Evidence map›Paper›PMID 26413210›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2015

GENETIC ANALYSIS OF STRUCTURAL BRAIN CONNECTIVITY USING DICCCOL MODELS OF DIFFUSION MRI IN 522 TWINS.

Dajiang Zhu, Liang Zhan, Joshua Faskowitz, Madelaine Daianu, Neda Jahanshad, Greig I de Zubicaray, Katie L McMahon, Nicholas G Martin, Margaret J Wright, Paul M Thompson

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 31% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

10 authors at 3 institutions in 2 countries.

Dajiang ZhuImaging Genetics Center, University of Southern California, CA, USA.
Liang ZhanImaging Genetics Center, University of Southern California, CA, USA.
Joshua FaskowitzImaging Genetics Center, University of Southern California, CA, USA.
Madelaine DaianuImaging Genetics Center, University of Southern California, CA, USA.
Neda JahanshadImaging Genetics Center, University of Southern California, CA, USA.
Greig I de ZubicaraySchool of Psychology, University of Queensland, Brisbane, Australia.
Katie L McMahonCenter for Advanced Imaging, University of Queensland, Brisbane, Australia.
Nicholas G MartinQIMR Berghofer Medical Research Institute, Brisbane, Australia.
Margaret J WrightQIMR Berghofer Medical Research Institute, Brisbane, Australia.
Paul M ThompsonImaging Genetics Center, University of Southern California, CA, USA.
University of Southern California · USQIMR Berghofer Medical Research Institute · AUUniversity of Queensland · AU

Funding

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
Genetic influences on the brain: A twin imaging studyR01HD050735 · NICHD · QUEENSLAND INSTITUTE OF MEDICAL RESEARCH · PI WRIGHT, MARGARET JANE · 2007 to 2011
$2.6M
NIBIB NIH HHS P41 EB015922NIBIB NIH HHS U54 EB020403NICHD NIH HHS R01 HD050735
6 · The paper itself

Abstract

Genetic and environmental factors affect white matter connectivity in the normal brain, and they also influence diseases in which brain connectivity is altered. Little is known about genetic influences on brain connectivity, despite wide variations in the brain's neural pathways. Here we applied the "DICCCOL" framework to analyze structural connectivity, in 261 twin pairs (522 participants, mean age: 21.8 y ± 2.7SD). We encoded connectivity patterns by projecting the white matter (WM) bundles of all "DICCCOLs" as a tracemap (TM). Next we fitted an A/C/E structural equation model to estimate additive genetic (A), common environmental (C), and unique environmental/error (E) components of the observed variations in brain connectivity. We found 44 "heritable DICCCOLs" whose connectivity was genetically influenced (

Indexed as

DICCCOLheritability

Identifiers

PMID26413210
PMCPMC4578156
OpenAlexW1483721262

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.