Fusion of white and gray matter geometry: A framework for investigating brain development |
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| Institution: | 1. Laboratory for Mathematics in Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;2. Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA;3. Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA;4. Children’s Hospital of Philadelphia, Philadelphia, PA, USA;1. Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;2. Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;3. Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;1. Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States;2. Center for Autism Research, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States;1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA;2. Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea;1. Cognitive Neuroscience Unit, Department of Psychology, School of Psychology, Faculty of Health, Deakin University, 221 Burwood Highway, Victoria, Australia;2. Monash Alfred Psychiatry Research Centre, The Alfred and Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia;3. Precision Brain Spine and Pain Centre, Australia;4. Department of Neurosurgery, Royal Melbourne Hospital, Australia;1. Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA;2. Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children''s Hospital of Philadelphia, Philadelphia, PA, USA |
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| Abstract: | Current neuroimaging investigation of the white matter typically focuses on measurements derived from diffusion tensor imaging, such as fractional anisotropy (FA). In contrast, imaging studies of the gray matter oftentimes focus on morphological features such as cortical thickness, folding and surface curvature. As a result, it is not clear how to combine findings from these two types of approaches in order to obtain a consistent picture of morphological changes in both gray and white matter.In this paper, we propose a joint investigation of gray and white matter morphology by combining geometrical information from white and the gray matter. To achieve this, we first introduce a novel method for computing multi-scale white matter tract geometry. Its formulation is based on the differential geometry of curve sets and is easily incorporated into a continuous scale-space framework.We then incorporate this method into a novel framework for “fusing” white and gray matter geometrical information. Given a set of fiber tracts originating in a particular cortical region, the key idea is to compute two scalar fields that represent geometrical characteristics of the white matter and of the surface of the cortical region. A quantitative marker is created by combining the distributions of these scalar values using Mutual Information. This marker can be then used in the study of normal and pathological brain structure and development. We apply this framework to a study on autism spectrum disorder in children. Our preliminary results support the view that autism may be characterized by early brain overgrowth, followed by reduced or arrested growth (Courchesne, 2004). |
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| Keywords: | Geometry Cortex White matter Neurodevelopment Autism |
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