Characterizing White Matter Microstructure in Asymptomatic Older Adults at Elevated Risk for Alzheimer's Disease
Bibliographic record
Abstract
Growing evidence suggests that Alzheimer’s disease (AD) is associated with axonal tract alterations. These white matter (WM) changes may emerge very early, prior to clinical symptom onset and may precede cortical grey matter changes. However, reliably characterizing these WM alterations in vivo and differentiating them from normal age-related change has been challenging. To address this challenge, the overarching goal of this dissertation was to examine differences in WM microstructure attributable to known AD-risk factors: age, genetics, and the presence of AD-related pathology. Advanced diffusion-imaging methods were used to characterize WM microstructure in a large sample of older adults at elevated familial risk for AD who remained clinically asymptomatic (n=146). Additionally, participants underwent genetic testing, lumbar punctures, and positron emission tomography (PET) scanning to derive AD-risk biomarkers. In Study 1, I implemented a multivariate, data-driven statistical technique, Partial Least Squares (PLS), to identify covariance patterns between whole-brain, voxelwise white matter microstructure and AD-risk factors. Neurite Orientation Dispersion and Density Imaging (NODDI) data were collected to derive three WM microstructure indices: neurite density (NDI), orientation dispersion (ODI), and isotropic volume fraction (ISOVF). Each of these measures was associated with age, APOE4 genotype, and amyloid-beta and tau pathology biomarkers. Older age was associated with all three NODDI WM indices. NDI was uniquely sensitive to AD-risk indexed by AD pathology biomarkers. Study 2 extended these analyses to examine WM microstructural associations with cognition (episodic memory, processing speed, and executive control) in the same preclinical AD cohort using a whole-brain exploratory approach. WM microstructure, indexed by NODDI, was associated with episodic memory and executive control. However, most associations did not remain when accounting for age-related variance, suggesting that WM-cognition associations may not be specific to AD-risk factors. This dissertation represents one of the first, and among the most comprehensive investigations into WM microstructure in the context of multiple AD-risk factors, occurring before clinical syndrome onset. These findings demonstrate WM microstructural alterations are among the earliest neural changes to accompany AD-related pathology, providing a window into the impact of AD on brain structure, and informing novel opportunities for surveillance and intervention at the earliest disease stages.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".