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Record W4252811609 · doi:10.1016/j.jalz.2014.05.053

IC‐P‐049: AMYLOIDOSIS CHANGES ASSOCIATIONS BETWEEN HIPPOCAMPUS VOLUME AND BRAIN METABOLIC DECLINES

2014· article· en· W4252811609 on OpenAlexaffabout
Min Su Kang, Maxime Parent, Monica Shin, Eduardo R. Zimmer, Antonia Aliaga, Axel Mathieu, Sulantha Mathotaarachchi, Sara Mohades, Sarinporn Manitsirikul, Jean‐Paul Soucy, Serge Gauthier, A. Claudio Cuello, Pedro Rosa‐Neto

Bibliographic record

VenueAlzheimer s & Dementia · 2014
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill Genome CentreMcGill University Health CentreDouglas Mental Health University InstituteDouglas CollegeMcGill University
Fundersnot available
KeywordsHippocampusTauopathyHippocampal formationAmyloidosisPositron emission tomographyMagnetic resonance imagingBrain sizePittsburgh compound BNeuroimagingMedicineNuclear medicineInternal medicineNeurodegenerationPathologyEndocrinologyNeuroscienceAlzheimer's diseaseBiologyRadiologyDisease

Abstract

fetched live from OpenAlex

The cerebral metabolic rate of glucose measured by Positron Emission Tomography (PET) using [18 F]FDG is often used as a biomarker of neurodegeneration in Alzheimer's disease (AD) and healthy aging. Mechanism underlying hypometabolism in these populations has been attributed to brain amyloidosis, tauopathy or cell depletion. Here, we investigate the link between hippocampal volume and regional hypometabolism in normal and an AD animal model (transgenic rat McGill-R-Thy1-APP) harbouring brain amyloidosis. This model shows the effects of amyloidosis on biomarkers in the absence of tauopathy or cell depletion. We hypothesized a distinct association pattern between rate of metabolic decline in hippocampal projections and hippocampal volume in TG animals. A total of 15 rats with a mean age of 16.6 months were used for this study. Hippocampus volumes were computed using structural Magnetic Resonance Imaging (MRI). The images [18F]FDG were acquired using PET at baseline and follow-up. Then the percent change of FDG values were calculated with respect to the baseline values. Whole brain images were obtained by MRI using Fast Imaging with Steady State Precession. PET images were then correlated at a voxel level with the hippocampus volume from the MRI images. In WT, average volumes in total brain and hippocampus (mm3) at baseline were 2418.91 ± 142.68 and 78.76 ± 5.98, respectively. In TG, average volume in total brain and hippocampus were 2390.31 ± 210.00 and 81.68 ± 8.28, respectively. In WT, average follow-up total brain volume and hippocampus were 2454.45 ± 131.99 and 76.9875 ± 2.67, respectively; and 2392.13 ± 208.64 and 75.48 ± 8.75, respectively in TG. Changes in FDG and hippocampal volume in WT were -0.72 ± 6.7% and 5.3 ± 5.0%, respectively and in TG were 6.1 ± 11% and 6.2 ± 3.4%, respectively. In WT, 6-month hypometabolism in frontal, temporal, cingulate cortex and striatum were associated with individual baseline hippocampal volumes. In TG, 6-month hypometabolism in the amygdala, basal forebrain, cingulate cortex and striatum were associated with individual baseline hippocampal volumes.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.314
Teacher spread0.279 · 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 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

Citations0
Published2014
Admission routes2
Has abstractyes

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