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A Novel Multi-Faceted Strategy to Identify the In Vivo Dynamics of Spinal Locomotor Networks

Abstract

Improving our understanding of the anatomy of spinal locomotor networks, how they physiologically interact with proprioception to execute locomotion in vivo, and how ES combined with physical training mechanistically reorganizes spinal networks will provide crucial information in how to use ES optimally to improve clinical outcomes along with inspiring future design of epidural electrodes and training programs. This thesis aims to adapt an engineering and big data workflow to answer these pressing biological problems, namely finding a way to generate large sets of data from current immunohistochemistry (IHC) techniques known to generate very little and to find a way to analyze this large amount of data in a time efficient manner when the current gold standard has been manual analysis.

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