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      The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large‐scale resting‐state functional brain networks in aging

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          Abstract

          The literature on large‐scale resting‐state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within‐network and increased between‐network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher‐order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within‐ and between‐network altered patterns and speed of dynamic connectivity. Research on within‐subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age‐related changes may contribute to the cognitive decline often seen in older adults.

          Abstract

          Although the literature on large‐scale, resting state functional networks in aging has been reviewed previously, we offer the first systematic qualitative and quantitative synthesis of the evidence. The novel synthesis stems from the adoption of PRISMA method and the breadth of network measures reviewed. The review offers a contemporary summary of the strength of the evidence, theoretical implications, and recommendations for further research.

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          Most cited references319

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            GRADE: an emerging consensus on rating quality of evidence and strength of recommendations.

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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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                Author and article information

                Contributors
                sharna.jamadar@monash.edu
                Journal
                Psychophysiology
                Psychophysiology
                10.1111/(ISSN)1469-8986
                PSYP
                Psychophysiology
                John Wiley and Sons Inc. (Hoboken )
                0048-5772
                1469-8986
                15 September 2022
                January 2023
                : 60
                : 1 ( doiID: 10.1111/psyp.v60.1 )
                : e14159
                Affiliations
                [ 1 ] Turner Institute for Brain and Mental Health Monash University Melbourne Victoria Australia
                [ 2 ] Monash Biomedical Imaging Monash University Melbourne Victoria Australia
                [ 3 ] Peninsula Clinical School, Central Clinical School Monash University Frankston Victoria Australia
                [ 4 ] Department of Geriatric Medicine Peninsula Health Frankston Victoria Australia
                [ 5 ] Australian Research Council Centre of Excellence for Integrative Brain Function Melbourne Victoria Australia
                Author notes
                [*] [* ] Correspondence

                S. D. Jamadar, Monash Biomedical Imaging, Monash University, 770 Blackburn Rd, Melbourne 3800, VIC, Australia.

                Email: sharna.jamadar@ 123456monash.edu

                Author information
                https://orcid.org/0000-0002-1052-4516
                https://orcid.org/0000-0003-1872-3314
                https://orcid.org/0000-0002-3186-4026
                https://orcid.org/0000-0001-7222-7181
                Article
                PSYP14159 PsyP-2022-0050.R2
                10.1111/psyp.14159
                10909558
                36106762
                82ef23df-0594-4db9-a295-e96cc853a31c
                © 2022 The Authors. Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.

                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
                : 22 July 2022
                : 31 January 2022
                : 25 July 2022
                Page count
                Figures: 5, Tables: 2, Pages: 39, Words: 30082
                Funding
                Funded by: Australian National Health and Medical Research Council
                Award ID: APP1174164
                Funded by: Australian Research Council Centre of Excellence for Integrative Brain Function , doi 10.13039/100013102;
                Award ID: CE114100007
                Categories
                Review
                Review
                Custom metadata
                2.0
                January 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.8 mode:remove_FC converted:03.03.2024

                Neurology
                aging,fmri,functional connectivity,large‐scale networks,lifespan,pet,prisma,resting‐state networks,systematic review

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