In vivo mean diffusivity is associated with neuropathology markers of Alzheimer's disease
Bibliographic record
Abstract
Abstract Background Diffusion weighted imaging (DWI) of gray matter can identify microstructural changes that have recently been shown to correlate with biomarkers of Alzheimer's disease (AD). Hence DWI‐based metrics such as mean diffusivity (MD) hold great promise to serve as an early and sensitive biomarker of AD pathology (Spotorno et al. 2023, Sun et al. 20). However, its underlying neuropathological determinants remain unclear. We therefore aimed to assess the association between MD and postmortem AD neuropathology. Method Cases with in‐vivo DWI within 6 years of death and postmortem neuropathology assessment were obtained from the National Alzheimer's Coordinating Center (NACC) database ( N = 43). Participant‐specific MD values were extracted from 132 regions of the Allen Anatomical Human Brain Atlas. MD values were harmonized across protocols, and residualized for age at death, sex, and MRI‐to‐death time interval. Partial Least Squares (PLS) analyses were then performed to assess the relationships between regional MD and pathology markers, including Thal amyloid phase, CERAD neuritic plaque score, Braak neurofibrillary stage, cerebral amyloid angiopathy (CAA), white matter rarefaction, and presence of microinfarcts Result The PLS analyses revealed one significant latent variable explaining 66% of the shared covariance between MD and neuropathology markers (ppermutation < 0.05). Significant neuropathology measures, in order of strongest contributor, included a higher Braak stage (β=0.57, 95%CI=[0.36,0.74]), Thal phase (β=0.55, 95%CI=[0.35, 0.71]), neuritic plaque score (β=0.47, 95%CI=[0.23, 0.66]) and CAA score (β=0.290572, 95%CI=[0.009, 0.56]). Occipital, parietal, temporal and frontal regions had the strongest association with neuropathology (bootstrap ratio (BSR) > 3.5, p <0.05), followed by cingulate cortex, basal forebrain, insula, and hippocampus and amygdala. Conclusion These results suggest that MD values, derived from in‐vivo DWI scans, are associated with a combination of neuropathology markers. In other words, the accumulation of both tau and amyloid leading to more severe neuropathology causes microstructural changes that are detectable by non‐invasive in‐vivo DWI biomarkers. This is a first step to validate the use of DWI as a non‐invasive tool to assess the severity of neuropathology in 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 | 0.000 |
| 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) | 0.003 | 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".