Volume 9, Issue 4 (July & August 2018 2018)                   BCN 2018, 9(4): 275-288 | Back to browse issues page


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Borjkhani M, Bahrami F, Janahmadi M. Assessing the Effects of Opioids on Pathological Memory by a Computational Model. BCN 2018; 9 (4) :275-288
URL: http://bcn.iums.ac.ir/article-1-962-en.html
1- Motor Control and Computational Neuroscience Laboratory, School of Electrical & Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
2- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Abstract:  

Introduction: Opioids hijack learning and memory formation mechanisms of brain and induce a pathological memory in the hippocampus. This effect is mainly mediated by modifications in glutamatergic system. Speaking more precisely, Opioids presence in a synapse inhibits blockage of N-Methyl-D-Aspartate Receptor (NMDAR) by Mg2+ , enhances conductance of NMDAR and thus, induces false Long-Term Potentiation (LTP). 
Methods: Based on experimental observations of different researchers, we developed a mathematical model for a pyramidal neuron of the hippocampus to study this false LTP. The model contains a spine of the pyramidal neuron with NMDAR, α-Amino-3-hydroxy-5-Methyl-4-isoxazole Propionic Acid Receptors (AMPARs), and Voltage-Gated Calcium Channels (VGCCs). The model also describes Calmodulin-dependent protein Kinase II (CaMKII) and AMPAR phosphorylation processes which are assumed to be the indicators of LTP induction in the synapse. 
Results: Simulation results indicate that the effect of inhibition of blockage of NMDARs by Mg2+ on the false LTP is not as crucial as the effect of NMDAR’s conductance modification by opioids. We also observed that activation of VGCCs has a dominant role in inducing pathological LTP. 
Conclusion: Our results confirm that preventing this pathological LTP is possible by three different mechanisms: 1. By decreasing NMDAR’s conductance; and 2. By attenuating VGCC’s mediated current; and 3. By enhancing glutamate clearance rate from the synapse. 

Type of Study: Original | Subject: Computational Neuroscience
Received: 2017/06/7 | Accepted: 2017/10/4 | Published: 2018/07/1

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