ABSTRACT
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
Subject(s)
Small-Conductance Calcium-Activated Potassium Channels , Cerebellar Nuclei , Computer SimulationABSTRACT
Introduction: The main objective of the present study was to investigate the effect of preceding pictorial stimulus on the emotional autonomic responses of the subsequent one
Methods: To this effect, physiological signals, including Electrocardiogram [ECG], Pulse Rate [PR], and Galvanic Skin Response [GSR] were collected. As these signals have random and chaotic nature, nonlinear dynamics of these physiological signals were evaluated with the methods of nonlinear system theory. Considering the hypothesis that emotional responses are usually associated with previous experiences of a subject, the subjective ratings of 4 emotional states were also evaluated. Four nonlinear characteristics [including Detrended Fluctuation Analysis [DFA], based parameters, Lyapunov exponent, and approximate entropy] were implemented. Nine standard features [including mean, standard deviation, minimum, maximum, median, mode, the second, third, and fourth moment] were also extracted
Results: To evaluate the ability of features in discriminating different types of emotions, some classification approaches were appraised, of them, Probabilistic Neural Network [PNN] led to the best classification rate of 100%. The results show that considering the emotional sequences, GSR is the best candidate for the representation of the physiological changes
Discussion: Lower discrimination was attained when the sequence occurred in the diagonal line of valence-arousal coordinates [for instance, positive valence and positive arousal versus negative valence and negative arousal]. By employing self-assessment ranks, no obvious improvement was achieved