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="http://cloudfront.jove.com/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="http://cloudfront.jove.com/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>

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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 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.

<table border="0" cellpadding="0" cellspacing="0" width="750">
	<tbody>
		<tr height="21">
			<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 ...

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

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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

High Density Event-related Potential  Data Acquisition in Cognitive Neuroscience

Functional magnetic resonance imaging (fMRI) is currently the standard method of evaluating brain function in the field of Cognitive Neuroscience, in part because fMRI data acquisition and analysis techniques are readily available. Because fMRI has excellent spatial resolution but poor temporal resolution, this method can only be used to identify the spatial location of brain activity associated with a given cognitive process (and reveals virtually nothing about the time course of brain activity). By contrast, event-related potential (ERP) recording, a method that is used much less frequently than fMRI, has excellent temporal resolution and thus can track rapid temporal modulations in neural activity. Unfortunately, ERPs are under utilized in Cognitive Neuroscience because data acquisition techniques are not readily available and low density ERP recording has poor spatial resolution. In an effort to foster the increased use of ERPs in Cognitive Neuroscience, the present article details key techniques involved in high density ERP data acquisition. Critically, high density ERPs offer the promise of excellent temporal resolution and good spatial resolution (or excellent spatial resolution if coupled with fMRI), which is necessary to capture the spatial-temporal dynamics of human brain function.

Event-related potential (ERP) recording is under utilized in Cognitive Neuroscience because data acquisition techniques are not readily available and this method often has poor spatial resolution. To foster the increased use of ERPs in Cognitive Neuroscience, the present article details key techniques involved in high density ERP data acquisition.

Functional magnetic resonance imaging (fMRI) is currently the standard method of evaluating brain function in the field of Cognitive Neuroscience, in part ...

Basics of Multivariate Analysis in Neuroimaging Data

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.

The current article describes the basics of multivariate analysis and contrasts it to the more commonly used voxel-wise univariate analysis. Both types of analysis are applied to a clinical-neuroscience data set. Supplementary split-half simulations show better replication of the multivariate results in independent data sets.

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be ...

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 ...

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator&#39;s coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes.

This paper describes a method for collecting and analyzing electroencephalography (EEG) data during concurrent transcranial magnetic stimulation (TMS) guided by activations revealed with functional magnetic resonance imaging (fMRI). A method for TMS artifact removal and extraction of event related potentials is described as well as considerations in paradigm design and experimental setup.

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological ...

Analyzing Synaptic Modulation of Drosophila melanogaster Photoreceptors after Exposure to Prolonged Light

The nervous system has the remarkable ability to adapt and respond to various stimuli. This neural adjustment is largely achieved through plasticity at the synaptic level. The Active Zone (AZ) is the region at the presynaptic membrane that mediates neurotransmitter release and is composed of a dense collection of scaffold proteins. AZs of Drosophila melanogaster (Drosophila) photoreceptors undergo molecular remodeling after prolonged exposure to natural ambient light. Thus the level of neuronal activity can rearrange the molecular composition of the AZ and contribute to the regulation of the functional output.
Starting from the light exposure set-up preparation to the immunohistochemistry, this protocol details how to quantify the number, the spatial distribution, and the delocalization level of synaptic molecules at AZs in Drosophila photoreceptors. Using image analysis software, clusters of the GFP-fused AZ component Bruchpilot were identified for each R8 photoreceptor (R8) axon terminal. Detected Bruchpilot spots were automatically assigned to individual R8 axons. To calculate the distribution of spot frequency along the axon, we implemented a customized software plugin. Each axon&#39;s start-point and end-point were manually defined and the position of each Bruchpilot spot was projected onto the connecting line between start and end-point. Besides the number of Bruchpilot clusters, we also quantified the delocalization level of Bruchpilot-GFP within the clusters. These measurements reflect in detail the spatially resolved synaptic dynamics in a single neuron under different environmental conditions to stimuli.

Analyzing Synaptic Modulation of Drosophila melanogaster Photoreceptors after Exposure to Prolonged Light

Here we show how to quantify the number and spatial distribution of synaptic active zones in Drosophila melanogaster&#160;photoreceptors, highlighted with a genetically encoded molecular marker, and their modulation after prolonged exposure to light.

The nervous system has the remarkable ability to adapt and respond to various stimuli. This neural adjustment is largely achieved through plasticity at ...

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Brainstem evoked response audiometry (BERA) is of central relevance in the clinical neurophysiology. As other evoked potential (EP) techniques, such as visually evoked potentials (VEPs) or somatosensory evoked potentials (SEPs), the auditory evoked potentials (AEPs) are triggered by the repetitive presentation of identical stimuli, the electroencephalographic (EEG) response of which is subsequently averaged resulting in distinct positive (p) and negative (n) deflections. In humans, both the amplitude and the latency of individual peaks can be used to characterize alterations in synchronization and conduction velocity in the underlying neuronal circuitries. Importantly, AEPs are also applied in basic and preclinical science to identify and characterize the auditory function in pharmacological and genetic animal models. Even more, animal models in combination with pharmacological testing are utilized to investigate for potential benefits in the treatment of sensorineural hearing loss (e.g., age- or noise-induced hearing deficits). Here we provide a detailed and integrative description of how to record auditory brainstem-evoked responses (ABRs) in mice using click and tone-burst application. A specific focus of this protocol is on pre-experimental animal housing, anesthesia, ABR recording, ABR filtering processes, automated wavelet-based amplitude growth function analysis, and latency detection.

Brainstem evoked response audiometry is an important tool in clinical neurophysiology. Nowadays, brainstem evoked response audiometry is also applied in the basic science and preclinical studies involving both pharmacological and genetic animal models. Here we provide a detailed description of how auditory brainstem responses can be successfully recorded and analyzed in mice.

Brainstem evoked response audiometry (BERA) is of central relevance in the clinical neurophysiology. As other evoked potential (EP) techniques, such as ...

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Current web resources provide limited, user friendly tools to compute spectrograms for visualizing and quantifying electroencephalographic (EEG) data. This paper describes a Windows-based, open source code for creating EEG multitaper spectrograms. The compiled program is accessible to Windows users without software licensing. For Macintosh users, the program is limited to those with a MATLAB software license. The program is illustrated via EEG spectrograms that vary as a function of states of sleep and wakefulness, and opiate-induced alterations in those states. The EEGs of C57BL/6J mice were wirelessly recorded for 4 h after intraperitoneal injection of saline (vehicle control) and antinociceptive doses of morphine, buprenorphine, and fentanyl. Spectrograms showed that buprenorphine and morphine caused similar changes in EEG power at 1&#8722;3 Hz and 8&#8722;9 Hz. Spectrograms after administration of fentanyl revealed maximal average power bands at 3 Hz and 7 Hz. The spectrograms unmasked differential opiate effects on EEG frequency and power. These computer-based methods are generalizable across drug classes and can be readily modified to quantify and display a wide range of rhythmic biological signals.

This protocol provides an open source, compiled MATLAB program that generates multitaper spectrograms for electroencephalographic data.

Current web resources provide limited, user friendly tools to compute spectrograms for visualizing and quantifying electroencephalographic (EEG) data. ...

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 ...