Longitudinal changes in microstructural white matter metrics in Alzheimer's disease
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current avenues of AD research focus on pre-symptomatic biomarkers that will assist with early diagnosis of AD. The majority of magnetic resonance imaging (MRI) based biomarker research to date has focused on neuronal loss in grey matter and there is a paucity of research on white matter. METHODS: Longitudinal DTI data from the Alzheimer's Disease Neuroimaging Initiative 2 database were used to examine 1) the within-group microstructural white matter changes in individuals with AD and healthy controls at baseline and year one; and 2) the between-group microstructural differences in individuals with AD and healthy controls at both time points. RESULTS: 1) Within-group: longitudinal Tract-Based Spatial Statistics revealed that individuals with AD and healthy controls both had widespread reduced fractional anisotropy (FA) and increased mean diffusivity (MD) with changes in the hippocampal cingulum exclusive to the AD group. 2) Between-group: relative to healthy controls, individuals with AD had lower FA and higher MD in the hippocampal cingulum, as well as the corpus callosum, internal and external capsule; corona radiata; posterior thalamic radiation; superior and inferior longitudinal fasciculus; fronto-occipital fasciculus; cingulate gyri; fornix; uncinate fasciculus; and tapetum. CONCLUSION: The current results indicate that sensitivity to white matter microstructure is a promising avenue for AD biomarker research. Additional longitudinal studies on both white and grey matter are warranted to further evaluate potential clinical utility.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it