MRI-Based Multi-Class Relevance Vector Machine Classification of Neurodegenerative Diseases
Bibliographic record
Abstract
Abstract Machine learning algorithms are a promising automated candidate that can help mitigate the growing need for dementia experts. Despite the substantial development in MRI-based machine learning analyses, case misclassification is a universal finding, yet the reasons behind misclassification are poorly understood. We implemented a multi-class classification approach that uses relevance vector machine and logistic classification to classify research participants based on their whole-brain T1-weighted MRI scans. A total of 468 participants from seven diagnostic classes were included: 144 healthy controls, 84 Alzheimer’s disease, 108 behavioral variant frontotemporal dementia (bvFTD), 30 semantic variant primary progressive aphasia (svPPA), 30 non-fluent variant primary progressive aphasia (nfvPPA), 30 corticobasal syndrome (CBS), and 42 progressive supranuclear palsy syndrome (PSPS). We compared the algorithm‘s diagnostic accuracy against the clinical, pathological, genetic, and quantitative imaging data. The exact neurodegenerative syndrome was predicted in 71% of the cases, the neurodegenerative disease spectrum was predicted in 80% of the cases, and the algorithm distinguished controls from any dementia in 85% of the cases. The algorithm showed high performance in diagnosing healthy controls, moderate performance in diagnosing AD, bvFTD, and svPPA, and low performance in diagnosing CBS, nfvPPA, and PSPS. Based on the quantitative imaging data, most of the misclassified neurodegenerative cases had minimal atrophy and brain volumes comparable to healthy controls. In AD, early-onset AD cases with minimal brain atrophy represented most of the misclassified cases. In bvFTD, FTD genetic mutation carriers (predominantly C9orf72 repeat expansion), FTD phenocopy, patients meeting only possible bvFTD criteria represented most misclassified cases. Case misclassification in machine learning studies in neurodegenerative diseases results from neurodegenerative disease heterogeneity and the limitations of structural MRI’s ability to capture the whole gamut of biological changes. Larger and more inclusive datasets that are representative of population biologic heterogeneity are needed to train better machine learning techniques, and a margin of error is expected and should be acceptable, like the uncertainty of a clinical diagnosis by a dementia expert.
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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 | 0.005 | 0.012 |
| 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 | 0.001 |
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".