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Record W2094026877 · doi:10.1016/j.jalz.2012.05.036

IC‐P‐004: Cerebral hypoperfusion in young transgenic APP mice predicts spatial distribution of amyloid deposition

2012· article· en· W2094026877 on OpenAlexaff
Marilyn Grand’Maison, François Hébert, Ming‐Kai Ho, Barry J. Bedell

Bibliographic record

VenueAlzheimer s & Dementia · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsPerfusionPathologyStatistical parametric mappingGenetically modified mouseImmunohistochemistryAmyloid (mycology)Brain atlasMedicineVoxelPerfusion scanningMagnetic resonance imagingNuclear medicineChemistryBiologyTransgeneRadiologyNeuroscience

Abstract

fetched live from OpenAlex

Regional cerebral hypoperfusion has been identified in Alzheimer's disease (AD) patients by arterial spin labeling (ASL) MRI. The association between hypoperfusion and β-amyloid deposition, however, remains poorly understood. In order to elucidate this relationship, we have performed in vivo 3D ASL MRI in young and old mutant human amyloid precursor protein (APP) transgenic (TG) mice and correlated regional perfusion data with gold-standard, post-mortem quantitative immunohistochemistry (qIHC) measures of β-amyloid burden. Anatomical and perfusion MRI data were acquired from 3-4 month-old (young) and 18-19 month-old (aged) APP TG and wild-type (WT) mice (n = 20 per group). Images were acquired using 3D b-SSFP sequence (anatomical) and pseudo-continuous 3D ASL sequence (perfusion) covering the entire brain on a 7T animal MRI system. MR images were processed using a fully-automated pipeline. The animals were sacrificed following completion of MRI scans and the brains were fixed. The formalin-fixed, paraffin-embedded brains were serially-sectioned from olfactory bulb through brainstem. The sections underwent IHC staining with 4G8 anti-amyloid monoclonal antibody and were counterstained with Acid Blue 129. The IHC sections were digitized using a MIRAX Scan automated, ultra-high-resolution slide scanner. The digitized tissue sections were then reconstructed into 3D β-amyloid parametric volumes. The anatomical/perfusion MRI volumes and 3D IHC parametric volumes were then spatially normalized to a customized anatomical MRI template/atlas. Voxel-wise and ROI-based statistical analyses were performed on these parametric volumes. Young APP TG mice demonstrated significantly reduced cerebral perfusion in several neuroanatomical regions relative to age-matched WT mice. At this age, the mice did not demonstrate IHC-visible β-amyloid deposits. However, the spatial distribution of hypoperfusion in the young TG mice and high β-amyloid burden in the aged APP TG mice were strongly correlated. We have demonstrated significant regional differences in blood flow between young, pre-plaque stage APP and WT mice using ASL MRI. Our unique approach of co-registering quantitative MRI and IHC volumes revealed that this pattern of hypoperfusion appears to predict the distribution of β-amyloid deposition in later life. As such, ASL perfusion MRI may serve as a powerful imaging biomarker for the assessment of disease evolution and therapeutic intervention in clinical AD studies.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.026
GPT teacher head0.247
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2012
Admission routes1
Has abstractyes

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