Exploring the power of MRI and clinical measures in predicting Alzheimer’s disease neuropathology
Bibliographic record
Abstract
Abstract Background The ability to predict Alzheimer’s disease (AD) before diagnosis is a topic of intense research. Early diagnosis would aid in improving treatment and intervention options, however, there are no current methods that can accurately predict AD years in advance. This study examines a novel machine learning approach that integrates the combined effects of vascular (white matter hyperintensities, WMHs), and structural brain changes (gray matter, GM) with clinical factors (cognitive status) to predict post-mortem neuropathological outcomes. Methods Healthy older adults, participants with mild cognitive impairment, and AD from the Alzheimer’s Disease Neuroimaging Initiative dataset with both post-mortem neuropathology data and antemortem MRI and clinical data were included. Longitudinal data were analyzed across three intervals before death (post-mortem data): 0-4 years, 4-8 years, and 8-14 years. Additionally, cross-sectional data at the last visit or interval (within four years, 0-4 years) before death were also examined. Machine learning models including gradient boosting, bagging, support vector regression, and linear regression were implemented. These models were applied towards feature selection of the top seven MRI, clinical, and demographic data to identify the best performing set of variables that could predict postmortem neuropathology outcomes (i.e., neurofibrillary tangles, neuritic plaques, diffuse plaques, senile/amyloid plaques, and amyloid angiopathy). Results A total of 94 participants (55-90 years of age) were included in the study. At last visit, the best-performing model included total and temporal lobe WMHs and achieved r =0.87( RMSE =0.62) during cross-validation for neuritic plaques. For longitudinal assessments across different intervals, the best-performing model included regional GM (i.e., hippocampus, amygdala, caudate) and frontal lobe WMH and achieved r =0.93( RMSE =0.59) during cross-validation for neurofibrillary tangles. For MRI and clinical predictors and clinical-only predictors, t -tests demonstrated significant differences at all intervals before death ( t [-13.60-7.90], p -values<0.001). Overall, post-mortem neuropathology outcome were predicted up to 14 years before death with high accuracies (∼90%). Conclusions Prediction accuracy was higher for post-mortem neuropathology outcomes that included MRI (WMHs, GM) and clinical features compared to clinical-only features. These findings highlight that MRI features are critical to successfully predict AD-related pathology years in advance which will improve participant selection for clinical trials, treatments, and intervention options.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".