IC‐P‐128: SEX DIFFERENCES IN THE RELATIONSHIP BETWEEN CORTICAL NEURODEGENERATION AND FDG‐PET HYPOMETABOLISM IN AD AND PROGRESSIVE MCI
Bibliographic record
Abstract
To better understand the relationship between cortical thinning (neurodegeneration) and cortical glucose hypometabolism throughout disease progression and across genders, the correlation between measures was compared in individuals with Alzheimer's Disease (AD, n = 223), Mild Cognitive Impairment (MCI, n= 261) who convert to AD, MCI who remain stable (n = 370), and normal controls (n = 282). All T1-MPRAGE and FDG-PET scans were downloaded from the Alzheimer's Disease Neuroimaging Initiative website (cohorts ADNI-1&2) and underwent further processing. MPRAGE scans underwent FreeSurfer processing, correcting for geometric inaccuracies or topological defects. PET data were projected onto the FreeSurfer cortical white surface. These processing steps resulted in vectors of cortical GM thickness and cortical GM FDG-PET uptake data, correspondingly indexed over the FreeSurfer template white surface and smoothed to the same degree across all subjects. Individual Pearson correlation coefficients were computed to assess subject-wise concordance of cortical thickness and cortical FDG-PET uptake measures. Linear mixed effects models assessing the effects of time, gender, diagnostic category on correlations while accounting for age, education and APOE-4 status were examined. Linear mixed-effects models revealed significant effect of time on the correlation between cortical thickness and cortical FDG-PET uptake, such that concordance between measures decreased across all time points (p<.001). Female participants had increased correlations across all time points (p<.05), and showed higher concordance than males at worsening disease-states (Females > Males at pMCI & AD, p<.05).
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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 | 0.001 | 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".