title: Structural connectivity within neural ganglia: A default small-world network.
abstract: 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 of Aplysia californica (ABG) is analyzed using network theory. In addition to quantifying the regional physical proprieties of the fractional anisotropy and apparent diffusion coefficient, 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 by which these specific nodes communicate is probed through weighted clustering coefficient measurements. 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 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.
authors.affiliation: The National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA; Department of Chemical and Biomedical Engineering, Florida State University, FAMU-FSU College of Engineering, Tallahassee, FL, USA.
authors.forename: Abdol Aziz O
authors.initials: AA
authors.lastname: Ould Ismail
authors.affiliation: The National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA; Department of Physics, Florida State University, Tallahassee, FL, USA.
authors.forename: Ghoncheh
authors.initials: G
authors.lastname: Amouzandeh
authors.affiliation: The National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA; Department of Chemical and Biomedical Engineering, Florida State University, FAMU-FSU College of Engineering, Tallahassee, FL, USA. Electronic address: grant@magnet.fsu.edu.
authors.forename: Samuel C
authors.initials: SC
authors.lastname: Grant
mesh_terms: D000818:Animals; D001048:Aplysia; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D056324:Diffusion Tensor Imaging / Q000379:methods; D005724:Ganglia / Q000502:physiology*; D007091:Image Processing, Computer-Assisted / Q000379:methods; D009415:Nerve Net / Q000473:pathology* / Q000502:physiology; D009434:Neural Pathways / Q000473:pathology*
keywords: diffusion tensor imaging; graph theory; small-world networks; structural connectivity