Currently available treatments of Parkinson’s disease are of limited efficacy. There are symptoms such as freezing of gait which causes falls and is a significant source of morbidity in patients suffering from Parkinson’s disease. One possible approach to treat these patients is deep brain stimulation. However, often there is a remaining freezing of gait during the standard subthalamic nucleus deep brain stimulation. At the same time, there is experimental evidence of freezing improvement during simultaneous stimulation of the subthalamic nucleus and substantia nigra pars reticulata. This effect could be due to the connections of the substantia nigra pars reticulata to the midbrain regions responsible for posture stability and gait initiation such as pedunculopontine nucleus. A computational model explaining the observed improvement, which also accounts for the behavioral data could help to unravel the mechanisms behind the symptoms of Parkinson’s disease and potentially lead to more individualized treatment.
For this reason, we study the cortico-subcortical networks responsible for gait and the effects exerted on these networks via perturbations such as deep brain stimulation. To assess the differences between the two aforementioned stimulation modes, we compare the network dynamics during the healthy, the Parkinsonian and the deep brain stimulated states. Also, we compare the modelling outputs with pupillometry data, which is an indirect measure of locus coeruleus activity. This is of importance as abnormalities in afferent pathways of locus coeruleus – one of the outputs of the model, are associated with gait deterioration. Previous computational models do not account for the effects of interest as they are either lack biological detail or do not include midbrain regions.
As a first approach, we developed a firing rate network model comprising interconnected populations of Hodgkin-Huxley neurons representing basal ganglia nuclei and midbrain regions. The switch to the Parkinsonian state is achieved via the change in striatal conductances representing dopamine depletion – a hallmark of Parkinson’s disease. Deep brain stimulation is modeled as a current applied to the efferent axons of the neurons in the target regions. The resulting firing profile in the locus coeruleus is then compared to the pupillometry data.
We present simulations from the proposed computational model that qualitatively account for the firing rate data and their dynamics in the healthy, Parkinsonian and stimulated states. Moreover, the firing dynamics during the subthalamic nucleus deep brain stimulation is markedly different from the simultaneous stimulation of subthalamic nucleus and substantia nigra pars reticulata. Limitations of that firing rate modelling approach are discussed. Thus, the model accounts for the first time for the difference between two stimulation modes and suggests a possible mechanism of action behind the deep brain stimulation.