Longitudinal White and Gray Matter Response to Precision Medicine-Guided Intervention for Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a debilitating condition that is widely known to adversely affect gray matter (GM) and white matter (WM) tracts within the brain. Recently, precision medicine has shown promise in alleviating the clinical and gross morphological trajectories of patients with AD. However, regional morphological changes have not yet been adequately characterized. OBJECTIVE: Investigate regional morphological responses to a precision medicine-guided intervention with regards to white and gray matter in AD and mild cognitive impairment (MCI). METHODS: Clinical and neuroimaging data were compiled over a 9-month period from 25 individuals who were diagnosed with AD or MCI receiving individualized treatment plans. Structural T1-weighted MRI scans underwent segmentation and volumetric quantifications via Neuroreader. Longitudinal changes were calculated via annualized percent change of WM or GM ratios. RESULTS: Montreal Cognitive Assessment scores (p < 0.001) and various domains of the Computerized Neurocognitive Screening Vital Signs significantly improved from baseline to 9-month follow-up. There was regional variability in WM and GM atrophy or hypertrophy, but none of these observed changes were statistically significant after correction for multiple comparisons.
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 | 0.001 | 0.002 |
| 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".