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      Neuroimaging and cognitive correlates of retinal Optical Coherence Tomography (OCT) measures at late middle age in a twin sample

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          Abstract

          Sharing in embryology and function between the eye and brain has led to interest in whether assessments of the eye reflect brain changes seen in neurodegeneration. We aimed to examine the associations between measures of retinal layer thickness using optical coherence tomography (OCT) and multimodal measures of brain structure and function. Using a convenient sample of twins discordant for type 2 diabetes, we performed cognitive testing, structural brain MRI (tissue volumetry), diffusion tensor imaging (white matter microstructure), and arterial spin labelling (cerebral blood flow). OCT images were recorded and retinal thickness maps generated. We used mixed level modelling to examine the relationship between retinal layer thicknesses and brain measures. We enrolled 35 people (18 pairs, mean age 63.8 years, 63% female). Ganglion cell layer thickness was positively associated with memory, speed, gray matter volume, and altered mean diffusivity. Ganglion cell layer thickness was strongly positively associated with regional cerebral blood flow. We found only a limited number of associations between other retinal layer thickness and measures of brain structure or function. Ganglion cell layer thickness showed consistent associations with a range of brain measures suggesting it may have utility as a marker for future dementia risk.

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          Cortical surface-based analysis. I. Segmentation and surface reconstruction.

          Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.
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            Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.

            There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms; there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the "mean FA skeleton"). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present example TBSS results from several diffusion imaging studies.
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              Measuring the thickness of the human cerebral cortex from magnetic resonance images.

              Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.
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                Author and article information

                Contributors
                velandai.srikanth@monash.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 June 2022
                10 June 2022
                2022
                : 12
                : 9562
                Affiliations
                [1 ]National Centre for Healthy Ageing, Melbourne, Australia
                [2 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Department of Geriatric Medicine, , Peninsula Health and Central Clinical School, Monash University, ; Melbourne, Australia
                [3 ]GRID grid.267362.4, ISNI 0000 0004 0432 5259, Department of Aged Care, , Alfred Health, ; Melbourne, Australia
                [4 ]GRID grid.437485.9, ISNI 0000 0001 0439 3380, Royal Free London NHS Foundation Trust, ; London, UK
                [5 ]GRID grid.83440.3b, ISNI 0000000121901201, UCL Medical School, ; London, UK
                [6 ]GRID grid.1013.3, ISNI 0000 0004 1936 834X, Macular Research Group, , University of Sydney, ; Sydney, Australia
                [7 ]GRID grid.419789.a, ISNI 0000 0000 9295 3933, Department of Ophthalmology, , Monash Health, ; Melbourne, Australia
                Article
                13662
                10.1038/s41598-022-13662-8
                9187769
                35688899
                2def1a7e-7284-4112-9d44-ae4a17f67f23
                © The Author(s) 2022

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

                History
                : 25 February 2022
                : 26 May 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: 1063608
                Award ID: 1063608
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000948, Pfizer Australia;
                Award ID: WS1931543
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                cognitive ageing,cognitive neuroscience,dementia
                Uncategorized
                cognitive ageing, cognitive neuroscience, dementia

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