MRI deep learning demonstrates featured neuropathological deteriorations in the AD brain
Bibliographic record
Abstract
Abstract Background Deep learning (DL) of non‐invasive brain structural magnetic resonance imaging (sMRI) has shown superb performance in differentiating Alzheimer’s disease (AD) from cognitively healthy participants (HC). Attention is needed to tackle AD pathological progression on sMRI using interpretable DL methods. Here, we studied the feasibility of DL in identifying neurodegenerative progression patterns in AD. Method Data applied to the study were from the multi‐centre Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) with 2,369 T1‐weighted brain images of 1,005 subjects. An interpretable DL technique named the ensemble 3‐dimensional convolutional neural network (Ensemble 3DCNN) was used to detect the longitudinal trajectory of sMRI changes during AD progression. The model used multiple 3D convolutional neural networks and a meta‐classifier to assess the degree of brain changes through generating an index score, named P‐score. The score was derived as the outcome of training and verifying the Ensemble 3DCNN model for difference in the whole‐brain sMRI between AD and HC. The predictive P‐scores values were used to analyze the sMRI images for mining neurodegenerative brain regions. In addition, dementia stages, and the temporal and spatial connectivity patterns of neurodegeneration progression were also analyzed. Result Brain regions showing high P‐scores (> 0.73/1.00) included the amygdala, nucleus accumbens, agranular insular cortex, and the hippocampus, matching the isocortex, basal magnocellular complex, and transentorhinal regions identified in Braak staging. The P‐score increased over time in 82% of the degenerative brain regions. The impaired areas were often spatially connected, consistently across multiple time points in the progression of AD. The trajectory of regional brain changes displayed multiple patterns with complex individual variability. Conclusion The interpretable 3D DL model demonstrated featured deteriorations on the AD sMRI images. The finding confirmed the neuropathological degeneration of AD and captured additional whole‐brain changes, especially, relating to the heterogeneity of AD expression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".