DTI-Based Connectivity in Isolated Neural Ganglia

Diffusion Tensor Imaging (DTI) provides a unique contrast based on the restricted directionality of water movement in an anisotropic environment. As such, DTI-based tractography can be used to characterize and quantify the structural connectivity within neural tissue. Here, DTI-based connectivity within isolated abdominal ganglia (ABG) of aplysia Californica is analyzed using network theory. In addition to quantifying the regional physical proprieties of the fractional anisotropy (FA) and apparent diffusion coefficient (ADC), DTI tractography was used to probe inner-connections of local communities, yielding unweighted, undirected graphs that represent community structures. Local and global efficiency, characteristic path lengths and clustering analysis are performed on both experimental and simulated data. The relevant intensity and velocity by which these specific nodes communicate is probed through weighted clustering coefficient measurements for the descriptive weighted matrices. Both small-worldness and novel small world metrics were used as tools to verify the small-world properties for the experimental results. The aim of this manuscript is to categorize and quantify the properties exhibited by structural networks in a model neural tissue to derive unique mean field information that quantitatively describe macroscopic connectivity. For ABG, findings demonstrate a default structural network with preferential specific small-world properties when compared to simulated lattice and random networks that are equivalent in order and degree.