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      Increased fractional anisotropy in the motor tracts of Parkinson's disease suggests compensatory neuroplasticity or selective neurodegeneration

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          Abstract

          Objective

          To determine the differences in motor pathways and selected non-motor pathways of the basal ganglia in Parkinson’s disease (PD) patients compared to healthy controls (HCs).

          Methods

          We analysed diffusion weighted imaging data of 24 PD patients and 26 HCs. We performed deterministic tractography analysis using the spherical deconvolution-based damped Richardson-Lucy algorithm and subcortical volume analysis.

          Results

          We found significantly increased fractional anisotropy (FA) in the motor pathways of PD patients: the bilateral corticospinal tract (right; corrected p = 0.0003, left; corrected p = 0.03), bilateral thalamus-motor cortex tract (right; corrected p = 0.02, left; corrected p = 0.004) and the right supplementary area-putamen tract (corrected p = 0.001). We also found significantly decreased FA in the right uncinate fasiculus (corrected p = 0.01) and no differences of FA in the bilateral supero-lateral medial forebrain bundles (p > 0.05) of PD patients compared to HCs. There were no subcortical volume differences (p > 0.05) between the PD patients and HCs.

          Conclusion

          These results can inform biological models of neurodegeneration and neuroplasticity in PD. We suggest that increased FA values in the motor tracts in PD may reflect compensatory reorganization of neural circuits indicative of adaptive or extended neuroplasticity.

          Key points

          Fractional anisotropy was higher in motor pathways of PD patients compared to healthy controls.

          Fractional anisotropy was lower in the uncinate fasciculus of PD patients compared to healthy controls.

          Increased fractional anisotropy could suggest adaptive neuroplasticity or selective neurodegeneration.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s00330-015-4178-1) contains supplementary material, which is available to authorized users.

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          Most cited references36

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          Advances in functional and structural MR image analysis and implementation as FSL.

          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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            White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI.

            Diffusion-weighted MRI (DW-MRI) has been increasingly used in imaging neuroscience over the last decade. An early form of this technique, diffusion tensor imaging (DTI) was rapidly implemented by major MRI scanner companies as a scanner selling point. Due to the ease of use of such implementations, and the plausibility of some of their results, DTI was leapt on by imaging neuroscientists who saw it as a powerful and unique new tool for exploring the structural connectivity of human brain. However, DTI is a rather approximate technique, and its results have frequently been given implausible interpretations that have escaped proper critique and have appeared misleadingly in journals of high reputation. In order to encourage the use of improved DW-MRI methods, which have a better chance of characterizing the actual fiber structure of white matter, and to warn against the misuse and misinterpretation of DTI, we review the physics of DW-MRI, indicate currently preferred methodology, and explain the limits of interpretation of its results. We conclude with a list of 'Do's and Don'ts' which define good practice in this expanding area of imaging neuroscience. Copyright © 2012 Elsevier Inc. All rights reserved.
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              A Bayesian model of shape and appearance for subcortical brain segmentation.

              Automatic segmentation of subcortical structures in human brain MR images is an important but difficult task due to poor and variable intensity contrast. Clear, well-defined intensity features are absent in many places along typical structure boundaries and so extra information is required to achieve successful segmentation. A method is proposed here that uses manually labelled image data to provide anatomical training information. It utilises the principles of the Active Shape and Appearance Models but places them within a Bayesian framework, allowing probabilistic relationships between shape and intensity to be fully exploited. The model is trained for 15 different subcortical structures using 336 manually-labelled T1-weighted MR images. Using the Bayesian approach, conditional probabilities can be calculated easily and efficiently, avoiding technical problems of ill-conditioned covariance matrices, even with weak priors, and eliminating the need for fitting extra empirical scaling parameters, as is required in standard Active Appearance Models. Furthermore, differences in boundary vertex locations provide a direct, purely local measure of geometric change in structure between groups that, unlike voxel-based morphometry, is not dependent on tissue classification methods or arbitrary smoothing. In this paper the fully-automated segmentation method is presented and assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively, using an independent clinical dataset involving Alzheimer's disease. Median Dice overlaps between 0.7 and 0.9 are obtained with this method, which is comparable or better than other automated methods. An implementation of this method, called FIRST, is currently distributed with the freely-available FSL package. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                +44 029 2068 8352 , lindend@cardiff.ac.uk
                Journal
                Eur Radiol
                Eur Radiol
                European Radiology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0938-7994
                1432-1084
                15 January 2016
                15 January 2016
                2016
                : 26
                : 10
                : 3327-3335
                Affiliations
                [1 ]Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
                [2 ]Institute of Psychological Medicine and Clinical Neurosciences (IPMCN), School of Medicine, Cardiff University, Cardiff, UK
                [3 ]Department of Psychiatry, University of Bern, Bern, Switzerland
                [4 ]Department of Clinical Neurology, Institute of Neurology, University College London, London, UK
                [5 ]MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Road, Cathays, Cardiff, CF24 4HQ UK
                Article
                4178
                10.1007/s00330-015-4178-1
                5021738
                26780637
                55feb512-cc25-4a45-ace3-e1b271c8d1b9
                © The Author(s) 2016

                Open Access This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial 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
                : 19 August 2015
                : 15 October 2015
                : 27 October 2015
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000866, Cardiff University (GB);
                Award ID: MRes and PhD International studentships
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000780, European Commission (BE);
                Award ID: 602186
                Award Recipient :
                Funded by: Swiss National Science Foundation
                Award ID: PBBEP3_144797
                Award Recipient :
                Categories
                Neuro
                Custom metadata
                © European Society of Radiology 2016

                Radiology & Imaging
                deterministic tractography,parkinson’s disease,neuroplasticity,diffusion-weighted imaging,fractional anisotropy

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