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Record W2014935444 · doi:10.1093/brain/awu043

Association of brain amyloid-β with cerebral perfusion and structure in Alzheimer’s disease and mild cognitive impairment

2014· article· en· W2014935444 on OpenAlexfundno aff
Niklas Mattsson, Duygu Tosun, Philip S. Insel, Alix Simonson, Clifford R. Jack, Laurel Beckett, Michael Donohue, William Jagust, Norbert Schuff, Michael W. Weiner

Bibliographic record

VenueBrain · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersJanssen Alzheimer Immunotherapy Research And DevelopmentJohnson and Johnson Pharmaceutical Research and DevelopmentNational Institute on AgingNational Institutes of HealthGenentechIXICONational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchUniversity of California, Los AngelesServierEisaiNorthern California Institute for Research and EducationPfizerBiogenBioClinicaSynarcBayer HealthCareAlzheimer's Disease Neuroimaging InitiativeNational Center for Advancing Translational SciencesMeso Scale DiagnosticsMedpaceAstraZenecaBristol-Myers SquibbEli Lilly and CompanyNovartis Pharmaceuticals CorporationF. Hoffmann-La RocheAmorfix Life SciencesAlzheimer's Drug Discovery FoundationFoundation for the National Institutes of Health
KeywordsCerebral blood flowDementiaAlzheimer's diseasePrecuneusMedicinePositron emission tomographyTemporal cortexPathologyMagnetic resonance imagingNeuroimagingAmyloid (mycology)Cerebral cortexCardiologyBlood flowInternal medicinePsychologyNeuroscienceDiseaseFunctional magnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Patients with Alzheimer's disease have reduced cerebral blood flow measured by arterial spin labelling magnetic resonance imaging, but it is unclear how this is related to amyloid-β pathology. Using 182 subjects from the Alzheimer's Disease Neuroimaging Initiative we tested associations of amyloid-β with regional cerebral blood flow in healthy controls (n = 51), early (n = 66) and late (n = 41) mild cognitive impairment, and Alzheimer's disease with dementia (n = 24). Based on the theory that Alzheimer's disease starts with amyloid-β accumulation and progresses with symptoms and secondary pathologies in different trajectories, we tested if cerebral blood flow differed between amyloid-β-negative controls and -positive subjects in different diagnostic groups, and if amyloid-β had different associations with cerebral blood flow and grey matter volume. Global amyloid-β load was measured by florbetapir positron emission tomography, and regional blood flow and volume were measured in eight a priori defined regions of interest. Cerebral blood flow was reduced in patients with dementia in most brain regions. Higher amyloid-β load was related to lower cerebral blood flow in several regions, independent of diagnostic group. When comparing amyloid-β-positive subjects with -negative controls, we found reductions of cerebral blood flow in several diagnostic groups, including in precuneus, entorhinal cortex and hippocampus (dementia), inferior parietal cortex (late mild cognitive impairment and dementia), and inferior temporal cortex (early and late mild cognitive impairment and dementia). The associations of amyloid-β with cerebral blood flow and volume differed across the disease spectrum, with high amyloid-β being associated with greater cerebral blood flow reduction in controls and greater volume reduction in late mild cognitive impairment and dementia. In addition to disease stage, amyloid-β pathology affects cerebral blood flow across the span from controls to dementia patients. Amyloid-β pathology has different associations with cerebral blood flow and volume, and may cause more loss of blood flow in early stages, whereas volume loss dominates in late disease stages.

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)0.000
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 score0.014
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.279
Teacher spread0.270 · 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

Citations215
Published2014
Admission routes1
Has abstractyes

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