Age-trajectories of higher-order diffusion properties of major brain metabolites in cerebral and cerebellar grey matter using in vivo diffusion-weighted MR spectroscopy at 3T
Bibliographic record
Abstract
Abstract Healthy brain aging involves changes in both brain structure and function, including alterations in cellular composition and microstructure across brain regions. Unlike diffusion-weighted MRI (dMRI), diffusion-weighted MR spectroscopy (dMRS) can assess cell-type specific microstructural changes, providing indirect information on both cell composition and microstructure through the quantification and interpretation of metabolites’ diffusion properties. This work investigates age-related changes in the higher-order diffusion properties of three major intracellular metabolites (N-Acetyl-aspartate, Creatine and Choline) beyond the classical apparent diffusion coefficient in cerebral and cerebellar grey matter of healthy human brain. Twenty-five subjects were recruited and scanned using a diffusion-weighted semi-LASER sequence in two brain regions-of-interest (ROI) at 3T: posterior-cingulate (PCC) and cerebellar cortices. Metabolites’ diffusion was characterized by quantifying metrics from both Gaussian and non-Gaussian signal representations and biophysical models. All studied metabolites exhibited lower apparent diffusivities and higher apparent kurtosis values in the cerebellum compared to the PCC, likely stemming from the higher microstructural complexity of cellular composition in the cerebellum. Multivariate regression analysis (accounting for ROI tissue composition as a covariate) showed slight decrease (or no change) of all metabolites’ diffusivities and slight increase of all metabolites’ kurtosis with age, none of which statistically significant (p>0.05). The proposed age-trajectories provide benchmarks for identifying anomalies in the diffusion properties of major brain metabolites which could be related to pathological mechanisms altering both the brain microstructure and cellular composition.
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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) | 0.001 | 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".