxels 10 × 10 × 10 mm3) from a 2D slab sampling bilateral frontal white matter (FWM) and anterior middle cingulate cortex (aMCC). PVS maps obtained from high-resolution (0.8 × 0.8 × 0.8 mm3) T1-weighted MRI were co-registered with MRS. In each MRS voxel, PVS volume and neurometabolite levels were measured. RESULTS: Linear regression accounting for age, sex, and BMI found greater PVS volume for higher levels of choline-containing compounds (Cho; P = 0.047) in FWM and lower PVS volume for higher levels of N-acetyl compounds (NAA; P = 0.012) in aMCC. Since (putatively) higher Cho is associated with inflammation while NAA has anti-inflammatory properties, these observations add to evidence that higher PVS load is a sign of inflammation. Additionally, lower Montreal Cognitive Assessment scores were associated with lower NAA in aMCC (P = 0.002), suggesting that local neuronal dysfunction and inflammation contribute to cognitive impairment in PD. CONCLUSION: These exploratory findings indicate that co-analysis of PVS and MRS is feasible and may help elucidate the cellular and metabolic substrates of glymphatic and inflammatory processes in PD.
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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".