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Voxel-based morphometry reveals reduced grey matter volume in the temporal cortex of developmental prosopagnosics

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      Abstract

      Individuals with developmental prosopagnosia exhibit severe and lasting difficulties in recognizing faces despite the absence of apparent brain abnormalities. We used voxel-based morphometry to investigate whether developmental prosopagnosics show subtle neuroanatomical differences from controls. An analysis based on segmentation of T1-weighted images from 17 developmental prosopagnosics and 18 matched controls revealed that they had reduced grey matter volume in the right anterior inferior temporal lobe and in the superior temporal sulcus/middle temporal gyrus bilaterally. In addition, a voxel-based morphometry analysis based on the segmentation of magnetization transfer parameter maps showed that developmental prosopagnosics also had reduced grey matter volume in the right middle fusiform gyrus and the inferior temporal gyrus. Multiple regression analyses relating three distinct behavioural component scores, derived from a principal component analysis, to grey matter volume revealed an association between a component related to facial identity and grey matter volume in the left superior temporal sulcus/middle temporal gyrus plus the right middle fusiform gyrus/inferior temporal gyrus. Grey matter volume in the lateral occipital cortex was associated with component scores related to object recognition tasks. Our results demonstrate that developmental prosopagnosics have reduced grey matter volume in several regions known to respond selectively to faces and provide new evidence that integrity of these areas relates to face recognition ability.

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      • Record: found
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      A fast diffeomorphic image registration algorithm.

      This paper describes DARTEL, which is an algorithm for diffeomorphic image registration. It is implemented for both 2D and 3D image registration and has been formulated to include an option for estimating inverse consistent deformations. Nonlinear registration is considered as a local optimisation problem, which is solved using a Levenberg-Marquardt strategy. The necessary matrix solutions are obtained in reasonable time using a multigrid method. A constant Eulerian velocity framework is used, which allows a rapid scaling and squaring method to be used in the computations. DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.
        Bookmark
        • Record: found
        • Abstract: found
        • Article: not found

        Unified segmentation.

        A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.
          Bookmark
          • Record: found
          • Abstract: found
          • Article: not found

          Voxel-based morphometry--the methods.

          At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data. Copyright 2000 Academic Press.
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            Author and article information

            Affiliations
            1 UCL Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, UK
            2 UCL Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK
            3 Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
            4 UCL Institute of Neurology, University College London, London WC1N 3BG, UK
            Author notes
            Correspondence to: Lúcia Garrido, Institute of Cognitive Neuroscience, Alexandra House, 17 Queen Square, London WC1N 3AR, UK E-mail: m.garrido@ 123456ucl.ac.uk
            Journal
            Brain
            brainj
            brain
            Brain
            Oxford University Press
            0006-8950
            1460-2156
            December 2009
            3 November 2009
            3 November 2009
            : 132
            : 12
            : 3443-3455
            2792372
            19887506
            10.1093/brain/awp271
            awp271
            © The Author(s) 2009. 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/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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