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 experiment or multiple studies containing a fixed number of responses (see &#34;Sample.xlsx&#34; for an example).</li>
	<li>Open the programming environment software, navigate to the &#34;QNSim1.0&#34; directory in the &#34;Current Folder&#34; window, and open the filed entitled &#34;Initialization.m.&#34; Click &#34;Run&#34; to launch the initialization screen (Figure 1).</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55595/55595fig1.jpg" /> 
Figure 1: Initialization Screen. This screen allows users to continue an existing project, by entering a previous *.mat project file (left), or to begin a new project from stored data of stimulated DA neurotransmission (right). Textboxes are provided to input the *.xlsx filename containing the data, a few essential descriptors of the data, and the *.mat project filename that will contain all the data relating to the project. <a href="https://cloudfront-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55595/55595fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Initialization of the Simulation Environment
Note: The initialization screen allows users to begin a new project (step 2.1) or continue with a previously saved project (step 2.2).
<ol>
	<li>Option 1: Beginning a new project
	<ol>
		<li>Under the New Project section, type in the name of the spreadsheet file that contains the DA response data for the project (e.g., Sample.xlsx) into the [DA] Data file textbox. 
		Note: The file must be in the same folder as the program files.</li>
		<li>Next to Simulation time, input the time points corresponding to the start and end of data collection (in seconds) relative to the start of the stimulation. For example, if the data collection begins 5 s before the stimulation and ends 35 s after, as in the Sample.xlsx, input &#34;-5&#34; and &#34;35.&#34;</li>
		<li>Input the number of responses in each study next to the corresponding text box.</li>
		<li>Next to sampling interval, input the experimental data sampling interval in seconds.</li>
		<li>Next to Save as (.mat), designate the filename, including the file type (.mat) where the project is to be saved. 
		Note: This is the file that will be accessed to create the simulations, and all work can be saved to this project file.</li>
		<li>Click Create New Project to launch the Simulator window (Figure 2).</li>
	</ol>
	</li>
	<li>Option 2: Continuing a previous project
	<ol>
		<li>In the Continue Previous Project section of the Initialization window, enter the.mat filename of a previously begun project (e.g., Sample.mat). 
		Note: This file must be contained within the same folder as the software program files.</li>
		<li>Click Load Existing Project to launch the Simulator window (Figure 2).</li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55595/55595fig2.jpg" /> 
Figure 2: Simulator Screen. The simulator screen allows users to select the experimental data in D and adjust simulation parameters in E to model the experimental data. Visual inspection of (A, B, and C) can then aid in the refinement of simulation parameters so that the simulation (blue dashed lines) models the experimental data (thick green lines). Here, (A) contains experimental and simulated DA concentration versus time data. (B) is a graph that aids in estimating post-stimulation DA release. (C) is a plot of the experimental and simulated first derivatives of DA concentration versus time data from A or the rate of evoked overflow (EO). The rate of EO is theoretically a balance of release-reuptake rates, which is superimposed on this graph. <a href="https://cloudfront-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55595/55595fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Using the Simulator Window
Note: The Simulator screen allows for selecting the experimental data to model (step 3.1), adjust parameters to simulate experimental DA response data (step 3.2), save/load modeling parameters for each DA response (step 3.3), and export the saved parameters (step 3.4).
<ol>
	<li>Selecting the experimental DA response to simulate.
	<ol>
		<li>Select the experimental DA response to simulate by inputting the study number, response number, and duration of the stimulation in the corresponding textboxes. Press the &#34;enter&#34; key or click simulate to begin the simulation process, which creates 3 graphs containing experimental data (thick green lines), simulated data (blue dashed lines), and simulated data that does not account for post-stimulation DA release (red dotted lines), as in Figure 2.</li>
	</ol>
	</li>
	<li>Modeling experimental DA responses. 
	Note: The objective of modeling is to adjust the model parameters relating to stimulated DA release (step 3.2.1), DA reuptake (step 3.2.2), and post-stimulation DA release (step 3.2.3) to accurately model experimental DA responses. Modeling is an iterative process whereby the starting parameters are refined until the parameters yield a simulated DA response that approximates the experimental data in the DA versus Time graph (Panel A), which can be aided by fitting the simulated data to the experimentally derived data in the Estimated post-stimulation component graph(Panel B)and the Rates of release, reuptake, and EO graph (Panel C).
	<ol>
		<li>Adjust the &#916;DAR, &#916;DAR&#964;, and DARss parameters associated with DA release (Equation 2) to match the amplitude of the stimulation. 
		Note: These parameters are only starting estimates that will get refined, but &#916;DAR&#964;=25 and a DARss value that is 1/5th of &#916;DAR are satisfactory starting conditions. Increasing any of these parameters will increase the amplitude in the DA versus Time graph and increase the DA release rate in the Estimated post-stimulation release component and Rates of release, reuptake, and EO graphs.</li>
		<li>Adjust the Vmax, Kmi, &#916;Km, Kminf, and k parameters associated with to DA reuptake (Equation 4), such that in Panel A, the simulated data approximates the shape of the rising phase of the experimental data (thick green line) and such that the simulation w/o post-stim release trace (red dotted line) is less than the experimental data trace for all post-stimulation time points. 
		Note: This step likely requires readjusting DA release parameters (step 3.2.1).</li>
		<li>Adjust the XR, &#964;R, and m parameters associated with post-stimulation DA release (Equation 3), such that the simulation approximates the experimental data in the DA versus Time graph. 
		Note: XR should take on a value between 0 and 1 and should generally be greater than 0.7.</li>
	</ol>
	</li>
	<li>Saving/loading the modeling parameters.
	<ol>
		<li>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.mat file for the project.</li>
		<li>If required, load previously saved parameters for a particular response by clicking Load Parameters. Ensure that the appropriate study number and response number are inputted in the corresponding text boxes.</li>
	</ol>
	</li>
	<li>Exporting saved parameters.
	<ol>
		<li>In the textbox next to the Export Parameters button, type in the filename (e.g., Sample.txt) and click Export Parameters to export a text file with all the parameters of the simulations. Delimit the text file using spaces to generate a spreadsheet of parameters, as in Figure 4 of the Representative Results section below.</li>
	</ol>
	</li>
</ol>

<ol>
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The authors have nothing to disclose.

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 neurotransmission data in terms of dynamic release and reuptake assumptions2. Moreover, the QN framework extends the possible regions of analysis with FSCV to sites that do not produce &#8220;convex&#8221; responses, like the dorsal striatum. These advancements allow for the regional characterization of in vivo DAergic pharmacodynamics6 and DAergic alterations in CNS disease models. The regional characterization of DA neurotransmission is especially important because the dorsal and ventral striatum have different functional implications, receive different DAergic innervation from different neuronal populations, and are differentially susceptible in disease-states like Parkinson&#8217;s31,32.
As Figure 5 demonstrates, the QN framework is capable of closely modeling experimental data; however, there are limitations with the modeling methods. With 12 adjustable parameters in the QN framework, multiple sets of parameterizations can closely simulate an experimental DA response, and it is important to determine which set of parameterizations most precisely and accurately reflects the underlying kinetics of DA release and reuptake. While it is not possible to extract model parameters that accurately reflect the underlying neurobiology with certainty, it is possible to systematically determine minimal estimates of release and reuptake parameters that fit the assumptions of the QN framework. Thus, the model parameters should be interpreted as conservative estimates of release and reuptake kinetics. To hone the accuracy of the model parameters, multiple DA responses elicited by different durations of stimulation should be obtained from the same recording site. This generates multiple substrates for modeling that can constrain simulation parameters to enhance their accuracy. Other constraints can systematically be placed on model parameters based on literature data (e.g., Kmi &#8776; 0.1-0.4 &#181;M and &#964;R &#8776; 1.2 for 60 Hz,10 s stimulations) to facilitate data modeling, especially for study designs where modeling relative temporal changes is more important than the absolute accuracy of model parameters. Strategies to enhance the precision and accuracy of model parameters are an ongoing effort.
The QN framework has strong theoretical foundations for its assumptions on how stimulation affects DA neurotransmission kinetics. The QN framework itself does not account for the possible effects of electrode response lag and diffusional distortion in the DA responses33,34, the existence and relative importance of which are debated in the field3. For instance, the regional kinetic variability of DA responses has previously been attributed to cytoarchitectural differences, with white matter tracts acting as a mass transport barrier in the dorsal striatum35. However, variability in DA response shapes are diminished by the administration of D2 antagonists or DAT inhibitors3,13, suggesting that regional response variability is linked to underlying differences in release and reuptake kinetics. If users see fit, DA responses can be deconvoluted to remove diffusional distortions according to previously published methods33,34,36, and this processed DA response data can still be modeled using the software program as usual.

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="https://cloudflare-jove-com.bdigitaluss.remotexs.co/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.

<table><tbody><tr><td>MATLAB R2016a for Mac&amp;nbsp;</td><td>Mathworks</td><td></td><td></td></tr><tr><td>QNsim1.0</td><td>In house software package</td><td></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 demonstrated in Figure 3, which serves as a template for organizing data to be modeled using the software program.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55595/55595fig3.jpg" /> 
Figure 3: Structuring the Data into a Readable Format for Modeling. The stimulated DA response data from Sample.xlsx is organized into columns, as above, with each column containing an individual stimulated DA response. Note that there are no columns relating to the time domain in this spreadsheet. This is accounted for in the initialization screen.
The sample data was modeled as described in step 3.2 (modeling experimental DA responses) using the parameters in Figure 4. This yielded simulations that approximated the experimental data well in both the D-Str (Figure 5A) and the NAc (Figure 5B).
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/55595/55595fig4.jpg" /> 
Figure 4: Exported Simulation Parameters for Modeling Sample Experimental Data. Each row contains simulation parameters correlating to an individual stimulated DA response. Here, rows 2-4 and 5-7 correspond to simulation parameters for the dorsal striatum and nucleus accumbens data (see Figure 3 above). <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55595/55595fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
FSCV experiments involving the collection of multiple DA responses generally incorporate sufficient inter-stimulation intervals to allow the system to restore itself to produce reproducible stimulated DA responses25. In an ideal scenario that produced reproducible responses, stimulated DA release parameters (&#916;DAR, &#916;DAT&#964;, and DARss) and reuptake parameters (Vmax, Kmi, &#916;Km, Kminf, and k) would be constant for baseline responses to the same frequency of stimulation. However, in practice, there are small changes in DA response shapes and amplitudes over time that translate to decreases in DA release metrics, like &#916;DAR in this dataset (compare row 2 to 3 and 5 to 6 in Figure 4), or to decreases in Vmax.
In contrast to stimulated DA release, post-stimulation DA release parameters change based upon the duration of stimulation. This is likely due to a stimulation-induced accumulation of intracellular Ca2+ that prolongs the post-stimulation release and increases the relative contribution of the slower linear decay component of post-stimulation DA release20,26. These stimulation duration-dependent effects can be seen in the representative modeling parameters, with the 10 s stimulation exhibiting larger &#964;R values and smaller XR values than the 5 s stimulations (compare row 2 to 3 and 5 to 6 in Figure 4).
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/55595/55595fig5.jpg" /> 
Figure 5: Simulations of Sample Data. Simulations demonstrate the close fit of simulated data (blue dashed lines) to the sample experimental data (thick green lines). Here, (A and B) depict simulations and experimental data collected in the dorsal striatum and nucleus accumbens, respectively. See Figure 4 for the parameters used to model the experimental data. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55595/55595fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Stimulated DA responses from the two regions produced very different response shapes, with concave rising shapes in the D-Str (Figure 5A) and convex rising shapes in the NAc (Figure 5B). Both response shapes could be modeled with a few notable differences in parameterizations. Although there is variability in response shapes and amplitudes even within a given region, Kminf is generally much lower in in the NAc compared to the D-Str. Moreover, Vmax, &#916;DAR, and &#916;DAR&#964; tend to be lower in the NAc as well, as is the case in this sample dataset (compare rows 2 and 5 in Figure 4).
Included in the sample dataset are responses to an MPH challenge. Although primarily a DAT inhibitor, MPH is known to have secondary effects on DA release22,27 and may alter the plasmalemmal expression of DAT like other DAT inhibitors28,29. Experimental responses were modeled after a 10 mg/kg MPH by holding the rapid component of post-stimulation release, &#964;R, constant pre- and post-MPH administration, making the arguable assumption that MPH-induced changes in stimulated DA neurotransmission kinetics are not due to changes in post-stimulation DA release. This allowed for the examination of changes in stimulated release and reuptake kinetics. In this example, MPH induced an increase in simulated Kmi, values as is expected for a competitive DAT inhibitor, but also a decrease in &#916;DAR&#964; and Vmax.
Supplemental File QNSim1.0.zip: <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55595/QNsim1_0.zip.zip" target="_blank">Please click here to download this file.</a>

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 ...

modeling-fast-scan-cyclic-voltammetry-data-from-electrically

Universidad San Sebastian

Research

JoVE Journal

Neuroscience

9.8K Views.  University of Pittsburgh. 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. 

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

Examination of Rapid Dopamine Dynamics with Fast Scan Cyclic Voltammetry During Intra-oral Tastant Administration in Awake Rats

Rapid, phasic dopamine (DA) release in the mammalian brain plays a critical role in reward processing, reinforcement learning, and motivational control. Fast scan cyclic voltammetry (FSCV) is an electrochemical technique with high spatial and temporal (sub-second) resolution that has been utilized to examine phasic DA release in several types of preparations. In vitro experiments in single-cells and brain slices and in vivo experiments in anesthetized rodents have been used to identify mechanisms that mediate dopamine release and uptake under normal conditions and in disease models. Over the last 20 years, in vivo FSCV experiments in awake, freely moving rodents have also provided insight of dopaminergic mechanisms in reward processing and reward learning. One major advantage of the awake, freely moving preparation is the ability to examine rapid DA fluctuations that are time-locked to specific behavioral events or to reward or cue presentation. However, one limitation of combined behavior and voltammetry experiments is the difficulty of dissociating DA effects that are specific to primary rewarding or aversive stimuli from co-occurring DA fluctuations that mediate reward-directed or other motor behaviors. Here, we describe a combined method using in vivo FSCV and intra-oral infusion in an awake rat to directly investigate DA responses to oral tastants. In these experiments, oral tastants are infused directly to the palate of the rat &#8211; bypassing reward-directed behavior and voluntary drinking behavior &#8211; allowing for direct examination of DA responses to tastant stimuli.

Rapid fluctuations in extracellular dopamine (DA) mediate both reward processing and motivated behavior in mammals. This manuscript describes the combined use of fast scan cyclic voltammetry (FSCV) and intra-oral tastant administration to determine how tastants alter rapid dopamine release in awake, freely moving rats.

Rapid, phasic dopamine (DA) release in the mammalian brain plays a critical role in reward processing, reinforcement learning, and motivational control. ...

Behavior

Presynaptic Dopamine Dynamics in Striatal Brain Slices with Fast-scan Cyclic Voltammetry

Extensive research has focused on the neurotransmitter dopamine because of its importance in the mechanism of action of drugs of abuse (e.g. cocaine and amphetamine), the role it plays in psychiatric illnesses (e.g. schizophrenia and Attention Deficit Hyperactivity Disorder), and its involvement in degenerative disorders like Parkinson's and Huntington's disease. Under normal physiological conditions, dopamine is known to regulate locomotor activity, cognition, learning, emotional affect, and neuroendocrine hormone secretion. One of the largest densities of dopamine neurons is within the striatum, which can be divided in two distinct neuroanatomical regions known as the nucleus accumbens and the caudate-putamen. The objective is to illustrate a general protocol for slice fast-scan cyclic voltammetry (FSCV) within the mouse striatum. FSCV is a well-defined electrochemical technique providing the opportunity to measure dopamine release and uptake in real time in discrete brain regions. Carbon fiber microelectrodes (diameter of ~ 7 &mu;m) are used in FSCV to detect dopamine oxidation. The analytical advantage of using FSCV to detect dopamine is its enhanced temporal resolution of 100 milliseconds and spatial resolution of less than ten microns, providing complementary information to in vivo microdialysis.

Using fast-scan cyclic voltammetry to measure electrically evoked presynaptic dopamine dynamics in striatal brain slices.

Extensive research has focused on the neurotransmitter dopamine because of its importance in the mechanism of action of drugs of abuse (e.g. cocaine and ...

Voltage Biasing, Cyclic Voltammetry, &amp; Electrical Impedance Spectroscopy for Neural Interfaces

Electrical impedance spectroscopy (EIS) and cyclic voltammetry (CV) measure properties of the electrode-tissue interface without additional invasive procedures, and can be used to monitor electrode performance over the long term. EIS measures electrical impedance at multiple frequencies, and increases in impedance indicate increased glial scar formation around the device, while cyclic voltammetry measures the charge carrying capacity of the electrode, and indicates how charge is transferred at different voltage levels. As implanted electrodes age, EIS and CV data change, and electrode sites that previously recorded spiking neurons often exhibit significantly lower efficacy for neural recording. The application of a brief voltage pulse to implanted electrode arrays, known as rejuvenation, can bring back spiking activity on otherwise silent electrode sites for a period of time. Rejuvenation alters EIS and CV, and can be monitored by these complementary methods. Typically, EIS is measured daily as an indication of the tissue response at the electrode site. If spikes are absent in a channel that previously had spikes, then CV is used to determine the charge carrying capacity of the electrode site, and rejuvenation can be applied to improve the interface efficacy. CV and EIS are then repeated to check the changes at the electrode-tissue interface, and neural recordings are collected. The overall goal of rejuvenation is to extend the functional lifetime of implanted arrays.

Voltage Biasing, Cyclic Voltammetry, & Electrical Impedance Spectroscopy for Neural Interfaces

The electrode-tissue interface of neural recording electrodes can be characterized with electrical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Application of voltage biasing changes the electrochemical properties of the electrode-tissue interface and can improve recording capability. Voltage biasing, EIS, CV, and neural recordings are complementary.

Electrical impedance spectroscopy (EIS) and cyclic voltammetry (CV) measure properties of the electrode-tissue interface without additional invasive ...

Single Cell Measurement of Dopamine Release with Simultaneous Voltage-clamp and Amperometry

After its release into the synaptic cleft, dopamine exerts its biological properties via its pre- and post-synaptic targets1. The dopamine signal is terminated by diffusion2-3, extracellular enzymes4, and membrane transporters5. The dopamine transporter, located in the peri-synaptic cleft of dopamine neurons clears the released amines through an inward dopamine flux (uptake). The dopamine transporter can also work in reverse direction to release amines from inside to outside in a process called outward transport or efflux of dopamine5. More than 20 years ago Sulzer et al. reported the dopamine transporter can operate in two modes of activity: forward (uptake) and reverse (efflux)5. The neurotransmitter released via efflux through the transporter can move a large amount of dopamine to the extracellular space, and has been shown to play a major regulatory role in extracellular dopamine homeostasis6. Here we describe how simultaneous patch clamp and amperometry recording can be used to measure released dopamine via the efflux mechanism with millisecond time resolution when the membrane potential is controlled. For this, whole-cell current and oxidative (amperometric) signals are measured simultaneously using an Axopatch 200B amplifier (Molecular Devices, with a low-pass Bessel filter set at 1,000 Hz for whole-cell current recording). For amperometry recording a carbon fiber electrode is connected to a second amplifier (Axopatch 200B) and is placed adjacent to the plasma membrane and held at +700 mV. The whole-cell and oxidative (amperometric) currents can be recorded and the current-voltage relationship can be generated using a voltage step protocol. Unlike the usual amperometric calibration, which requires conversion to concentration, the current is reported directly without considering the effective volume7. Thus, the resulting data represent a lower limit to dopamine efflux because some transmitter is lost to the bulk solution.

The amperometric technique measures dopamine release from a single cell by detecting the oxidative current produced by spontaneous dopamine oxidization. Simultaneous voltage clamp and amperometry methodology reveal the mechanistic relationship between the overall "activity" of dopamine transporter and the regulatory role of this activity on the reverse transport of dopamine.

After its release into the synaptic cleft, dopamine exerts its biological properties via its pre- and post-synaptic targets1. The dopamine signal is ...

Gold Nanoparticle Modified Carbon Fiber Microelectrodes for Enhanced Neurochemical Detection

For over 30 years, carbon-fiber microelectrodes (CFMEs) have been the standard for neurotransmitter detection. Generally, carbon fibers are aspirated into glass capillaries, pulled to a fine taper, and then sealed using an epoxy to create electrode materials that are used for fast scan cyclic voltammetry testing. The use of bare CFMEs has several limitations, though. First and foremost, the carbon fiber contains mostly basal plane carbon, which has a relatively low surface area and yields lower sensitivities than other nanomaterials. Furthermore, the graphitic carbon is limited by its temporal resolution, and its relatively low conductivity. Lastly, neurochemicals and macromolecules have been known to foul at the surface of carbon electrodes where they form non-conductive polymers that block further neurotransmitter adsorption. For this study, we modify CFMEs with gold nanoparticles to enhance neurochemical testing with fast scan cyclic voltammetry. Au3+ was electrodeposited or dipcoated from a colloidal solution onto the surface of CFMEs. Since gold is a stable and relatively inert metal, it is an ideal electrode material for analytical measurements of neurochemicals. Gold nanoparticle modified (AuNP-CFMEs) had a stability to dopamine response for over 4 h. Moreover, AuNP-CFMEs exhibit an increased sensitivity (higher peak oxidative current of the cyclic voltammograms) and faster electron transfer kinetics (lower &#916;EP or peak separation) than bare unmodified CFMEs. The development of AuNP-CFMEs provides the creation of novel electrochemical sensors for detecting fast changes in dopamine concentration and other neurochemicals at lower limits of detection. This work has vast applications for the enhancement of neurochemical measurements. The generation of gold nanoparticle modified CFMEs will be vitally important for the development of novel electrode sensors to detect neurotransmitters in vivo in rodent and other models to study neurochemical effects of drug abuse, depression, stroke, ischemia, and other behavioral and disease states.

In this study, we modify carbon-fiber microelectrodes with gold nanoparticles to enhance the sensitivity of neurotransmitter detection.

For over 30 years, carbon-fiber microelectrodes (CFMEs) have been the standard for neurotransmitter detection. Generally, carbon fibers are aspirated into ...

Chemistry

Dopamine Release at Individual Presynaptic Terminals Visualized with FFNs

The nervous system transmits signals between neurons via neurotransmitter release during synaptic vesicle fusion. To observe neurotransmitter uptake and release from individual presynaptic terminals directly, we designed fluorescent false neurotransmitters as substrates for the synaptic vesicle monoamine transporter. Using these probes to image dopamine release in the striatum, we made several observations pertinent to synaptic plasticity. We found that the fraction of synaptic vesicles releasing neurotransmitter per stimulus was dependent on the stimulus frequency. A kinetically distinct "reserve" synaptic vesicle population was not observed under these experimental conditions. A frequency-dependent heterogeneity of presynaptic terminals was revealed that was dependent in part on D2 dopamine receptors, indicating a mechanism for frequency-dependent coding of presynaptic selection.

Hui Zhang and Niko G. Gubernator contributed equally to this work.

A new means to measure neurotransmission optically using fluorescent dopamine analogs.

The nervous system transmits signals between neurons via neurotransmitter release during synaptic vesicle fusion. To observe neurotransmitter uptake and ...

Biology

Measuring In Vivo Changes in Extracellular Neurotransmitters During Naturally Rewarding Behaviors in Female Syrian Hamsters

The ability to measure neurotransmitter release on a rapid time scale allows patterns of neurotransmission to be linked to specific behaviors or manipulations; a powerful tool in elucidating underlying mechanisms and circuitry. While the technique of microdialysis has been used for decades to measure nearly any analyte of interest in the brain, this technique is limited in temporal resolution. Alternatively, fast scan cyclic voltammetry is both temporally precise and extremely sensitive; however, because this technically difficult method relies on the electroactivity of the analyte of interest, the possibility to detect nonelectroactive substances (e.g., the neurotransmitter glutamate) is eliminated. This paper details the use of a turn-key system that combines fixed-potential amperometry and enzymatic biosensing to measure both electroactive and nonelectroactive neurotransmitters with temporal precision. The pairing of these two powerful techniques allows for the measurement of both tonic and phasic neurotransmission with relative ease, and permits recording of multiple neurotransmitters simultaneously. The aim of this manuscript is to demonstrate the process of measuring dopamine and glutamate neurotransmission in vivo using a naturally rewarding behavior (i.e., sexual behavior) in female hamsters, with the ultimate goal of displaying the technical feasibility of this assay for examining other behaviors and experimental paradigms.

Measuring In Vivo Changes in Extracellular Neurotransmitters During Naturally Rewarding Behaviors in Female Syrian Hamsters

This paper details the use of fixed-potential amperometric recordings using carbon fiber electrodes and enzymatic biosensor technology to measure the release of dopamine and glutamate with high temporal resolution during natural rewarding behavior in the female hamster.

The ability to measure neurotransmitter release on a rapid time scale allows patterns of neurotransmission to be linked to specific behaviors or ...

Combined Infusion and Stimulation with Fast-Scan Cyclic Voltammetry (CIS-FSCV) to Assess Ventral Tegmental Area Receptor Regulation of Phasic Dopamine

Phasic dopamine (DA) release from the ventral tegmental area (VTA) to the nucleus accumbens plays a pivotal role in reward processing and reinforcement learning. Understanding how the diverse neuronal inputs into the VTA control phasic DA release can provide a better picture of the circuitry that controls reward processing and reinforcement learning. Here, we describe a method that combines intra-VTA cannula infusions of pharmacological agonists and antagonists with stimulation-evoked phasic DA release (combined infusion and stimulation, or CIS) as measured by in vivo fast-scan cyclic voltammetry (FSCV). Using CIS-FSCV in anesthetized rats, a phasic DA response can be evoked by electrically stimulating the VTA with a bipolar electrode fitted with a cannula while recording in the nucleus accumbens core. Pharmacological agonists or antagonists can be infused directly at the stimulation site to investigate specific VTA receptors&#39; roles in driving phasic DA release. A major benefit of CIS-FSCV is that VTA receptor function can be studied in vivo, building on in vitro studies.

Combined Infusion and Stimulation with Fast-Scan Cyclic Voltammetry CIS-FSCV to Assess Ventral Tegmental Area Receptor Regulation of Phasic Dopamine

The goal of this protocol is to directly manipulate ventral tegmental area receptors to study their contribution to subsecond dopamine release.

Phasic dopamine (DA) release from the ventral tegmental area (VTA) to the nucleus accumbens plays a pivotal role in reward processing and reinforcement ...

Cyclic Voltammetry (CV)

Source: Laboratory of Dr. Kayla Green &#8212; Texas Christian University
A Cyclic Voltammetry (CV) experiment involves the scan of a range of potential voltages while measuring current. In the CV experiment, the potential of an immersed, stationary electrode is scanned from a predetermined starting potential to a final value (called the switching potential) and then the reverse scan is obtained. This gives a &#39;cyclic&#39; sweep of potentials and the current vs. potential curve derived from the data is called a cyclic voltammogram. The first sweep is called the &#39;forward scan&#39; and the return wave is called the &#39;reverse scan&#39;. The potential extremes are termed the &#39;scan window&#39;. The magnitude of reduction and oxidation currents and the shape of the voltammograms are highly dependent on analyte concentration, scan rates, and experimental conditions. By varying these factors, cyclic voltammetry can yield information regarding the stability of transition metal oxidation state in the complexed form, reversibility of electron transfer reactions, and information regarding reactivity. This video will explain the basic setup for a cyclic voltammetry experiment including analyte preparation and setting up the electrochemical cell. A simple cyclic voltammetry experiment will be presented.

Cyclic Voltammetry CV: Measuring Redox Potentials and Currents

Source: Laboratory of Dr. Kayla Green &#8212; Texas Christian University
A Cyclic Voltammetry (CV) experiment involves the scan of a range of potential ...

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Monitoring Dopamine Release in a Rat Model with VTA Dopaminergic Neuron Receptor Manipulation

Source: Wickham, R. J., et al. <a href="https://app-jove-com.bdigitaluss.remotexs.co/v/60886">Combined Infusion and Stimulation with Fast-Scan Cyclic Voltammetry (CIS-FSCV) to Assess Ventral Tegmental Area Receptor Regulation of Phasic Dopamine.</a> J. Vis. Exp. (2020).
This video demonstrates the implantation of electrodes and cannulas in an anesthetized rat to measure dopamine release in the nucleus accumbens using a potentiostat and the infusion of drugs to modulate dopaminergic activity in the ventral tegmental area.

All procedures involving animal models have been reviewed by the local institutional animal care committee and the JoVE veterinary review board.
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Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Summary

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Voltage Biasing, Cyclic Voltammetry, & Electrical Impedance Spectroscopy for Neural Interfaces

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Combined Infusion and Stimulation with Fast-Scan Cyclic Voltammetry (CIS-FSCV) to Assess Ventral Tegmental Area Receptor Regulation of Phasic Dopamine

Cyclic Voltammetry (CV)

Monitoring Dopamine Release in a Rat Model with VTA Dopaminergic Neuron Receptor Manipulation