Modeling Isotropic and Anisotropic Diffusion in Aging Brain White Matter with MRI
Bibliographic record
Abstract
The cognitive decline that accompanies normal adult aging is associated with degeneration of the brain’s connectivity backbone, the white matter (WM). Age-related WM degeneration includes both (i) neuronal disconnection resulting from a loss of WM fibers and (ii) reduced conduction efficiency among intact neuronal connections resulting from disorganization and degradation of the WM fibers that remain. The development of single-shell diffusion MRI has made it possible to probe WM degeneration in-vivo at its earliest microstructural stages by measuring the anisotropy of the microscopic diffusion of water molecules in WM tissue. Diffusional changes in aging reflect both processes specific to anisotropic WM fibers, such as their disorganization and degradation, as well as elevated isotropic diffusivity that results from extra-fiber processes, such as an enlargement of the extracellular space associated with fiber loss. These two classes of diffusion effects—anisotropic and isotropic—are presumably associated with different cognitive domains, so they can potentially serve as differential targets for preventing and treating pathological aging. Unfortunately, conventional scale-invariant measures of diffusion anisotropy (the most common being fractional anisotropy, FA) inherently conflate anisotropic with isotropic effects. The purpose of this PhD thesis is to investigate diffusion anisotropy measures that, rather than being scale-invariant, are invariant under elevated isotropic diffusivity, so that fiber-specific diffusional changes and elevated isotropic diffusivity can be distinguished. I investigate how these measures can be extracted by fitting both a one- and two-compartment model of diffusion to single-shell diffusion MRI data, and I test a hypothesis that these measures can be used to uncover positive age associations of anisotropy in regions of complex fiber architecture to reflect selective degeneration of secondary crossing fibers. I further devise a mathematical framework for interpreting the outcome of these model fits to single-shell diffusion MRI data, and I derive a nonlinear relationship between the one- and two-compartment models that implicates the sensitivity and explanatory power of their respectively derived metrics to age-related processes. Ultimately, this thesis provides a foundation for understanding how to harness the power of single-shell diffusion MRI data for probing age-related WM degeneration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".