DTI Metrics Correlation to FLAIR Biomarkers, Cognition, and Neurodegenerative Diseases
Bibliographic record
Abstract
A novel approach was proposed to correlate Diffusion Tensor Imaging (DTI) biomarkers to FLAIR biomarkers in normal appearing brain matter regions (NABM). In this work, we also investigated the relation of the NABM region to cognition and used the NABM region to show the difference between different forms of dementia, emphasizing mild cognitive impairment, and vascular mild cognitive impairment. Mean Diffusivity (MD) was used as the metric of choice for NABM analysis. The DTI to FLAIR correlation analysis found MD to be highly correlated (p<0.05) to FLAIR biomarkers with the highest correlations seen between MD and Microstructural integrity, Macro and Microstructural Damage and NABM to ICV Volume Ratio. We concluded that the NABM region can be effectively correlated with FLAIR biomarkers, be used to differentiate between mild cognitive impairment and vascular mild cognitive impairment, however it did not show statistical significance to cognition though it did follow expected trends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".