Plasma neurofilament light and cerebrospinal fluid biomarkers are associated with white matter damage in Alzheimer's disease
Bibliographic record
Abstract
Abstract The most replicated blood biomarker for monitoring Alzheimer’s disease is neurofilament light (NFL). Recent evidence revealed that the plasma level of the NFL has a strong predictive value in cognitive decline and is elevated in AD patients. The Diffusion Tensor Imaging (DTI) is understood to reflect white matter disruption, neurodegeneration largely, and synaptic damage in AD. However, there is no investigation of the association between plasma NFL and white matter microstructure integrity. we have investigated the cross-sectional associations of plasma NFL, CSF tau, p tau, and Aβ with white matter microstructural changes as measured by DTI in 92 mild cognitive impairment (MCI) participants. We investigated potential correlations of the DTI values of each region of the MNI atlas, with plasma NFL, CSF total tau, CSF p tau, and as well as CSF Aβ, separately using a partial correlation model controlled for the effect of age, sex and APOE ε4 genotype. Our findings revealed a significant correlation between plasma and CSF biomarkers with altered white matter microstructural changes in widespread brain regions. Plasma NFL has a negative correlation with FA and positive correlation with RD, AD, and MD values in different regions. Plasma NFL promises to be an early biomarker of microstructural changes in MCI and for MCI progression to 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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".