| Abstract: | Spinal cord injury (SCI) is a traumatic event that can result in loss of motor, sensory, and autonomic functions. There is currently no cure for SCI, but potential treatments and therapies are informed by knowledge of how spinal cord circuits respond to injury, including assessment of remaining functional axons post-SCI. Computational approaches to analyze axon anatomy after injury require large computational resources and are inadequate at assessing continuity of long-distance axon tracts in the spinal cord, crucial for distinguishing severed from spared axons. In this work, we combined tissue clearing and LSFM to acquire 3D volumes of murine spinal cords with fluorescently-labeled neuronal classes. We employed a DTI-based tractography method, CAPTURE, that creates streamlines following strictly continuous axon tracts to assess axon sparing post-SCI. We investigated the limits of CAPTURE regarding image resolution and quality, finding limitations with both decrease in resolution based on axon tract size and increased noise. We then investigated segmentation algorithms to best distinguish signal from background in these images. Finally, we used this CAPTURE-based method to analyze axon sparing post-SCI in spinal cords with fluorescently-labeled V2a interneurons and corticospinal tract (CST) neurons. These data showed that a CAPTURE-based pipeline has potential for use in quantifying axon sparing post-SCI, as well as representing neural circuit anatomy in uninjured spinal cords. The electronic version of this dissertation is accessible from https://hdl.handle.net/1969.1/198133 |
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