Neurovascular coupling and functional connectivity changes through the Alzheimer's disease spectrum: Effects of simvastatin treatment
Bibliographic record
Abstract
INTRODUCTION: Alzheimer's disease (AD) is a leading cause of dementia, with vascular dysfunction being an early pathogenic event. Cardiovascular interventions show therapeutic promise, hence may improve neurovascular coupling (NVC) and resting-state functional connectivity (RSFC) hemodynamic responses. METHODS: NVC and RSFC were recorded longitudinally in control and AD transgenic mice treated or not with simvastatin (SV) using optical imaging of intrinsic signals (OIS), together with memory testing. RESULTS: AD mice showed early decreases in NVC and bilateral connectivity (BC) in motor and cingulate cortex, and hypoconnectivity within the sensory-motor network. Early default-mode network (DMN) hyperconnectivity was followed by hypoconnectivity, together with a decreased BC in somatosensory (S) cortex. SV restored NVC, prevented aberrant DMN hyperconnectivity, improved BC of S, and preserved memory. DISCUSSION: Our results indicate that OIS can detect early AD-related changes in NVC and RSFC, and that a preventive cardiovascular strategy may bear therapeutic promise in at-risk individuals. HIGHLIGHTS: Optical imaging of intrinsic signals captures Alzheimer's disease (AD) progression and response to therapy. Neurovascular coupling (NVC) and resting-state functional connectivity (RSFC) disruptions precede memory decline in AD transgenic mice. Simvastatin prevented NVC and AD-specific RSFC disruptions and protected memory. This study supports translational value of hemodynamic signal in early AD detection.
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 | 0.000 | 0.000 |
| 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".