Dysregulation of cerebral perfusion dynamics is associated with Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: A novel physio-marker, termed "cerebrovascular dynamics index" (CDI), was developed and evaluated in a multi-center National Institutes of Health (NIH)-funded study for improved diagnosis of mild cognitive impairment (MCI) and its transition to Alzheimer's disease (AD). METHODS: The CDI quantifies the regulation dynamics of cerebral perfusion and oxygenation (which adjust autonomously blood flow and oxygen delivery over time) through predictive dynamic modeling using relevant time-series data. RESULTS: Cross-sectional results demonstrated excellent diagnostic performance of CDI in differentiating 90 MCI/AD patients from 77 controls (area under the curve (AUC) = 0.96), which surpassed the commonly used biomarker of amyloid positron emission tomography-standardized uptake value ratio (PET-SUVR) (AUC = 0.78) or cognitive screening tests of Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) (AUC = 0.91 and 0.92, respectively). The CDI can also be used for disease staging because it differentiated 56 MCI from 34 mild AD participants (AUC = 0.98). CONCLUSION: These findings offer the promise of a high-performance diagnostic physio-marker for MCI and AD, which can be obtained in a comfortable, rapid, and automated manner in clinical settings. Highlights: Novel physio-marker (cerebrovascular dynamics index [CDI]) quantifies the regulation dynamics of cerebral perfusion.The CDI was shown to improve mild cognitive impairment/Alzheimer's disease (MCI/AD) diagnosis (area under the curve [AUC] >0.95) relative to existing markers.The CDI is obtained non-invasively, objectively, rapidly, and inexpensively.The CDI performance supports the key role of cerebrovascular dysfunction in AD.The CDI is obtained via dynamic modeling of hemodynamic/oxygenation time-series data.
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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 | 0.001 |
| 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".