ABSTRACT
BACKGROUND: Focal Axonal Swellings arise in several leading neurodegenerative diseases of the central nervous system and are hallmark features of concussions and traumatic brain injuries. Recent theories mapped how the shape of each swelling affects the propagation of spike trains and consequently the information encoded in them. Spikes can be selectively deleted, have their speed affected, or blocked depending upon the severity of the swelling. NEW METHOD: Our computational toolbox extracts meaningful geometrical parameters from sequential images of injured axon segments. The algorithm provides a principled approach for dealing with imaging distortions caused by experimental artifacts in order to extract the cross-section of an axon by detecting local symmetries, turning points and turning regions. RESULTS: Our characterization of the Focal Axonal Swelling allows for an assessment of its impact on spike propagation, leading to a color coding of the axon that highlights problematic regions for information propagation. COMPARISON WITH EXISTING METHODS: Many theoretical works reported distortions in spike propagation related to axonal enlargements. Such estimates, however, were not incorporated to a toolbox that could classify axonal swellings directly from experimental images. CONCLUSIONS: Our MATLAB toolbox thus highlights potential trouble spots of axonal morphology, and similar to car traffic maps, identify blocked or impaired routes for information flow. This computational framework is a promising starting point for diagnosing and assessing the impact of axonal swellings implicated in concussions, Alzheimer's and Parkinson's disease, Multiple Sclerosis and other neurological pathologies.
