Abstract
From the inception of the neuron doctrine (i.e., the understanding that nervous systems are composed of individual cells), the morphology of neurons has been a predictable focus in the study of their physiology. After all, a practical understanding of the development, maintenance, and functional robustness of a neuron’s geometry is prerequisite to a full appreciation of the organization and adaptability (i.e., plasticity) of neural networks. In addition, a variety of neuromorphological abnormalities, which are often conspicuous, have been implicated in human diseases such as neurodegeneration, neuropsychiatric disorders (e.g., Autism Spectrum Disorder, Major Depressive Disorder), cerebrovascular pathology, and neurotrauma. However, the sheer complexity of neuron morphologies precludes consistent, objective analysis without the aid of computers. Automated methods of reconstructing entire neuron “skeletons” continue to improve; but for the time being, manual reconstruction in three dimensions remains the most reliable way of analyzing the structure of the entire neuropil. Two species that have been useful for neuromorphology analysis are the Jonah crab (Cancer borealis) and the American lobster (Homarus americanus), both of which possess elegant and physiologically analogous stomatogastric ganglia (STG). This thesis reports on forty-eight manually-reconstructed STG neurons from these two species, including five electrophysiologically-characterized cell types and two developmental stages. Quantitative analyses reveal remarkable variability across all the considered parameters. No obvious morphological differences between STG cell types can be identified; however, branching complexity, soma size, and the space occupied by the neuropil are all shown to increase significantly between juvenile and adult developmental stages. Furthermore, branching patterns in these cells—specifically tortuosity, path length, and branch order—are not well-predicted by a leading computational algorithm. Finally, double-neuron reconstructions of synaptically-related neurons can readily identify candidate synaptic appositions. Ultimately, these efforts present a promising collection of quantitative tools which may be used to characterize fine structures within neuropils. As global repositories of neuronal reconstructions grow—and as automated methods of reconstruction continue to improve—such quantitative approaches are likely to improve our understanding of how neuronal structure relates to functionality, development, and even disease.