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      Multimodal population brain imaging in the UK Biobank prospective epidemiological study

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          Abstract

          Medical imaging has enormous potential for early disease prediction, but is impeded by the difficulty and expense of acquiring datasets prior to symptom onset. UK Biobank aims to address this problem directly by acquiring high quality, consistently acquired imaging data from 100,000 predominantly healthy participants, with health outcomes tracked over coming decades. The brain imaging includes structural, diffusion and functional modalities. Along with body and cardiac imaging, genetics, lifestyle measures, biological phenotyping and health records, this is expected to enable discovery of imaging markers of a broad range of diseases at their earliest stages, as well as provide unique insight into disease mechanisms. We describe UK Biobank brain imaging, and present results derived from the first 5,000 participants’ data release. Although that covers just 5% of the ultimate cohort, it already yields a rich range of associations between brain imaging and other measures collected by UK Biobank.

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          Most cited references 84

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          A default mode of brain function.

          A baseline or control state is fundamental to the understanding of most complex systems. Defining a baseline state in the human brain, arguably our most complex system, poses a particular challenge. Many suspect that left unconstrained, its activity will vary unpredictably. Despite this prediction we identify a baseline state of the normal adult human brain in terms of the brain oxygen extraction fraction or OEF. The OEF is defined as the ratio of oxygen used by the brain to oxygen delivered by flowing blood and is remarkably uniform in the awake but resting state (e.g., lying quietly with eyes closed). Local deviations in the OEF represent the physiological basis of signals of changes in neuronal activity obtained with functional MRI during a wide variety of human behaviors. We used quantitative metabolic and circulatory measurements from positron-emission tomography to obtain the OEF regionally throughout the brain. Areas of activation were conspicuous by their absence. All significant deviations from the mean hemisphere OEF were increases, signifying deactivations, and resided almost exclusively in the visual system. Defining the baseline state of an area in this manner attaches meaning to a group of areas that consistently exhibit decreases from this baseline, during a wide variety of goal-directed behaviors monitored with positron-emission tomography and functional MRI. These decreases suggest the existence of an organized, baseline default mode of brain function that is suspended during specific goal-directed behaviors.
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            FSL.

            FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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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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                Author and article information

                Affiliations
                [1 ]Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
                [2 ]Department of Electrical Engineering, Brigham Young University, Provo, USA
                [3 ]Institute of Neurology, University College London, London, UK
                [4 ]Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, USA
                [5 ]Icahn School of Medicine at Mount Sinai, New York, USA
                [6 ]Department of Neuroradiology, University of Heidelberg, Germany
                [7 ]Siemens Healthcare UK, Frimley, UK
                [8 ]UK Biobank, Stockport, UK
                [9 ]Nuffield Department of Population Health, University of Oxford, Oxford, UK
                [10 ]Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK
                Author notes
                [* ]Corresponding author: karla@ 123456fmrib.ox.ac.uk
                Journal
                9809671
                21092
                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                1097-6256
                1546-1726
                12 September 2016
                19 September 2016
                November 2016
                19 March 2017
                : 19
                : 11
                : 1523-1536
                27643430 5086094 10.1038/nn.4393 EMS69790

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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