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      The default network of the human brain is associated with perceived social isolation

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          Abstract

          Humans survive and thrive through social exchange. Yet, social dependency also comes at a cost. Perceived social isolation, or loneliness, affects physical and mental health, cognitive performance, overall life expectancy, and increases vulnerability to Alzheimer’s disease-related dementias. Despite severe consequences on behavior and health, the neural basis of loneliness remains elusive. Using the UK Biobank population imaging-genetics cohort ( n = ~40,000, aged 40–69 years when recruited, mean age = 54.9), we test for signatures of loneliness in grey matter morphology, intrinsic functional coupling, and fiber tract microstructure. The loneliness-linked neurobiological profiles converge on a collection of brain regions known as the ‘default network’. This higher associative network shows more consistent loneliness associations in grey matter volume than other cortical brain networks. Lonely individuals display stronger functional communication in the default network, and greater microstructural integrity of its fornix pathway. The findings fit with the possibility that the up-regulation of these neural circuits supports mentalizing, reminiscence and imagination to fill the social void.

          Abstract

          Here, using pattern-learning analyses of structural, functional, and diffusion brain scans in ~40,000 UK Biobank participants, the authors provide population-scale evidence that the default network is associated with perceived social isolation.

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          Most cited references81

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          LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

          Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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            Fast robust automated brain extraction.

            An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Copyright 2002 Wiley-Liss, Inc.
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              The minimal preprocessing pipelines for the Human Connectome Project.

              The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                nathan.spreng@gmail.ca
                danilo.bzdok@mcgill.ca
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                15 December 2020
                15 December 2020
                2020
                : 11
                : 6393
                Affiliations
                [1 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Laboratory of Brain and Cognition, Montreal Neurological Institute, Department of Neurology and Neurosurgery, Faculty of Medicine, , McGill University, ; Montreal, QC Canada
                [2 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Departments of Psychiatry and Psychology, , McGill University, ; Montreal, QC Canada
                [3 ]GRID grid.412078.8, ISNI 0000 0001 2353 5268, Douglas Mental Health University Institute, ; Verdun, QC HRH 1R3 Canada
                [4 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, McConnell Brain Imaging Centre, , Montreal Neurological Institute (MNI), McGill University, ; Montreal, QC Canada
                [5 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Department of Biomedical Engineering, Faculty of Medicine, , McGill University, ; Montreal, QC Canada
                [6 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, School of Business and Economics, , Vrije Universiteit Amsterdam, ; Amsterdam, The Netherlands
                [7 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Marketing Department, the Wharton School, , University of Pennsylvania, ; Pennsylvania, PA USA
                [8 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Human Development, , Cornell University, ; Ithaca, NY USA
                [9 ]GRID grid.5386.8, ISNI 000000041936877X, Division of Geriatrics and Palliative Medicine, Weill Cornell Medical College, ; New York, NY USA
                [10 ]GRID grid.412301.5, ISNI 0000 0000 8653 1507, Department of Neurosurgery, Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), , RWTH Aachen University Hospital, ; Aachen, Germany
                [11 ]Quantopian Inc., Boston, MA USA
                [12 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, , Massachusetts General Hospital, ; Boston, MA 02114 USA
                [13 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Psychiatry, Massachusetts General Hospital, , Harvard Medical School, ; Boston, MA 02114 USA
                [14 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Athinoula A. Martinos Center for Biomedical Imaging, , Massachusetts General Hospital, ; Charlestown, MA 02129 USA
                [15 ]GRID grid.66859.34, Stanley Center for Psychiatric Research, , Broad Institute of MIT and Harvard, ; Cambridge, MA 02138 USA
                [16 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, School of Computer Science, , McGill University, ; Montreal, QC Canada
                [17 ]GRID grid.47100.32, ISNI 0000000419368710, Departments of Psychology and Psychiatry, , Yale University, ; New Haven, CA 06520 USA
                [18 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Department of Electrical & Computer Engineering, Centre for Sleep & Cognition, Clinical Imaging Research Centre, N.1 Institute for Health, , National University of Singapore, ; Singapore, Singapore
                [19 ]GRID grid.21100.32, ISNI 0000 0004 1936 9430, Department of Psychology, , York University, ; Toronto, ON Canada
                [20 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Department of Experimental Psychology, , University of Oxford, ; Oxford, UK
                [21 ]Mila - Quebec Artificial Intelligence Institute, Montreal, QC Canada
                Author information
                http://orcid.org/0000-0003-1530-8916
                http://orcid.org/0000-0002-0945-5779
                http://orcid.org/0000-0001-7413-0412
                http://orcid.org/0000-0001-6251-5630
                http://orcid.org/0000-0002-1163-3634
                http://orcid.org/0000-0001-6583-803X
                http://orcid.org/0000-0002-9982-9702
                http://orcid.org/0000-0003-3466-6620
                Article
                20039
                10.1038/s41467-020-20039-w
                7738683
                33319780
                0f19090c-e62d-4cfc-ac38-01d5fbe4ae6b
                © The Author(s) 2020

                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
                : 6 July 2020
                : 14 October 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: R01AG068563A
                Award Recipient :
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                © The Author(s) 2020

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                neuroscience,health care
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                neuroscience, health care

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