Identifying the regional substrates predictive of Alzheimer’s disease progression through a convolutional neural network model and occlusion
Bibliographic record
Abstract
Abstract Progressive brain atrophy is a key neuropathological hallmark of Alzheimer’s disease (AD). However, atrophy patterns along the progression of AD are diffuse and variable. Consequently, identifying the major regional atrophy patterns underlying AD progression is challenging. In the current study, we propose a method that evaluates the degree to which specific regional atrophy are predictive of AD progression, while holding all other atrophy changes constant. We first trained a dense convolutional neural network model to differentiate individuals with mild cognitive impairment (MCI) who progress to AD vs. those with a stable MCI diagnosis. Then, we retested the model multiple times, each time occluding major regions from the model’s testing set’s input. This revealed that the hippocampus, fusiform, and inferior temporal gyri, were the strongest predictors of AD progression, in agreement with established staging models. These results shed light on the major regional patterns of atrophy predictive of AD progression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".