Functionality and Robustness of Injured Connectomic Dynamics in C. elegans: Linking Behavioral Deficits to Neural Circuit Damage

• Published in: PLoS computational biology 2017-01, Vol.13 (1), p.e1005261-e1005261
• Main Authors: Kunert, James M, Maia, Pedro D, Kutz, J Nathan
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Animals; Applied mathematics; Behavior; Biology and Life Sciences; Caenorhabditis elegans; Caenorhabditis elegans - physiology; Computational Biology; Computer and Information Sciences; Concussion; Connectome; Dementia; Engineering and Technology; Funding; Medicine and Health Sciences; Models, Neurological; Multiple sclerosis; Nematoda; Nematodes; Nerve Net - injuries; Nerve Net - physiopathology; Neural circuitry; Neural networks; Neurosciences; Parkinson's disease; Physiological aspects; Research and Analysis Methods; Studies; Traumatic brain injury; Visualization
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005261

Using a model for the dynamics of the full somatic nervous system of the nematode C. elegans, we address how biological network architectures and their functionality are degraded in the presence of focal axonal swellings (FAS) arising from neurodegenerative disease and/or traumatic brain injury. Using biophysically measured FAS distributions and swelling sizes, we are able to simulate the effects of injuries on the neural dynamics of C. elegans, showing how damaging the network degrades its low-dimensional dynamical responses. We visualize these injured neural dynamics by mapping them onto the worm's low-dimensional postures, i.e. eigenworm modes. We show that a diversity of functional deficits arise from the same level of injury on a connectomic network. Functional deficits are quantified using a statistical shape analysis, a procrustes analysis, for deformations of the limit cycles that characterize key behaviors such as forward crawling. This procrustes metric carries information on the functional outcome of injuries in the model. Furthermore, we apply classification trees to relate injury structure to the behavioral outcome. This makes testable predictions for the structure of an injury given a defined functional deficit. More critically, this study demonstrates the potential role of computational simulation studies in understanding how neuronal networks process biological signals, and how this processing is impacted by network injury.