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Addressing head motion dependencies for small-world topologies in functional connectomics

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      Graph theoretical explorations of functional interactions within the human connectome, are rapidly advancing our understanding of brain architecture. In particular, global and regional topological parameters are increasingly being employed to quantify and characterize inter-individual differences in human brain function. Head motion remains a significant concern in the accurate determination of resting-state fMRI based assessments of the connectome, including those based on graph theoretical analysis (e.g., motion can increase local efficiency, while decreasing global efficiency and small-worldness). This study provides a comprehensive examination of motion correction strategies on the relationship between motion and commonly used topological parameters. At the individual-level, we evaluated different models of head motion regression and scrubbing, as well as the potential benefits of using partial correlation (estimated via graphical lasso) instead of full correlation. At the group-level, we investigated the utility of regression of motion and mean intrinsic functional connectivity before topological parameters calculation and/or after. Consistent with prior findings, none of the explicit motion-correction approaches at individual-level were able to remove motion relationships for topological parameters. Global signal regression (GSR) emerged as an effective means of mitigating relationships between motion and topological parameters; though at the risk of altering the connectivity structure and topological hub distributions when higher density graphs are employed (e.g., >6%). Group-level analysis correction for motion was once again found to be a crucial step. Finally, similar to recent work, we found a constellation of findings suggestive of the possibility that some of the motion-relationships detected may reflect neural or trait signatures of motion, rather than simply motion-induced artifact.

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          Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

            Author and article information

            1Nathan Kline Institute for Psychiatric Research Orangeburg, NY, USA
            2Center for the Developing Brain, Child Mind Institute New York, NY, USA
            3The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center New York, NY, USA
            4State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
            5Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University Beijing, China
            Author notes

            Edited by: Alan Evans, McGill University, Canada

            Reviewed by: Qingbao Yu, The Mind Research Network, USA; Alex Fornito, University of Melbourne, Australia

            *Correspondence: Chao-Gan Yan, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA e-mail: ycg.yan@ ;
            Michael P. Milham, Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022, USA e-mail: michael.milham@

            This article was submitted to the journal Frontiers in Human Neuroscience.

            Front Hum Neurosci
            Front Hum Neurosci
            Front. Hum. Neurosci.
            Frontiers in Human Neuroscience
            Frontiers Media S.A.
            26 December 2013
            : 7
            Copyright © 2013 Yan, Craddock, He and Milham.

            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: 11, Tables: 3, Equations: 0, References: 51, Pages: 19, Words: 11833
            Original Research Article


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