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      Evaluating the reliability of different preprocessing steps to estimate graph theoretical measures in resting state fMRI data

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          With resting-state functional MRI (rs-fMRI) there are a variety of post-processing methods that can be used to quantify the human brain connectome. However, there is also a choice of which preprocessing steps will be used prior to calculating the functional connectivity of the brain. In this manuscript, we have tested seven different preprocessing schemes and assessed the reliability between and reproducibility within the various strategies by means of graph theoretical measures. Different preprocessing schemes were tested on a publicly available dataset, which includes rs-fMRI data of healthy controls. The brain was parcellated into 190 nodes and four graph theoretical (GT) measures were calculated; global efficiency (GEFF), characteristic path length (CPL), average clustering coefficient (ACC), and average local efficiency (ALE). Our findings indicate that results can significantly differ based on which preprocessing steps are selected. We also found dependence between motion and GT measurements in most preprocessing strategies. We conclude that by using censoring based on outliers within the functional time-series as a processing, results indicate an increase in reliability of GT measurements with a reduction of the dependency of head motion.

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          Most cited references 47

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          Complex network measures of brain connectivity: uses and interpretations.

          Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox ( accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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            Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.

            An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
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              Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

              Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements. Copyright © 2011 Elsevier Inc. All rights reserved.

                Author and article information

                1Faculdade de Engenharia, PUCRS Porto Alegre, Brazil
                2Instituto do Cérebro do Rio Grande do Sul (InsCer-RS), PUCRS Porto Alegre, Brazil
                3Faculdade de Física, PUCRS Porto Alegre, Brazil
                4Faculdade de Medicina, PUCRS Porto Alegre, Brazil
                Author notes

                Edited by: Lourens Waldorp, University of Amsterdam, Netherlands

                Reviewed by: Elisha Moses, Weizmann Institute, Israel; Xi-Nian Zuo, Chinese Academy of Sciences, China

                *Correspondence: Alexandre R. Franco, Instituto do Cérebro do Rio Grande do Sul (InsCer-RS), PUCRS, Av. Ipiranga 6690, Porto Alegre 90610-000, Brazil e-mail: alexandre.franco@

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience.

                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                19 February 2015
                : 9
                4333797 10.3389/fnins.2015.00048
                Copyright © 2015 Aurich, Alves Filho, Marques da Silva and Franco.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Figures: 3, Tables: 4, Equations: 1, References: 47, Pages: 10, Words: 7222
                Original Research Article


                pre-processing, reliability, graph theory, functional mri, resting state


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