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Record W2621215132 · doi:10.1093/brain/awx112

Cerebrovascular resistance: effects on cognitive decline, cortical atrophy, and progression to dementia

2017· article· en· W2621215132 on OpenAlexfundno aff
Belinda Yew, Daniel A. Nation

Bibliographic record

VenueBrain · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institute on AgingNational Institutes of HealthFoundation for the National Institutes of Health
KeywordsCerebral blood flowDementiaAtrophyCognitive declineCardiologyPositron emission tomographyAlzheimer's diseaseInternal medicineMedicineBlood pressureBlood flowPsychologyMagnetic resonance imagingNeurosciencePathologyDiseaseRadiology

Abstract

fetched live from OpenAlex

See Markus (doi:10.1093/awx161) for a scientific commentary on this article.Evidence for vascular contributions to Alzheimer's disease has been increasingly identified, with increased blood pressure and decreased cerebral blood flow both linked to in vivo biomarkers and clinical progression of Alzheimer's disease. We therefore hypothesized that an elevated ratio of blood pressure to cerebral blood flow, indicative of cerebrovascular resistance, would exhibit earlier and more widespread associations with Alzheimer's disease than cerebral blood flow alone. Further, we predicted that increased cerebrovascular resistance and amyloid retention would synergistically influence cognitive performance trajectories, independent of neuronal metabolism. Lastly, we anticipated associations between cerebrovascular resistance and later brain atrophy, prior to amyloid accumulation. To evaluate these hypotheses, we investigated associations between cerebrovascular resistance and amyloid retention, cognitive decline, and brain atrophy, controlling for neuronal metabolism. North American older adults (n = 232) underwent arterial spin labelling magnetic resonance imaging to measure regional cerebral blood flow in brain regions susceptible to ageing and Alzheimer's disease. An estimated cerebrovascular resistance index was then calculated as the ratio of mean arterial pressure to regional cerebral blood flow. Positron emission tomography with 18F-florbetapir and fludeoxyglucose was used to quantify amyloid retention and neuronal metabolism, respectively. Cognitive performance was evaluated via annual assessments of global cognition, memory, and executive function. Results indicated diminished inferior parietal and temporal cerebral blood flow for patients with Alzheimer's disease (n = 33) relative to both non-demented groups, but no cerebral blood flow differences between non-demented amyloid-positive (n = 87) and amyloid-negative (n = 112) cases. In contrast, the cerebrovascular resistance index was significantly elevated in amyloid-positive versus amyloid-negative cases, with additional elevation in patients with Alzheimer's disease. Furthermore, cerebrovascular resistance index group differences were of greater statistical effect size and encompassed a greater number of brain regions than those for cerebral blood flow alone. Cognitive decline over 2-year follow-up was accelerated by elevated baseline cerebrovascular resistance index, particularly for amyloid-positive individuals. Increased baseline cerebrovascular resistance index also predicted greater progression to dementia, beyond that attributable to amyloid-positivity. Finally, increased cerebrovascular resistance index predicted greater regional atrophy among non-demented older adults who were amyloid-negative. Findings suggest that increased cerebrovascular resistance may represent a previously unrecognized contributor to Alzheimer's disease that is independent of neuronal hypometabolism, predates changes in brain perfusion, exacerbates and works synergistically with amyloidosis to produce cognitive decline, and drives amyloid-independent brain atrophy during the earliest stage of disease.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.355
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations138
Published2017
Admission routes1
Has abstractyes

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