Combining Structural Brain Changes Improves the Prediction of Alzheimer’s Disease and Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Several structural brain changes have been associated with Alzheimer's disease (AD). This study investigated the prediction of AD by combining multiple brain changes with the hallmark medial temporal lobe atrophy (MTA). METHODS: High-resolution magnetic resonance imaging (MRI) data of people with mild AD (n = 39), mild cognitive impairment (MCI; n = 82), and of healthy controls (HC; n = 58) were obtained at baseline. Among these people, 26 AD, 53 MCI, and 46 HC subjects had 24-month follow-up MRI scans. Bilateral MTA was evaluated using a medial temporal lobe atrophy scale (MTAS). Common changes in the aging brain were summarized using a brain atrophy and lesion index (BALI). The performance of the MTAS, BALI, and a score combining both, using a logistic regression model, were assessed. RESULTS: The MTAS and BALI scores were closely correlated (r(2) > 0.56); each differed between the diagnostic groups. Having an unfavorable MTAS score was associated with an increased risk of MCI-AD conversion (OR = 3.71, p = 0.039), adjusted for age, sex, and education; having an unfavorable BALI score marginally contributed to such risks (OR = 4.08, p = 0.080). Combining MTAS and BALI components resulted in a greater OR (8.99, p = 0.007) and an improved predictive accuracy (75.9%, p = 0.002). CONCLUSION: Multiple structural changes have an additive effect on AD. The data support potential future roles of combining multiple coexisting structural changes to benefit AD diagnosis, progression monitoring, and/or treatment effect evaluation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".