Glutamine + glutamate level predicts the magnitude of microstructural organization in the gray matter in the healthy elderly
Bibliographic record
Abstract
BACKGROUND: Diffusion tensor imaging (DTI), which is a technique for measuring the degree and direction of movement of water molecules in tissue, has been widely used to noninvasively assess white matter (WM) or gray matter (GM) microstructures in vivo. Mean diffusivity (MD), which is the average diffusion across all directions, has been considered as a marker of WM tract degeneration or extracellular space enlargement in GM. Recent lines of evidence suggest that cortical MD can better identify early-stage Alzheimer's disease than structural morphometric parameters in magnetic resonance imaging. However, knowledge of the relationships between cortical MD and other biological factors in the same cortical region, e.g. metabolites, is still limited. METHODS: Thirty-three healthy elderly individuals [aged 50-77 years (mean, 63.8±7.4 years); 11 males and 22 females] were enrolled. We estimated the associations between cortical MD and neurotransmitter levels. Specifically, we measured levels of γ-aminobutyric acid (GABA) and glutamate + glutamine (Glx), which are inhibitory and excitatory neurotransmitters, respectively, in medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) using MEGA-PRESS magnetic resonance spectroscopy, and we measured regional cortical MD using DTI. RESULTS: Cortical MD was significantly negatively associated with Glx levels in both mPFC and PCC. No significant association was observed between cortical MD and GABA levels in either GM region. CONCLUSION: Our findings suggest that degeneration of microstructural organization in GM, as determined on the basis of cortical MD measured by DTI, is accompanied by the decline of Glx metabolism within the same GM region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".