The olfactory bulb (OB) is one of the first regions of the brain affected by Parkinson’s disease (PD) as measured by both dysfunction and presence of alpha-synuclein aggregation. Better understanding of how PD affects OB function could lead to earlier diagnosis and potential treatment. By simulating damage to the OB in a computational model, it may be possible to identify regions of interest or markers of early disease. We modified a simple rate-based computational model of the olfactory bulb and simulated damage to various components of the network. This was done for several configurations of the network, at different sizes and with 1D and 2D connectivity structures. We found that, in almost every case, activity of 2D networks were more robust to damage than 1D networks, leading us to conclude that a connection scheme of at least 2D is vital to computational modeling of the OB. We also found that certain types of damage (namely, seeded damage to the granule cell layer and to the synapses between mitral and granule cells) resulted in a peak of the oscillatory power of the network as a function of damage. This result is testable experimentally and bears further investigation utilizing more sophisticated computational models. If proven accurate, this rise in oscillatory power in the OB has the potential to be an early marker of PD.
bioRxiv Subject Collection: Neuroscience