Diffusion anisotropy in subcortical white matter and cortical gray matter: Changes with aging and the role of CSF‐suppression
Bibliographic record
Abstract
PURPOSE: To determine the relevance of cerebrospinal fluid (CSF)-suppression for the measurement of diffusion anisotropy in well-localized areas of the brain, particularly the subcortical white matter (WM) within the gyri and cortical gray matter (GM), in young and elderly subjects, and to assess the changes of water diffusivity in the brain with normal aging. MATERIALS AND METHODS: Quantitative measures of anisotropy in 26 regions, including subcortical WM (i.e., in the gyri), cortical GM, major deep WM, and deep GM regions of young (21-25 years, N = 8) and elderly (61-74 years, N = 10) normal volunteers, were assessed with CSF-suppressed diffusion tensor imaging (DTI) relative to standard DTI. RESULTS: CSF-suppressed DTI demonstrated significant increases in fractional anisotropy (FA) of 3-12% in the young and 2-14% in the elderly groups with the largest changes being in the subcortical WM of the gyri. Furthermore, FA decreased by 10-19% in the subcortical WM of the gyri of the elderly subjects relative to the young, primarily due to increases in the perpendicular diffusivity, lambda(3), with age. CONCLUSION: CSF-suppressed DTI yields more accurate measures of quantitative anisotropy in cortical and subcortical brain regions. Reductions of anisotropy with aging were predominantly observed in subcortical WM of the gyri.
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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) | 0.000 | 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".