Geometric representation of neuroanatomical data observed in mouse brain at cellular and gross levels

This dissertation studies two problems related to geometric representation of neuroanatomical data: (i) spatial representation and organization of individual neurons, and (ii) reconstruction of three-dimensional neuroanatomical regions from sparse two-dimensional drawings. This work has been motivated by nearby development of new technology, Knife-Edge Scanning Microscopy (KESM), that images a whole mouse brain at cellular level in less than a month. A method is introduced to represent neuronal data observed in the mammalian brain at the cellular level using geometric primitives and spatial indexing. A data representation scheme is defined that captures the geometry of individual neurons using traditional geometric primitives, points and cross-sectional areas along a trajectory. This representation captures inferred synapses as directed links between primitives and spatially indexes observed neurons based on the locations of their cell bodies. This method provides a set of rules for acquisition, representation, and indexing of KESMgenerated data. Neuroanatomical data observed at the gross level provides the underlying regional framework for neuronal circuits. Accumulated expert knowledge on neuroanatomical organization is usually given as a series of sparse two-dimensional contours. A data structure and an algorithm are described to reconstruct separating surfaces among multiple regions from these sparse cross-sectional contours. A topology graph is defined for each region that describes the topological skeleton of the region’s boundary surface and that shows between which contours the surface patches should be generated. A graph-directed triangulation algorithm is provided to reconstruct surface patches between contours. This graph-directed triangulation algorithm combined together with a piecewise parametric curve fitting technique ensures that abutting or shared surface patches are precisely coincident. This method overcomes limitations in i) traditional surfaces-from-contours algorithms that assume binary, not multiple, regionalization of space, and in ii) few existing separating surfaces algorithms that assume conversion of input into a regular volumetric grid, which is not possible with sparse inter-planar resolution

Segmentation strategies for polymerized volume data sets

A new technique, called the polymerization algorithm, is described for the hierarchical segmentation of polymerized volume data sets (PVDS) using the Lblock data structure. The Lblock data structure is defined as a 3dimensional isorectangular block of enhanced vertex information. Segmentation of the PVDS is attained by intersecting and merging Lblock coverings of the enhanced volumetric data. The data structure allows for easy compression, storage, segmentation, and reconstruction of volumetric data obtained from scanning a mammalian brain at submicron resolution, using threedimensional light microscopy (knifeedge scanning microscopy (KESM), confocal microscopy (CFM), and multiphoton microscopy (MPM)). A hybrid technique using the polymerization algorithm and an existing vectorbased tracing algorithm is developed. Both the polymerized and the hybrid algorithm have been tested and their analyzed results are presented

Das Konnektom des Hirnstamms der Laborratte

Die Konnektomanalyse des Hirnstammes geht von 310 unilateralen Regionen im Hirnstamm aus. Sie wurden als Hierarchie und als 3D-Visualisierung uni- und bilateral dargestellt. In drei Datenbanken wurden 587 Publikationen identifiziert, die vor allem aus intrinsischen Verbindungen bestehen und u.a. von mir erfasst wurden

Das Konnektom der Basalganglien der Ratte

Der Begriff Konnektom umfasst die Gesamtheit aller neuronalen Verbindungen innerhalb des Nervensystems in einem Organismus. Tract-Tracing-Studien, bezogen auf die Kerngebiete der Basalganglien der Ratte, wurden ausgewertet und mit Hilfe des Programmes neuroVIISAS umfassend analysiert. Bei der Auswertung wurden neben ipsi- auch kontralaterale Konnektivitäten auf mehreren hierarchischen Ebenen berücksichtigt. Besondere Bedeutung kommt den Kerngebieten der Substantia nigra pars compacta und des Caudatus-Putamen-Komplexes zu