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      Structural connectivity of autonomic, pain, limbic, and sensory brainstem nuclei in living humans based on 7 Tesla and 3 Tesla MRI

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          Abstract

          Autonomic, pain, limbic, and sensory processes are mainly governed by the central nervous system, with brainstem nuclei as relay centers for these crucial functions. Yet, the structural connectivity of brainstem nuclei in living humans remains understudied. These tiny structures are difficult to locate using conventional in vivo MRI, and ex vivo brainstem nuclei atlases lack precise and automatic transformability to in vivo images. To fill this gap, we mapped our recently developed probabilistic brainstem nuclei atlas developed in living humans to high‐spatial resolution (1.7 mm isotropic) and diffusion weighted imaging (DWI) at 7 Tesla in 20 healthy participants. To demonstrate clinical translatability, we also acquired 3 Tesla DWI with conventional resolution (2.5 mm isotropic) in the same participants. Results showed the structural connectome of 15 autonomic, pain, limbic, and sensory (including vestibular) brainstem nuclei/nuclei complex (superior/inferior colliculi, ventral tegmental area‐parabrachial pigmented, microcellular tegmental–parabigeminal, lateral/medial parabrachial, vestibular, superior olivary, superior/inferior medullary reticular formation, viscerosensory motor, raphe magnus/pallidus/obscurus, parvicellular reticular nucleus‐alpha part), derived from probabilistic tractography computation. Through graph measure analysis, we identified network hubs and demonstrated high intercommunity communication in these nuclei. We found good ( r = .5) translational capability of the 7 Tesla connectome to clinical (i.e., 3 Tesla) datasets. Furthermore, we validated the structural connectome by building diagrams of autonomic/pain/limbic connectivity, vestibular connectivity, and their interactions, and by inspecting the presence of specific links based on human and animal literature. These findings offer a baseline for studies of these brainstem nuclei and their functions in health and disease, including autonomic dysfunction, chronic pain, psychiatric, and vestibular disorders.

          Abstract

          Autonomic, pain, limbic, and sensory processes are mainly governed by the central nervous system, with brainstem nuclei as relay centers for these crucial functions and yet the structural connectivity of brainstem nuclei in living humans remains understudied due to difficulty to locate using conventional in vivo MRI, and ex vivo brainstem nuclei atlases lack precise and automatic transformability to in vivo images. To fill this gap, we mapped our recently developed probabilistic brainstem nuclei atlas developed in living humans to high spatial resolution (1.7 mm isotropic) and diffusion weighted imaging (DWI) at 7 Tesla in 20 healthy participants and demonstrate clinical translatability, using 3 Tesla DWI with conventional resolution (2.5 mm isotropic) in the same participants. We show the structural connectome of 15 autonomic, pain, limbic, and sensory (including vestibular) brainstem nuclei/nuclei complex (superior/inferior colliculi, ventral tegmental area‐parabrachial pigmented, microcellular tegmental–parabigeminal, lateral/medial parabrachial, vestibular, superior olivary, superior/inferior medullary reticular formation, viscerosensory motor, raphe magnus/pallidus/obscurus, parvicellular reticular nucleus‐alpha part), derived from probabilistic tractography computation.

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          An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

          In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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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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              Complex brain networks: graph theoretical analysis of structural and functional systems.

              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.
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                Author and article information

                Contributors
                ksingh0@mgh.harvard.edu
                martab@mgh.harvard.edu
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                19 March 2022
                July 2022
                : 43
                : 10 ( doiID: 10.1002/hbm.v43.10 )
                : 3086-3112
                Affiliations
                [ 1 ] Brainstem Imaging Laboratory, Department of Radiology Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School Boston Massachusetts USA
                [ 2 ] Escuela Nacional de Estudios Superiores, Juriquilla Universidad Nacional Autónoma de México Querétaro Mexico
                [ 3 ] Life Sciences Institute Sant'Anna School of Advanced Studies Pisa Italy
                [ 4 ] Research Center E. Piaggio University of Pisa Pisa Italy
                [ 5 ] Department of Psychiatry and Psychology Mayo Clinic Rochester Minnesota USA
                [ 6 ] Department of Otorhinolaryngology – Head and Neck Surgery Mayo Clinic Rochester Minnesota USA
                [ 7 ] Department of Biomedical and Dental Sciences and Morphofunctional Imaging University of Messina Italy
                [ 8 ] Laboratory of Neuromotor Physiology IRCCS Santa Lucia Foundation Rome Italy
                [ 9 ] Division of Sleep Medicine Harvard University Boston Massachusetts USA
                Author notes
                [*] [* ] Correspondence

                Kavita Singh and Marta Bianciardi, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Building 149, Room 2301, 13th Street, Charlestown, Boston, MA 02129, USA.

                Email: ksingh0@ 123456mgh.harvard.edu and martab@ 123456mgh.harvard.edu

                Author information
                https://orcid.org/0000-0002-4772-372X
                https://orcid.org/0000-0001-5633-3419
                Article
                HBM25836
                10.1002/hbm.25836
                9188976
                35305272
                b3bc354d-3d72-47d7-a38a-6eeb041ff207
                © 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 09 February 2022
                : 25 October 2021
                : 06 March 2022
                Page count
                Figures: 12, Tables: 2, Pages: 27, Words: 19080
                Funding
                Funded by: Harvard Mind Brain Behavior
                Funded by: Italian Ministry of Health
                Award ID: RF‐2019‐12369194
                Award ID: IRCCS Fondazione Santa Lucia
                Funded by: MGH Claflin Award
                Funded by: National Institute of Biomedical Imaging and Bioengineering , doi 10.13039/100000070;
                Award ID: NIBIB‐K01EB019474
                Funded by: National Institute on Deafness and Other Communication Disorders , doi 10.13039/100000055;
                Award ID: NIDCD‐R21DC015888
                Funded by: NIA‐R01AG063982
                Funded by: U.S. Department of Defense Congressionally Directed Medical Research Program
                Award ID: W81XWH1810760 PT170028
                Funded by: U.S. Department of Defense , doi 10.13039/100000005;
                Funded by: Ministry of Health , doi 10.13039/100009647;
                Funded by: National Institutes of Health , doi 10.13039/100000002;
                Award ID: PT170028
                Award ID: W81XWH1810760
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                July 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:12.06.2022

                Neurology
                7 tesla mri,autonomic/pain/limbic/sensory network,brainstem,human structural connectome

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