Neurovascular coupling, functional connectivity, and cerebrovascular endothelial extracellular vesicles as biomarkers of mild cognitive impairment
Bibliographic record
Abstract
INTRODUCTION: Mild cognitive impairment (MCI) is a prodromal stage of dementia. Understanding the mechanistic changes from healthy aging to MCI is critical for comprehending disease progression and enabling preventative intervention. METHODS: Patients with MCI and age-matched controls (CN) were administered cognitive tasks during functional near-infrared spectroscopy (fNIRS) recording, and changes in plasma levels of extracellular vesicles (EVs) were assessed using small-particle flow cytometry. RESULTS: Neurovascular coupling (NVC) and functional connectivity (FC) were decreased in MCI compared to CN, prominently in the left-dorsolateral prefrontal cortex (LDLPFC). We observed an increased ratio of cerebrovascular endothelial EVs (CEEVs) to total endothelial EVs in patients with MCI compared to CN, correlating with structural MRI small vessel ischemic damage in MCI. LDLPFC NVC, CEEV ratio, and LDLPFC FC had the highest feature importance in the random Forest group classification. DISCUSSION: NVC, CEEVs, and FC predict MCI diagnosis, indicating their potential as markers for MCI cerebrovascular pathology. HIGHLIGHTS: Neurovascular coupling (NVC) is impaired in mild cognitive impairment (MCI). Functional connectivity (FC) compensation mechanism is lost in MCI. Cerebrovascular endothelial extracellular vesicles (CEEVs) are increased in MCI. CEEV load strongly associates with cerebral small vessel ischemic lesions in MCI. NVC, CEEVs, and FC predict MCI diagnosis over demographic and comorbidity factors.
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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 | 0.001 | 0.000 |
| 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".