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      White matter damage and cognitive impairment after traumatic brain injury

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          Abstract

          White matter disruption is an important determinant of cognitive impairment after brain injury, but conventional neuroimaging underestimates its extent. In contrast, diffusion tensor imaging provides a validated and sensitive way of identifying the impact of axonal injury. The relationship between cognitive impairment after traumatic brain injury and white matter damage is likely to be complex. We applied a flexible technique—tract-based spatial statistics—to explore whether damage to specific white matter tracts is associated with particular patterns of cognitive impairment. The commonly affected domains of memory, executive function and information processing speed were investigated in 28 patients in the post-acute/chronic phase following traumatic brain injury and in 26 age-matched controls. Analysis of fractional anisotropy and diffusivity maps revealed widespread differences in white matter integrity between the groups. Patients showed large areas of reduced fractional anisotropy, as well as increased mean and axial diffusivities, compared with controls, despite the small amounts of cortical and white matter damage visible on standard imaging. A stratified analysis based on the presence or absence of microbleeds (a marker of diffuse axonal injury) revealed diffusion tensor imaging to be more sensitive than gradient-echo imaging to white matter damage. The location of white matter abnormality predicted cognitive function to some extent. The structure of the fornices was correlated with associative learning and memory across both patient and control groups, whilst the structure of frontal lobe connections showed relationships with executive function that differed in the two groups. These results highlight the complexity of the relationships between white matter structure and cognition. Although widespread and, sometimes, chronic abnormalities of white matter are identifiable following traumatic brain injury, the impact of these changes on cognitive function is likely to depend on damage to key pathways that link nodes in the distributed brain networks supporting high-level cognitive functions.

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              Many image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation is the need to define the initial cluster-forming threshold. This threshold is arbitrary, and yet its exact choice can have a large impact on the results, particularly at the lower (e.g., t, z < 4) cluster-forming thresholds frequently used. Furthermore, the amount of spatial pre-smoothing is also arbitrary (given that the expected signal extent is very rarely known in advance of the analysis). In the light of such problems, we propose a new method which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems. The method takes a raw statistic image and produces an output image in which the voxel-wise values represent the amount of cluster-like local spatial support. The method is thus referred to as "threshold-free cluster enhancement" (TFCE). We present the TFCE approach and discuss in detail ROC-based optimisation and comparisons with cluster-based and voxel-based thresholding. We find that TFCE gives generally better sensitivity than other methods over a wide range of test signal shapes and SNR values. We also show an example on a real imaging dataset, suggesting that TFCE does indeed provide not just improved sensitivity, but richer and more interpretable output than cluster-based thresholding.
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                Author and article information

                Journal
                Brain
                brain
                brain
                Brain
                Oxford University Press
                0006-8950
                1460-2156
                February 2011
                30 December 2010
                30 December 2010
                : 134
                : 2
                : 449-463
                Affiliations
                1 Department of Psychology, Goldsmiths, University of London, London, UK
                2 Institute of Neurology, Division of Clinical Neurology, University College London, London, UK
                3 Computational, Cognitive, and Clinical Neuroimaging Laboratory, Clinical Neuroscience, Centre for Neuroscience, Division of Experimental Medicine, Department of Medicine, Imperial College London, London, UK
                4 MRC Clinical Sciences Centre, Experimental and Clinical Neuroscience Section, Cognitive Neuroimaging Research Group, Faculty of Medicine, Imperial College London, London, UK
                5 Imaging Department, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK
                6 MRC Clinical Sciences Centre, Experimental and Clinical Neuroscience Section, Neonatal Medicine Research Group, Faculty of Medicine, Imperial College London, London, UK
                Author notes
                Correspondence to: Dr David J. Sharp, Computational, Cognitive, and Clinical Neuroimaging Laboratory, 3rd Floor, Burlington Danes Building, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK E-mail: david.sharp@ 123456imperial.ac.uk
                Article
                awq347
                10.1093/brain/awq347
                3030764
                21193486
                124c6108-6e7f-4422-88af-5107d13132c1
                © The Author(s) 2010. Published by Oxford University Press on behalf of Brain.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 4 July 2010
                : 29 September 2010
                : 15 October 2010
                Categories
                Original Articles

                Neurosciences
                diffusion tensor,traumatic brain injury,brain behaviour and relationships,diffuse axonal injury,cognitive impairment

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