Evaluation of Whole Brain Health in Aging and Alzheimer's Disease: A Standard Procedure for Scoring an MRI-Based Brain Atrophy and Lesion Index
Bibliographic record
Abstract
BACKGROUND: The Brain Atrophy and Lesion Index (BALI), a semi-quantitative rating scale, has been developed to evaluate whole brain structural changes in aging and Alzheimer's disease (AD). OBJECTIVE: This study describes a standard procedure to score the BALI and train new raters for reliable BALI evaluation following this procedure. METHODS: Structural MRI of subjects in the Alzheimer's Disease Neuroimaging Initiative dataset who had 3.0T, T1, and T2 weighted MRI scans at baseline and at 6, 12, and 24 month follow-ups were retrieved (n = 122, including 24 AD, 51 mild cognitive impairment patients, and 47 healthy control subjects). Images were evaluated by four raters following training with a step-by-step BALI process. Seven domains of structural brain changes were evaluated, and a total score was calculated as the sum of the sub-scores. RESULTS: New raters achieved >90% accuracy after two weeks of training. Reliability was shown in both intra-rater correlation coefficients (ICC ≥ 0.92, p < 0.001) and inter-rater correlation coefficients (ICC ≥0.88, p < 0.001). Mean BALI total scores differed by diagnosis (F ≥ 2.69, p ≤ 0.049) and increased consistently over two years. CONCLUSION: The BALI can be introduced using a standard procedure that allows new users to achieve highly reliable evaluation of structural brain changes. This can advance its potential as a robust method for assessing global brain health in aging, AD, and mild cognitive impairment.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".