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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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          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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            Unified segmentation

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              Comparing an Individual's Test Score Against Norms Derived from Small Samples

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                Author and article information

                Journal
                Brain
                brainj
                brain
                Brain
                Oxford University Press
                0006-8950
                1460-2156
                December 2009
                3 November 2009
                3 November 2009
                : 132
                : 12
                : 3443-3455
                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
                Article
                awp271
                10.1093/brain/awp271
                2792372
                19887506
                63738aaf-cabe-45c1-be01-0b6e9d6dd6ec
                © 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.

                History
                : 27 May 2009
                : 21 August 2009
                : 15 September 2009
                Categories
                Original Articles

                Neurosciences
                temporal cortex,voxel-based morphometry,object recognition,developmental prosopagnosia,perception,face recognition

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