Gray Matter Volume Associations with Montreal Cognitive Assessment Domains in an ADNI Cohort of Early-Onset Mild Cognitive Impairment and Alzheimer’s Disease
Bibliographic record
Abstract
Background/Objectives: T1-weighted magnetic resonance imaging (MRI) and the Montreal Cognitive Assessment are standard, efficient, and swift clinical and research tools used when interrogating cognitively impairing (CI) conditions, such as Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). However, the associations between gross cognitive impairment (CI) as compared to domain-specific functioning and underlying neuroanatomical correlates have not been investigated among individuals with early-onset Mild Cognitive Impairment (MCI) or Alzheimer’s disease (EOAD), who can benefit greatly from early diagnosis and intervention strategies. Methods: We analyzed T1-weighted MRIs and Montreal Cognitive Assessment (MoCA) scores from the ADNI database in individuals < 65 years old who were either cognitively normal (CN) or had MCI or EOAD. Gray matter volume (GMV) was estimated in voxel-based morphometry (VBM) and ROI-parcellation general linear models examining associations with individual MoCA scores after adjusting for demographic covariates. Results: Results from 120 subjects (44 CN, 62 MCI, and 14 EOAD), identified significant global but also individually distinct domain-specific topographical signatures spanning the temporal, parietal, limbic, occipital, frontal lobes, and cingulate gyri. Conclusions: The results highlight neural correlates of cognitive functioning in a sample of young patients representative of the AD continuum, in addition to studying the structural MRI and functional cognitive difference.
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 | 0.001 |
| 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 | 0.001 | 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".