In Vivo Cortical Microstructure: Relationships With Tauopathy and Cognitive Impairment in the Elderly
Bibliographic record
Abstract
ABSTRACT Positron Emission Tomography (PET) of tau is considered “the” indicator of Alzheimer's pathology. However, non‐PET proxies would be helpful for wider accessibility. We used Neurite Orientation Dispersion and Density Imaging (NODDI)‐derived indices (i.e., orientation dispersion [ODI], neurite density [NDI], and free‐water [fISO]) to determine relationships of gray matter microstructure with tau and cognitive impairment. We assessed the fit between NODDI indices, cortical thickness/subcortical volume (CT/ScVol), and tau via multiple factor analysis (MFA) using data from 80 participants from the ADNI‐3 dataset with overlapping multishell diffusion‐weighted and tau‐PET scans ([ 18 F]AV‐1451); 49 were considered cognitively normal older adults (age ~74 years), 26 were diagnosed with mild cognitive impairment (age ~75 years), and five 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 (i.e., CT/ScVol) were 7% less related to this first MFA dimension than fISO and 8% less than tau. Subsequent mediation analyses demonstrated that fISO mediated the relationship between CT/ScVol and tau, explaining all of the variance. Our results suggest that microstructural features derived from NODDI such as fISO might be useful adjunct markers to tau, which needs to be confirmed in longitudinal studies. Cortical fISO, rather than macrostructure (i.e., CT/ScVol), may represent tau's impact on the brain (and, by extension, cognition). image
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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) | 0.000 | 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".