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      Disrutpted resting-state functional architecture of the brain after 45-day simulated microgravity

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          Abstract

          Long-term spaceflight induces both physiological and psychological changes in astronauts. To understand the neural mechanisms underlying these physiological and psychological changes, it is critical to investigate the effects of microgravity on the functional architecture of the brain. In this study, we used resting-state functional MRI (rs-fMRI) to study whether the functional architecture of the brain is altered after 45 days of −6° head-down tilt (HDT) bed rest, which is a reliable model for the simulation of microgravity. Sixteen healthy male volunteers underwent rs-fMRI scans before and after 45 days of −6° HDT bed rest. Specifically, we used a commonly employed graph-based measure of network organization, i.e., degree centrality (DC), to perform a full-brain exploration of the regions that were influenced by simulated microgravity. We subsequently examined the functional connectivities of these regions using a seed-based resting-state functional connectivity (RSFC) analysis. We found decreased DC in two regions, the left anterior insula (aINS) and the anterior part of the middle cingulate cortex (MCC; also called the dorsal anterior cingulate cortex in many studies), in the male volunteers after 45 days of −6° HDT bed rest. Furthermore, seed-based RSFC analyses revealed that a functional network anchored in the aINS and MCC was particularly influenced by simulated microgravity. These results provide evidence that simulated microgravity alters the resting-state functional architecture of the brains of males and suggest that the processing of salience information, which is primarily subserved by the aINS–MCC functional network, is particularly influenced by spaceflight. The current findings provide a new perspective for understanding the relationships between microgravity, cognitive function, autonomic neural function, and central neural activity.

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          Most cited references32

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          An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.

          Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Identification and Classification of Hubs in Brain Networks

            Brain regions in the mammalian cerebral cortex are linked by a complex network of fiber bundles. These inter-regional networks have previously been analyzed in terms of their node degree, structural motif, path length and clustering coefficient distributions. In this paper we focus on the identification and classification of hub regions, which are thought to play pivotal roles in the coordination of information flow. We identify hubs and characterize their network contributions by examining motif fingerprints and centrality indices for all regions within the cerebral cortices of both the cat and the macaque. Motif fingerprints capture the statistics of local connection patterns, while measures of centrality identify regions that lie on many of the shortest paths between parts of the network. Within both cat and macaque networks, we find that a combination of degree, motif participation, betweenness centrality and closeness centrality allows for reliable identification of hub regions, many of which have previously been functionally classified as polysensory or multimodal. We then classify hubs as either provincial (intra-cluster) hubs or connector (inter-cluster) hubs, and proceed to show that lesioning hubs of each type from the network produces opposite effects on the small-world index. Our study presents an approach to the identification and classification of putative hub regions in brain networks on the basis of multiple network attributes and charts potential links between the structural embedding of such regions and their functional roles.
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              Unified segmentation

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                Author and article information

                Contributors
                Journal
                Front Behav Neurosci
                Front Behav Neurosci
                Front. Behav. Neurosci.
                Frontiers in Behavioral Neuroscience
                Frontiers Media S.A.
                1662-5153
                05 June 2014
                2014
                : 8
                : 200
                Affiliations
                [1] 1Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences Beijing, China
                [2] 2University of Chinese Academy of Sciences Beijing, China
                [3] 3National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center Beijing, China
                Author notes

                Edited by: Francesca Cirulli, Istituto Superiore di Sanità, Italy

                Reviewed by: Gustav Schelling, Ludwig-Maximilians University, Germany; Francesca Cirulli, Istituto Superiore di Sanità, Italy

                *Correspondence: Shan-Guang Chen, National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Haidian District, Beijing 100094, China e-mail: shanguang_chen@ 123456126.com ;
                Shu Li, Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 4A Datun Road, Chaoyang District, Beijing 100101, China e-mail: lishu@ 123456psych.ac.cn ; s.li@ 123456unswalumni.com

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

                Article
                10.3389/fnbeh.2014.00200
                4046318
                24926242
                fc36e60f-48ad-44f1-a52b-250641252311
                Copyright © 2014 Zhou, Wang, Rao, Liang, Chen, Zheng, Tan, Tian, Wang, Bai, Chen and Li.

                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.

                History
                : 28 January 2014
                : 18 May 2014
                Page count
                Figures: 3, Tables: 3, Equations: 0, References: 69, Pages: 10, Words: 8002
                Categories
                Neuroscience
                Original Research Article

                Neurosciences
                anterior insula,cingulate cortex,head-down tilt bed rest,functional magnetic resonance imaging (fmri),functional connectivity,resting state

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