P2‐403: CORTICAL IRON DEPOSITION IN ALZHEIMER'S DISEASE CONTRASTS WITH AGE‐RELATED SUBCORTICAL DEPOSITION
Bibliographic record
Abstract
Recently, brain iron overload has been linked to the development of AD. Iron accumulates during aging, resulting in oxidative stress and neurodegenerative processes. Animal models show iron colocalization with amyloid and tau deposits, though this has yet to be extended to humans. We sought to compare brain iron contents across healthy elderly and the AD spectrum. Preliminary data from 149 participants (34 AD, 26 MCI, 89 CN elderly) of the McGill University Research Centre for Studies in Aging (MCSA) were used for this analysis. Patients in the MCSA study have been diagnosed with AD or MCI, by a panel of neurologists based on their cognitive evaluation and the presence of both amyloid and tau neuropathology. Multi-echo T2* data were collected from all participants and iron (R2*) was quantitatively assessed by a mono-exponential fit on a voxel-wise basis. Neuroimaging analyses were performed using the VoxelStats toolbox, a MATLAB-based analytical framework that allows for the execution of voxel-wise multimodal neuroimaging analyses. Iron load and age showed a positive correlation in the caudate and putamen (Figure 1). Furthermore, in our cohort, AD patients showed increased iron levels in the prefrontal cortex (Figure 2). Results were corrected for multiple comparisons using an FDR threshold of 0.05 and cluster threshold of 0.001.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 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".