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      The detailed organization of the human cerebellum estimated by intrinsic functional connectivity within the individual

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          Abstract

          Abstract

          Distinct regions of the cerebellum connect to separate regions of the cerebral cortex forming a complex topography. Although cerebellar organization has been examined in group-averaged data, study of individuals provides an opportunity to discover features that emerge at a higher spatial resolution. Here, functional connectivity MRI was used to examine the cerebellum of two intensively sampled individuals (each scanned 31 times). Connectivity to somatomotor cortex showed the expected crossed laterality and topography of the body maps. A surprising discovery was connectivity to the primary visual cortex along the vermis with evidence for representation of the central field. Within the hemispheres, each individual displayed a hierarchical progression from the inverted anterior lobe somatomotor map through to higher-order association zones. The hierarchy ended at Crus I/II and then progressed in reverse order through to the upright somatomotor map in the posterior lobe. Evidence for a third set of networks was found in the most posterior extent of the cerebellum. Detailed analysis of the higher-order association networks revealed robust representations of two distinct networks linked to the default network, multiple networks linked to cognitive control, as well as a separate representation of a language network. Although idiosyncratic spatial details emerged between subjects, each network could be detected in both individuals, and seed regions placed within the cerebellum recapitulated the full extent of the spatially specific cerebral networks. The observation of multiple networks in juxtaposed regions at the Crus I/II apex confirms the importance of this zone to higher-order cognitive function and reveals new organizational details.

          NEW & NOTEWORTHY Stable, within-individual maps of cerebellar organization reveal orderly macroscale representations of the cerebral cortex with local juxtaposed zones representing distinct networks. In addition, individuals reveal idiosyncratic organizational features.

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          Most cited references87

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          FSL.

          FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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            The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

            Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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              FreeSurfer.

              FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                J Neurophysiol
                J Neurophysiol
                jn
                J Neurophysiol
                JN
                Journal of Neurophysiology
                American Physiological Society (Bethesda, MD )
                0022-3077
                1522-1598
                1 February 2021
                2 December 2020
                1 February 2022
                : 125
                : 2
                : 358-384
                Affiliations
                [1] 1Department of Electrical and Computer Engineering, National University of Singapore , Singapore, Singapore
                [2] 2Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine , Singapore, Singapore
                [3] 3N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore , Singapore, Singapore
                [4] 4Department of Psychiatry, Massachusetts General Hospital , Charlestown, Massachusetts
                [5] 5Department of Psychology, Center for Brain Science, Harvard University , Cambridge, Massachusetts
                [6] 6Department of Neurology, Northwestern University Feinberg School of Medicine , Chicago, Illinois
                [7] 7Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Charlestown, Massachusetts
                [8] 8NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore , Singapore, Singapore
                Author notes
                Correspondence: R. Buckner ( randy_buckner@ 123456harvard.edu ); B. T. T. Yeo ( thomas.yeo@ 123456nus.edu.sg ).
                Author information
                https://orcid.org/0000-0002-0208-5808
                https://orcid.org/0000-0001-7562-0755
                Article
                JN-00561-2020 JN-00561-2020
                10.1152/jn.00561.2020
                7948146
                33427596
                98c78057-82a7-45e9-9d7f-0bf4ab294a8c
                Copyright © 2021 the Authors

                Licensed under Creative Commons Attribution CC-BY 4.0. Published by the American Physiological Society.

                History
                : 18 September 2020
                : 25 November 2020
                : 26 November 2020
                Funding
                Funded by: HHS | National Institutes of Health (NIH) 10.13039/100000002
                Award ID: R01MH124004
                Award ID: P50MH106435
                Award ID: R00MH117226
                Award ID: S10OD020039
                Funded by: National Science Foundation (NSF) 10.13039/100000001
                Award ID: DGE1745303
                Categories
                Research Article
                Higher Neural Functions and Behavior

                Neurology
                association cortex,bayesian,default network
                Neurology
                association cortex, bayesian, default network

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