IC‐P‐038: DIFFERENTIAL GREY AND WHITE MATTER MICROSTRUCTURAL ABNORMALITIES IN EARLY AND LATE‐ONSET ALZHEIMER'S DISEASE AND MILD COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Early-onset Alzheimer's disease (EOAD) has different pathological and clinical patterns and disease course from late-onset AD (LOAD). Diffusion tensor imaging (DTI) studies have demonstrated white-matter (WM) and grey-matter (GM) microstructural changes that could be markers for AD progression. We therefore sought to demonstrate distinct patterns of brain microstructural in patients with early- and late-onset AD and mild cognitive impairment (MCI). 44 young healthy-controls (YHC, ≤65 years-old), 41 older HC (>65 years-old), 31 early-onset MCI (EOMCI), 59 late-onset MCI (LOMCI), 21 EOAD and 72 LOAD underwent T1 and diffusion MRI imaging. The free-water (FW) method was applied to derive individual FW (GM and WM) and tissue compartment fractional anisotropy (FAt) maps from DTI data. To assess age, stage, and interactions effects, we carried out ANCOVA analyses on WM-FW, WM-FAt, GM-thickness, and GM-FW images. We further tested the associations of brain-measures with Montreal Cognitive Assessment (MoCA) scores. In early-onset patients, minimal WM and GM microstructural changes were seen at the MCI stage, followed by steep damage from the MCI to AD stage. In late-onset patients, progressive microstructural alterations were demonstrated along the AD continuum (Fig. 1B&3B). Across all groups, older subjects had greater WM-FW and lower WM-tissue FAt than the younger counterparts (Fig.1). For GM-thickness, there were no age-related differences at pre-dementia stages, although EOAD had lower thickness than LOAD in the parietal regions (precuneus), while LOAD had lower thickness than EOAD in the precentral gyrus and superior-temporal gyrus (Fig. 2). In contrast to GM-thickness, GM-FW increases appeared early at the pre-dementia stage (LOMCI>EOMCI). EOAD had greater GM-FW increases in the parietal/precuneus and middle-temporal regions than LOAD (Fig. 3). Lastly, parietal GM-FW increases contributed to global cognitive impairment in MCI while parietal and temporal WM abnormalities and GM thinning related to lower MoCA in dementia (Table 1).
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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) | 0.003 | 0.001 |
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".