Evidence map›Paper›PMID 37156449›Full record

ArticleNeuroImage2023

Identifying sex-specific risk architectures for predicting amyloid deposition using neural networks.

Linghai Wang, Antonija Kolobaric, Howard Aizenstein, Brian Lopresti, Dana Tudorascu, Beth Snitz, William Klunk, Minjie Wu

Open access · goldAbstract read
In one paragraph

Article in NeuroImage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Observational
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Linghai WangUniversity of Pittsburgh, Pittsburgh, Pennsylvania, United States. Electronic address: L.Wang@pitt.edu.
Antonija KolobaricUniversity of Pittsburgh, Pittsburgh, Pennsylvania, United States.
Howard AizensteinUniversity of Pittsburgh, Pittsburgh, Pennsylvania, United States; Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, United States; School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States.
Brian LoprestiUniversity of Pittsburgh, Pittsburgh, Pennsylvania, United States.
Dana TudorascuDepartment of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States.
Beth SnitzUniversity of Pittsburgh, Pittsburgh, Pennsylvania, United States.
William KlunkDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, United States; School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States.
Minjie WuDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, United States.
University of Pittsburgh · US

Funding

Roles of Gray Matter Brain Aging and Small Vessel Disease in AD PathophysiologyRF1AG025516 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI AIZENSTEIN, HOWARD J, VILLEMAGNE, VICTOR LUIS · 2014 to 2021
$9.0M
A Cerebrovascular Basis for Sex Differences in Alzheimer's Disease RiskR01AG067018 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI WU, MINJIE · 2020 to 2023
$1.7M
NIA NIH HHS R01 AG067018NIA NIH HHS RF1 AG025516
6 · The paper itself

Abstract

In older adults without dementia, White Matter Hyperintensities (WMH) in MRI have been shown to be highly associated with cerebral amyloid deposition, measured by the Pittsburgh compound B (PiB) PET. However, the relation to age, sex, and education in explaining this association is not well understood. We use the voxel counts of regional WMH, age, one-hot encoded sex, and education to predict the regional PiB using a multilayer perceptron with only rectilinear activations using mean squared error. We then develop a novel, robust metric to understand the relevance of each input variable for prediction. Our observations indicate that sex is the most relevant predictor of PiB and that WMH is not relevant for prediction. These results indicate that there is a sex-specific risk architecture for Aβ deposition.

Indexed as

Alzheimer DiseasePositron-Emission TomographyAgedAmyloid beta-PeptidesAniline CompoundsBrainFemaleHumansMagnetic Resonance ImagingMaleAmyloid beta-PeptidesAniline CompoundsAlzheimer's diseaseBeta-amyloidMachine learningSex differencesSmall vessel disease

Identifiers

PMID37156449
PMCPMC10905666
OpenAlexW4372311232

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.