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Unformatted text preview: Digital Object Identifier (DOI) 10.1007/s00791-002-0084-6 Comput Visual Sci 5: 13–34 (2002) Computing and Visualization in Science Springer-Verlag 2002 Regular article A framework for computational anatomy Paul M. Thompson, Arthur W. Toga Laboratory of Neuro Imaging, Dept. of Neurology, Division of Brain Mapping, UCLA School of Medicine, Los Angeles, CA 90095, USA (e-mail: [email protected]) Received: 13 August 2001 Communicated by: C. Johnson and M. Rumpf Abstract. The rapid collection of brain images from healthy and diseased subjects has stimulated the development of pow- erful mathematical algorithms to compare, pool and average brain data across whole populations. Brain structure is so complex and variable that new approaches in computer vi- sion, partial differential equations, and statistical field the- ory are being formulated to detect and visualize disease- specific patterns. We present some novel mathematical strate- gies for computational anatomy, focusing on the creation of population-based brain atlases. These atlases describe how the brain varies with age, gender, genetics, and over time. We review applications in Alzheimer’s disease, schizophrenia and brain development, outlining some current challenges in the field. 1 Diversity of brain maps Recent developments in brain imaging have greatly empow- ered medicine and neuroscience. The ability to image the structure and function of the living brain has also acceler- ated the collection and databasing of brain maps. These maps store information on anatomy and physiology, from whole- brain to molecular scales, some capturing dynamic changes that occur over milliseconds or even over entire lifetimes (see e.g. Toga and Mazziotta 1996; Frackowiak et al. 1997, for re- cent reviews). Since the development of computerized tomography (CT; Hounsfield 1973) and magnetic resonance imaging tech- niques (Lauterbur 1973), maps of brain structure have typ- ically been based upon 3D tomographic images (Damasio 1995). Angiographic or spiral CT techniques can also visual- ize vascular anatomy (Fishman 1997), while diffusion tensor images can even reveal fiber topography in vivo (Turner et al. 1991; Mori et al. 2001; Jacobs and Fraser 1994). These brain maps can be supplemented with high-resolution infor- mation from anatomic specimens (Talairach and Tournoux 1988; Ono et al. 1990; Duvernoy 1991) and a variety of his- tologic preparations which reveal regional cytoarchitecture (Brodmann 1909) and regional molecular content such as myelination patterns (Smith 1907; Mai et al. 1997), recep- tor binding sites (Geyer et al. 1997), protein densities and mRNA distributions. Other brain maps have concentrated on function, quantified by positron emission tomography (PET; Minoshima et al. 1994), functional MRI (Le Bihan 1996), electrophysiology (Avoli et al. 1991; Palovcik et al. 1992) or optical imaging (Cannestra et al. 1996). Additional maps have been developed to represent neuronal connectivity and...
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This note was uploaded on 01/16/2012 for the course BI 200 taught by Professor Potter during the Fall '11 term at Montgomery College.

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