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      The architecture of the human default mode network explored through cytoarchitecture, wiring and signal flow

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          Abstract

          The default mode network (DMN) is implicated in many aspects of complex thought and behavior. Here, we leverage postmortem histology and in vivo neuroimaging to characterize the anatomy of the DMN to better understand its role in information processing and cortical communication. Our results show that the DMN is cytoarchitecturally heterogenous, containing cytoarchitectural types that are variably specialized for unimodal, heteromodal and memory-related processing. Studying diffusion-based structural connectivity in combination with cytoarchitecture, we found the DMN contains regions receptive to input from sensory cortex and a core that is relatively insulated from environmental input. Finally, analysis of signal flow with effective connectivity models showed that the DMN is unique amongst cortical networks in balancing its output across the levels of sensory hierarchies. Together, our study establishes an anatomical foundation from which accounts of the broad role the DMN plays in human brain function and cognition can be developed.

          Abstract

          The default mode network (DMN) is implicated in cognition and behavior. Here, the authors show that the DMN is cytoarchitecturally heterogeneous, it contains regions receptive to input from the sensory cortex and a core relatively insulated from environmental input, and it uniquely balances its output across sensory hierarchies.

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          Most cited references133

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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.
              • Record: found
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              Cortical surface-based analysis. I. Segmentation and surface reconstruction.

              Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.

                Author and article information

                Contributors
                c.paquola@fz-juelich.de
                Journal
                Nat Neurosci
                Nat Neurosci
                Nature Neuroscience
                Nature Publishing Group US (New York )
                1097-6256
                1546-1726
                28 January 2025
                28 January 2025
                2025
                : 28
                : 3
                : 654-664
                Affiliations
                [1 ]McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, ( https://ror.org/01pxwe438) Montréal, Quebec Canada
                [2 ]Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, ( https://ror.org/02nv7yv05) Jülich, Germany
                [3 ]Translational Neuromodeling Unit (TNU), University of Zurich and ETH Zurich, ( https://ror.org/02crff812) Zurich, Switzerland
                [4 ]Max Planck Institute for Cognitive and Brain Sciences, ( https://ror.org/0387jng26) Leipzig, Germany
                [5 ]Institute for Systems Neuroscience, Heinrich Heine Universistät Dusseldorf, ( https://ror.org/024z2rq82) Dusseldorf, Germany
                [6 ]Integrative Neuroscience & Cognition Center (INCC – UMR 8002), University of Paris, Centre national de la recherche scientifique (CNRS), Paris, France
                [7 ]Institute for Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, ( https://ror.org/02nv7yv05) Jülich, Germany
                [8 ]Department of Psychology, University of York, ( https://ror.org/04m01e293) York, UK
                [9 ]Department of Psychology, Queen’s University, ( https://ror.org/02y72wh86) Kingston, Ontario Canada
                Author information
                http://orcid.org/0000-0002-0190-4103
                http://orcid.org/0000-0002-8011-2226
                http://orcid.org/0000-0002-4448-8998
                http://orcid.org/0000-0002-2917-1212
                http://orcid.org/0000-0003-2998-6849
                http://orcid.org/0000-0002-8880-9204
                http://orcid.org/0000-0003-3841-6098
                http://orcid.org/0000-0001-5828-0867
                http://orcid.org/0000-0002-3826-4330
                http://orcid.org/0000-0002-7298-2459
                http://orcid.org/0000-0001-9256-6041
                Article
                1868
                10.1038/s41593-024-01868-0
                11893468
                39875581
                64099a0f-0cda-45f3-b2de-f476d4da4e6a
                © The Author(s) 2025, corrected publication 2025

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 November 2023
                : 6 December 2024
                Funding
                Funded by: Helmholtz International BigBrain Analytics Learning Laboratory
                Funded by: Helmholtz International BigBrain Analytics Learning Laboratory National Science and Engineering Research Council of Canada (NSERC Discovery-1304413) Canadian Institutes of Health Research (CIHR FDN-154298, PRJ-174995)
                Categories
                Article
                Custom metadata
                © Springer Nature America, Inc. 2025

                Neurosciences
                cognitive neuroscience,computational neuroscience
                Neurosciences
                cognitive neuroscience, computational neuroscience

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