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      White matter microstructure pathology in classic galactosemia revealed by neurite orientation dispersion and density imaging

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          Abstract

          White matter abnormalities have been observed in patients with classic galactosemia, an inborn error of galactose metabolism. However, magnetic resonance imaging (MRI) data collected in the past were generally qualitative in nature. Our objective was to investigate white matter microstructure pathology and examine correlations with outcome and behaviour in this disease, by using multi-shell diffusion weighted imaging. In addition to standard diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI) was used to estimate density and orientation dispersion of neurites in a group of eight patients (aged 16–21 years) and eight healthy controls (aged 15–20 years). Extensive white matter abnormalities were found: neurite density index (NDI) was lower in the patient group in bilateral anterior areas, and orientation dispersion index (ODI) was increased mainly in the left hemisphere. These specific regional profiles are in agreement with the cognitive profile observed in galactosemia, showing higher order cognitive impairments, and language and motor impairments, respectively. Less favourable white matter properties correlated positively with age and age at onset of diet, and negatively with behavioural outcome (e.g. visual working memory). To conclude, this study provides evidence of white matter pathology regarding density and dispersion of neurites in these patients. The results are discussed in light of suggested pathophysiological mechanisms.

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          The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
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            A study with low statistical power has a reduced chance of detecting a true effect, but it is less well appreciated that low power also reduces the likelihood that a statistically significant result reflects a true effect. Here, we show that the average statistical power of studies in the neurosciences is very low. The consequences of this include overestimates of effect size and low reproducibility of results. There are also ethical dimensions to this problem, as unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established but often ignored methodological principles.
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              Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.

              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

                Contributors
                0031 43 381135 , inge.timmers@maastrichtuniversity.nl
                Journal
                J Inherit Metab Dis
                J. Inherit. Metab. Dis
                Journal of Inherited Metabolic Disease
                Springer Netherlands (Dordrecht )
                0141-8955
                1573-2665
                25 October 2014
                25 October 2014
                2015
                : 38
                : 2
                : 295-304
                Affiliations
                [ ]Department of Cognitive Neuroscience, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
                [ ]Department of Pediatrics, Maastricht University Medical Center, PO Box 5800, 6202 AZ Maastricht, The Netherlands
                [ ]Department of Computer Science and Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT London, UK
                [ ]Maastricht Brain Imaging Center (M-BIC), PO Box 616, 6200 MD Maastricht, The Netherlands
                [ ]Laboratory Genetic Metabolic Diseases, Maastricht University Medical Center, PO Box 5800, 6202 AZ Maastricht, The Netherlands
                Author notes

                Communicated by: Nicole Wolf

                Article
                9780
                10.1007/s10545-014-9780-x
                4341012
                25344151
                d1825e38-8895-4b43-86d9-6cbc9e092606
                © The Author(s) 2014

                Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

                History
                : 12 February 2014
                : 2 October 2014
                : 6 October 2014
                Categories
                Original Article
                Custom metadata
                © SSIEM 2015

                Internal medicine
                Internal medicine

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