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Record W2046176479 · doi:10.1371/journal.pone.0047905

Dissociation between Brain Amyloid Deposition and Metabolism in Early Mild Cognitive Impairment

2012· article· en· W2046176479 on OpenAlexafffund
Liyong Wu, Jared Rowley, Sara Mohades, Antoine Leuzy, Marina Tedeschi Dauar, Monica Shin, Vladimir Fonov, Jianping Jia, Serge Gauthier, Pedro Rosa‐Neto

Bibliographic record

VenuePLoS ONE · 2012
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMontreal Neurological Institute and HospitalDouglas Mental Health University InstituteMcGill University
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchUniversity of California, Los AngelesNational Institutes of HealthEisaiNational Natural Science Foundation of ChinaNorthern California Institute for Research and EducationUniversity of California, San DiegoBioClinicaBayer HealthCareAlzheimer's Disease Neuroimaging InitiativeBiogenBristol-Myers SquibbEli Lilly and CompanyAstraZenecaAmorfix Life SciencesSynarcNational Institute on AgingAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsPrecuneusNeuroimagingPosterior cingulateStatistical parametric mappingCognitive impairmentVoxelAlzheimer's diseaseMedicineAmyloid (mycology)Voxel-based morphometryInternal medicineNeurosciencePathologyMagnetic resonance imagingBiologyCognitionDiseaseWhite matterRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: The hypothetical model of dynamic biomarkers for Alzheimer's disease (AD) describes high amyloid deposition and hypometabolism at the mild cognitive impairment (MCI) stage. However, it remains unknown whether brain amyloidosis and hypometabolism follow the same trajectories in MCI individuals. We used the concept of early MCI (EMCI) and late MCI (LMCI) as defined by the Alzheimer's disease Neuroimaging Initiative (ADNI)-Go in order to compare the biomarker profile between EMCI and LMCI. OBJECTIVES: To examine the global and voxel-based neocortical amyloid burden and metabolism among individuals who are cognitively normal (CN), as well as those with EMCI, LMCI and mild AD. METHODS: In the present study, 354 participants, including CN (n = 109), EMCI (n = 157), LMCI (n = 39) and AD (n = 49), were enrolled between September 2009 and November 2011 through ADNI-GO and ADNI-2. Brain amyloid load and metabolism were estimated using [(18)F]AV45 and [(18)F]fluorodeoxyglucose ([(18)F]FDG) PET, respectively. Uptake ratio images of [(18)F]AV45 and [(18)F]FDG were calculated by dividing the summed PET image by the median counts of the grey matter of the cerebellum and pons, respectively. Group differences of global [(18)F]AV45 and [(18)F]FDG were analyzed using ANOVA, while the voxel-based group differences were estimated using statistic parametric mapping (SPM). RESULTS: EMCI patients showed higher global [(18)F]AV45 retention compared to CN and lower uptake compared to LMCI. SPM detected higher [(18)F]AV45 uptake in EMCI compared to CN in the precuneus, posterior cingulate, medial and dorsal lateral prefrontal cortices, bilaterally. EMCI showed lower [(18)F]AV45 retention than LMCI in the superior temporal, inferior parietal, as well as dorsal lateral prefrontal cortices, bilaterally. Regarding to the global [(18)F]FDG, EMCI patients showed no significant difference from CN and a higher uptake ratio compared to LMCI. At the voxel level, EMCI showed higher metabolism in precuneus, hippocampus, entorhinal and inferior parietal cortices, as compared to LMCI. CONCLUSIONS: The present results indicate that brain metabolism remains normal despite the presence of significant amyloid accumulation in EMCI. These results suggest a role for anti-amyloid interventions in EMCI aiming to delay or halt the deposition of amyloid and related metabolism impairment.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.297
Teacher spread0.260 · 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

Citations52
Published2012
Admission routes2
Has abstractyes

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