Evidence map›Paper›PMID 29725294›Full record

ArticleFrontiers in human neuroscience2018

Effects of Gradient Coil Noise and Gradient Coil Replacement on the Reproducibility of Resting State Networks.

Epifanio Bagarinao, Erina Tsuzuki, Yukina Yoshida, Yohei Ozawa, Maki Kuzuya, Takashi Otani, Shuji Koyama, Haruo Isoda, Hirohisa Watanabe, Satoshi Maesawa and 2 more

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

Epifanio BagarinaoBrain and Mind Research Center, Nagoya University, Nagoya, Japan.
Erina TsuzukiDepartment of Radiological Technology, School of Health Sciences, Nagoya University, Nagoya, Japan.
Yukina YoshidaDepartment of Radiological Technology, School of Health Sciences, Nagoya University, Nagoya, Japan.
Yohei OzawaDepartment of Radiological Technology, School of Health Sciences, Nagoya University, Nagoya, Japan.
Maki KuzuyaDepartment of Radiological Technology, School of Health Sciences, Nagoya University, Nagoya, Japan.
Takashi OtaniDepartment of Radiological Technology, School of Health Sciences, Nagoya University, Nagoya, Japan.
Shuji KoyamaBrain and Mind Research Center, Nagoya University, Nagoya, Japan.
Haruo IsodaBrain and Mind Research Center, Nagoya University, Nagoya, Japan.
Hirohisa WatanabeBrain and Mind Research Center, Nagoya University, Nagoya, Japan.
Satoshi MaesawaBrain and Mind Research Center, Nagoya University, Nagoya, Japan.
Shinji NaganawaBrain and Mind Research Center, Nagoya University, Nagoya, Japan.
Gen SobueBrain and Mind Research Center, Nagoya University, Nagoya, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The stability of the MRI scanner throughout a given study is critical in minimizing hardware-induced variability in the acquired imaging data set. However, MRI scanners do malfunction at times, which could generate image artifacts and would require the replacement of a major component such as its gradient coil. In this article, we examined the effect of low intensity, randomly occurring hardware-related noise due to a faulty gradient coil on brain morphometric measures derived from T1-weighted images and resting state networks (RSNs) constructed from resting state functional MRI. We also introduced a method to detect and minimize the effect of the noise associated with a faulty gradient coil. Finally, we assessed the reproducibility of these morphometric measures and RSNs before and after gradient coil replacement. Our results showed that gradient coil noise, even at relatively low intensities, could introduce a large number of voxels exhibiting spurious significant connectivity changes in several RSNs. However, censoring the affected volumes during the analysis could minimize, if not completely eliminate, these spurious connectivity changes and could lead to reproducible RSNs even after gradient coil replacement.

Indexed as

gradient coil noisegradient coil replacementreproducibilityresting state fMRIresting state networks

Identifiers

PMID29725294
PMCPMC5917444

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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.