An MRI Brain Atrophy and Lesion Index to Assess the Progression of Structural Changes in Alzheimer's Disease, Mild Cognitive Impairment, and Normal Aging: A Follow-Up Study
Bibliographic record
Abstract
BACKGROUND: A brain atrophy and lesion index (BALI) based on high-field magnetic resonance imaging (MRI) has recently been validated to evaluate structural changes in the aging brain. The present study investigated the two-year progression of brain structural deficits in people with Alzheimer's disease (AD) and mild cognitive impairment (MCI), and in healthy control older adults (HC) using the BALI rating. METHODS: T1-weighted high-resolution anatomical imaging data using 3 Tesla MRI at baseline (AD = 39, MCI = 82, HC = 58) and at 24-months were obtained from the Alzheimer's disease Neuroimaging Initiative database. Lesions in various brain structures, including the infratentorial and basal ganglia areas, and the periventricular and deep white matter and global atrophy, were evaluated and combined into the BALI scale. RESULTS: Mean progression of brain deficits over two years was evident in all diagnostic groups (p < 0.001) and was statistically greater in MCI-AD converters than in the non-converters (p = 0.044). An increase in the BALI score was significantly associated with cognitive test scores (p = 0.008 for the Mini-Mental State Examination MMSE and p = 0.013 for the Alzheimer's Disease Assessment Scale-Cognitive Subscale ADAS-cog) in a model that adjusted for age, sex, and education. CONCLUSION: The BALI rating quantified the progression of brain deficits over two years, which is associated with cognitive decline. BALI ratings may be used to help summarize AD-associated structural variations.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".