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Integration of routine QA data into mega‐analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies

Overview of attention for article published in Human Brain Mapping, November 2017
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Title
Integration of routine QA data into mega‐analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies
Published in
Human Brain Mapping, November 2017
DOI 10.1002/hbm.23900
Pubmed ID
Authors

Peter Kochunov, Erin W. Dickie, Joseph D. Viviano, Jessica Turner, Peter B. Kingsley, Neda Jahanshad, Paul M. Thompson, Meghann C. Ryan, Els Fieremans, Dmitry Novikov, Jelle Veraart, Elliot L. Hong, Anil K. Malhotra, Robert W. Buchanan, Sofia Chavez, Aristotle N. Voineskos

Abstract

A novel mega-analytical approach that reduced methodological variance was evaluated using a multisite diffusion tensor imaging (DTI) fractional anisotropy (FA) data by comparing white matter integrity in people with schizophrenia to controls. Methodological variance was reduced through regression of variance captured from quality assurance (QA) and by using Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising. N = 192 (119 patients/73 controls) data sets were collected at three sites equipped with 3T MRI systems: GE MR750, GE HDx, and Siemens Trio. DTI protocol included five b = 0 and 60 diffusion-sensitized gradient directions (b = 1,000 s/mm2 ). In-house DTI QA protocol data was acquired weekly using a uniform phantom; factor analysis was used to distil into two orthogonal QA factors related to: SNR and FA. They were used as site-specific covariates to perform mega-analytic data aggregation. The effect size of patient-control differences was compared to these reported by the enhancing neuro imaging genetics meta-analysis (ENIGMA) consortium before and after regressing QA variance. Impact of MP-PCA filtering was evaluated likewise. QA-factors explained ∼3-4% variance in the whole-brain average FA values per site. Regression of QA factors improved the effect size of schizophrenia on whole brain average FA values-from Cohen's d = .53 to .57-and improved the agreement between the regional pattern of FA differences observed in this study versus ENIGMA from r = .54 to .70. Application of MP-PCA-denoising further improved the agreement to r = .81. Regression of methodological variances captured by routine QA and advanced denoising that led to a better agreement with a large mega-analytic study.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 25%
Student > Master 4 14%
Researcher 3 11%
Professor > Associate Professor 3 11%
Student > Postgraduate 2 7%
Other 3 11%
Unknown 6 21%
Readers by discipline Count As %
Medicine and Dentistry 7 25%
Neuroscience 5 18%
Psychology 2 7%
Physics and Astronomy 1 4%
Social Sciences 1 4%
Other 3 11%
Unknown 9 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 November 2017.
All research outputs
#12,302,943
of 13,904,332 outputs
Outputs from Human Brain Mapping
#2,681
of 2,987 outputs
Outputs of similar age
#335,312
of 400,499 outputs
Outputs of similar age from Human Brain Mapping
#97
of 105 outputs
Altmetric has tracked 13,904,332 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,987 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.8. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 105 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.