Optimization of rs-fMRI Pre-processing for Enhanced Signal-Noise Separation, Test-Retest Reliability, and Group Discrimination

Shirer, William R.; Jiang, Hongsi; Price, Chester W.; Ng, Bernard; Greicius, Michael D.

doi:10.1016/j.neuroimage.2015.05.015

Cited by 179 publications

(195 citation statements)

References 61 publications

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“…We did not find convincing evidence for using slice-timing correction. In agreement with previous studies [18], [19], [20], [21] and following our results, we suggest to not use the filtering of global signal in the stage of data preprocessing. To further investigate the impact of preprocessing pipeline on functional connectivity, we will test the features for group discriminability, which is crucial when evaluating potential markers of a disease.…”

Section: Discussionsupporting

confidence: 93%

“…These studies, however, mostly target only a specific effect of preprocessing of the data. Follow-up studies such as [17], [18], [19], [20], [21] evaluated specific preprocessing steps and their impact of diverse features of functional connectivity.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we examine different preprocessing pipelines covering usage of STC, RETROICOR [15] for cardiac and breathing filtering, high pass filtering, movement correction, WM, CSF and global signal (GS) filtering. Our study is inspired by the consensual pipeline for structural brain networks evaluating different parcellation atlases [22] and is a direct extension to [20], [18], [21].…”

Section: Introductionmentioning

confidence: 99%

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The impact of diverse preprocessing pipelines on brain functional connectivity

Výtvarová

Fousek

Bartoň

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Brain functional connectivity measured by functional magnetic resonance imaging was shown to be influenced by preprocessing procedures. We aim to describe this influence separately for different preprocessing factors and in 20 different most used preprocessing pipelines. We evaluate the effects of slice-timing correction and physiological noise filtering by RETROICOR, diverse levels of motion correction, and white matter, cerebrospinal fluid, and global signal filtering. With usage of three datasets, we show the impact on global metrics of restingstate functional brain networks and their reliability. We show negative effect of RETROICOR on reliability of metrics and disrupting effect of global signal regression on network topology. We do not support the use of slice-timing correction because it does not significantly influence any of the measured features. We also show that the selected types of preprocessing may affect averaged node strength, normalized clustering coefficient, normalized characteristic path length and modularity.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The impact of diverse preprocessing pipelines on brain functional connectivity

Výtvarová

Fousek

Bartoň

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…ICCs for fcMRI in other disciplines have thus far proved widely disparate in range depending on the scan duration, preprocessing, modeling, and network tested [5,10,14,31]. Current estimates suggest that fcMRI ICCs range from 0.35–0.93, with ICCs of 0.5–0.6 most commonly reported.…”

Section: Discussionmentioning

confidence: 99%

Test-retest reliability of pain-related functional brain connectivity compared with pain self-report

Letzen

Boissoneault

Sevel

et al. 2016

Pain

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Test-retest reliability, or reproducibility of results over time, is poorly established for functional brain connectivity (fcMRI) during painful stimulation. As reliability informs the validity of research findings, it is imperative to examine, especially given recent emphasis on using functional neuroimaging as a tool for biomarker development. Although proposed pain neural signatures have been derived using complex, multivariate algorithms, even the reliability of less complex fcMRI findings has yet to be reported. The present study examined the test-retest reliability for fcMRI of pain-related brain regions, as well as self-reported pain [via visual analogue scales (VASs)]. Thirty-two healthy individuals completed three consecutive fMRI runs of a thermal pain task. Functional connectivity analyses were completed on pain-related brain regions. Intraclass correlations (ICC) were conducted on fcMRI values and VAS scores across the fMRI runs. ICC coefficients for fcMRI values varied widely (range = −.174–.766), with fcMRI between right nucleus accumbens and medial prefrontal cortex showing the highest reliability (range = .649–.766). ICC coefficients for VAS scores ranged from .906–.947. Overall, self-reported pain was more reliable than fcMRI data. These results highlight that fMRI findings might be less reliable than inherently assumed, and have implications for future studies proposing pain markers.

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“…Others contend that biased correlation patterns cannot be explained solely as a consequence of using global signal correction within preprocessing and may have a biological basis (Chai et al, 2012;Fox et al, 2009). In recently published research identifying the quality of the outcome data based on different processing pipelines, global signal regression significantly increased signal-noise separation and did not affect the classification of clinical patients versus controls (Shirer et al, 2015). While this research was not designed to address the controversy, variable RSFC (Batterink et al, 2010;Dong et al, 2014;Stice et al, 2013).…”

Section: Resting State Connectivity and Bulimic Symptoms 13mentioning

confidence: 98%