Longer Repetition Time Proton MR Spectroscopy Shows Increasing Hippocampal and Parahippocampal Metabolite Concentrations with Aging
Bibliographic record
Abstract
ABSTRACT BACKGROUND AND PURPOSE Previous magnetic resonance spectroscopy (MRS) studies have concluded that hippocampal and parahippocampal metabolite concentrations remain stable during healthy adult aging. However, these studies used short repetition times (TR ≤ 2 seconds), which lead to incomplete longitudinal magnetization recovery, and thus, heavily T 1 ‐weighted measurements. It is important to accurately characterize brain metabolites changes with age to enable appropriate interpretations of MRS findings in the context of neurodegenerative diseases. Our goal was to assess hippocampal brain metabolite concentrations in a large cohort of diversely aged healthy volunteers using a longer TR of 4 seconds. METHODS Left hippocampal MR spectra were collected from 38 healthy volunteers at 3T. Absolute metabolite concentrations were determined for total N‐acetyl‐aspartate (tNAA), total creatine (tCr), total choline (tCho), glutamate and glutamine (Glx), and myoinositol (mI). Individual partial correlations between each metabolite with age were assessed using demographic information and voxel compartmentation as confounders. RESULTS Hippocampal tNAA, tCr, tCho, and mI all increased with age (NAA: R 2 = .17, P = .041; tCr: R 2 = .45, P = .0002; tCho: R 2 = .37, P = .001; mI: R 2 = .44, P = .0003). There were no relationships between age and signal to noise ratio, linewidth, or scan date, indicating the correlations were not confounded by spectral quality. Furthermore, we did not observe a trend with age in the voxel tissue compartmentations. CONCLUSIONS We observed increases in hippocampal/parahippocampal metabolite concentrations with age, a finding that is in contrast to previous literature. Our findings illustrate the importance of using a sufficiently long TR in MRS to avoid T 1 ‐relaxation effects influencing the measurement of absolute metabolite concentrations.
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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) | 0.002 | 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".