Automatic prediction of cognitive and functional decline using baseline MRI and cognitive scores
Bibliographic record
Abstract
Abstract Background Patients in the early stages of AD dementia experience decline in their cognitive abilities at different rates. Accurately predicting the progression rate would enable the enrichment of patient populations in clinical trials. Using the early AD‐related pattern of atrophy and cognitive scores from a baseline visit, we trained a model to predict cognitive and functional decline in early stages of AD. Method Data included 312 patients with mild Alzheimer’s disease from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and were chosen based on amyloid positivity, the Clinical Dementia Rating (CDR=0.5) and the Mini‐Mental State Exam (MMSE, range 24‐30) scores. SNIPE (Scoring by Nonlocal Image Patch Estimator) was used to measure AD‐related atrophy patterns in the hippocampi (HC) and entorhinal cortex (EC) (Coupé et al. 2019). A balanced random forest was used to train models with feature sets including MR‐based z‐scored features (SNIPE scores for HC and EC), and cognitive test scores (Alzheimer’s Disease Assessment Scale (ADAS‐13), MoCA (Montreal Cognitive Assessment), and MMSE) from baseline data. Classifiers were trained using different combinations of features plus age. The classification performance was evaluated based on the measured sensitivity, specificity, and accuracy to predict future functional and cognitive decline (defined as a 2‐point change in CSD‐SB). Result Table 1 shows the classification performance of all trained models for two and three year follow up periods. Using hippocampal grading scores in addition to MoCA, ADAS‐13 and MMSE, yields the highest accuracy (76.9%) in predicting cognitive decline at 2 years. Figure 1 shows the ROC curve for two models: One consisting of all cognitive features and the other using both cognitive and MRI features. Comparing results between the classifier using only the baseline cognitive score and the corresponding classifier with the added MRI features showed that for both follow‐up periods, the accuracy of prediction is increased when adding MRI features. Conclusion Microscopic changes due to AD pathology that occur in the HC and EC can be used as a feature in a predict the cognitive decline and increase the accuracy of prediction, even at the early stages of the AD trajectory.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".