The role of the autonomic nervous system in cerebral blood flow regulation in dementia: A review
Bibliographic record
Abstract
In this review we will examine the role of the autonomic nervous system in the control of cerebral blood flow (CBF) in dementia. Worldwide, 55 million people currently live with dementia, and this figure will increase as the global population ages. Understanding the changes in vascular physiology in dementia could pave the way for novel therapeutic approaches. Reductions in CBF have been demonstrated in multiple dementia sub-types, in addition to increased cerebrovascular resistance and reduced vasoreactivity. Cerebral autoregulation (CA) is a key mechanism for the maintenance of cerebral perfusion, but remains largely intact in cognitive disorders, despite reductions in global and regional CBF. However, the tight coupling between neuronal activity and CBF (neurovascular coupling - NVC) is lost in dementia, which may be a key driver of cognitive dysfunction. Despite numerous studies investigating disturbances in the control of CBF in dementia, less is known about the specific mechanisms responsible for the observed changes. Disturbances could be related to one of a number of pathways and mechanisms including disruption of the autonomic component. In this review we will explore clinical and animal studies, which specifically investigated the autonomic component of CBF control in dementia, drawing on the clinical implications and potential for novel biomarker and therapeutic targets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".