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Introduction: Investigating an effective controller to shift hippocampal epileptic periodicity to normal chaotic behavior will be new hope for epilepsy treatment. Astrocytes nourish and protect neurons and maintain synaptic transmission and network activity. Therefore, this study explored the ameliorating effect of the astrocyte computational model on epileptic periodicity. Methods: Modified Morris-Lecar equations were used to model the hippocampal CA3 network. Network inhibitory parameters were employed to generate oscillation-induced epileptiform periodicity. The astrocyte controller was based on a functional dynamic mathematical model of brain astrocytic cells. Results: Results demonstrated that the synchronization of two neural networks shifted the brain's chaotic state to periodicity. Applying an astrocytic controller to the synchronized networks returned the system to the desynchronized chaotic state. Conclusion: It is concluded that astrocytes are probably a good model for controlling epileptic periodicity. However, more research is needed to delineate this effect. Highlights: Modeling of CA3 neurons reproduced synchronized periodic epileptiform discharges.An astrocyte mathematical model modulated neuronal network excitability.The astrocyte controller desynchronized neural network periodic oscillations.Application of the astrocyte model restored a chaotic desynchronized state.Results suggest astrocytes may control hypersynchronous epileptiform activity. Plain Language Summary: This study looked at whether a mathematical model of brain cells called astrocytes could help control seizure activity. Seizures happen when groups of brain cells become overly active and synchronized. Normally, brain cell activity is chaotic and unsynchronized. The researchers modeled a small network of hippocampus brain cells using equations. We adjusted the model to create seizure-like periodic synchronized activity. Then we added a mathematical astrocyte model to try to disrupt this unwanted synchronization. Astrocytes are a type of glial cell in the brain. They help nourish neurons and regulate brain cell communication. The researchers modeled astrocyte activity using equations based on calcium levels. Calcium levels affect how astrocytes communicate with brain cells. When the researchers added the astrocyte model to the seizure-like network activity, it was able to restore chaotic unsynchronized activity. The astrocyte model accomplished this by affecting the excitability of the neuronal network. These results suggest astrocytes could potentially be used to control seizure activity. More research is needed to further test this astrocyte model. Currently, many seizure patients do not respond fully to medication. Astrocyte-based treatments could potentially provide an alternative approach. The findings are notable because they demonstrate a biologically-based method to restore normal chaotic brain activity. Most previous efforts have used electrical stimulation. An astrocyte-based approach could modulate communication between brain cells in a more natural way.
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Digital foot scanners have been developed in recent years to yield anthropometrists digital image of insole with pressure distribution and anthropometric information. In this paper, a hybrid algorithm containing gray level spatial correlation (GLSC) histogram and Shanbag entropy is presented for analysis of scanned foot images. An evolutionary algorithm is also employed to find the optimum parameters of GLSC and transform function of the membership values. Resulting binary images as the thresholded images are undergone anthropometric measurements taking in to account the scale factor of pixel size to metric scale. The proposed method is finally applied to plantar images obtained through scanning feet of randomly selected subjects by a foot scanner system as our experimental setup described in the paper. Running computation time and the effects of GLSC parameters are investigated in the simulation results.
