A model for low-frequency bursts in subthalamic nucleus neurons

DOI

10.2991/jrnal.2014.1.1.10How to use a DOI?

Keywords

subthalamic nucleus, cortex, globus pallidus, Parkinson’s disease, burst oscillations

Abstract

Burst firing in subthalamic nucleus (STN) has been suggested to be highly associated with the motor symptoms in Parkinson’s disease, which result from a loss of dopamine. Although it is clinically very important to clarify the mechanism underlying the bursting dynamics, complex interactions between STN and other brain areas make it difficult to understand. In anesthetized rats, STN neurons exhibit low-frequency (1 Hz) bursts, which are synchronous with cortical slow oscillations and are significantly strengthened by dopamine depletion. To reproduce these low-frequency bursts, we examine a conductance-based model of an STN neuron that includes NMDA-type glutamatergic inputs reflecting cortical oscillations. In addition, the neuron model contains GABAergic inhibitory inputs, which are assumed to result from the activities of globus pallidus (GP). We show that the STN neuron model can reproduce low-frequency bursts synchronized with cortical activity, in the presence of GABAergic inhibition. In addition, we demonstrate that increased GABA activity leads to enhanced burstiness whereas increased NMDA conductances mainly augment STN firing rate. The induction of burst firing additionally decreases the coherence between STN and cortical activities. These results may give insights into how the complicated interactions between the STN, cortex, and GP can modulate the dynamics of bursting oscillations in the basal ganglia.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Shigeru Kubota
AU  - Jonathan E. Rubin
PY  - 2014
DA  - 2014/06/30
TI  - A model for low-frequency bursts in subthalamic nucleus neurons
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 49
EP  - 53
VL  - 1
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2014.1.1.10
DO  - 10.2991/jrnal.2014.1.1.10
ID  - Kubota2014
ER  -