MRI-based brain atrophy and lesion index assessment of whole-brain structural changes during aging
Bibliographic record
Abstract
Objective To assess the value of brain atrophy and lesion index(BALI), based on magnetic resonance imaging(MRI), in the evaluation of brain aging. Methods 169 healthy older adults were divided into five age groups (40-49, 50-59, 60-69, 70-79, and 80-89 years). MRI scans were evaluated by the 3.0T GE signa and the BALI rating schemes, based on the T1 weighted(T1WI), T2 weighted(T2WI), and T2 weighted fluid attenuated inversion recovery(T2-FLAIR), and T2*weighted gradient-recalled echo(T2*GRE)images were recorded. Results Based on T1WI, T2WI, T2-FLAIR and T2*GRE, total scores of BALI increased with age, and showed significant differences between the five age groups(F=35.35, 42.87, 46.57, and 54.15, respectively; all P=0.000). In addition, BALI scores from each sequence were correlated with age(T1WI: r=0.71; T2WI: r=0.73; T2-FLAIR: r=0.73; T2*GRE: r=0.77; all P<0.01). Furthermore, T2*GRE was most sensitive to microbleeds and T2-FLAIR revealed a greater level of deep white matter(χ2=53.47, P=0.000)and periventricular lesions(χ2=29.93, P=0.000)than other sequences. Conclusions The T1WI, T2WI, T2-FLAIR and T2*GRE, BALI scores can be used to assess whole brain structural changes with aging and provide semi-quantitative indicators for the assessment of brain health. Key words: Magnetic resonance imaging; Brain injuries
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".