Evidence map›Paper›PMID 29201284›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2017

APPROXIMATING PRINCIPAL GENETIC COMPONENTS OF SUBCORTICAL SHAPE.

Boris A Gutman, Fabrizio Pizzagalli, Neda Jahanshad, Margaret J Wright, Katie L McMahon, Greig de Zubicaray, Paul M Thompson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 85% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

7 authors at 4 institutions in 2 countries.

Boris A GutmanImaging Genetics Center, University of Southern California, Los Angeles, CA, USA.
Fabrizio PizzagalliImaging Genetics Center, University of Southern California, Los Angeles, CA, USA.
Neda JahanshadImaging Genetics Center, University of Southern California, Los Angeles, CA, USA.
Margaret J WrightQueensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
Katie L McMahonQueensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
Greig de ZubicarayQueensland University of Technology, Brisbane, QLD, Australia.
Paul M ThompsonImaging Genetics Center, University of Southern California, Los Angeles, CA, USA.
University of Southern California · USARC Centre of Excellence in Advanced Molecular Imaging · AUNational Imaging Facility · AUQueensland University of Technology · AU

Funding

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
NIA NIH HHS RF1 AG041915NIBIB NIH HHS U54 EB020403
6 · The paper itself

Abstract

Optimal representations of the genetic structure underlying complex neuroimaging phenotypes lie at the heart of our quest to discover the genetic code of the brain. Here, we suggest a strategy for achieving such a representation by decomposing the genetic covariance matrix of complex phenotypes into maximally heritable and genetically independent components. We show that such a representation can be approximated well with eigenvectors of the genetic covariance based on a large family study. Using 520 twin pairs from the QTIM dataset, we estimate 500 principal genetic components of 54,000 vertex-wise shape features representing fourteen subcortical regions. We show that our features maintain their desired properties in practice. Further, the genetic components are found to be significantly associated with the CLU and PICALM genes in an unrelated Alzheimer's Disease (AD) dataset. The same genes are not significantly associated with other volume and shape measures in this dataset.

Indexed as

Alzheimer’s diseasebrain imaginggenome-wide association studyimaging geneticssubcortical shape

Identifiers

PMID29201284
PMCPMC5705101
OpenAlexW2664356294

What OpenQuestion holds

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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.