Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks

Abstract

Traumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seen in vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury.

Plasticity impairment exposes CA3 vulnerability in a hippocampal network model of mild traumatic brain injury

“…Finally, our work adds to past studies that examine other forms of neuropathology in TBI and begins to form a more comprehensive view of how different injury mechanisms perturb the function of damaged neural circuits. In our previous work on STDP and injury, we investigated how STDP can act as a homeostatic mechanism to restore baseline function in networks after injury, enabling the network to absorb damage and mitigate functional deficits (Gabrieli et al, 2020;Schumm et al, 2020). Potentiation impairment, as modeled here, would likely reduce the protective, or insulating, role that STDP provides against neurodegeneration.…”

“…The total current aggregates ionic currents through AMPA, NMDA, and GABA-A receptors as well as a noise input used to drive the network, using a gamma distribution (k = 2, θ = ½) (Gabrieli et al, 2020;Gabrieli et al, 2021;Izhikevich & Edelman, 2008;Schumm et al, 2020) (see Section 2.3 for more detail on the noise current). The NMDA receptor currents have longer duration and shorter amplitude as compared to AMPA receptor currents (Gabrieli et al, 2021).…”

A multilayer network model of neuron-astrocyte populations in vitro reveals mGluR5 inhibition is protective following traumatic injury

“…Conceptually, increased mean nodal strength in the neuronal layer following injury may result from local changes in synaptic strength from plasticity, or may also result from NMDA receptor subtypes influencing the remodeling of synaptic strength. On its own, spike timing dependent plasticity can compensate quickly for reductions in activity that occur when a fraction of the neuronal population is inactivated, leading to a recovery in connectivity within the microcircuit and an increase in remaining synaptic strength ( Gabrieli, Schumm, Vigilante, Parvesse, & Meaney, 2020 ; Schumm, Gabrieli, & Meaney, 2020 ), in turn increasing the functional connectivity in the network. Our model of mechanical trauma may lead to local increases in functional connectivity within neurons containing a large fraction of NMDAR receptors containing the GluN 2 A subunit ( Patel et al, 2014 ).…”

Young and Aged Neuronal Tissue Dynamics With a Simplified Neuronal Patch Cellular Automata Model

“…The activation function can be modified into similar input-output mapping in frequency domain or voltage-current domain, and can be used as the rule for our CA model. Our computational modeling framework can be utilized for large scale simulation of different neuronal conditions such as Parkinson's disease (Bevan et al, 2002;Kang and Lowery, 2014), Alzheimer's disease, and chronic traumatic encephalopathy (Gabrieli et al, 2020;Wickramaratne et al, 2020).…”