Development and validation of structural Magnetic Resonance Imaging (MRI)-based biomarkers for diagnosis and prognosis of prodromal Alzheimer's disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia.Currently in Canada, there are about 750,000 people living with dementia with more than 60,000 new cases diagnosed each year.Considering the relative failures in recent clinical trials, combined with the promising prospect of life-style changes, early prediction of the future onset of dementia in the early stages of Alzheimer's disease continuum can be highly beneficial.Advances in better identifying future progressors may both enhance population design for clinical trials and better identify the at-risk populations for life-style interventions.This thesis focuses on enhancement of dementia onset in patients with Mild Cognitive Impairment (MCI) using structural Magnetic Resonance Images (MRI) driven markers.The volume of the hippocampus is one of the best-known MRI-based biomarkers for Alzheimer's disease.Here, it is investigated using four different automatic hippocampus segmentation methods, each enhanced with bias error correction.Results show that multi-atlasbased hippocampus segmentation methods are accurate and show high conformity with manual delineation, and they can be further enhanced using a bias correction technique.The methods compared are not significantly different in their ability to capture AD related pathology.The Cohen's d group difference in hippocampal volume between patients with Alzheimer's dementia and healthy controls is high for all the methods and is of medium size between patients with MCI who convert to dementia in the near future and to those who remain stable for all the methods.Due to the wide-spread use of hippocampal volume a recent effort has been made by researchers in the field to harmonize the hippocampus segmentation protocol.The resulting new protocol, known as the EADC-ADNI harmonized protocol or the HarP, needs more validation.I validate the protocol using a large multi-center database designed for AD biomarker research.The results
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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 | 0.012 | 0.014 |
| 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 | 0.002 | 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".