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      Brain cortical characteristics of lifetime cognitive ageing

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          Abstract

          Regional cortical brain volume is the product of surface area and thickness. These measures exhibit partially distinct trajectories of change across the brain’s cortex in older age, but it is unclear which cortical characteristics at which loci are sensitive to cognitive ageing differences. We examine associations between change in intelligence from age 11 to 73 years and regional cortical volume, surface area, and thickness measured at age 73 years in 568 community-dwelling older adults, all born in 1936. A relative positive change in intelligence from 11 to 73 was associated with larger volume and surface area in selective frontal, temporal, parietal, and occipital regions ( r < 0.180, FDR-corrected q < 0.05). There were no significant associations between cognitive ageing and a thinner cortex for any region. Interestingly, thickness and surface area were phenotypically independent across bilateral lateral temporal loci, whose surface area was significantly related to change in intelligence. These findings suggest that associations between regional cortical volume and cognitive ageing differences are predominantly driven by surface area rather than thickness among healthy older adults. Regional brain surface area has been relatively underexplored, and is a potentially informative biomarker for identifying determinants of cognitive ageing differences.

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          The online version of this article (doi:10.1007/s00429-017-1505-0) contains supplementary material, which is available to authorized users.

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          Most cited references48

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            A hybrid approach to the skull stripping problem in MRI.

            We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skull-stripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skull-stripping tools. Copyright 2004 Elsevier Inc.
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              Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex.

              Highly accurate surface models of the cerebral cortex are becoming increasingly important as tools in the investigation of the functional organization of the human brain. The construction of such models is difficult using current neuroimaging technology due to the high degree of cortical folding. Even single voxel misclassifications can result in erroneous connections being created between adjacent banks of a sulcus, resulting in a topologically inaccurate model. These topological defects cause the cortical model to no longer be homeomorphic to a sheet, preventing the accurate inflation, flattening, or spherical morphing of the reconstructed cortex. Surface deformation techniques can guarantee the topological correctness of a model, but are time-consuming and may result in geometrically inaccurate models. In order to address this need we have developed a technique for taking a model of the cortex, detecting and fixing the topological defects while leaving that majority of the model intact, resulting in a surface that is both geometrically accurate and topologically correct.
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                Author and article information

                Contributors
                0131 650 8493 , simon.cox@ed.ac.uk
                Journal
                Brain Struct Funct
                Brain Struct Funct
                Brain Structure & Function
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1863-2653
                1863-2661
                6 September 2017
                6 September 2017
                2018
                : 223
                : 1
                : 509-518
                Affiliations
                [1 ]ISNI 0000 0004 1936 7988, GRID grid.4305.2, Centre for Cognitive Ageing and Cognitive Epidemiology, , University of Edinburgh, ; 7 George Square, Edinburgh, EH8 9JZ UK
                [2 ]ISNI 0000 0004 1936 7988, GRID grid.4305.2, Department of Psychology, , University of Edinburgh, ; Edinburgh, UK
                [3 ]Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
                [4 ]ISNI 0000 0004 1936 7988, GRID grid.4305.2, Brain Research Imaging Centre, Neuroimaging Sciences, , University of Edinburgh, ; Edinburgh, UK
                [5 ]ISNI 0000 0001 0725 8811, GRID grid.411276.7, Department of Computer Science, , Lagos State University, ; Lagos, Nigeria
                [6 ]ISNI 0000 0004 1936 7988, GRID grid.4305.2, Division of Psychiatry, , University of Edinburgh, ; Edinburgh, UK
                Article
                1505
                10.1007/s00429-017-1505-0
                5772145
                28879544
                d69fe20b-c073-4e98-b0e1-76a5842415eb
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.

                History
                : 9 June 2017
                : 20 August 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/M013111/1
                Award ID: MR/K026992/1
                Award ID: G1001245
                Award ID: G0701120
                Funded by: Age UK (GB)
                Award ID: The Disconnected Mind project
                Categories
                Original Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2018

                Neurology
                ageing,intelligence,mri,cortex,thickness,surface area
                Neurology
                ageing, intelligence, mri, cortex, thickness, surface area

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