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      A gradient of hemisphere-specific dorsal to ventral processing routes in parieto-premotor networks

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          Abstract

          Networks in the parietal and premotor cortices enable essential human abilities regarding motor processing, including attention and tool use. Even though our knowledge on its topography has steadily increased, a detailed picture of hemisphere-specific integrating pathways is still lacking. With the help of multishell diffusion magnetic resonance imaging, probabilistic tractography, and the Graph Theory Analysis, we investigated connectivity patterns between frontal premotor and posterior parietal brain areas in healthy individuals. With a two-stage node characterization approach, we defined the network role of precisely mapped cortical regions from the Julich-Brain atlas. We found evidence for a third, left-sided, medio-dorsal subpathway in a successively graded dorsal stream, referencing more specialized motor processing on the left. Supplementary motor areas had a strongly lateralized connectivity to either left dorsal or right ventral parietal domains, representing an action-attention dichotomy between hemispheres. The left sulcal parietal regions primarily coupled with areas 44 and 45, mirrored by the inferior frontal junction (IFJ) on the right, a structural lateralization we termed as “Broca’s-IFJ switch.” We were able to deepen knowledge on gyral and sulcal pathways as well as domain-specific contributions in parieto-premotor networks. Our study sheds new light on the complex lateralization of cortical routes for motor activity in the human brain.

          Author Summary

          Human motor abilities are processed via specialized yet intertwined pathways in the parietal and premotor cortex. These can be parcellated into networks of brain areas, sharing connection patterns that differ between hemispheres. We differentiated a set of graded pathways, connecting definable network hubs. The well-known Dorsal Stream for visuomotor transformation appears to be left-dominantly divisible into three distinct substreams, providing more anatomical detail about its specialization into visual and semantic segments. Supplementary motor areas show lateralized couplings with left dorsal and right ventral parietal areas, respectively, while left-sided AIPS connectivity to area 44/45 is mirrored by right-sided intersulcal links to the inferior frontal sulcus, both deepening our understanding of incorporated multi-task faculties like attention and speech.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Collective dynamics of 'small-world' networks.

            Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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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 (http://www.brain-connectivity-toolbox.net) 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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                Author and article information

                Contributors
                Role: Role: Role: Role: Role: Role: Role: Role: Role:
                Role: Role: Role: Role: Role:
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                Journal
                Netw Neurosci
                Netw Neurosci
                netn
                Network Neuroscience
                MIT Press (255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA journals-info@mit.edu )
                2472-1751
                2024
                10 December 2024
                : 8
                : 4
                : 1563-1589
                Affiliations
                [1]Department of Clinical Cognition Science, Clinic of Neurology at the RWTH Aachen University Faculty of Medicine, ZBMT, Aachen, Germany
                [2]Institute for Neuroscience and Medicine (INM-1), Research Centre Jülich GmbH, Jülich, Germany
                [3]Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
                [4]JARA-BRAIN, Juelich-Aachen Research Alliance, Juelich, Germany
                [5]Institute for Neuroscience and Medicine (INM-4), Research Center Jülich GmbH, Jülich, Germany
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                * Corresponding Author: fbinkofski@ 123456ukaachen.de
                [†]

                Both authors contributed equally as senior authors to this work.

                Handling Editor: Alex Fornito

                Author information
                https://orcid.org/0000-0002-6750-943X
                Article
                netn_a_00407
                10.1162/netn_a_00407
                11675101
                39735515
                ca2121c5-cb3b-436a-97c7-16508c1e97d2
                © 2024 Massachusetts Institute of Technology

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/.

                History
                : 19 January 2024
                : 06 June 2024
                Page count
                Pages: 27
                Funding
                Funded by: Horizon 2020 Framework Programme, DOI 10.3030/945539;
                Award ID: 945539; HBP SGA3
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                Jüchtern, M., Shaikh, U. J., Caspers, S., & Binkofski, F. (2024). A gradient of hemisphere-specific dorsal to ventral processing routes in parieto-premotor networks. Network Neuroscience, 8(4), 1563–1589. https://doi.org/10.1162/netn_a_00407

                dorsal stream,frontoparietal,graph theory analysis,structural connectivity,tractography

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