Name: modeling fast-scan cyclic voltammetry data from electrically stimulated dopamine neurotransmission data using qnsim1.0
Uploaded: 2017-06-05T12:30:00
Description: Watch this Scientific Journal Video about Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0 at JoVE.com

We acknowledge the UPMC Rehabilitation Institute for supporting this work.

1. Installation, Data Preparation, and Launching QNsim1.0
<ol>
	<li>Download &#34;QNsim1.0.zip&#34; (provided as a supplement) and extract it to a desired directory.</li>
	<li>Prepare stimulated DA response data for modeling with the software program by organizing a spreadsheet in which each column contains a temporal DA response converted to &#181;M DA concentrations. Save this (.xlsx) file to the same directory as the program files. 
	Note: The spreadsheet can contain multiple responses in an individual...

<ol>
	<li>Taylor, I. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinetic+diversity+of+dopamine+transmission+in+the+dorsal+striatum.">Kinetic diversity of dopamine transmission in the dorsal striatum.</a> J Neurochem. 133 (4), 522-531 (2015).</li><li>Harun, R., Grassi, C. M., Munoz, M. J., Torres, G. E., Wagner, A. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neurobiological+model+of+stimulated+dopamine+neurotransmission+to+interpret+fast-scan+cyclic+voltammetry+data.">Neurobiological model of stimulated dopamine neurotransmission to interpret fast-scan cyclic voltammetry data.</a> Brain Res. 1599, 67-84 (2015).</li><li>Taylor, I. M., Jaquins-Gerstl, A., Sesack, S. R., Michael, A. 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M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heterogeneity+of+stimulated+dopamine+overflow+within+rat+striatum+as+observed+with+in+vivo+voltammetry.">Heterogeneity of stimulated dopamine overflow within rat striatum as observed with in vivo voltammetry.</a> Brain Res. 487 (2), 311-320 (1989).</li><li>Harun, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast-scan+cyclic+voltammetry+demonstrates+that+L-DOPA+produces+dose-dependent+regionally+selective,+bimodal+effects+on+striatal+dopamine+kinetics+in+vivo.">Fast-scan cyclic voltammetry demonstrates that L-DOPA produces dose-dependent regionally selective, bimodal effects on striatal dopamine kinetics in vivo.</a> J Neurochem. , (2015).</li><li>Jones, S. R., Garris, P. A., Wightman, R. 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A., Monchi, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+effects+of+dopaminergic+therapies+on+dorsal+and+ventral+striatum+in+Parkinson's+disease:+implications+for+cognitive+function.">Differential effects of dopaminergic therapies on dorsal and ventral striatum in Parkinson's disease: implications for cognitive function.</a> Parkinsons Dis. 2011, 572743 (2011).</li><li>Kile, B. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimizing+the+Temporal+Resolution+of+Fast-Scan+Cyclic+Voltammetry.">Optimizing the Temporal Resolution of Fast-Scan Cyclic Voltammetry.</a> ACS Chem Neurosci. 3 (4), 285-292 (2012).</li><li>Venton, B. J., Troyer, K. P., Wightman, R. 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The use of FSCV to study in vivo stimulated DA neurotransmission originated in the 1980s30 and still continues to be a rich source of in vivo neurotransmission data with unparalleled spatial and temporal resolution. Stimulated DA responses reflect a complex balance of DA release and reuptake that are modulated by the electrical stimulations themselves. The QN model incorporates principles from contemporary neurotransmission research to model in vivo stimulated DA neurotr...

Dopamine (DA) neurotransmission plays an essential role in various cognitive and behavioral functions, and its dysfunction is implicated in several common diseases and disorders. As such, it is critical to develop accurate methods of quantitatively studying DA neurotransmission in vivo to evaluate how DA neurotransmission is altered in the contexts of disease models and drug pharmacology. Fast-scan cyclic voltammetry (FSCV) allows for monitoring in vivo DA neurotransmission with fine spatial and temporal resolution. While it is possible to monitor physiological DA neurotransmission in awake, freely behaving animals, the electrical stimulation of ascending dopaminergic pathways in anesthetized animals can produce robust DA responses that are amenable to the enhanced kinetic analysis of DA neurotransmission.
Electrically stimulated DA responses reflect a dynamic interplay of DA release and reuptake, and interpretations of these responses have predominantly used a simple model of stimulated DA neurotransmission called the Michaelis-Menten (M-M) model12. The M-M model consists of 3 variables to describe DA responses in terms of a constant DA release rate and a constant reuptake efficiency (i.e., the relationship between the DA reuptake rate and extracellular DA concentrations), as described by Equation 1: 
<img alt="Equation 1" src="/files/ftp_upload/55595/55595eq1.jpg" /> 
(DA release) (DA reuptake)
In Equation 1, f is the frequency of stimulation; [DA]p is the estimated DA concentration increase per pulse of stimulation; Vmax represents the estimated maximal reuptake rate; and Km is the estimated M-M constant, which is theoretically equivalent to the extracellular DA concentration that saturates 50% of DAT, leading to a half-maximal reuptake rate. This differential equation can be integrated to simulate experimental DA responses by estimating the [DA]p, Vmax, and Km parameters.
Although the M-M model has facilitated significant advances in the understanding of DA neurotransmission kinetics in various experimental contexts, the M-M model makes simplistic fundamental assumptions that limit its applicability when modeling DA responses elicited by supraphysiological stimulations2,13. For instance, the M-M model can only approximate DA response shapes if they rise in a convex manner, but it cannot account for the gradual (concave) rising responses found in dorsal striatal regions12. Thus, the M-M model assumptions do not accurately capture the dynamic release and reuptake processes of stimulated DA neurotransmission.
To model stimulated DA responses according to a realistic quantitative framework, the quantitative neurobiological (QN) framework was developed based on principles of stimulated neurotransmission kinetics derived from complementary research and experimentation2. Various lines of neurotransmission research demonstrate that (1) stimulated neurotransmitter release is a dynamic process that decreases in rate over the course of stimulation14, (2) release continues in the post-stimulation phase with biphasic decay kinetics15, and (3) DA reuptake efficiency is progressively inhibited during the duration of the stimulation itself2,16. These three concepts serve as the foundation of the QN framework, and the three equations consisting of 12 parameters describing the dynamics of DA release and reuptake (Table 1). The QN framework can closely simulate heterogeneous experimental DA response types, as well as the predicted effects of experimental manipulations of stimulation parameters and drug administration2,6. Although further research is necessary to refine the data modeling approach, future experiments can greatly benefit from this neurobiologically grounded modeling approach, which significantly adds to the inferences drawn from the stimulated DA neurotransmission paradigm.
<img alt="Table 1" src="/files/ftp_upload/55595/55595tbl1.jpg" /> 
Table 1: Modeling Equations and Parameters. <a href="http://cloudflare.jove.com/files/ftp_upload/55595/55595tbl1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
This tutorial describes how to model stimulated DA response data to estimate DA release and reuptake kinetics using QNsim 1.0. The actual experimental data collection and processing is not described here and only requires temporal DA concentration data. The theoretical support and foundations of the QN framework have been extensively described previously2, but a practical perspective on applying the QN framework to model DA response data is described below.
The QN framework models the dynamic interplay between: 1) dynamic DA release, 2) DA reuptake, and 3) the effects of supraphysiological stimulations on these processes to extract meaningful kinetic information from DA response data. The QN framework is best suited for modeling FSCV data acquired using highly supraphysiological stimulations of long duration (e.g., 60 Hz, 10 s stimulations), which produce robust DA responses that are amenable for kinetic analysis. Following the accurate modeling of the underlying release and reuptake processes, the model parameters can be used to simulate a DA response that should approximate the shape of the experimental DA response.
The equations of the QN framework describe the rates of DA release and reuptake over the course of the stimulated DA responses. The QN framework describes the stimulated DA release rate as a function of time from the start of stimulation (tstim), when the DA release rate exponentially decreases over the course of stimulation. This is consistent with the depletion of a readily releasable pool, with an added steady-state DA release rate (DARss) to account for vesicle replenishment, similar to other reports (Equation 2)14,17.
<img alt="Equation 2" src="/files/ftp_upload/55595/55595eq2.jpg" />
Manipulations that increases the DA release rate, such as increasing &#916;DAR, &#916;DAR&#964;, or DARss, lead to increased response amplitudes on DA versus time plots. Each parameter contributes differentially to DA response shapes. Increasing DARss and &#916;DAR&#964; both make the rising phase of responses more linear (less convex). Decreasing &#916;DAR&#964; promotes convexity, which is controlled by the magnitude of &#916;DAR. Based on modeling experience, DARss is generally less than 1/5th of &#916;DAR; thus, &#916;DAR is the release parameter that primarily determines the overall response amplitude of a DA response.
The post-stimulation DA release rate is modeled by Equation 3 as a continuation of the stimulated DA release rate from the end of stimulation (DARES) as a function of time after stimulation (tpost). The post-stimulation DA release rate follows a biphasic decay pattern, as previously described15, with a rapid exponential decay phase and a prolonged linear decay phase to model two calcium-dependent neurotransmitter release processes.
<img alt="Equation 4" src="/files/ftp_upload/55595/55595eq4.jpg" />
(Rapid exponential decay)&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; (Prolonged linear decay)
It is not currently possible to determine how much post-stimulation DA release occurs. This limitation can be addressed by systematically minimizing estimates of post-stimulation DA release and validating model parameters across a set of experimental DA responses collected from the same recording site using varying stimulation durations. This minimization allows users to make conservative estimates of release and reuptake. Because electrical stimulations lead to the calcium accumulation that promotes post-stimulation neurotransmitter release, the duration of stimulation influences the post-stimulation neurotransmitter release parameters18,19. Based on modeling experience, it was found that as the stimulation duration increases, &#964;R increases and XR decreases, consistent with the anticipated effects of a greater calcium accumulation20.
Equation 4 describes the DA reuptake rate as an extension of the M-M framework and incorporates a dynamic Km term, which increases during stimulation to model a progressively decreasing reuptake efficiency caused by the supraphysiological stimulations2,16. The Km after stimulation is held constant at the Km value at the end of stimulation (KmES).
<img alt="Equation 5" src="/files/ftp_upload/55595/55595eq5.jpg" />
where,
<img alt="Equation 6" src="/files/ftp_upload/55595/55595eq6.jpg" />
(During stimulation)&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; (After stimulation)
Stimulated DA responses, especially from ventral striatal regions, are often insensitive to changes in the initial Km value (Kmi), which makes defining a Kmi value problematic. Thus, like the original M-M framework, Kmi is approximated at 0.1-0.4 &#181;M for DA responses collected from control untreated animals12. The &#916;Km term determines the extent of reuptake efficiency change during stimulation, which from our experience is about 20 &#181;M over the course of a 60-Hz, 10-s stimulation. The k and Kminf values determine how Km changes over time, and increasing either of these terms promotes the concavity of the rising phase. Vmax is the maximal reuptake rate that partly relates to local DA transporter density, which exhibits a ventromedial to dorsolateral gradient21. Accordingly, Vmax values in the dorsal striatum (D-Str) are generally greater than 30 &#181;M/s but generally less than 30 &#181;M/s in the ventral regions, like the nucleus accumbens (NAc)6.
The general guidelines above can aid in modeling experimental DA response data, but generating a simulation that approximates the experimental DA response requires iteratively adjusting model parameters. The accuracy of the model parameters can be improved by obtaining DA responses to supraphysiological stimulations that provide a robust substrate for simulation, as well as by obtaining and modeling multiple DA responses to stimulations of varying durations at the same recording site (e.g., 60-Hz, 5-s and 10-s stimulations) to validate the accuracy of the parameters (see the sample data). To demonstrate, a dataset is included with the software package containing regiospecific stimulated DA responses collected in the nucleus accumbens and dorsal striatum, before and after a pharmacological challenge that was already modeled using the QN framework. By extension, users will find this methodology can similarly be applied to characterize the kinetics of DA neurotransmission in various disease contexts and pharmacological manipulations.

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			<td height="21" style="height:21px;width:216px;">MATLAB R2016a for Mac&#160;</td>
			<td style="width:99px;">Mathworks</td>
			<td style="width:119px;"></td>
			<td style="width:317px;"></td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">QNsim1.0</td>
			<td style="width:99px;">In house software package</td>
			<td style="width:119px;"></td>
			<td>Software to model FSCV data using the QN framework</td>
		</tr>
	</tbody>
</table>

modeling fast-scan cyclic voltammetry data from electrically stimulated dopamine neurotransmission data using qnsim1.0

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.

Included with the software program are sample DA neurotransmission data obtained from the rat dorsal striatum (Study 1) and the nucleus accumbens (Study 2) that are compiled into &#34;Sample.xlsx.&#34; The spreadsheet contains the DA concentration data of the baseline responses to 60 Hz, 10 s and 5 s stimulations and a response to a 60 Hz, 5 s stimulation 35 min after the administration of the DA transport inhibitor methylphenidate (MPH) (10 mg/kg, i.p.). The stimulated DA response data is organized into columns, as demo...

Watch this Scientific Journal Video about Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0 at JoVE.com

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

Fast-scan cyclic voltammetry can monitor in vivo dopamine neurotransmission in the context of drugs, disease, and other experimental manipulations. This work describes the implementation of QNsim1.0, a software to model electrically stimulated dopamine responses according to the quantitative neurobiological model to quantify estimates of dopamine release and reuptake dynamics.

Central dopaminergic (DAergic) pathways have an important role in a wide range of functions, such as attention, motivation, and movement. Dopamine (DA) is ...

The overall goal of this software program is to estimate the kinetics of electrically stimulated dopamine neurotransmission from the experimental fast-scan cyclic voltammetry data. This method can facilitate study designs to help answer key questions about dopamine neurotransmission, such as how electrically evoked striatal dopamine neurotransmission is altered by the drugs and disease states. The main advantage of this software is that it provides a user-friendly platform to model experimental voltammetry data using a quantitative neurobiological framework.In this procedure, download QNsim1.0. zip and extract it to a desired directory. Next, prepare the stimulated dopamine response data for modeling by organizing a spreadsheet in which each column contains a temporal dopamine response converted to micromolar dopamine concentration, and save this file to the same directory as the program files.After that, open the programming software, navigate to the QNsim1.0 directory in the current folder window and open the file entitled Initialization.m. Then click Run to launch the Initialization screen. To begin a new project, under the New Project section type the name of the spreadsheet file that contains the dopamine response data for the project in the dopamine data file text box.This spreadsheet contains three responses from two studies, one collected in the dorsal striatum, and one collected in the nucleus accumbens. Next to Simulation Time input the time points corresponding to the start and end of data collection relative to the start of the stimulation. For the sample data, there are responses with a 5-second prestimulation interval and 35 seconds of data collection after the start of the stimulation.Following that, input the number of responses in each study next to the corresponding text box. Next to Sampling Interval, input the experimental data sampling interval in seconds, which was every 0.1 seconds for our data. Then, next to Save As, designate the file name, including the file type where the project is to be saved.Click Create New Project to launch the simulator window. Alternatively, to continue a previous project, in the Continue Previous Project section of the initialization window, enter the dot mat file name of a previously begun project. Afterward, click Load Existing Project to launch the simulator window.In this procedure, select the experimental dopamine response to simulate by inputting the study number, response number, and duration of the stimulation in the corresponding text boxes. Press the Enter key or click Simulate to begin the simulation process, which creates three graphs containing experimental data, simulated data, and simulated data that does not account for post stimulation dopamine release. To model the experimental dopamine responses, adjust the delta DAR, delta DAR tau, and dar steady state parameters associated with dopamine release to match the amplitude of the stimulation.Then adjust the VMAX, KM initial, delta KM, KM inflection. Then adjust the VMAX, KM initial, delta KM, KM inflection, and K parameters associated to dopamine reuptake such that in Panel A the simulated data approximates the shape of the rising phase of the experimental data and the simulation without post stim release trace is less than the experimental data trace for all post stimulation time points. This modeling approach is an iterative process of adjusting model parameters.Thus, it will likely be necessary to modify DA release parameters to match the shape of the rising phase. Then adjust XR, tau R, and M parameters associated with post stimulation dopamine release such that the simulated data approximates the experimental data in the Dopamine versus Time graph. In order to validate the accuracy of simulation parameters it is critical to ascertain that the same set of stimulated release and reuptake parameters can approximate experimental responses to different durations of stimulation from the same sampling site.Once a set of parameters closely models the experimental data, click Save Parameters, which will save that set of parameters for the given response to the dot mat file for the project. If required, load previously saved parameters for a particular response by clicking Load Parameters. Ensure that the appropriate study number and response number are entered in the corresponding text boxes.To export the saved parameters, in the text box next to the Export Parameters button type in the file name and click Export Parameters to export a text file with all the parameters of the simulations. Delimit the cells in Excel by spaces to view this data in a table format. The left and right graph shown here depict the simulations in experimental data collected in the dorsal striatum and nucleus accumbens respectively.The two regions generally exhibit different response shapes with concave rising shapes in the dorsal striatum and convex rising shapes in the nucleus accumbens. Although there is variability in response shapes and amplitudes, even within a given region, both response shapes could be modeled with a few notable differences and parameterizations. Generally, VMAX is lower, and KM inflection is much lower in the nucleus accumbens, compared to the dorsal striatum.While attempting this procedure, it's important to remember to be systematic in your approach by minimizing estimates of dopamine release and reuptake parameters and validating parameters on multiple experimental responses from the same sampling site. After watching this video, you should have a good understanding of how to model the stimulated dopamine responses in order to estimate the kinetics of electrically stimulated dopamine neurotransmission in the striatum.

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