IC‐P‐138: Spatial distribution of white matter hyperintensities in elderly individuals
Bibliographic record
Abstract
White matter hyperintensities (WMHs) are lesions in the white matter tissue of the brain seen in normal aging as well as in patients with Alzheimer's disease (AD), and other dementias. Age is a strong predictor of WMH load (Au et al. 2006) and impacts cognition of otherwise healthy elderly people as well as patients with MCI and dementia (Yoshita et al. 2005). We wished to investigate the spatial distribution of WMHs in the brain. We used two data sets: (1) 120 normal control (NC) adults at risk of AD aged 55 years or older from PREVENT-AD, a longitudinal cohort study of healthy persons with a parental history of AD dementia which had T1-w, T2*, and FLAIR MRI; (2) 80 elderly individuals 70-90 years of age with normal cognition, mild cognitive impairment (MCI), or dementia, with T1-w, double-echo PD/T2-w, and FLAIR scans at enrollment at the University of California, Davis Alzheimer's Disease Center (ADC). The WMHs were segmented using our validated automatic linear regression WMH classifier (Dadar, AAIC 2015, submitted). In short, after preprocessing (image intensity non-uniformity correction and normalization, co-registration of T2*, T2-w, PD and FLAIR images to the T1-w, and non-linear registration to an average ADNI AD template), 17 features are used to segment WMHs. Using the automatic segmentations, average maps of WMH loads were calculated for each population separately. Figure 1 shows 12 transverse slices of the average WMH maps for the entire PREVENT-AD and ADC populations overlapped on the ADNI template.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".