Fatma Gargouri, Fathi Kallel, Sebastien Delphine, Ahmed Ben Hamida, Stéphane Lehéricy, Romain Valabregue
Abstract
Resting state functional MRI (rs-fMRI) is an imaging technique that allows the spontaneous activity of the brain to be measured. Measures of functional connectivity highly depend on the quality of the BOLD signal data processing. In this study, our aim was to study the influence of preprocessing steps and their order of application on small-world topology and their efficiency in resting state fMRI data analysis using graph theory. We applied the most standard preprocessing steps: slice-timing, realign, smoothing, filtering, and the tCompCor method. In particular, we were interested in how preprocessing can retain the small-world economic properties and how to maximize the local and global efficiency of a network while minimizing the cost. Tests that we conducted in 54 healthy subjects showed that the choice and ordering of preprocessing steps impacted the graph measures. We found that thecsr(where we applied realignment, smoothing, and tCompCor as a final step) and thescr(where we applied realignment, tCompCor and smoothing as a final step) strategies had the highest mean values of global efficiency
(eg)
. Furthermore, we found that thefscrstrategy (where we applied realignment, tCompCor, smoothing, and filtering as a final step), had the highest mean local efficiency
(el)
values. These results confirm that the graph theory measures of functional connectivity depend on the ordering of the processing steps, with the best results being obtained using smoothing and tCompCor as the final steps for global efficiency with additional filtering for local efficiency.
This research output has an Altmetric Attention Score of 6. 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 22 February 2018.
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#6,475,284
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Outputs from Frontiers in Computational Neuroscience
#279
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Outputs of similar age
#123,028
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Outputs of similar age from Frontiers in Computational Neuroscience
#8
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Altmetric has tracked 25,375,376 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,456 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has done well, scoring higher than 80% of its peers.
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We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
