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      Differential relationships between apathy and depression with white matter microstructural changes and functional outcomes

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          Abstract

          Small vessel disease pathways disrupts subcortical pathways that are important for emotion regulation. Hollocks et al. use brain imaging and statistical modelling to show that white-matter damage is associated with apathy, but not depression, although the latter still has a significant impact on quality of life.

          Abstract

          Small vessel disease pathways disrupts subcortical pathways that are important for emotion regulation. Hollocks et al. use brain imaging and statistical modelling to show that white-matter damage is associated with apathy, but not depression, although the latter still has a significant impact on quality of life.

          Abstract

          Small vessel disease is a stroke subtype characterized by pathology of the small perforating arteries, which supply the sub-cortical structures of the brain. Small vessel disease is associated with high rates of apathy and depression, thought to be caused by a disruption of white matter cortical-subcortical pathways important for emotion regulation. It provides an important biological model to investigate mechanisms underlying these key neuropsychiatric disorders. This study investigated whether apathy and depression can be distinguished in small vessel disease both in terms of their relative relationship with white matter microstructure, and secondly whether they can independently predict functional outcomes. Participants with small vessel disease ( n = 118; mean age = 68.9 years; 65% male) defined as a clinical and magnetic resonance imaging confirmed lacunar stroke with radiological leukoaraiosis were recruited and completed cognitive testing, measures of apathy, depression, quality of life and diffusion tensor imaging. Healthy controls ( n = 398; mean age = 64.3 years; 52% male) were also studied in order to interpret the degree of apathy and depression found within the small vessel disease group. Firstly, a multilevel structural equation modelling approach was used to identify: (i) the relationships between median fractional anisotropy and apathy, depression and cognitive impairment; and (ii) if apathy and depression make independent contributions to quality of life in patients with small vessel disease. Secondly, we applied a whole-brain voxel-based analysis to investigate which regions of white matter were associated with apathy and depression, controlling for age, gender and cognitive functioning. Structural equation modelling results indicated both apathy (r = −0.23, P ≤ 0.001) and depression (r = −0.41, P ≤ 0.001) were independent predictors of quality of life. A reduced median fractional anisotropy was significantly associated with apathy (r = −0.38, P ≤ 0.001), but not depression (r = −0.16, P = 0.09). On voxel-based analysis, apathy was associated with widespread reduction in white matter integrity, with the strongest effects in limbic association tracts such as the anterior cingulum, fornix and uncinate fasciculus. In contrast, when controlling for apathy, we found no significant relationship between our white matter parameters and symptoms of depression. In conclusion, white matter microstructural changes in small vessel disease are associated with apathy but not directly with depressive symptoms. These results suggest that apathy, but not depression, in small vessel disease is related to damage to cortical-subcortical networks associated with emotion regulation, reward and goal-directed behaviour.

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          Most cited references48

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          Development and validation of a geriatric depression screening scale: a preliminary report.

          A new Geriatric Depression Scale (GDS) designed specifically for rating depression in the elderly was tested for reliability and validity and compared with the Hamilton Rating Scale for Depression (HRS-D) and the Zung Self-Rating Depression Scale (SDS). In constructing the GDS a 100-item questionnaire was administered to normal and severely depressed subjects. The 30 questions most highly correlated with the total scores were then selected and readministered to new groups of elderly subjects. These subjects were classified as normal, mildly depressed or severely depressed on the basis of Research Diagnostic Criteria (RDC) for depression. The GDS, HRS-D and SDS were all found to be internally consistent measures, and each of the scales was correlated with the subject's number of RDC symptoms. However, the GDS and the HRS-D were significantly better correlated with RDC symptoms than was the SDS. The authors suggest that the GDS represents a reliable and valid self-rating depression screening scale for elderly populations.
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            Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

            All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.
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              Estimation of the effective self-diffusion tensor from the NMR spin echo.

              The diagonal and off-diagonal elements of the effective self-diffusion tensor, Deff, are related to the echo intensity in an NMR spin-echo experiment. This relationship is used to design experiments from which Deff is estimated. This estimate is validated using isotropic and anisotropic media, i.e., water and skeletal muscle. It is shown that significant errors are made in diffusion NMR spectroscopy and imaging of anisotropic skeletal muscle when off-diagonal elements of Deff are ignored, most notably the loss of information needed to determine fiber orientation. Estimation of Deff provides the theoretical basis for a new MRI modality, diffusion tensor imaging, which provides information about tissue microstructure and its physiologic state not contained in scalar quantities such as T1, T2, proton density, or the scalar apparent diffusion constant.
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                Author and article information

                Journal
                Brain
                Brain
                brainj
                brain
                Brain
                Oxford University Press
                0006-8950
                1460-2156
                December 2015
                21 October 2015
                21 October 2015
                : 138
                : 12
                : 3803-3815
                Affiliations
                1 Stroke Research Group, University of Cambridge, Department of Clinical Neurosciences, R3, Box 183, Addenbrooke’s Biomedical Campus, Cambridge, CB2 0QQ, UK
                2 St. Georges, University of London, Neurosciences Research Centre, Cardiovascular and Cell Sciences Research Institute, London, UK
                3 King’s College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychology, London, UK
                4 University of Oxford, Nuffield Department of Clinical Neurosciences, Oxford, UK
                Author notes
                Correspondence to: Matthew J. Hollocks, Stroke Research Group, University of Cambridge, Department of Clinical Neurosciences, R3, Box 183, Addenbrooke’s Biomedical Campus, Cambridge, CB2 0QQ, UK E-mail: mjh249@ 123456medschl.cam.ac.uk
                Article
                awv304
                10.1093/brain/awv304
                4655344
                26490330
                191a70b6-7941-427f-a25d-5fea770a363e
                © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 May 2015
                : 24 August 2015
                : 26 August 2015
                Page count
                Pages: 13
                Categories
                Original Articles
                1090
                Editor's Choice

                Neurosciences
                diffusion tensor imaging,emotion,lacunar stroke,motivation,vascular dementia
                Neurosciences
                diffusion tensor imaging, emotion, lacunar stroke, motivation, vascular dementia

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