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Patterns of progressive atrophy vary with age in Alzheimer's disease patients

2017· article· en· W2769374905 on OpenAlexfundno aff
Cassidy M. Fiford, Gerard R. Ridgway, David M. Cash, Marc Modat, Jennifer M. Nicholas, Emily N. Manning, Ian B. Malone, Geert Jan Biessels, Sébastien Ourselin, Owen Carmichael, M. Jorge Cardoso, Josephine Barnes

Bibliographic record

VenueNeurobiology of Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesUCLH Biomedical Research CentreGenentechEngineering and Physical Sciences Research CouncilAlzheimer's SocietyCanadian Institutes of Health ResearchAlzheimer's Drug Discovery FoundationNational Institutes of HealthH. Lundbeck A/SMedical Research CouncilServierUniversity College London Hospitals NHS Foundation TrustFoundation for the National Institutes of HealthUniversity of Southern CaliforniaEisaiElanWolfson FoundationBrain Research TrustNational Institute on AgingAlzheimer SocietyNational Institute for Health and Care ResearchNovartis Pharmaceuticals CorporationSeventh Framework ProgrammeNorthern California Institute for Research and EducationPfizerBiogenBioClinicaF. Hoffmann-La RocheIXICOTakeda Pharmaceutical CompanyAbbVieNational Institute on Handicapped ResearchUniversity College LondonLundbeckfondenAlzheimer's Research TrustU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbWellcome TrustRocheMerckAlzheimer's AssociationFujirebio EuropePennington Biomedical Research FoundationGE HealthcareDoD Alzheimer's Disease Neuroimaging InitiativeAlzheimer's Disease Neuroimaging InitiativeNational Institute of Biomedical Imaging and BioengineeringJohnson and JohnsonMeso Scale Diagnostics
KeywordsAtrophyMagnetic resonance imagingPrecuneusHyperintensityCardiologyVoxel-based morphometryWhite matterBrain sizeMedicinePsychologyAlzheimer's diseasePosterior cingulateInternal medicinePathologyDiseaseNeuroscienceRadiologyCognition

Abstract

fetched live from OpenAlex

Age is not only the greatest risk factor for Alzheimer's disease (AD) but also a key modifier of disease presentation and progression. Here, we investigate how longitudinal atrophy patterns vary with age in mild cognitive impairment (MCI) and AD. Data comprised serial longitudinal 1.5-T magnetic resonance imaging scans from 153 AD, 339 MCI, and 191 control subjects. Voxel-wise maps of longitudinal volume change were obtained and aligned across subjects. Local volume change was then modeled in terms of diagnostic group and an interaction between group and age, adjusted for total intracranial volume, white-matter hyperintensity volume, and apolipoprotein E genotype. Results were significant at p < 0.05 with family-wise error correction for multiple comparisons. An age-by-group interaction revealed that younger AD patients had significantly faster atrophy rates in the bilateral precuneus, parietal, and superior temporal lobes. These results suggest younger AD patients have predominantly posterior progressive atrophy, unexplained by white-matter hyperintensity, apolipoprotein E, or total intracranial volume. Clinical trials may benefit from adapting outcome measures for patient groups with lower average ages, to capture progressive atrophy in posterior cortices.

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 score0.005
Threshold uncertainty score0.254

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 head0.019
GPT teacher head0.310
Teacher spread0.291 · 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

Citations45
Published2017
Admission routes1
Has abstractyes

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