Prediction of clinical and biomarker conformed Alzheimer’s disease and mild cognitive impairment from multi-feature brain structural MRI
Bibliographic record
Abstract
Abstract Background: Structural neuroimaging has been applied towards identification of individuals with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). However, these methods are greatly impacted by age limiting their utility for detection of preclinical pathology. Therefore, careful consideration of age effects in the modeling of AD degenerative patterns could provide more sensitive detection of the earliest stages of brain disease. Methods: We built linear models for age based on multiple combined structural features (cortical thickness, subcortical structural volumes, ratio of gray to white matter signal intensity, white matter signal abnormalities, total intracranial volume) in 272 healthy adults across a wide age range (D1: age 36-108). These models were then used to create a new support vector machine (SVM) training model with 10-fold cross validation in 136 AD and 268 control participants (D2) based on deviations from the expected age-effects found in the initial sample. Subsequent validation assessed the accuracy of the SVM model to correctly classify AD patients in a new dataset (D3). Finally, we applied the classifier to individuals with MCI to evaluate prediction for early impairment and longitudinal cognitive change. Results: Optimal cross-validation accuracy was 93.07% in the D2, compared to 91.83% without age detrending in D1. In the validation dataset (D3), the classifier obtained an accuracy of 84.85% (56/66), sensitivity of 85.36% (35/41) and specificity of 84% (21/25). In the MCI dataset, we observed significantly greater longitudinal cognitive decline in MCI who were classified as more ‘AD-like’ (MCI-AD), and this effect was pronounced in individuals who were late MCI. The top five contributive features were volumes of left hippocampus, right hippocampus, left amygdala, the thickness of left and right medial temporal & parahippocampus gyrus. Conclusions: Linear detrending for age in SVM for combined structural features resulted in good performance for classification of AD and generalization of MCI prediction. Such procedures should be employed in future work.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".