Evidence map›Paper›PMID 30702182›Full record

ArticleJournal of neuroimaging : official journal of the American Society of Neuroimaging2019

Variability of Resting-State Functional MRI Graph Theory Metrics across 3T Platforms.

Ranliang Hu, Deqiang Qiu, Ying Guo, Yujie Zhao, Christopher Leatherday, Junjie Wu, Jason W Allen

Open access · greenAbstract read
In one paragraph

Article in Journal of neuroimaging : official journal of the American Society of Neuroimaging, 2019. 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
0.4field-weighted citation impact, top 42% 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, 5 citations in OpenAlex.

  1. Article
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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 1 institution in 1 country.

Ranliang HuDepartment of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA.ORCID 0000-0001-7767-1652
Deqiang QiuDepartment of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA.
Ying GuoDepartment of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA.
Yujie ZhaoDepartment of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA.
Christopher LeatherdayDepartment of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA.
Junjie WuDepartment of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA.
Jason W AllenDepartment of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA.
Emory University · US

Funding

Statistical ICA Methods for Analysis & Integration of Multi-dimensional Data Diversity SupplementR01MH105561 · NIMH · EMORY UNIVERSITY · PI GUO, YING, KANG, JIAN · 2014 to 2024
$4.3M
Statistical Methods for Analyzing Complex, Multi-dimensional Data from Cross-sectional and Longitudinal Mental Health StudiesR01MH118771 · NIMH · EMORY UNIVERSITY · PI GUO, YING, MANATUNGA, AMITA K. · 2019 to 2023
$3.1M
Method Development of Agreement Measures and Applications in Mental HealthR01MH079448 · NIMH · EMORY UNIVERSITY · PI GUO, YING, MANATUNGA, AMITA K. · 2008 to 2016
$2.2M
NIMH NIH HHS R01 MH079448NIMH NIH HHS R01 MH105561NIMH NIH HHS R01 MH118771
6 · The paper itself

Abstract

background and purposeGraph theory analysis of brain connectivity data is a promising tool for studying the function of the healthy and diseased brain. The consistency of resting-state functional MRI (rsfMRI) connectivity measures across multiple scanner types is an important factor in designing multi-institutional research studies and has important implications for the potential use of this technique in a heterogeneous clinical setting. We sought to quantitatively study the interscanner variability of rsfMRI graph theory metrics obtained from healthy volunteers scanned on three different scanner platforms.

methodsIn this prospective Institutional Review Board approved study, 9 healthy volunteers were enrolled for brain MRI on three 3T scanners (Magnetom Prisma, Skyra, and Trio, Siemens, Erlangen, Germany) in three separate scan sessions within approximately 1 week. Standard preprocessing of rsfMRI was performed with SPM12. Subject scans were normalized to Montreal Neurologic Institute (MNI) space, and connectivity of 116 regions-of-interests based on the automated anatomic labeling (AAL) atlas was calculated using Conn toolbox. Whole-network graph theory metrics were calculated using Brain Connectivity Toolbox, and intraclass correlation (ICC) across three scan sessions was assessed.

resultsA total of 25 rsfMRI exams were completed in 9 subjects with a median-intersession time of 3 days. Among all three sessions, there was good to excellent agreement in characteristic path length and global efficiency (ICC: .79, .79) and good agreement in the transitivity, local efficiency, and clustering coefficient (ICC = .72, .69, .62).

conclusionsThere was high consistency of graph theory metrics of rsfMRI connectivity networks among healthy volunteers scanned on three different generation 3T MRI scanners.

Indexed as

AdultAlgorithmsBrainBrain MappingFemaleHealthy VolunteersHumansMagnetic Resonance ImagingMaleNerve NetProspective Studiesgraph theoryresting state functional MRIvariability

Identifiers

PMID30702182
PMCPMC6506355
OpenAlexW2914176589

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.