Evidence map›Paper›PMID 29511103›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2018

Susceptibility of brain atrophy to

Marco Lorenzi, Andre Altmann, Boris Gutman, Selina Wray, Charles Arber, Derrek P Hibar, Neda Jahanshad, Jonathan M Schott, Daniel C Alexander, Paul M Thompson and 2 more

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
3.2field-weighted citation impact, top 8% 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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 53 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
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  8. GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023
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  13. Review
  14. Article
  15. Inflammatory modulation of the associations between prenatal maternal depression and neonatal brain.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021
    Article
  16. Article
  17. Developmental exposure to PbChemosphere · 2020
    Article
  18. Article
  19. Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020
    Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 3 countries.

Marco LorenziTranslational Imaging Group, Centre for Medical Image Computing, University College London, London WC1E 7JE, United Kingdom; marco.lorenzi@inria.fr.
Andre AltmannTranslational Imaging Group, Centre for Medical Image Computing, University College London, London WC1E 7JE, United Kingdom.
Boris GutmanImaging Genetics Center, University of Southern California, Marina Del Rey, CA 90292.
Selina WrayDepartment of Molecular Neuroscience, University College London Institute of Neurology, London WC1N 1PJ, United Kingdom.
Charles ArberDepartment of Molecular Neuroscience, University College London Institute of Neurology, London WC1N 1PJ, United Kingdom.
Derrek P HibarImaging Genetics Center, University of Southern California, Marina Del Rey, CA 90292.
Neda JahanshadImaging Genetics Center, University of Southern California, Marina Del Rey, CA 90292.
Jonathan M SchottDepartment of Neurodegeneration, Dementia Research Centre, Institute of Neurology, London WC1N 3BG, United Kingdom.
Daniel C AlexanderCentre of Medical Image Computing, University College London, London WC1E 7JE, United Kingdom.
Paul M ThompsonImaging Genetics Center, University of Southern California, Marina Del Rey, CA 90292.
Sebastien OurselinTranslational Imaging Group, Centre for Medical Image Computing, University College London, London WC1E 7JE, United Kingdom.
Alzheimer’s Disease Neuroimaging Initiative
University College London · GBUniversity of Southern California · USUK Dementia Research Institute · GB

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VAN HORN, JOHN DARRELL · 2012 to 2022
$14.1M
Training U54EB020403 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2014 to 2018
$10.1M
Growth factors, neuroinflammation, exercise, and brain integrityRF1AG041915 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2014 to 2014
$2.9M
Alzheimer's disease risk analyzed using population imaging genomicsR01AG040060 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2011 to 2016
$2.5M
CIHRDepartment of HealthMedical Research Council MR/J01107X/1Medical Research Council MR/L016311/1Medical Research Council MR/L023784/1NIA NIH HHS R01 AG040060NIA NIH HHS RF1 AG041915NIA NIH HHS U01 AG024904NIBIB NIH HHS P41 EB015922NIBIB NIH HHS U54 EB020403
6 · The paper itself

Abstract

The joint modeling of brain imaging information and genetic data is a promising research avenue to highlight the functional role of genes in determining the pathophysiological mechanisms of Alzheimer's disease (AD). However, since genome-wide association (GWA) studies are essentially limited to the exploration of statistical correlations between genetic variants and phenotype, the validation and interpretation of the findings are usually nontrivial and prone to false positives. To address this issue, in this work, we investigate the functional genetic mechanisms underlying brain atrophy in AD by studying the involvement of candidate variants in known genetic regulatory functions. This approach, here termed functional prioritization, aims at testing the sets of gene variants identified by high-dimensional multivariate statistical modeling with respect to known biological processes to introduce a biology-driven validation scheme. When applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, the functional prioritization allowed for identifying a link between tribbles pseudokinase 3 (

Indexed as

AgedAlzheimer DiseaseAtrophyBrainCell Cycle ProteinsCognitive DysfunctionCohort StudiesFemaleGenetic Predisposition to DiseaseHumansMagnetic Resonance ImagingMaleMultivariate AnalysisPolymorphism, Single NucleotideProtein Serine-Threonine KinasesRepressor ProteinsCell Cycle ProteinsProtein Serine-Threonine KinasesRepressor ProteinsTRIB3 protein, humanAlzheimer’s diseasebrain atrophyimaging–geneticsneuroimagingTRIB3

Identifiers

PMID29511103
PMCPMC5866534
OpenAlexW2794167820

What OpenQuestion holds

Textmetadata
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.