Automated Brain Mapping to Evaluate the Relationship between Neurodegeneration, Cerebral Small Vessel Disease and Structural Covariance Network Disruption in Alzheimer's Disease
Bibliographic record
Abstract
Humans are currently living longer than any other period in history. However, gains in life expectancy do not portend a commensurate increase in quality of life years, particularly in persons with dementia. This is pertinent as the number of persons suffering from Alzheimerâ s disease (AD) is expected to double to over a million within a generation in Canada. The pathological underpinnings of AD remain unclear but are thought to target large-scale brain systems. It is posited that disease of the brainâ s small blood vessels may contribute to AD progression by disrupting structural brain networks that subserve complex cognitive routines. Therefore, the overarching aim of this dissertation is to determine the contribution of cerebral small vessel disease (SVD) versus coexistent neurodegeneration to brain network disruption in AD. The first part of this thesis presents and validates a state-of-the-art automated hippocampal segmentation technique for magnetic resonance imaging to accurately measure neurodegenerative status in mixed disease samples. The second part of this thesis assesses the relationship between hippocampal volume, SVD burden and grey matter cortico- and subneocortico-cortical network hub disruption. Finally, we extend these findings by introducing a method we call covariance-based connectomics to characterize how cortex-wide polysynaptic (grey-white matter) systems are disrupted in relation to SVD and neurodegenerative markers in AD versus age-matched controls. In summary, this thesis presents innovative human brain mapping techniques and the application of these methods to explore the relationship between SVD and AD in vivo. The SVD-signature of network disruption in AD is elucidated, and evidence is presented which supports the idea that SVD is differentially associated with neural network degradation in older adults and persons with mild AD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".