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      Structural connectome with high angular resolution diffusion imaging MRI: assessing the impact of diffusion weighting and sampling on graph-theoretic measures

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          Abstract

          Purpose

          Advances in computational network analysis have enabled the characterization of topological properties of human brain networks (connectomics) from high angular resolution diffusion imaging (HARDI) MRI structural measurements. In this study, the effect of changing the diffusion weighting ( b value) and sampling (number of gradient directions) was investigated in ten healthy volunteers, with specific focus on graph theoretical network metrics used to characterize the human connectome.

          Methods

          Probabilistic tractography based on the Q-ball reconstruction of HARDI MRI measurements was performed and structural connections between all pairs of regions from the automated anatomical labeling (AAL) atlas were estimated, to compare two HARDI schemes: low b value ( b = 1000) and low direction number ( n = 32) (LBLD); high b value ( b = 3000) and high number ( n = 54) of directions (HBHD).

          Results

          LBLD and HBHD data sets produced connectome images with highly overlapping hub structure. Overall, the HBHD scheme yielded significantly higher connection probabilities between cortical and subcortical sites and allowed detecting more connections. Small worldness and modularity were reduced in HBHD data. The clustering coefficient was significantly higher in HBHD data indicating a higher level of segregation in the resulting connectome for the HBHD scheme.

          Conclusion

          Our results demonstrate that the HARDI scheme as an impact on structural connectome measures which is not automatically implied by the tractography outcome. As the number of gradient directions and b values applied may introduce a bias in the assessment of network properties, the choice of a given HARDI protocol must be carefully considered when comparing results across connectomic studies.

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          Most cited references27

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          Rich-club organization of the human connectome.

          The human brain is a complex network of interlinked regions. Recent studies have demonstrated the existence of a number of highly connected and highly central neocortical hub regions, regions that play a key role in global information integration between different parts of the network. The potential functional importance of these "brain hubs" is underscored by recent studies showing that disturbances of their structural and functional connectivity profile are linked to neuropathology. This study aims to map out both the subcortical and neocortical hubs of the brain and examine their mutual relationship, particularly their structural linkages. Here, we demonstrate that brain hubs form a so-called "rich club," characterized by a tendency for high-degree nodes to be more densely connected among themselves than nodes of a lower degree, providing important information on the higher-level topology of the brain network. Whole-brain structural networks of 21 subjects were reconstructed using diffusion tensor imaging data. Examining the connectivity profile of these networks revealed a group of 12 strongly interconnected bihemispheric hub regions, comprising the precuneus, superior frontal and superior parietal cortex, as well as the subcortical hippocampus, putamen, and thalamus. Importantly, these hub regions were found to be more densely interconnected than would be expected based solely on their degree, together forming a rich club. We discuss the potential functional implications of the rich-club organization of the human connectome, particularly in light of its role in information integration and in conferring robustness to its structural core.
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            The small world of the cerebral cortex.

            While much information is available on the structural connectivity of the cerebral cortex, especially in the primate, the main organizational principles of the connection patterns linking brain areas, columns and individual cells have remained elusive. We attempt to characterize a wide variety of cortical connectivity data sets using a specific set of graph theory methods. We measure global aspects of cortical graphs including the abundance of small structural motifs such as cycles, the degree of local clustering of connections and the average path length. We examine large-scale cortical connection matrices obtained from neuroanatomical data bases, as well as probabilistic connection matrices at the level of small cortical neuronal populations linked by intra-areal and inter-areal connections. All cortical connection matrices examined in this study exhibit "small-world" attributes, characterized by the presence of abundant clustering of connections combined with short average distances between neuronal elements. We discuss the significance of these universal organizational features of cortex in light of functional brain anatomy. Supplementary materials are at www.indiana.edu/~cortex/lab.htm.
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              Connectivity Predicts deep brain stimulation outcome in Parkinson disease.

              The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort.
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                Author and article information

                Contributors
                +39 08996 5082 , faesposito@unisa.it
                Journal
                Neuroradiology
                Neuroradiology
                Neuroradiology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0028-3940
                1432-1920
                8 March 2018
                8 March 2018
                2018
                : 60
                : 5
                : 497-504
                Affiliations
                [1 ]MRI Research Center SUN-FISM – Neurological Institute for Diagnosis and Care “Hermitage Capodimonte”, 80131 Naples, Italy
                [2 ]ISNI 0000 0001 2200 8888, GRID grid.9841.4, Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, , University of Campania “Luigi Vanvitelli”, ; Naples, Italy
                [3 ]ISNI 0000 0001 2200 8888, GRID grid.9841.4, Magnetic Resonance Imaging Research Center of the Second University of Naples-Italian Foundation for Multiple Sclerosis, , Second University of Naples, ; Naples, Italy
                [4 ]ISNI 0000 0004 1937 0335, GRID grid.11780.3f, Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, , University of Salerno, ; Via S. Allende, 84081 Baronissi, Salerno Italy
                [5 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, , Maastricht University, ; 6201BC Maastricht, The Netherlands
                Author information
                http://orcid.org/0000-0002-5099-9786
                Article
                2003
                10.1007/s00234-018-2003-7
                5906499
                29520641
                041f3241-01b2-412c-83be-56ecb7ada4c0
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 10 November 2017
                : 26 February 2018
                Funding
                Funded by: Maastricht University
                Categories
                Functional Neuroradiology
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2018

                Radiology & Imaging
                diffusion mri,tractography,networks,connectivity,gradient sampling schemes,connectome

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