Deriving structural properties of the rat hippocampus: A computational approach

12:00 noon, April 08, Tuesday, 2008, by Deepak Ropireddy, ST2, 430A

Abstract

Synaptic micro-circuit properties in neuronal networks are fundamental to spatial information processing in brain systems. These properties, intrinsic to brain structure, are directly related to neural activity and function. Characterization of this micro-circuitry has been a long standing challenge in mammalian hippocampal research. One of the stumbling blocks in this endeavor is the absence of a construct to integrate the available anatomical knowledge. Thus, we devised an approach to map existing morphological data onto an in-silico based template of the rat hippocampus. Towards this goal, we have reconstructed a high resolution 3D model of the rat hippocampus from thin cryostatic slices and computationally transformed the digitally traced stack into volumetric representation with arbitrary voxel size. A computational framework is developed to embed morphological reconstructions of individual neurons by orienting their principal axes along the transversal and longitudinal planes.

In my talk, I will focus on three issues that we are addressing using this construct through computational means. Firstly, I will explain the importance of mapping the available hippocampal morphological data onto this template in various hippocampal regions and sub-structures and show the results. Secondly, this framework is applicable to estimating the macro-level stereological properties (total volumes, spatial occupancy and overlaps) within the various cytoarchitectural layers of the hippocampus. We digitally packed dentate and cornu ammonis (CA) cellular layers to emulate the in-vivo tissue properties and estimated the spatial occupancy and overlaps of dendritic segments. I will discuss preliminary results of the macroscopic stereological estimates in a portion of the dentate and cornu ammonis volumes. Thirdly, this framework is suitable to compute potential synaptic connectivity patterns within principal cells and interneurons within the hippocampus using existing mathematical formulas. The concept of “potential synapse” will be utilized in probing the proximity of dendritic and axonal segments. In the final part of my talk, I will discuss preliminary results of four different types of CA pyramidal axonal arbor potential synaptic connectivity patterns within the hippocampus.

Short Bio

Deepak Ropireddy received his Bachelor in Technology degree from National Institute of Technology, Warangal, India in Materials and Metallurgical Engineering in 1999. He received his M.S in Computer Science from Texas A&M University-Commerce, Texas in 2002. He is now a PhD candidate in the Interdisciplinary Neuroscience PhD Program here at George Mason University and doing his current research at Krasnow Institute for Advanced Study, GMU.