IC‐P‐122: Atrophy‐specific MRI brain template for Alzheimer's disease and mild cognitive impairment
Bibliographic record
Abstract
Rapid brain loss is characteristic for the patients with mild cognitive impairment (MCI) and Alzheimer disease (AD) [1]. Increase of the lateral ventricular volume is strongly correlated with the progression of the disease. High variability in the degree of atrophy for subjects with AD and MCI makes use of a single disease-specific template challenging. We propose a novel approach to generate a continuous four-dimensional template, where the 4 dimension is a surrogate measure of overall brain atrophy. We used MRI scans obtained from the ADNI database (www.loni.ucla.edu/ADNI). Automated methods to estimate intracranial capacity (ICC) and lateral ventricles volume (LVV) [2] was applied to all available datasets at base line. The ratio between LVV and ICC (RLVV) was used as a surrogate measure of overall brain atrophy with mean(standard deviation) value of 2.46(0.87)%. Subsets from all subjects (CN, MCI and AD) were selected with uniform distribution of RLVV from 1.0 to 6.0% , resulting in a total of 160 subjects. Our algorithm [3] was modified to perform simultaneous 1) creation of the template and 2) linear regression of image intensity and shape versus RLVV. The ratio between LVV and ICC yielded values of mean(sd) 2.13(0.72)% for NC, 2.45(0.84)% for MCI and 2.84(0.91)% for AD. The continuous, four dimensional anatomical template was created. For a given RLVV, an appropriate three dimensional anatomical template may be constructed, reflecting the average shape of the brain and the contrast between different tissue types for the given level of atrophy. Figure 1 shows images through 6 example values of increasing RLVV.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".