Contribution of somatic and dendritic SK channels in the firing rate of deep cerebellar nuclei: implication in cerebellar ataxia

Introduction: Loss of inhibitory output from Purkinje cells leads to hyperexcitability of the Deep Cerebellar Nuclei [DCN], which results in cerebellar ataxia. Also, inhibition of small-conductance calcium-activated potassium [SK] channel increases firing rate of DCN, which could cause cerebellar ataxia. Therefore, SK channel activators can be effective in reducing the symptoms of this disease, and used for the treatment of cerebellar ataxia. In this regard, we hypothesized that blockade of SK channels in different compartments of DCN would increase firing rate with different value. The location of these channels has different effects on increasing firing rate

Methods: In this study, multi-compartment computational model of DCN was used. This computational stimulation allowed us to study the changes in the firing activity of DCN neuron without concerns about interfering parameters in the experiment

Results: The simulation results demonstrated that blockade of somatic and dendritic SK channel increased the firing rate of DCN. In addition, after hyperpolarization [AHP] amplitude increased with blocking SK channel, and its regularity and resting potential changed. However, action potentials amplitude and duration had no significant changes. The simulation results illustrated a more significant contribution of SK channels on the dendritic tree to the DCN firing rate. SK channels in the proximal dendrites have more impact on firing rate compared to distal dendrites

Discussion: Therefore, inhibition of SK channel in DCN can cause cerebellar ataxia, and SK channel openers can have a therapeutic effect on cerebellar ataxia. In addition, the location of SK channels could be important in therapeutic goals. Dendritic SK channels can be a more effective target compared to somatic SK channels

grey box neural network model of basal ganglia for gait signal of patients with Huntington disease

Introduction: Huntington disease [HD] is a progressive neurodegenerative disease which affects movement control system of the brain. HD symptoms lead to patient's gait change and influence stride time intervals. In this study, we present a grey box mathematical model to simulate HD disorders. This model contains main physiological findings about BG

Methods: We used artificial neural networks [ANN] and predetermined data to model healthy state behavior, and then we trained patients with HD with this model. All blocks and relations between them were designed based on physiological findings

Results: According to the physiological findings, increasing or decreasing model connection weights are indicative of change in secretion of respective neurotransmitters. Our results show the simulating ability of the model in normal condition and different disease stages

Conclusion: Fine similarity between the presented model and BG physiological structure with its high ability in simulating HD disorders, introduces this model as a powerful tool to analyze HD behavior

gray-box neural network model of parkinson's disease using gait signal

In this study, we focused on the gait of Parkinson's disease [PD] and presented a gray box model for it. We tried to present a model for basal ganglia structure in order to generate stride time interval signal in model output for healthy and PD states. Because of feedback role of dopamine neurotransmitter in basal ganglia, this part is modelled by "Elman Network", which is a neural network structure based on a feedback relation between each layer. Remaining parts of the basal ganglia are modelled with feed-forward neural networks. We first trained the model with a healthy person and a PD patient separately. Then, in order to extend the model generality, we tried to generate the behaviour of all subjects of our database in the model. Hence, we extracted some features of stride signal including mean, variance, fractal dimension and five coefficients from spectral domain. With adding 10% tolerance to above mentioned neural network weights and using genetic algorithm, we found proper parameters to model every person in the used database. The following points may be regarded as clues for the acceptability of our model in simulating the stride signal: the high power of the network for simulating normal and patient states, high ability of the model in producing the behaviour of different persons in normal and patient cases, and the similarities between the model and physiological structure of basal ganglia