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J Vis Exp. 2017 Jun 5;(124). doi: 10.3791/55595.

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0.

Author information

1
Center for Neuroscience, University of Pittsburgh; Department of Physical Medicine & Rehabilitation, University of Pittsburgh, School of Medicine; Safar Center for Resuscitation Research, University of Pittsburgh; rah28@pitt.edu.
2
Department of Physical Medicine & Rehabilitation, University of Pittsburgh, School of Medicine.
3
Department of Physical Medicine & Rehabilitation, University of Pittsburgh, School of Medicine; Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University.
4
Center for Neuroscience, University of Pittsburgh; Department of Physical Medicine & Rehabilitation, University of Pittsburgh, School of Medicine; Safar Center for Resuscitation Research, University of Pittsburgh.

Abstract

Central dopaminergic (DAergic) pathways have an important role in a wide range of functions, such as attention, motivation, and movement. Dopamine (DA) is implicated in diseases and disorders including attention deficit hyperactivity disorder, Parkinson's disease, and traumatic brain injury. Thus, DA neurotransmission and the methods to study it are of intense scientific interest. In vivo fast-scan cyclic voltammetry (FSCV) is a method that allows for selectively monitoring DA concentration changes with fine temporal and spatial resolution. This technique is commonly used in conjunction with electrical stimulations of ascending DAergic pathways to control the impulse flow of dopamine neurotransmission. Although the stimulated DA neurotransmission paradigm can produce robust DA responses with clear morphologies, making them amenable for kinetic analysis, there is still much debate on how to interpret the responses in terms of their DA release and clearance components. To address this concern, a quantitative neurobiological (QN) framework of stimulated DA neurotransmission was recently developed to realistically model the dynamics of DA release and reuptake over the course of a stimulated DA response. The foundations of this model are based on experimental data from stimulated DA neurotransmission and on principles of neurotransmission adopted from various lines of research. The QN model implements 12 parameters related to stimulated DA release and reuptake dynamics to model DA responses. This work describes how to simulate DA responses using QNsim1.0 and also details principles that have been implemented to systematically discern alterations in the stimulated dopamine release and reuptake dynamics.

PMID:
28605373
DOI:
10.3791/55595
[Indexed for MEDLINE]

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