In Vivo Cortical Microstructure: A Proxy for Tauopathy and Cognitive impairment in the Elderly with and without MCI/Dementia
Bibliographic record
Abstract
Abstract Aggregation of hyperphosphorylated tau protein is currently one of the most reliable indicators of Alzheimer’s pathology and cognitive impairment in older adults. However, it would be useful to have a non-invasive, accessible proxy measure that does not rely on Positron Emission Tomography (PET). We used data from multi-shell diffusion-weighted imaging (DWI) to assess indices from the Neurite Orientation Dispersion and Density Imaging (NODDI) model to determine possible proxies for tau and relationship with cognitive impairment. After controlling for age, sex, and the time difference between the scan acquisitions (DWI vs. PET), we used multiple factor analysis (MFA) to assess the fit between NODDI indices (orientation dispersion [ODI], neurite density [NDI], and free-water [fISO]), cortical thickness, and tau binding (via PET). We used data from 80 participants from the ADNI-3 sample who had a multi-shell DWI and an [ 18 F]AV-1451 (tau) PET scan. Of these 80, 49 individuals were considered cognitively normal older adults (age ~74 years), 26 individuals had a diagnosis of mild cognitive impairment (age ~75 years), and five individuals had Alzheimer’s dementia (age ~78 years). fISO and tau shared a large amount of spatial overlap, and both strongly correlated with the first MFA dimension. Macrostructural features (such as cortical thickness and subcortical volume) introduced in a follow-up analysis were less related to this first MFA dimension than fISO and eight percent less than tau. Subsequent mediation analyses demonstrated that fISO mediated the relationship between cortical thickness and tau, explaining all of the variance. Microstructural features derived from advanced DWI acquisitions such as fISO may be useful proxies for tau. Cortical fISO, rather than cortical thickness, may represent the impact of tau on the brain (and, by extension, cognition).
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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) | 0.001 | 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) | 0.001 | 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".