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Author Janahmadi, Mahyar ♦ Bahrami, Fariba ♦ Borjkhani, Mehdi
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Frontiers Media S.A.
File Format HTM / HTML
Date Created 2018-08-02
Copyright Year ©2018
Language English
Subject Domain (in LCC) RC321-571
Subject Keyword Neuropsychiatry ♦ Biological psychiatry ♦ Memory of addiction ♦ Neurosciences ♦ Opioid ♦ Internal medicine ♦ Synchronization ♦ Medicine ♦ Computational modeling ♦ Astrocyte
Abstract There are several experimental studies which suggest opioids consumption forms pathological memories in different brain regions. For example it has been empirically demonstrated that the theta rhythm which appears during chronic opioid consumption is correlated with the addiction memory formation. In this paper, we present a minimal computational model that shows how opioids can change firing patterns of the neurons during acute and chronic opioid consumption and also during withdrawal periods. The model consists of a pre- and post-synaptic neuronal circuits and the astrocyte that monitors the synapses. The output circuitry consists of inhibitory interneurons and excitatory pyramidal neurons. Our simulation results demonstrate that acute opioid consumption induces synchronous patterns in the beta frequency range, while, chronic opioid consumption provokes theta frequency oscillations. This allows us to infer that the theta rhythm appeared during chronic treatment can be an indication of brain engagement in opioid-induced memory formation. Our results also suggest that changing the inputs of the interneurons and the inhibitory neuronal network is not an appropriate method for preventing the formation of pathological memory. However, the same results suggest that prevention of pathological memory formation is possible by manipulating the input of the stimulatory network and the excitatory connections in the neuronal network. They also show that during withdrawal periods, firing rate is reduced and random fluctuations are generated in the modeled neural network. The random fluctuations disappear and synchronized patterns emerge when the activities of the astrocytic transporters are decreased. These results suggest that formation of the synchronized activities can be correlated with the relapse. Our model also predicts that reduction in gliotransmitter release can eliminate the synchrony and thereby it can reduce the likelihood of the relapse occurrence.
ISSN 16625188
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2018-08-01
e-ISSN 16625188
Journal Frontiers in Computational Neuroscience
Volume Number 12

Source: Directory of Open Access Journals (DOAJ)