IC‐P‐152: MRI‐Derived Indication of Disparities in Very Early Adulthood for AD and AMCI Individuals
Bibliographic record
Abstract
We have used a recent MRI patch-based hippocampal segmentation technique (NeuroImage 54 (2011)) to grade a new hippocampus according to information from a training set of images. Rather than using the diagnostic information, the proposed grading uses the chronological age of the training set individuals, thereby providing a calculated “hippocampal age” for a new participant. The intuitive understanding of this metric is that the new image (i.e. the hippocampus of the new individual) looks like an “older”, “equal”, or “younger” hippocampus than what is normal for chronological age. In this work we wished to assess differences in calculated hippocampal age in a well-characterized cohort of AD, MCI and CTRL subjects. We analysed released data from the Hippocampus Harmonization project (www.hippocampal-protocol.net), namely images and hippocampal labels for 119 individuals with the best reference model registration, out of the 135 ADNI participants in the Harmonization project (35CTRL, 41 MCI, 43 AD). We performed leave-one-out patch-based segmentation and calculated hippocampal ages for all subjects. The reported score is calculated as the weighted sum of all CTRL template subject’s chronological ages used by the patch segmentation, over the total sum of the weights from all (CTRL and AD) templates. The delta (Δ) hippocampal age score is the difference between this calculated hippocampal age and chronological age. Shown in Figure 1 is the difference between calculated hippocampal age and the chronological age, with Δhippocampal age normalized to the chronological age of the subject. Clearly, AD subjects have an “older” hippocampal age than MCI, and in turn than from CTRL. When interpolating the resulting fits to the x-axis (i.e. the point at which their hippocampi should be completely “normal”) we find that AD subjects in particular start departing from normality in their mid-30s. Robust fitting provides intercepts (age, SD) as follows: NC: 53 (6.0) years old; MCI 43.3 (7.4) y.o.; and AD 31.7 (20.6) y.o.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".