Differential Regional Distribution of Juxtacortical White Matter Signal Abnormalities in Aging and Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: White matter signal abnormalities (WMSA) (also known as 'hyperintensities') on MRI are commonly seen in normal aging and increases have been noted in Alzheimer's disease (AD), but whether there is a spatial specificity to these increases is unknown. OBJECTIVE: To discern whether or not there is a spatial pattern of WMSA in the brains of individuals with AD that differs from those who exhibit cognitively healthy aging. METHOD: Structural MRI data from the Alzheimer's Disease Neuroimaging Initiative public database were used to quantify WMSA in 35 regions of interest (ROIs). Regional measures were compared between cognitively healthy older controls (OC; n = 107) and individuals with a clinical diagnosis of AD (n = 127). Regional WMSA volume was also assessed in individuals with mild cognitive impairment (MCI; n = 74) who were 6, 12, and 24 months away from AD conversion. RESULTS: WMSA volume was significantly greater in AD compared to OC in 24 out of 35 ROIs after controlling for age, and nine were significantly higher after normalizing for total WMSA. Regions with greater WMSA volume in AD included rostral frontal, inferior temporal, and inferior parietal WM. In MCI, frontal and temporal regions demonstrated significantly greater WMSA volume with decreasing time-to-AD-conversion. DISCUSSION: Individuals with AD have greater regional volume of WMSA compared to OC regardless of age or total WMSA volume. Accumulation of regional WMSA is linked to time to AD conversion in individuals with MCI. These findings indicate WMSA is an important pathological component of AD development.
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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 | 0.001 | 0.001 |
| 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".