Evidence map›Paper›PMID 25670955›Full record

ArticlePsychiatry investigation2015

Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment.

Seok Woo Moon, Ivo D Dinov, Alen Zamanyan, Ran Shi, Alex Genco, Sam Hobel, Paul M Thompson, Arthur W Toga, Alzheimer's Disease Neuroimaging Initiative (ADNI)

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Psychiatry investigation, 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
–field-weighted citation impact
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.

  1. Article
  2. Review
  3. Review
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

9 authors.

Seok Woo MoonDepartment of Psychiatry, Konkuk University School of Medicine, Chungju, Republic of Korea.
Ivo D DinovLaboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA. ; Statistics Online Computational Resource, UMSM, University of Michigan, Ann Arbor, MI, USA.
Alen ZamanyanLaboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
Ran ShiLaboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
Alex GencoLaboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
Sam HobelLaboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
Paul M ThompsonLaboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
Arthur W TogaLaboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
Alzheimer's Disease Neuroimaging Initiative (ADNI)

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
VASCULAR RISK AND COGNITIVE STATUS IN A LATINO POPULATIONP50AG005142 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ARANDA, MARIA P. · 1985 to 2019
$41.0M
UC Davis Alzheimer's Disease Core CenterP30AG010129 · NIA · UNIVERSITY OF CALIFORNIA DAVIS · PI JIN, LEE-WAY · 1991 to 2020
$28.3M
Center for Computational Biology (RMI)U54RR021813 · NCRR · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI TOGA, ARTHUR W · 2004 to 2010
$25.3M
FUNCTION BIRNU24RR021992 · NCRR · UNIVERSITY OF CALIFORNIA-IRVINE · PI POTKIN, STEVEN G · 2006 to 2010
$25.0M
YALE CENTER FOR THE: LEARNING &ATTENTIONP41RR013642 · NCRR · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BEARER, ELAINE L · 1998 to 2011
$21.9M
Pilot and Feasibility (P and F) ProgramP30DK089503 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Joyce Lee · 2010 to 2026
$20.3M
BIO-INFORMATICS RESEARCH NETWORK COORDINATING CENTER (BIRN-CC)U24RR025736 · NCRR · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KESSELMAN, CARL · 2009 to 2011
$15.0M
TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VAN HORN, JOHN DARRELL · 2012 to 2022
$14.1M
TrainingU54EB020406 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TOGA, ARTHUR W · 2014 to 2018
$12.5M
Public Outreach and Education CoreP50NS091856 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALBIN, ROGER L · 2014 to 2019
$10.1M
Training U54EB020403 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2014 to 2018
$10.1M
NCRR NIH HHS P41 RR013642NCRR NIH HHS R21 RR019771NCRR NIH HHS U24 RR021992NCRR NIH HHS U24 RR025736NCRR NIH HHS U54 RR021813NIA NIH HHS K01 AG030514NIA NIH HHS P30 AG010129NIA NIH HHS P50 AG005142NIA NIH HHS RF1 AG041915NIA NIH HHS U01 AG024904NIBIB NIH HHS P41 EB015922NIBIB NIH HHS U54 EB020403NIBIB NIH HHS U54 EB020406NIDDK NIH HHS P30 DK089503NIMH NIH HHS R01 MH071940NINDS NIH HHS P50 NS091856NINR NIH HHS P20 NR015331NLM NIH HHS R01 LM005639
6 · The paper itself

Abstract

objectiveThis article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers.

methodsNine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers.

resultsWe identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area).

conclusionWe observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.

Indexed as

ADNIAlzheimer's diseaseEarly-onsetGeneticsMemoryMild cognitive impairmentNeuroimaging

Identifiers

PMID25670955
PMCPMC4310910

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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.