Name: a visual guide to sorting electrophysiological recordings using 'spikesorter'
Uploaded: 2017-02-10T15:00:00
Description: Watch this Scientific Journal Video about A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter' at JoVE.com

We thank those individuals and groups who have used SpikeSorter and who have provided requests for file format support and suggestions and feedback on how to improve it. These include Youping Xiao, Felix Fung, Artak Khachatryan, Eric Kuebler, Curtis Baker, Amol Gharat and Dongsheng Xiao. We thank Adrien Peyrache for the false positive and negative figures given in 'Representative Results'.

1. Program Setup
<ol>
	<li>Go to <a href="http://www.swindale.ecc.ubc.ca/SpikeSorter">http://www.swindale.ecc.ubc.ca/SpikeSorter</a> to download the program. Copy the supplied executable file to the directory of your choice. Read the accompanying documentation. 
	NOTE: No formal installation or compilation is required.</li>
	<li>Before opening any file to be sorted, ensure that there is enough free RAM to contain the entire duration of the recording. Also make sure a valid channel configuration file, as described ...

<ol>
	<li>Buzs&#225;ki, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Large-scale+recording+of+neuronal+ensembles.">Large-scale recording of neuronal ensembles.</a> Nat. Neurosci. 7, 446-451 (2004).</li><li>Blanche, T. J., Spacek, M. A., Hetke, J. F., Swindale, N. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Polytrodes:+High+Density+Silicon+Electrode+Arrays+for+Large+Scale+Multiunit+Recording.">Polytrodes: High Density Silicon Electrode Arrays for Large Scale Multiunit Recording.</a> J. Neurophys. 93, 2987-3000 (2005).</li><li>Lewicki, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+review+of+methods+for+spike+sorting:+the+detection+and+classi&#64257;cation+of+neuronal+action+potentials.">A review of methods for spike sorting: the detection and classi&#64257;cation of neuronal action potentials.</a> Network. 9, R53-R78 (1998).</li><li>Letelier, J. C., Weber, P. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spike+sorting+based+on+discrete+wavelet+transform+coefficients.">Spike sorting based on discrete wavelet transform coefficients.</a> J. Neurosci. Methods. 101, 93-106 (2000).</li><li>Quiroga, R. Q., Nadasdy, Z., Ben-Shaul, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unsupervised+spike+detection+and+sorting+with+wavelets+and+superparamagnetic+clustering.">Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering.</a> Neural Computation. 16, 1661-1687 (2004).</li><li>Franke, F., Natora, M., Boucsein, C., Munk, M., Obermayer, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+online+spike+detection+and+spike+classi&#64257;cation+algorithm+capable+of+instantaneous+resolution+of+overlapping+spikes.">An online spike detection and spike classi&#64257;cation algorithm capable of instantaneous resolution of overlapping spikes.</a> J. Comput. Neurosci. 29, 127-148 (2010).</li><li>J&#228;ckel, D., Frey, U., Fiscella, M., Franke, F., Hierlemann, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Applicability+of+independent+component+analysis+on+high-density+microelectrode+array+recordings.">Applicability of independent component analysis on high-density microelectrode array recordings.</a> J. Neurophysiol. 108, 334-348 (2012).</li><li>Rossant, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spike+sorting+for+large,+dense+electrode+arrays.">Spike sorting for large, dense electrode arrays.</a> Nature Neuroscience. 19, 634-641 (2016).</li><li>Vandecasteele, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Large-scale+recording+of+neurons+by+movable+silicon+probes+in+behaving+rodents.">Large-scale recording of neurons by movable silicon probes in behaving rodents.</a> JoVE. (61), e3568 (2012).</li><li>Schjetnan, A. G. P., Luczak, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recording+large-scale+neuronal+ensembles+with+silicon+probes+in+the+anesthetized+rat.">Recording large-scale neuronal ensembles with silicon probes in the anesthetized rat.</a> JoVE. (56), e3282 (2011).</li><li>Swindale, N. V., Spacek, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spike+sorting+for+polytrodes:+a+divide+and+conquer+approach.">Spike sorting for polytrodes: a divide and conquer approach.</a> Frontiers in Systems Neuroscience. 8, 1-21 (2014).</li><li>Swindale, N. V., Spacek, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spike+detection+methods+for+polytrodes+and+high+density+microelectrode+arrays.">Spike detection methods for polytrodes and high density microelectrode arrays.</a> J. Comput. Neurosci. 38, 249-261 (2015).</li><li>Swindale, N. V., Spacek, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Veri&#64257;cation+of+multichannel+electrode+array+integrity+by+use+of+cross-channel+correlations.">Veri&#64257;cation of multichannel electrode array integrity by use of cross-channel correlations.</a> J. Neurosci. Meth. 263, 95-102 (2016).</li><li>Fukunaga, K., Hostetler, L. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+estimation+of+the+gradient+of+a+density+function,+with+applications+in+pattern+recognition.">The estimation of the gradient of a density function, with applications in pattern recognition.</a> IEEE Transactions on Information Theory (IEEE). 21, 32-40 (1975).</li><li>Mitelut, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Standardizing+spike+sorting:+an+in+vitro,+in+silico+and+in+vivo+study+to+develop+quantitative+metrics+for+sorting+extracellularly+recorded+spiking+activity.">Standardizing spike sorting: an in vitro, in silico and in vivo study to develop quantitative metrics for sorting extracellularly recorded spiking activity.</a> Soc. Neurosci. Abstr. 598 (10), (2015).</li><li>Hazan, L., Zugaro, M., Buzs&#225;ki, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Klusters,+NeuroScope,+NDManager:+A+free+software+suite+for+neurophysiological+data+processing+and+visualization.">Klusters, NeuroScope, NDManager: A free software suite for neurophysiological data processing and visualization.</a> J. Neurosci. Meth. 155, 207-216 (2006).</li><li>Harris, K. D., Henze, D. A., Csicsvari, J., Hirase, H., Buzs&#225;ki, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accuracy+of+tetrode+spike+separation+as+determined+by+simultaneous+intracellular+and+extracellular+measurements.">Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements.</a> J. Neurophysiol. 84, 401-414 (2000).</li><li>Anastassiou, C. A., Perin, R., Buzs&#225;ki, G., Markram, H., Koch, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell-type+and+activity+dependent+extracellular+correlates+of+intracellular+spiking.">Cell-type and activity dependent extracellular correlates of intracellular spiking.</a> J. Neurophysiol. 114, 608-623 (2015).</li><li>Wohrer, A., Humphries, M. D., Machens, C. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Population-wide+distributions+of+neural+activity+during+perceptual+decision-making.">Population-wide distributions of neural activity during perceptual decision-making.</a> Prog. Neurobiol. 103, 156-193 (2013).</li><li>Mizuseki, K., Buzs&#225;ki, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preconfigured,+skewed+distribution+of+firing+rates+in+the+hippocampus+and+entorhinal+cortex.">Preconfigured, skewed distribution of firing rates in the hippocampus and entorhinal cortex.</a> Cell Reports. 4, 1010-1021 (2013).</li><li>Schmitzer-Torbert, N., Jackson, J., Henze, D., Harris, K., Redish, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+measures+of+cluster+quality+for+use+in+extracellular+recordings.">Quantitative measures of cluster quality for use in extracellular recordings.</a> Neuroscience. 131, 1-11 (2005).</li><li>Barnett, A. H., Magland, J. F., Greengard, L. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validation+of+neural+spike+sorting+algorithms+without+ground-truth+information.">Validation of neural spike sorting algorithms without ground-truth information.</a> J. Neurosci. Meth. 264, 65-77 (2016).</li></ol>

File Formats
Currently supported file formats include Neuralynx (.ntt and .ncs), Plexon (.plx), Neuroscope (.xml + .dat), MultiChannel Systems (.mcd), Blackrock (.nev) and Intan (.rhd). For unsupported formats, there are two options. One is to request addition of the file format to an upcoming release (an email link to the developer is provided in the &#39;Help - About&#39; dialog). The other is to convert the file to a supported format. A simple option is to use the time-spike-format &#39;.tsf&#...

Anyone who records extracellular signals from the brain using methods more sophisticated than simple on-line thresholding and windowing faces the task of identifying and separating the signals from different neurons from the noisy voltage signals recorded by the electrode. This task is commonly known as spike sorting. The difficulty of spike sorting is compounded by various factors. Neurons can be very close together so that the signals recorded from them by a nearby electrode are likely to be similar and hard to distinguish. The signals produced by a single neuron may vary over time, perhaps because of movements of the electrode, variable sodium channel kinetics during periods of high firing rate, variable degrees of activation of voltage conductances in dendrites that are close to the electrode, or possibly as a result of changes in brain state. These problems can be mitigated by using multi-electrode arrays (MEAs) with many closely spaced (20 - 100 &#181;m) recording channels which allows better spatial definition of the signals from single neurons since they are typically spread out over several channels1,2. However, this, combined with the fact that signals from neurons spread along the entire length of the electrode overlap in space, results in a potentially very high dimensional space within which clusters corresponding to unique neurons need to be identified. This problem becomes computationally intractable for more than a small number of electrode channels. To date, there is no generally agreed-upon best method for spike sorting, though many solutions have been proposed3,4,5,6,7,8 and recordings from MEAs are becoming increasingly common9,10. Because spike sorting is not an end in itself, but is simply a necessary preliminary step before further data analysis, there is a need for an easily usable package that will read in raw recording data files and convert them to sorted spike trains with as little user input, and as quickly and reliably, as possible.
This paper provides a tutorial for the use of SpikeSorter &#8212; a program developed with the aim of meeting these needs. The program is based on algorithms described in previously published papers11,12,13. The goals in designing the program were that a) it should have a user-friendly interface requiring little or no prior knowledge of computer programming or of spike sorting methodology; b) few or no other specialized software components beyond standard Windows or Linux operating systems should be needed; c) a wide range of recording data formats for data import and export should be supported; d) the need for user input during sorting should be minimized, and e) sorting times should scale in a reasonable way, ideally linearly, with recording duration and the number of channels on the electrode. The algorithms implemented in the program include a) a flexible set of pre-processing and event detection strategies; b) an automated divide and conquer strategy of dimension reduction which clusters voltage waveforms based on the principal components (PC) distributions obtained from subsets of channels assigned to specific clusters; c) automated clustering of PC distributions with a fast clustering procedure based on the mean-shift algorithm3,14, and d) partially automated pairwise merging and splitting of clusters to ensure that each is as distinct as possible from all others. To this, a set of procedures has been added that allow manual splitting or merging of clusters based on inspection of PC distributions, cross- and auto-correlograms of spike trains and time-amplitude plots of spike waveforms. Recordings from tetrodes, tetrode arrays, Utah arrays as well as single and multi-shank MEAs can be read and sorted. The current limit on the number of channels is 256 but this may be increased in future.
Another cross-platform open-source implementation, &#34;spyke&#34; (http://spyke.github.io), is also available. Written by one of us (MS) in Python and Cython, spyke uses the same overall approach as SpikeSorter, with some differences: to reduce memory demands, raw data is loaded in small blocks, and only when absolutely necessary; clusters are exclusively displayed, manipulated, and sorted in 3D; and principal component and independent component analysis are both used as complementary dimension reduction methods. Spyke requires more user interaction, but relies heavily on keyboard and mouse shortcuts and an undo/redo queue to rapidly explore the effects of various factors on the clustering of any given subset of spikes. These factors include spike channel and time range selection, spike alignment, clustering dimensions and spatial bandwidth (sigma)11.
The following is a brief description of the algorithms and strategies used for sorting. More complete descriptions can be found in previous publications11,12,13 and in annotations that can be accessed via help buttons (identified with a &#39;?&#39;) within SpikeSorter. After loading a raw extracellular voltage file and filtering out the lower frequency components, an initial stage of event detection results in a set of events, each of which consists of a brief voltage snapshot before and after the event time. If the electrode sites are sufficiently closely spaced (&#60; 100 &#181;m), single unit signals will generally appear on several neighboring channels. A central channel is automatically chosen for each event, corresponding to the channel on which the peak-to-peak voltage of the event is largest. Automated sorting starts by forming a single initial cluster for each electrode channel, consisting of all the events that were localized to that channel. A unit located midway between channels may give rise to spikes that are localized (perhaps randomly) to different channels: the clusters from these two sets of spikes will be identified as similar and merged at a later stage. The average waveform of the events in each initial cluster is then calculated. This is referred to as the cluster template. Subsidiary channels are assigned to each cluster based on the amplitudes and standard deviation of the template waveforms on each channel. Principal component values are then calculated for each cluster based on the waveforms on the assigned set of channels. The user can choose the number of principal component dimensions to use: usually 2 is sufficient. Each cluster is then split into a further set of clusters, and this is repeated until none can be further split by automated clustering.
At this point, an initial set of say, 64 clusters from a 64-channel electrode, may be split into two or three times that number, depending on the number of units that was present in the recording. But because of the variable assignment of events from single units to different channels, the number of clusters found at this stage is almost certainly larger than it should be. The next stage of sorting is to correct the oversplitting by comparing pairs of clusters and merging similar pairs or reassigning events from one to another. This stage of sorting is referred to as &#39;merge and split&#39;.
Merging and Splitting
For N clusters, there are N*(N-1)/2 pairs and hence the number of pairs grows as N2, which is undesirable. However, many pairs can be excluded from the comparison because the two members of the pair are physically far apart. This reduces the dependence to something that is more linearly related to the number of channels. In spite of this shortcut, the merge and split stage can still be quite time consuming. It works in the following way. Each cluster pair that is to be compared (those that are physically close together, as judged by the overlap in the channel sets assigned to each) is temporarily merged, though keeping the identities of the spikes in the two member clusters known. The principal components of the merged pair are then calculated. A measure of the overlap between the points in the two clusters is calculated based on the distribution of the first two principal components.
The way the overlap measure is calculated is described in more detail elsewhere11. Its value is zero if the clusters do not overlap at all, i.e. the nearest neighbor of each point is in the same cluster. Its value is close to 1 if the clusters overlap completely, i.e. the probability of the nearest neighbor being in the same cluster is the same as that predicted from a uniform mixing of points.
Various decisions are made which take the overlap measure into account. If the overlap is greater than a certain value, clusters may be merged. If the overlap is very small, the cluster pair may be defined as distinct and left alone. Intermediate values, indicating incomplete separation of the cluster pair, may signal that the pair should be merged and then re-split, the desired outcome being a pair of clusters with less overlap. These procedures are run first in an automated stage and then in a manually guided stage.
In the automated stage, cluster pairs with a high overlap value are merged; then cluster pairs with intermediate to low overlap values are merged and re-split. In the second, user-guided stage, the user is presented with all the remaining ambiguous cluster pairs (i.e. those with overlap values in a defined intermediate range) in sequence and is asked to choose whether a) to merge the pair, b) merge and resplit the pair, c) to declare the pair to be distinct (which will override the significance of the overlap measure), or d) to define the relation between the pair as &#39;ambiguous&#39; indicating that the spikes in the pair are unlikely to be well sorted. Various tools are provided to help with these decisions, including auto- and cross-correlograms and time series plots of spike height and PC values.
Ideally, at the end of the merging and splitting stages, every cluster should be distinct from all others, either because it has few or no channels in common with other clusters, or because the overlap index is less than a defined value. This value is user-selectable but is typically 0.1. Clusters (units) that pass this test are defined as &#39;stable&#39;, those that do not (because the overlap with one or more other clusters is greater than the threshold) are defined as &#39;unstable&#39;. In practice, the great majority of units end up being defined as &#39;stable&#39; at the finish of sorting, leaving the remainder to either be discarded or treated as potentially multi-unit.
Software Requirements
SpikeSorter is compatible with 64 bit versions of Windows 7 and Windows 10, and has also been run successfully under Linux using the Wine emulator. Data files are loaded completely into memory (for speed) hence available RAM needs to scale with the size of the recording (allow about 2 GB for the program itself). Electrophysiological data files larger than 130 GB in size have been successfully sorted in both Windows and Linux environments. Options are accessed through standard Windows menus, a toolbar and dialogs. The layout of items on the menu matches roughly the order of operations in sorting, beginning with the &#39;File&#39; menu on the left for data input and the &#39;Export&#39; menu on the right allowing for export of sorted data. Toolbar buttons provide shortcuts to commonly used menu items.
The Channel Configuration File
Many recording data formats do not store channel locations. However, knowing these is essential for spike sorting. Channels may also be numbered in various ways by acquisition software: SpikeSorter requires that channels are numbered in sequence, beginning with channel 1. Thus, an ancillary electrode configuration file has to be created that can remap channel numbers to follow the sequential rule, and to store channel locations. The channel configuration file is a text file with a single row of text for each channel. The first line of the file stores a text name, up to 16 characters long, that identifies the electrode. The numbers in subsequent lines can be separated by tabs, a single comma, or spaces. There are four numbers in each row providing (in order): the channel number in the file, the channel number to which it is to be mapped (i.e. the number that will be used by SpikeSorter), and the x and y coordinates of the channel, in microns. The x coordinate would normally be taken as perpendicular to the direction of electrode insertion and the y coordinate accordingly would be depth into the tissue. The configuration file has to be placed in the same directory as the recording file. There is some flexibility in how it can be named. The program will first search for a file that has the same name as the raw data file but with a .cfg extension. If that file is not found, it will search for the file &#39;electrode.cfg&#39;. If that file in turn is not found an error message is generated to indicate a lack of channel layout information.

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			<td class="xl63" height="21" style="height:15.75pt;width:162pt" width="216">spikesorter.exe</td>
			<td class="xl63" style="width:53pt" width="71">N/A</td>
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			<td class="xl64" style="width:125pt" width="167">program file currently available through invited dropbox link</td>
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a visual guide to sorting electrophysiological recordings using 'spikesorter'

Few stand-alone software applications are available for sorting spikes from recordings made with multi-electrode arrays. Ideally, an application should be user friendly with a graphical user interface, able to read data files in a variety of formats, and provide users with a flexible set of tools giving them the ability to detect and sort extracellular voltage waveforms from different units with some degree of reliability. Previously published spike sorting methods are now available in a software program, SpikeSorter, intended to provide electrophysiologists with a complete set of tools for sorting, starting from raw recorded data file and ending with the export of sorted spikes times. Procedures are automated to the extent this is currently possible. The article explains and illustrates the use of the program. A representative data file is opened, extracellular traces are filtered, events are detected and then clustered. A number of problems that commonly occur during sorting are illustrated, including the artefactual over-splitting of units due to the tendency of some units to fire spikes in pairs where the second spike is significantly smaller than the first, and over-splitting caused by slow variation in spike height over time encountered in some units. The accuracy of SpikeSorter's performance has been tested with surrogate ground truth data and found to be comparable to that of other algorithms in current development.

Figure 7 shows the display (obtained by going to &#39;View - Sorted waveforms&#39;) for a typical sorted recording. The default view option is just to show the waveforms on the center channel for each cluster. A common experience is that waveforms for a cluster pair on the same channel look identical, but when the &#39;Compare pairs&#39; dialog is used to examine the two clusters there are distinct clusters in the PC projection, most often resulting from waveform differen...

Watch this Scientific Journal Video about A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter' at JoVE.com

A Visual Guide to Sorting Electrophysiological Recordings Using &#039;SpikeSorter&#039;

The article shows how to use the program SpikeSorter to detect and sort spikes in extracellular recordings made with multi-electrode arrays.

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Few stand-alone software applications are available for sorting spikes from recordings made with multi-electrode arrays. Ideally, an application should be ...

The overall goal of this video, is to show electrophysiologists how to use the program, SpikeSorter, to detect and sort spikes in extracellular recordings from neural tissue made with microelectrode arrays. Our goal was to provide a simple solution to this task of spike sorting using a program with a user-friendly interface. SpikeSorter can read files in a variety of formats and offers a variety of pre-processing and event detection strategies, in addition to sorting and data export options.The implications of this technique extends towards the analysis of neural behaviors, studied by extracellular recordings with multielectrode arrays. Generally, individuals new to this method will struggle because of the complexity of the task and the lack of standard methodology and objective validation. We first had the idea for this method, when we were faced with the problem of sorting our own recordings, done with 54 channel multi electrode arrays and found no available solution.To begin this procedure, open the program. Go to file and open, and then select the recording file format from the drop-down list, at the bottom right of the resulting open file dialog. Next, select the file and click open.Click OK when reading has finished. The recording signals are now shown in the voltage waveform display. If the recording is unfiltered and contains the local field potential, remove it by clicking on the filter icon on the toolbar.Select high pass butterworth filter, a suitable cutoff frequency and the number of poles, then press Do It.Press OK, then done, then inspect the filtered waveform in the display. Voltage signals can be inspected by holding down the left mouse button and then dragging. Use the scroll wheel to zoom in on particular features and out again.Hover the mouse over specific waveform points to display corresponding voltage values in the top left hand corner. Double-clicking on the window, brings up a dialog that can be also used to quickly examine any part of the recording and to examine specific events in channels or clusters, once event detection and sorting have been completed. Next, check for channels that may be faulty and need to be masked.Click on the channel check icon and inspect the graph. To mask an outlying channel, either select the channel number or select it from the problem list. When finished, close the dialog, then click yes at the prompt, to save the mask values.Masked channels are grayed out in the voltage display. In this procedure, go to pre-process event detection to bring up the event detection dialog. Use the slider on the top right to inspect the noise level on the particular channels.Careful inspection of the voltage display, may also reveal silent or unusually noisy channels that need to be masked. Choose variable thresholding for event detection. The recommended range of thresholds is from 4.0 to 6.0 times noise.Select 4.5. Choose the dynamic multiphasic filter detection method from the drop-down list. Set the window to be roughly half the width of a typical spike waveform, with the values in the range, 0.15 to 0.5 milliseconds.After that, select the alignment method by choosing the option that best identifies a single, temporally localized feature of the spikes that are being sorted. Leave other options at their default values and press Start. Press Done to exit the dialog.The voltage waveform window shows the detected events in gray on their assigned center channel. Event detection parameters can be adjusted based on the results seen at this point. For example, event detection may be rerun with a lower detection threshold, in order to detect more events.The next step is not normally performed before routine sorting, but it is useful to do it when sorting for the first time, or when encountering unfamiliar data. Go to sort, convert channels to clusters, to create a single cluster for each unmasked electrode channel, assuming that each channel has some events assigned to it. Examine these clusters by going to review, view, clean and split clusters to bring up another dialog.Use the spin control to select the cluster to be viewed. Select a cluster that has two or more distinct subclusters, then press the realign button to change the time of each event, so as to better match it to the shape of the template. Next, press Autosplit to cluster the events based on the principal components'distribution.Subsequently, click on the checkboxes to examine the waveforms in the subclusters. Use the slider control to examine the corresponding waveforms. As an exercise, use one of the small split buttons to create a new cluster and examine it.Notice how the red cluster, now reveals two subclusters more clearly, after the green one is split off. Sorting could continue manually this way, but it is preferable to use the faster Autosort procedure. Press Done and go to Sort, Autosort.Leave the skip event detection option checked if event detection has already been done, as here. If it is not checked, event detection will be run automatically, using parameter values and choices inherited from the event detection dialog. In the clustering panel below, select a temporal window large enough to contain the entirety of the spike waveform, preceding and following the alignment point.Next, select a realignment option to be used during clustering and choose a minimum cluster size. Decide on the number of dimensions in the PC space that will be used for clustering and leave other options at their default settings. After that, press Start to begin Autosort.After Autosort is complete, press next to go to the manually guided merge and split stage, then press begin. In the new dialog, cluster pairs that are not clearly similar or distinct will be presented in sequence. For each presented pair, use the buttons to choose whether to merge the pair, merge and re-split it, to mark the pair as distinct or as ambiguous, if it is unclear as to what is the best decision.Click on the checkboxes, to display a graph of either spike peak height or the first or second principal component against time or auto and cross correlation histograms. These may help with decisions. Here, we see an example of a merge decision.Followed by a merge and split decision. If the merge and split option is unable to find clearly separable clusters, use the slider in the prompted dialog to manually vary a clustering parameter and find a split that looks satisfactory. If this cannot be achieved, use the revert button to go back to the original state of the two clusters.When finished, press split as shown. Now go to review, post-processing. Use the alignment cleaning button to remove events from the clusters that are bad matches to their cluster template.Then, use the recluster button to reassign unclustered events to particular templates based on match value. The reclaimed events are marked as a subcluster of each parent cluster and can be inspected, using the view, clean and split clusters dialog. These events will remain in the cluster and be exported as such, unless they are deleted.Next, return to the post-processing dialog. Use the delete button and the spin control next to it, to delete the clusters with a signal-to-noise ratio less than the selected threshold. Subsequently, use the sort button to renumber the clusters according to a chosen criterion.This figure shows the display, obtained by going to view, sorted waveforms for the recording that was just sorted. Common experience, is that waveforms for a cluster pair on the same channel, look identical, but the compare pairs dialog nevertheless, shows distinct clusters in the PC projection. Merging decisions following the previous stages of sorting are sometimes suggested by amplitude time plots and cross correlograms.This illustrates a merging decision based on the presence of a strong asymmetric, cross correlation at short time intervals, coupled with a clear relationship between the first principal component values of the two units, shown here on the vertical axis. This figure shows a case where similar evidence for merging is lacking. The cross correlogram is not strongly asymmetric and the autocorrelograms are different.Though waveform shapes are similar, the two units should arguably not be merged, because of the difference in the principal components'distributions. This figure shows a case where waveform shapes are similar and the first principal component values of two units blend when one of them stops firing and the other starts. In this case, the decision to merge seems straightforward.We hope this brief introduction to SpikeSorter has been useful. We plan to continue to develop the program and add new features where they can be useful. Towards this end, we welcome bug reports, constructive feedback and suggestions for improvement.

a-visual-guide-to-sorting-electrophysiological-recordings-using

Research

JoVE Journal

Neuroscience

Chronic Imaging of Mouse Visual Cortex Using a Thinned-skull Preparation

In vivo imaging using two-photon laser scanning microscopy (2PLSM) allows the study of living cells and neuronal processes in the intact brain. The technique presented here allows the imaging of the same area of the brain at several time points (chronic imaging) with microscopic resolution allowing the tracking of dendritic spines which are the small structures that represent the majority of postsynaptic excitatory sites in the CNS. The ability to clearly resolve fine cortical structures over several time points has many advantages, specifically in the study of brain plasticity in which morphological changes at synapses and circuit remodeling may help explain underlying mechanisms. In this video and supplementary material, we show a protocol for chronic in vivo imaging of the intact brain using a thinned-skull preparation. The thinned-skull preparation is a minimally invasive approach, which avoids potential damage to the dura and/or cortex, thus reducing the onset of an inflammatory response. When this protocol is performed correctly, it is possible to clearly monitor changes in dendritic spine characteristics in the intact brain over a prolonged period of time.

In this video and supplemental material, we show a protocol for chronic in vivo imaging of the intact brain using a thinned-skull preparation.

In vivo imaging using two-photon laser scanning microscopy (2PLSM) allows the study of living cells and neuronal processes in the intact brain. The ...

Electrophysiological Recordings from the Giant Fiber Pathway of D. melanogaster

When startled adult D. melanogaster react by jumping into the air and flying away. In many invertebrate species, including D. melanogaster, the "escape" (or "startle") response during the adult stage is mediated by the multi-component neuronal circuit called the Giant Fiber System (GFS). The comparative large size of the neurons, their distinctive morphology and simple connectivity make the GFS an attractive model system for studying neuronal circuitry. The GFS pathway is composed of two bilaterally symmetrical Giant Fiber (GF) interneurons whose axons descend from the brain along the midline into the thoracic ganglion via the cervical connective. In the mesothoracic neuromere (T2) of the ventral ganglia the GFs form electro-chemical synapses with 1) the large medial dendrite of the ipsilateral motorneuron (TTMn) which drives the tergotrochanteral muscle (TTM), the main extensor for the mesothoracic femur/leg, and 2) the contralateral peripherally synapsing interneuron (PSI) which in turn forms chemical (cholinergic) synapses with the motorneurons (DLMns) of the dorsal longitudinal muscles (DLMs), the wing depressors. The neuronal pathway(s) to the dorsovental muscles (DVMs), the wing elevators, has not yet been worked out (the DLMs and DVMs are known jointly as indirect flight muscles - they are not attached directly to the wings, but rather move the wings indirectly by distorting the nearby thoracic cuticle) (King and Wyman, 1980; Allen et al., 2006). The di-synaptic activation of the DLMs (via PSI) causes a small but important delay in the timing of the contraction of these muscles relative to the monosynaptic activation of TTM (~0.5 ms) allowing the TTMs to first extend the femur and propel the fly off the ground. The TTMs simultaneously stretch-activate the DLMs which in turn mutually stretch-activate the DVMs for the duration of the flight. The GF pathway can be activated either indirectly by applying a sensory (e.g."air-puff" or "lights-off") stimulus, or directly by a supra-threshold electrical stimulus to the brain (described here). In both cases, an action potential reaches the TTMs and DLMs solely via the GFs, PSIs, and TTM/DLM motoneurons, although the TTMns and DLMns do have other, as yet unidentified, sensory inputs. Measuring "latency response" (the time between the stimulation and muscle depolarization) and the "following to high frequency stimulation" (the number of successful responses to a certain number of high frequency stimuli) provides a way to reproducibly and quantitatively assess the functional status of the GFS components, including both central synapses (GF-TTMn, GF-PSI, PSI-DLMn) and the chemical (glutamatergic) neuromuscular junctions (TTMn-TTM and DLMn-DLM). It has been used to identify genes involved in central synapse formation and to assess CNS function.

Electrophysiological Recordings from the Giant Fiber Pathway of D. melanogaster

The Giant Fiber System is a simple neuronal circuit of adult Drosophila melanogaster containing the largest neurons in the fly. We describe the protocol for monitoring synaptic transmission through this pathway by recording post synaptic potentials in dorsal longitudinal (DLM) and tergotrochanteral (TTM) muscles following direct stimulation of the Giant Fiber interneurons.

When startled adult D. melanogaster react by jumping into the air and flying away. In many invertebrate species, including D. melanogaster, the "escape" ...

Transretinal ERG Recordings from Mouse Retina: Rod and Cone Photoresponses

There are two distinct classes of image-forming photoreceptors in the vertebrate retina: rods and cones. Rods are able to detect single photons of light whereas cones operate continuously under rapidly changing bright light conditions. Absorption of light by rod- and cone-specific visual pigments in the outer segments of photoreceptors triggers a phototransduction cascade that eventually leads to closure of cyclic nucleotide-gated channels on the plasma membrane and cell hyperpolarization. This light-induced change in membrane current and potential can be registered as a photoresponse, by either classical suction electrode recording technique1,2 or by transretinal electroretinogram recordings (ERG) from isolated retinas with pharmacologically blocked postsynaptic response components3-5. The latter method allows drug-accessible long-lasting recordings from mouse photoreceptors and is particularly useful for obtaining stable photoresponses from the scarce and fragile mouse cones. In the case of cones, such experiments can be performed both in dark-adapted conditions and following intense illumination that bleaches essentially all visual pigment, to monitor the process of cone photosensitivity recovery during dark adaptation6,7. In this video, we will show how to perform rod- and M/L-cone-driven transretinal recordings from dark-adapted mouse retina. Rod recordings will be carried out using retina of wild type (C57Bl/6) mice. For simplicity, cone recordings will be obtained from genetically modified rod transducin &alpha;-subunit knockout (T&alpha;-/-) mice which lack rod signaling8.

We describe a relatively simple method of transretinal electroretinogram (ERG) recordings for obtaining rod and cone photoresponses from intact mouse retina. This approach takes advantage of the block of synaptic transmission from photoreceptors to isolate their light responses and record them using field electrodes placed across the isolated flat-mounted retina.

There are two distinct classes of image-forming photoreceptors in the vertebrate retina: rods and cones. Rods are able to detect single photons of light ...

Dual Electrophysiological Recordings of Synaptically-evoked Astroglial and Neuronal Responses in Acute Hippocampal Slices

Astrocytes form together with neurons tripartite synapses, where they integrate and modulate neuronal activity. Indeed, astrocytes sense neuronal inputs through activation of their ion channels and neurotransmitter receptors, and process information in part through activity-dependent release of gliotransmitters. Furthermore, astrocytes constitute the main uptake system for glutamate, contribute to potassium spatial buffering, as well as to GABA clearance. These cells therefore constantly monitor synaptic activity, and are thereby sensitive indicators for alterations in synaptically-released glutamate, GABA and extracellular potassium levels. Additionally, alterations in astroglial uptake activity or buffering capacity can have severe effects on neuronal functions, and might be overlooked when characterizing physiopathological situations or knockout mice. Dual recording of neuronal and astroglial activities is therefore an important method to study alterations in synaptic strength associated to concomitant changes in astroglial uptake and buffering capacities. Here we describe how to prepare hippocampal slices, how to identify stratum radiatum astrocytes, and how to record simultaneously neuronal and astroglial electrophysiological responses. Furthermore, we describe how to isolate pharmacologically the synaptically-evoked astroglial currents.

The preparation of acute brain slices from isolated hippocampi, as well as the simultaneous electrophysiological recordings of astrocytes and neurons in stratum radiatum during stimulation of schaffer collaterals is described. The pharmacological isolation of astroglial potassium and glutamate transporter currents is demonstrated.

Astrocytes form together with neurons tripartite synapses, where they integrate and modulate neuronal activity. Indeed, astrocytes sense neuronal inputs ...

The Swimmeret System of Crayfish: A Practical Guide for the Dissection of the Nerve Cord and Extracellular Recordings of the Motor Pattern

Here we demonstrate the dissection of the crayfish abdominal nerve cord. The preparation comprises the last two thoracic ganglia (T4, T5) and the chain of abdominal ganglia (A1 to A6). This chain of ganglia includes the part of the central nervous system (CNS) that drives coordinated locomotion of the pleopods (swimmerets): the swimmeret system. It is known for over five decades that in crayfish each swimmeret is driven by its own independent pattern generating kernel that generates rhythmic alternating activity 1-3. The motor neurons innervating the musculature of each swimmeret comprise two anatomically and functionally distinct populations 4. One is responsible for the retraction (power stroke, PS) of the swimmeret. The other drives the protraction (return stroke, RS) of the swimmeret. Motor neurons of the swimmeret system are able to produce spontaneously a fictive motor pattern, which is identical to the pattern recorded in vivo 1.
The aim of this report is to introduce an interesting and convenient model system for studying rhythm generating networks and coordination of independent microcircuits for students&#8217; practical laboratory courses. The protocol provided includes step-by-step instructions for the dissection of the crayfish&#8217;s abdominal nerve cord, pinning of the isolated chain of ganglia, desheathing the ganglia and recording the swimmerets fictive motor pattern extracellularly from the isolated nervous system.
Additionally, we can monitor the activity of swimmeret neurons recorded intracellularly from dendrites. Here we also describe briefly these techniques and provide some examples. Furthermore, the morphology of swimmeret neurons can be assessed using various staining techniques. Here we provide examples of intracellular (by iontophoresis) dye filled neurons and backfills of pools of swimmeret motor neurons. In our lab we use this preparation to study basic functions of fictive locomotion, the effect of sensory feedback on the activity of the CNS, and coordination between microcircuits on a cellular level.

Here we describe the dissection of the crayfish abdominal nerve cord. We also demonstrate an electrophysiological technique to record fictive locomotion from swimmeret motor neurons.

Here we demonstrate the dissection of the crayfish abdominal nerve cord. The preparation comprises the last two thoracic ganglia (T4, T5) and the chain of ...

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

The combination of patch clamp recordings from two (or more) synaptically coupled neurons (paired recordings) in acute brain slice preparations with simultaneous intracellular biocytin filling allows a correlated analysis of their structural and functional properties. With this method it is possible to identify and characterize both pre- and postsynaptic neurons by their morphology and electrophysiological response pattern. Paired recordings allow studying the connectivity patterns between these neurons as well as the properties of both chemical and electrical synaptic transmission. Here, we give a step-by-step description of the procedures required to obtain reliable paired recordings together with an optimal recovery of the neuron morphology. We will describe how pairs of neurons connected via chemical synapses or gap junctions are identified in brain slice preparations. We will outline how neurons are reconstructed to obtain their 3D morphology of the dendritic and axonal domain and how synaptic contacts are identified and localized. We will also discuss the caveats and limitations of the paired recording technique, in particular those associated with dendritic and axonal truncations during the preparation of brain slices because these strongly affect connectivity estimates. However, because of the versatility of the paired recording approach it will remain a valuable tool in characterizing different aspects of synaptic transmission at identified neuronal microcircuits in the brain.

Patch-clamp recordings and simultaneous intracellular biocytin filling of synaptically coupled neurons in acute brain slices allow a correlated analysis of their structural and functional properties. The aim of this protocol is to describe the essential technical steps of electrophysiological recording from neuronal microcircuits and their subsequent morphological analysis.

The combination of patch clamp recordings from two (or more) synaptically coupled neurons (paired recordings) in acute brain slice preparations with ...

Whole-cell Patch-clamp Recordings for Electrophysiological Determination of Ion Selectivity in Channelrhodopsins

Over the past decade, channelrhodopsins became indispensable in neuroscientific research where they are used as tools to non-invasively manipulate electrical processes in target cells. In this context, ion selectivity of a channelrhodopsin is of particular importance. This article describes the investigation of chloride selectivity for a recently identified anion-conducting channelrhodopsin of Proteomonas sulcata via electrophysiological patch-clamp recordings on HEK293 cells. The experimental procedure for measuring light-gated photocurrents demands a fast switchable &#8211; ideally monochromatic &#8211; light source coupled into the microscope of an otherwise conventional patch-clamp setup. Preparative procedures prior to the experiment are outlined involving preparation of buffered solutions, considerations on liquid junction potentials, seeding and transfection of cells, and pulling of patch pipettes. The actual recording of current-voltage relations to determine the reversal potentials for different chloride concentrations takes place 24 h to 48 h after transfection. Finally, electrophysiological data are analyzed with respect to theoretical considerations of chloride conduction.

This article describes how the ion selectivity of channelrhodopsin is determined with electrophysiological whole-cell patch-clamp recordings using HEK293 cells. Here, the experimental procedure for investigating chloride selectivity of an anion-selective channelrhodopsin is demonstrated. However, the procedure is transferable to other channelrhodopsins of distinct selectivity.

Over the past decade, channelrhodopsins became indispensable in neuroscientific research where they are used as tools to non-invasively manipulate ...

Electrophysiological Method for Whole-cell Voltage Clamp Recordings from Drosophila Photoreceptors

Whole-cell voltage clamp recordings from Drosophila melanogaster photoreceptors have revolutionized the field of invertebrate visual transduction, enabling the use of D. melanogaster molecular genetics to study inositol-lipid signaling and Transient Receptor Potential (TRP) channels at the single-molecule level. A handful of labs have mastered this powerful technique, which enables the analysis of the physiological responses to light under highly controlled conditions. This technique allows control over the intracellular and extracellular media; the membrane voltage; and the fast application of pharmacological compounds, such as a variety of ionic or pH indicators, to the intra- and extracellular media. With an exceptionally high signal-to-noise ratio, this method enables the measurement of dark spontaneous and light-induced unitary currents (i.e.&#160;spontaneous and quantum bumps) and macroscopic Light-induced Currents (LIC) from single D. melanogaster photoreceptors. This protocol outlines, in great detail, all the key steps necessary to perform this technique, which includes both electrophysiological and optical recordings. The fly retina dissection procedure for the attainment of intact and viable ex vivo isolated ommatidia in the bath chamber is described. The equipment needed to perform whole-cell and fluorescence imaging measurements are also detailed. Finally, the pitfalls in using this delicate preparation during extended experiments are explained.

Electrophysiological Method for Whole-cell Voltage Clamp Recordings from Drosophila Photoreceptors

Whole-cell recordings from Drosophila melanogaster photoreceptors enable the measurement of spontaneous dark bumps, quantum bumps, macroscopic responses to light, and current-voltage relationships under various conditions. In combination with D. melanogaster genetic manipulation tools, this method enables the study of the ubiquitous inositol-lipid signaling pathway and its target, the TRP channel.

Whole-cell voltage clamp recordings from Drosophila melanogaster photoreceptors have revolutionized the field of invertebrate visual transduction, ...

Acute In Vivo Electrophysiological Recordings of Local Field Potentials and Multi-unit Activity from the Hyperdirect Pathway in Anesthetized Rats

Converging evidence shows that many neuropsychiatric diseases should be understood as disorders of large-scale neuronal networks. To better understand the pathophysiological basis of these diseases, it is necessary to precisely characterize in which way the processing of information is disturbed between the different neuronal parts of the circuit. Using extracellular in vivo electrophysiological recordings, it is possible to accurately delineate neuronal activity within a neuronal network. The application of this method has several advantages over alternative techniques, e.g., functional magnetic resonance imaging and calcium imaging, as it allows a unique temporal and spatial resolution and does not rely on genetically engineered organisms. However, the use of extracellular in vivo recordings is limited since it is an invasive technique that cannot be universally applied. In this article, a simple and easy to use method is presented with which it is possible to simultaneously record extracellular potentials such as local field potentials and multiunit activity at multiple sites of a network. It is detailed how a precise targeting of subcortical nuclei can be achieved using a combination of stereotactic surgery and online analysis of multi-unit recordings. Thus, it is demonstrated, how a complete network such as the hyperdirect cortico-basal ganglia loop can be studied in anesthetized animals in vivo.

Acute In Vivo Electrophysiological Recordings of Local Field Potentials and Multi-unit Activity from the Hyperdirect Pathway in Anesthetized Rats

In this study, the methodology is presented on how to perform multi-site in vivo electrophysiological recordings from the hyperdirect pathway under urethane anesthesia.

Converging evidence shows that many neuropsychiatric diseases should be understood as disorders of large-scale neuronal networks. To better understand the ...

Visual Evoked Potential Recordings in Mice Using a Dry Non-invasive Multi-channel Scalp EEG Sensor

For scalp EEG research environments with laboratory mice, we designed a dry-type 16 channel EEG sensor which is non-invasive, deformable, and re-usable because of the plunger-spring-barrel structural facet and mechanical strengths resulting from metal materials. The whole process for acquiring the VEP responses in vivo from a mouse consists of four steps: (1) sensor assembly, (2) animal preparation, (3) VEP measurement, and (4) signal processing. This paper presents representative measurements of VEP responses from multiple mice with a submicro-voltage signal resolution and sub-hundred millisecond temporal resolution. Although the proposed method is safer and more convenient compared to other previously reported animal EEG acquiring methods, there are remaining issues including how to enhance the signal-to-noise ratio and how to apply this technique with freely moving animals. The proposed method utilizes easily available resources and shows a repetitive VEP response with a satisfactory signal quality. Therefore, this method could be utilized for longitudinal experimental studies and reliable translational research exploiting non-invasive paradigms.

We designed a dry-type 16 channel EEG sensor which is non-invasive, deformable, and re-usable. This paper describes the whole process from manufacturing the proposed EEG electrode to signal processing of visual evoked potential (VEP) signals measured on a mouse scalp using a dry non-invasive multi-channel EEG sensor.

For scalp EEG research environments with laboratory mice, we designed a dry-type 16 channel EEG sensor which is non-invasive, deformable, and re-usable ...