Plasma NFL Disrupt Cognitive Integrity Via Coupling the Interactions of Core Subsystem and Frontoparietal Network in Alzheimer's Disease
Bibliographic record
Abstract
Abstract Backgroud: Plasma neurofilament light chain (NFL) is a potential biomarker for neurodegenerative diseases. Both NFL and the interactions of core subsystem and frontoparietal network (FPN) are associated with the cognitive integrity. The present study was to investigate the underlying mechanism and application of plasmaNFL couplingcore-FPN in Alzheimer’s disease (AD). Methods: A total of 224 AD-spectrum participants with complete resting-state fMRI, neuropsychological tests and plasma NFL at baseline (n=112) and after approximately 17 months follow up (n=112) . Brain networks construction of core subsystem and FPN wereperformed in these subjects. The follow-up data were used to test and verify these baseline characteristics. Furthermore, Receiver Operating Characteristic analysiswas used to explore the classification by the association of plasma NFL and core-FPN, and the mediation analysis were appliedto investigate the significance of plasma NFL coupling networks on cognitive impairments in these subjects. Results: The discernment ofdisease-relatedinteractions of core subsystem and FPN maybe the neural network fundamentals of plasmaNFL regarding to cognitive decline in AD-spectrum patients. Furthermore, the clinical significance of plasma NFL coupling networks on AD identification and monitoring cognitive impairments were observed in these subjects. Conclusions: The characteristic change of plasma NFL coupling networks could expect to be used as an potential indicator of future targeted therapies and prevention strategies in AD patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".