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      An improved neuroanatomical model of the default-mode network reconciles previous neuroimaging and neuropathological findings

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          Abstract

          The brain is constituted of multiple networks of functionally correlated brain areas, out of which the default-mode network (DMN) is the largest. Most existing research into the DMN has taken a corticocentric approach. Despite its resemblance with the unitary model of the limbic system, the contribution of subcortical structures to the DMN may be underappreciated. Here, we propose a more comprehensive neuroanatomical model of the DMN including subcortical structures such as the basal forebrain, cholinergic nuclei, anterior and mediodorsal thalamic nuclei. Additionally, tractography of diffusion-weighted imaging was employed to explore the structural connectivity, which revealed that the thalamus and basal forebrain are of central importance for the functioning of the DMN. The contribution of these neurochemically diverse brain nuclei reconciles previous neuroimaging with neuropathological findings in diseased brains and offers the potential for identifying a conserved homologue of the DMN in other mammalian species.

          Abstract

          Pedro Alves et al. use a functional alignment approach to build an improved map of the default-mode network (DMN) from resting state fMRI-based individual DMN maps. They find that thalamus and basal forebrain are central to the DMN and validate these findings through tractography and graph theory analysis of structural connectivity in their DMN model.

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          Situating the default-mode network along a principal gradient of macroscale cortical organization.

          Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.
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            Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

            All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.
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              Remembering the past to imagine the future: the prospective brain.

              A rapidly growing number of recent studies show that imagining the future depends on much of the same neural machinery that is needed for remembering the past. These findings have led to the concept of the prospective brain; an idea that a crucial function of the brain is to use stored information to imagine, simulate and predict possible future events. We suggest that processes such as memory can be productively re-conceptualized in light of this idea.
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                Author and article information

                Contributors
                pedronascimentoalves@gmail.com
                michel.thiebaut@gmail.com
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                10 October 2019
                10 October 2019
                2019
                : 2
                : 370
                Affiliations
                [1 ]ISNI 0000 0001 2308 1657, GRID grid.462844.8, Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, ; Paris, France
                [2 ]ISNI 0000 0004 0620 5939, GRID grid.425274.2, Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, ; CNRS UMR 7225 Paris, France
                [3 ]ISNI 0000 0001 2295 9747, GRID grid.411265.5, Department of Neurosciences and Mental Health, Neurology, Hospital de Santa Maria, CHULN, ; Lisbon, Portugal
                [4 ]ISNI 0000 0001 2181 4263, GRID grid.9983.b, Language Research Laboratory, Faculty of Medicine, Universidade de Lisboa, ; Lisbon, Portugal
                [5 ]ISNI 0000 0004 1936 9924, GRID grid.89336.37, Computational Neuroimaging Laboratory, Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, ; Austin, TX USA
                [6 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, FMRIB centre, John Radcliffe Hospital, University of Oxford, ; Oxford, UK
                [7 ]ISNI 0000 0001 2186 3954, GRID grid.5328.c, INRIA, Parietal Team, ; Saclay, France
                [8 ]GRID grid.457334.2, Neurospin, CEA, ; Gif-sur-Yvette, France
                [9 ]ISNI 0000 0001 0728 696X, GRID grid.1957.a, Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, ; Aachen, Germany
                [10 ]GRID grid.494742.8, JARA-BRAIN, Jülich-Aachen Research Alliance, ; Jülich, Germany
                [11 ]ISNI 0000 0001 2150 9058, GRID grid.411439.a, Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, ; Paris, France
                [12 ]ISNI 0000 0001 2112 9282, GRID grid.4444.0, Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, ; Bordeaux, France
                Author information
                http://orcid.org/0000-0002-7822-2653
                http://orcid.org/0000-0003-2074-6910
                http://orcid.org/0000-0003-3466-6620
                http://orcid.org/0000-0002-0329-1814
                Article
                611
                10.1038/s42003-019-0611-3
                6787009
                31633061
                68cdac3b-8204-4fbb-81bf-547a1f8b72ac
                © The Author(s) 2019

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 24 January 2019
                : 16 September 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 818521
                Award Recipient :
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                © The Author(s) 2018

                human behaviour,brain,neural circuits
                human behaviour, brain, neural circuits

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