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      Postretinal Structure and Function in Severe Congenital Photoreceptor Blindness Caused by Mutations in the GUCY2D Gene

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          Abstract

          Purpose

          To examine how severe congenital blindness resulting from mutations of the GUCY2D gene alters brain structure and function, and to relate these findings to the notable preservation of retinal architecture in this form of Leber congenital amaurosis (LCA).

          Methods

          Six GUCY2D-LCA patients (ages 20–46) were studied with optical coherence tomography of the retina and multimodal magnetic resonance imaging (MRI) of the brain. Measurements from this group were compared to those obtained from populations of normally sighted controls and people with congenital blindness of a variety of causes.

          Results

          Patients with GUCY2D-LCA had preservation of the photoreceptors, ganglion cells, and nerve fiber layer. Despite this, visual function in these patients ranged from 20/160 acuity to no light perception, and functional MRI responses to light stimulation were attenuated and restricted. This severe visual impairment was reflected in substantial thickening of the gray matter layer of area V1, accompanied by an alteration of resting-state correlations within the occipital lobe, similar to a comparison group of congenitally blind people with structural damage to the retina. In contrast to the comparison blind population, however, the GUCY2D-LCA group had preservation of the size of the optic chiasm, and the fractional anisotropy of the optic radiations as measured with diffusion tensor imaging was also normal.

          Conclusions

          These results identify dissociable effects of blindness upon the visual pathway. Further, the relatively intact postgeniculate white matter pathway in GUCY2D-LCA is encouraging for the prospect of recovery of visual function with gene augmentation therapy.

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          Most cited references67

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          A hybrid approach to the skull stripping problem in MRI.

          We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skull-stripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skull-stripping tools. Copyright 2004 Elsevier Inc.
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            Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex.

            Highly accurate surface models of the cerebral cortex are becoming increasingly important as tools in the investigation of the functional organization of the human brain. The construction of such models is difficult using current neuroimaging technology due to the high degree of cortical folding. Even single voxel misclassifications can result in erroneous connections being created between adjacent banks of a sulcus, resulting in a topologically inaccurate model. These topological defects cause the cortical model to no longer be homeomorphic to a sheet, preventing the accurate inflation, flattening, or spherical morphing of the reconstructed cortex. Surface deformation techniques can guarantee the topological correctness of a model, but are time-consuming and may result in geometrically inaccurate models. In order to address this need we have developed a technique for taking a model of the cortex, detecting and fixing the topological defects while leaving that majority of the model intact, resulting in a surface that is both geometrically accurate and topologically correct.
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              Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach.

              Abstract We describe a comprehensive linear approach to the problem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole strengths over the cortical surface is highly underdetermined, even given closely spaced EEG and MEG recordings. We have obtained much better solutions to this problem by explicitly incorporating both local cortical orientation as well as spatial covariance of sources and sensors into our formulation. An explicit polygonal model of the cortical manifold is first constructed as follows: (1) slice data in three orthogonal planes of section (needle-shaped voxels) are combined with a linear deblurring technique to make a single high-resolution 3-D image (cubic voxels), (2) the image is recursively flood-filled to determine the topology of the gray-white matter border, and (3) the resulting continuous surface is refined by relaxing it against the original 3-D gray-scale image using a deformable template method, which is also used to computationally flatten the cortex for easier viewing. The explicit solution to an error minimization formulation of an optimal inverse linear operator (for a particular cortical manifold, sensor placement, noise and prior source covariance) gives rise to a compact expression that is practically computable for hundreds of sensors and thousands of sources. The inverse solution can then be weighted for a particular (averaged) event using the sensor covariance for that event. Model studies suggest that we may be able to localize multiple cortical sources with spatial resolution as good as PET with this technique, while retaining a much finer grained picture of activity over time.
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                Author and article information

                Journal
                Invest Ophthalmol Vis Sci
                Invest. Ophthalmol. Vis. Sci
                iovs
                iovs
                IOVS
                Investigative Ophthalmology & Visual Science
                The Association for Research in Vision and Ophthalmology
                0146-0404
                1552-5783
                February 2017
                : 58
                : 2
                : 959-973
                Affiliations
                [1 ]Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
                [2 ]Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
                Author notes
                Correspondence: Geoffrey K. Aguirre, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; aguirreg@ 123456mail.med.upenn.edu .
                Article
                iovs-58-01-46 IOVS-16-20413R2
                10.1167/iovs.16-20413
                5308769
                28403437
                077d0528-73e0-4383-bd24-6e86604b601d
                Copyright 2017 The Authors

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : 28 July 2016
                : 27 December 2016
                Categories
                Visual Neuroscience

                visual cortex,functional imaging,retinal degeneration

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