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Record W4210789110 · doi:10.1002/alz.056469

Correspondence between cortical tau and atrophy in aged non‐demented adults with Down syndrome

2021· article· en· W4210789110 on OpenAlexaff
Lisa Taylor, Eric Doran, Jean‐Baptiste Poline, Dana Nguyen, Sharon J. Krinsky‐McHale, Julie C. Price, Mithra Sathishkumar, William Charles Kreisl, Christy Hom, Margaret B. Pulsifer, Florence Lai, H. Diana Rosas, Adam M. Brickman, Nicole Schupf, Wayne Silverman, Ira T. Lott, Michael A. Yassa, David B. Keator

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsMcGill University
Fundersnot available
KeywordsAtrophyVoxelDementiaVoxel-based morphometryPopulationPsychologyAlzheimer's diseasePathologyPosterior cortical atrophyNuclear medicineMedicineMagnetic resonance imagingNeuroscienceInternal medicineDiseaseWhite matterRadiology

Abstract

fetched live from OpenAlex

Abstract Background Individuals with Down syndrome (DS) have a higher likelihood of developing early‐onset Alzheimer’s disease which has been associated with abnormal tau proteins and atrophy in the brain [1]. Prior research has shown a spatial relationship between tau and atrophy in neurotypicals with dementia [2]. Although tau and atrophy certainly contribute independently to dementia, the synergistic relationship in a non‐demented population with DS is not well understood. This study aims to identify cortical regions with high tau and atrophy in aged, non‐demented participants with DS. Method Analysis included 28 non‐demented participants (49.8 +/‐ 6.4 years; 17 males) with DS from the Alzheimer’s Disease in Down Syndrome (ADDS) study. Tau PET (18F‐AV1451) and MRI scans were acquired within the same timeframe (1.8 months +/‐1.5). Gray‐matter cortical ribbons were extracted from T1 MRI segmentations with Freesurfer (RRID: SCR_001847). The cortical ribbons were used to mask the coregistered PET scan data and converted to standard uptake value ratio (SUVR) units using the cerebellar cortex reference region. The tau and gray‐matter images were converted to z‐score images using the group mean and standard deviation. We defined high tau as voxels with z‐scores >=2.0 and high atrophy as voxels with gray matter z‐scores <= ‐2.0. The z‐score images were spatially normalized into MNI space with ANTs (RRID: SCR_004757) and a voxel‐based correspondence analysis was performed. Voxels surviving the high tau and high atrophy thresholds were evaluated to determine anatomical localization using the Desikan/Killiany atlas [3]. Result We found high correspondence between tau and atrophy in the following regions (figure 1): entorhinal cortex, insula, fusiform, pars orbitalis, paracentral, precentral, superior temporal, medial orbitofrontal, lateral orbitofrontal, temporal pole, rostral middle frontal, superior frontal, pars triangularis, lingual, and the amygdala. Conclusion Our study demonstrated a correspondence between atrophy and tau in deep cortical structures, as well as frontotemporal regions in aged, non‐demented adults with DS. Many of these regions are consistent with findings in neurotypical populations and shown in our prior work to be regions associated with increased amyloid (18F‐AV‐45) as a function of disease severity in DS [4].

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)0.001
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.298
Teacher spread0.269 · 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
Published2021
Admission routes1
Has abstractyes

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