Structural covariance across the lifespan: Brain development and aging through the lens of inter‐network relationships
Recent studies have revealed that brain development is marked by morphological synchronization across brain regions. Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of...
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Veröffentlicht in: | Human brain mapping 2019-01, Vol.40 (1), p.125-136 |
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creator | Aboud, Katherine S. Huo, Yuankai Kang, Hakmook Ealey, Ashley Resnick, Susan M. Landman, Bennett A. Cutting, Laurie E. |
description | Recent studies have revealed that brain development is marked by morphological synchronization across brain regions. Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within‐network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big‐data framework and a novel application of covariate‐adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7–90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter‐network relationships. The shift from young to middle adulthood was marked by a significant decrease in inter‐network modularity and organization, which continued into older adulthood, potentially reflecting changes in brain organizational efficiency with age. This study is the first to characterize brain development and aging in terms of inter‐network structural covariance across the lifespan. |
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Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within‐network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big‐data framework and a novel application of covariate‐adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7–90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter‐network relationships. The shift from young to middle adulthood was marked by a significant decrease in inter‐network modularity and organization, which continued into older adulthood, potentially reflecting changes in brain organizational efficiency with age. 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Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within‐network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big‐data framework and a novel application of covariate‐adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7–90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter‐network relationships. The shift from young to middle adulthood was marked by a significant decrease in inter‐network modularity and organization, which continued into older adulthood, potentially reflecting changes in brain organizational efficiency with age. This study is the first to characterize brain development and aging in terms of inter‐network structural covariance across the lifespan.</description><subject>Adolescent</subject><subject>Adolescents</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Aging</subject><subject>Aging - physiology</subject><subject>Big Data</subject><subject>Brain</subject><subject>brain development</subject><subject>Cerebral Cortex - anatomy & histology</subject><subject>Cerebral Cortex - diagnostic imaging</subject><subject>Cerebral Cortex - growth & development</subject><subject>Cerebral Cortex - physiology</subject><subject>Child</subject><subject>Children</subject><subject>Cognitive ability</subject><subject>Covariance</subject><subject>Data processing</subject><subject>Female</subject><subject>Human Development - physiology</subject><subject>Humans</subject><subject>Life span</subject><subject>lifespan aging</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Modularity</subject><subject>MRI</subject><subject>Nerve Net - anatomy & histology</subject><subject>Nerve Net - diagnostic imaging</subject><subject>Nerve Net - physiology</subject><subject>Networks</subject><subject>Neuroimaging - methods</subject><subject>structural covariance</subject><subject>Synchronism</subject><subject>Synchronization</subject><subject>T1w</subject><subject>Trajectories</subject><subject>Young Adult</subject><issn>1065-9471</issn><issn>1097-0193</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kc1u1TAQhS0Eoj-w4AWQJTawSGvHjh2zQKIVUKQiFsDacpzJjUtiBzu5pbs-As_Ik-DblAqQWIw8kr85OjMHoSeUHFFCyuO-GY9Kzip1D-1TomRBqGL3d72oCsUl3UMHKV0QQmlF6EO0xwgTtVLVPvr-aY6LnZdoBmzD1kRnvAVsbAwp4bkHPLgO0mT8S3wSjfO4hS0MYRrBz9j4FpuN85tMxrBs-nUCfMKhw87PEH9e__AwX4b4FUcYzOyCT72b0iP0oDNDgse37yH68vbN59Oz4vzju_enr88LyzlTRcmtbClXsjGVJA3YmjMhFFdQkso2LeFl7mtbUyslFYZVuUQH0LUyozU7RK9W3WlpRmhttp131VN0o4lXOhin__7xrtebsNWCy5rKMgs8vxWI4dsCadajSxaGwXgIS9IlLYWi-fgyo8_-QS_CEn1eL1MVJ4JTtqNerNTNjSN0d2Yo0bs8dc5T3-SZ2ad_ur8jfweYgeMVuHQDXP1fSZ-dfFglfwFvUK0r</recordid><startdate>201901</startdate><enddate>201901</enddate><creator>Aboud, Katherine S.</creator><creator>Huo, Yuankai</creator><creator>Kang, Hakmook</creator><creator>Ealey, Ashley</creator><creator>Resnick, Susan M.</creator><creator>Landman, Bennett A.</creator><creator>Cutting, Laurie E.</creator><general>John Wiley & Sons, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QR</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-2362-6028</orcidid></search><sort><creationdate>201901</creationdate><title>Structural covariance across the lifespan: Brain development and aging through the lens of inter‐network relationships</title><author>Aboud, Katherine S. ; 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Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within‐network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big‐data framework and a novel application of covariate‐adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7–90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter‐network relationships. 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subjects | Adolescent Adolescents Adult Aged Aged, 80 and over Aging Aging - physiology Big Data Brain brain development Cerebral Cortex - anatomy & histology Cerebral Cortex - diagnostic imaging Cerebral Cortex - growth & development Cerebral Cortex - physiology Child Children Cognitive ability Covariance Data processing Female Human Development - physiology Humans Life span lifespan aging Magnetic Resonance Imaging Male Middle Aged Modularity MRI Nerve Net - anatomy & histology Nerve Net - diagnostic imaging Nerve Net - physiology Networks Neuroimaging - methods structural covariance Synchronism Synchronization T1w Trajectories Young Adult |
title | Structural covariance across the lifespan: Brain development and aging through the lens of inter‐network relationships |
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