3D generalization of brain model to visualize and analyze neuroanatomical data

Neuroscientists present data in a 3D form in order to convey a better real world visualization and understanding of the localization of data in relation to brain anatomy and structure. The problem with the visualization of cortical surface of the brain is that the brain has multiple, deep folds and the resulting structural overlap can hide data interweaved within the folds. On one hand, a 2D representation can result in a distorted view that may lead to incorrect localization and analysis of the data. On the other hand, a realistic 3D representation may interfere with our judgment or analysis by showing too many details. Alternatively, a 3D generalization can be used to simplify the model of the brain in order to visualize the hidden data and smooth some of the details. This dissertation addresses the following research question: Is 3D generalization of a brain model a viable approach for visualizing neuroanatomical data?

[1]  R. Fabio From point cloud to surface the modeling and visualization problem , 2003 .

[2]  Fabio Remondino From point cloud to surface , 2003 .

[3]  Jiann-Yeou Rau,et al.  LOD Generation for 3D Polyhedral Building Model , 2006, PSIVT.

[4]  Daniel R. Montello,et al.  Testing the First Law of Cognitive Geography on Point-Display Spatializations , 2003, COSIT.

[5]  H A Drury,et al.  Computational methods for reconstructing and unfolding the cerebral cortex. , 1995, Cerebral cortex.

[6]  U. Grenander,et al.  Statistical methods in computational anatomy , 1997, Statistical methods in medical research.

[7]  Tobias Höllerer,et al.  Resolving multiple occluded layers in augmented reality , 2003, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings..

[8]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[9]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[10]  Melanie Tory Mental registration of 2D and 3D visualizations (an empirical study) , 2003, IEEE Visualization, 2003. VIS 2003..

[11]  David C. Van Essen,et al.  Application of Information Technology: An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex , 2001, J. Am. Medical Informatics Assoc..

[12]  Bernhard Preim,et al.  VR Based Visualization and Exploration of Plant Biological Data , 2009, J. Virtual Real. Broadcast..

[13]  Mark Apperley,et al.  Data base navigation: an office environment for the professional , 1982 .

[14]  W. Gellert,et al.  The VNR concise encyclopedia of mathematics , 1977 .

[15]  Jock D. Mackinlay,et al.  The perspective wall: detail and context smoothly integrated , 1991, CHI.

[16]  Colin Ware,et al.  Zooming, multiple windows, and visual working memory , 2002, AVI '02.

[17]  William H. Beyer,et al.  CRC standard mathematical tables , 1976 .

[18]  Parashkev Nachev,et al.  A new method for automated high-dimensional lesion segmentation evaluated in vascular injury and applied to the human occipital lobe , 2014, Cortex.

[19]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[20]  J. Hencil Peter,et al.  An Optimised Density Based Clustering Algorithm , 2010 .

[21]  Willis F. Kern,et al.  Solid mensuration : with proofs , 1938 .

[22]  Arnold W. M. Smeulders,et al.  The Morphological Structure of Images: The Differential Equations of Morphological Scale-Space , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[24]  Andrea Forberg,et al.  Generalization of 3D building data based on a scale-space approach , 2007 .

[25]  Stephen C. Strother,et al.  A symbolic environment for visualizing activated foci in functional neuroimaging datasets , 1998, Medical Image Anal..

[26]  George Furnas,et al.  The FISHEYE view: A new look at structured files , 1986, CHI 1986.

[27]  Naftali Kadmon,et al.  A Polyfocal Projection for Statistical Surfaces , 1978 .

[28]  David M. Mark,et al.  The natural landscape metaphor in information visualization: The role of commonsense geomorphology , 2010, J. Assoc. Inf. Sci. Technol..

[29]  J A Fiez,et al.  Lesion segmentation and manual warping to a reference brain: Intra‐ and interobserver reliability , 2000, Human brain mapping.

[30]  R. Schafer,et al.  Morphological systems for multidimensional signal processing , 1990, Proc. IEEE.

[31]  P. Strick,et al.  Rabies as a transneuronal tracer of circuits in the central nervous system , 2000, Journal of Neuroscience Methods.

[32]  Stephen G. Eick,et al.  Data visualization sliders , 1994, UIST '94.

[33]  RP Dum,et al.  The origin of corticospinal projections from the premotor areas in the frontal lobe , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[34]  D. V. van Essen,et al.  Computerized Mappings of the Cerebral Cortex: A Multiresolution Flattening Method and a Surface-Based Coordinate System , 1996, Journal of Cognitive Neuroscience.

[35]  Z. Nadasdy,et al.  Neurons in the basal forebrain project to the cortex in a complex topographic organization that reflects corticocortical connectivity patterns: an experimental study based on retrograde tracing and 3D reconstruction. , 2015, Cerebral cortex.

[36]  Andreea C. Bostan,et al.  Cerebellar networks with the cerebral cortex and basal ganglia , 2013, Trends in Cognitive Sciences.

[37]  Colin Ware,et al.  Information Visualization: Perception for Design , 2000 .

[38]  Benjamin B. Kimia,et al.  Shapes, shocks, and deformations I: The components of two-dimensional shape and the reaction-diffusion space , 1995, International Journal of Computer Vision.

[39]  Colin Ware,et al.  Visual Thinking for Design , 2008 .

[40]  W. Denk,et al.  The Big and the Small: Challenges of Imaging the Brain’s Circuits , 2011, Science.

[41]  P. Strick,et al.  Frontal Lobe Inputs to the Digit Representations of the Motor Areas on the Lateral Surface of the Hemisphere , 2005, The Journal of Neuroscience.

[42]  U. Grenander,et al.  Computational anatomy: an emerging discipline , 1998 .

[43]  Petros Maragos,et al.  Morphological Signal and Image Processing , 2009 .

[44]  H. Heijmans Morphological image operators , 1994 .

[45]  Lauretta Passarelli,et al.  Cortical Connectivity Suggests a Role in Limb Coordination for Macaque Area PE of the Superior Parietal Cortex , 2013, The Journal of Neuroscience.

[46]  David Rudrauf,et al.  What affects detectability of lesion–deficit relationships in lesion studies? , 2014, NeuroImage: Clinical.

[47]  James D. Foley,et al.  Fundamentals of interactive computer graphics , 1982 .

[48]  Bennett Eisenberg,et al.  Random Triangles in n Dimensions , 1996 .

[49]  Stephen Brooks,et al.  Multilayer hybrid visualizations to support 3D GIS , 2008, Comput. Environ. Urban Syst..

[50]  Igor Jurisica,et al.  Interaction Techniques for Selecting and Manipulating Subgraphs in Network Visualizations , 2009, IEEE Transactions on Visualization and Computer Graphics.

[51]  Alice M. Agogino,et al.  An interface for interactive spatial reasoning and visualization , 1992, CHI '92.

[52]  Harvey S. Smallman,et al.  The Use of 2D and 3D Displays for Shape-Understanding versus Relative-Position Tasks , 2001, Hum. Factors.

[53]  E. A. Maxwell,et al.  Mathematical Gems II , 1976, The Mathematical Gazette.

[54]  W. Graf,et al.  Cerebellar inputs to intraparietal cortex areas LIP and MIP: functional frameworks for adaptive control of eye movements, reaching, and arm/eye/head movement coordination. , 2010, Cerebral cortex.

[55]  Chen Li,et al.  Motor and Nonmotor Domains in the Monkey Dentate , 2002, Annals of the New York Academy of Sciences.

[56]  M. Schwartz,et al.  Multivariate lesion‐symptom mapping using support vector regression , 2014, Human brain mapping.

[57]  Eric L. Schwartz,et al.  A Numerical Solution to the Generalized Mapmaker's Problem: Flattening Nonconvex Polyhedral Surfaces , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[58]  Martin Kada AUTOMATIC GENERALISATION OF 3D BUILDING MODELS , 2002 .

[59]  Yq Liu,et al.  Intention and Attention: Different functional roles for LIPd and LIPv , 2010, Nature Neuroscience.

[60]  Monika Sester,et al.  Segmentation of Buildings for 3 D-Generalisation , 2004 .

[61]  Jon Louis Bentley,et al.  Quad trees a data structure for retrieval on composite keys , 1974, Acta Informatica.

[62]  M. Hegarty,et al.  A dissociation between mental rotation and perspective-taking spatial abilities , 2004 .

[63]  Ben Shneiderman,et al.  Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays , 1994 .

[64]  G. Luppino,et al.  Anatomical Evidence for the Involvement of the Macaque Ventrolateral Prefrontal Area 12r in Controlling Goal-Directed Actions , 2011, The Journal of Neuroscience.

[65]  Maureen C. Stone,et al.  Enhanced dynamic queries via movable filters , 1995, CHI '95.

[66]  Marilyn Tremaine,et al.  Understanding visualization through spatial ability differences , 2005, VIS 05. IEEE Visualization, 2005..

[67]  William J. Schroeder,et al.  The Visualization Toolkit , 2005, The Visualization Handbook.

[68]  Nelson L. Max,et al.  A characterization of the scientific data analysis process , 1992, Proceedings Visualization '92.