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Analysis of neuro-behavioral Discovery™ data on the Macintosh computer
Journal of Neuroscience Methods 70 Ž1996. 131–140
Analysis of neuro-behavioral Discoverye data on the Macintosh computer Rokny Akhavein b
a,1
, Craig Weiss
a,)
, Charles R. Larson
b,2
, John F. Disterhoft
a
a Department of Cell and Molecular Biology, Northwestern UniÕersity, Chicago, IL 60611 USA Department of Communication Sciences and Disorders, Northwestern UniÕersity, Chicago, IL 60611 USA
Received 9 November 1995; revised 20 June 1996; accepted 20 June 1996
Abstract This paper describes a suite of routines using IgorPro, a powerful analysis and graphing software package for the Macintosh computer, to enhance the ability to analyze, manipulate, and display data recorded with the Discoverye acquisition software marketed by DataWave Technologies. The routines are able to time-align fast and slow data channels, and are especially useful for analyses that involve both neural and behavioral data. The software was designed for eyeblink conditioning and vocalization experiments, but it can easily be used for analyzing other types of neurobehavioral data. The data are first prepared on the PC with routines that inspect the header of the data file and translate the data file into a compact binary format that can be read by IgorPro. An option is also available to splice out data from unnecessary portions of an intertrial interval. The new file is then put on the Macintosh computer for display and analysis by IgorPro. These routines enable both neural and behavioral data to be quickly and easily reduced, manipulated, and statistically and graphically summarized. Keywords: BrainWave; DataWave; Electrophysiological instrumentation; Eyeblink conditioning; Hippocampus; Neurophysiology; On line system; Single neuron recording; Vocalization
1. Introduction A goal of laboratories in many different fields of neurobiology is to relate changes in behavior to changes in neural activity. A wide variety of transducers are available to convert behavior into an analog signal that can be used as input to a polygraph for a hardcopy record, or into a computer for collection and analysis. A variety of electrodes and microwires are also available to detect neural activity. Activity from an electrode is then amplified and filtered before being sent to the same computer that collects the behavioral data. If behavior in response to a stimulus is of interest, a stimulus marker is also needed for input to the computer. This signal is used to time-align the neural and behavioral data.
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Corresponding author. Tel.: Ž312. 503-3112; Fax: Ž312. 503-7912; E-mail: [email protected] 1 Present address: Biomedical Engineering Department, Northwestern University, Evanston, IL 60208, USA. 2 Present address: Dept. of Communication Sciences & Disorders, Northwestern University, Francis Searle Bld, Evanston, IL 60208, USA. 0165-0270r96r$15.00 Published by Elsevier Science B.V. PII S 0 1 6 5 - 0 2 7 0 Ž 9 6 . 0 0 1 1 0 - 0
The relation of behavior to single-unit activity becomes more difficult to analyze since the activity of one or a few neurons needs to be separated from a background of noise. The earliest discriminators of neural activity were based on delivering a short pulse whenever the signal exceeded a designated voltage level. This Schmitt trigger based discriminator was the standard for many years, and is still in use today. The simplest discriminators have one level to include activity that is above a critical threshold. This works well for multiple-unit recordings, or for well isolated single neurons. More advanced discriminators include a second threshold to exclude signals that are too large, e.g. stimulus artifacts. The best of these hardware based discriminators also include a time window to separate waveforms according to spike width Že.g., Olds, 1973; Bak and Schmidt, 1977.. These issues are discussed in a previous review of the instrumentation ŽSchmidt, 1984a. and algorithms ŽSchmidt, 1984b. used for spike separation. A disadvantage of most discriminators and separation systems is that the waveform of the spike is not saved unless the data is also stored on analog tape. The tape then needs to be replayed to recapture wave shape information for the single neuron. One computerized system that col-
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lects and replays waveforms is the Discoverye software package that is marketed by DataWave Technologies. It enables the asynchronous collection of both voltage-discriminated and continuous recordings. Voltage-discriminated data collection, referred to here as fast recording, is used to record brief, discrete events, such as action potentials and TTL pulses that exceed a user-defined voltage. The analog waveform of each event is digitized and timestamped. The duration and sampling frequency for the waveform is dependent upon the speed of the ArD board and the number of channels being sampled. Continuous data collection, referred to here as slow recording, may be used to monitor relatively long, sustained events, such as eyeblinks, vocalizations, or other behavioral and lowfrequency measurements such as EEG and EMG activity. The continuous analog signal is digitized and stored with one timestamp for the start of each block of data. The ability to digitize data asynchronously among fast and slow channels, and to replay and analyze the data off-line, has made the Discovery system a popular collection tool among neuroscientists. Our laboratories, for example, need to compare directly single-neuron activity collected on fast channels with eyeblink activity or vocalizations collected on slow channels. We facilitated these types of analyses by transferring the Discovery data file into a wave-based analysis system known as IgorPro ŽWaveMetrics, Inc., Lake Oswego, OR.. This software package provides a rich and flexible environment on Macintosh computers for the analysis, display and output of publication quality results. The suite of IgorPro routines described here provide a generalized analysis framework to enhance the ability to analyze, manipulate, and display data that was recorded with the Discovery acquisition software and separated into single-neuron activity with DataWave routines. The Igor routines were designed for eyeblink conditioning and vocalization experiments, but they can easily be used for analyzing other types of neurobehavioral data. The routines also work with the built-in analysis functions of Igor, which are not explicitly described here.
ing wave-type data. It operates with Macintosh 68030 and 68040 computers, and is available in native code for the PowerMac line of computers. A floating point processor is recommended for maximum processing speed on the 68030 and 68040 based Macintoshes.
3. Discovery collection parameters The setup procedure for collecting data with Discovery is dependent mostly upon two sets of parameters. The first set describes the collection of spike data; the second set describes the collection of the slow-wave data. Configuring these two sets of parameters does not require any programming experience and is accomplished easily. We also use one or more fast channels to collect TTL pulses that mark the start of individual trials or the delivery of stimuli. Those who are adept at programming can write ‘clock sequences’ to modify Discovery in several ways, e.g. event flags can be generated and collection can be automatically paused and re-started. We found that experiments were conducted most reliably when we had one computer controlling the behavioral experiment ŽAkase et al., 1994. and another operating Discovery to collect the single neuron and behavioral data. This configuration provided an excellent environment to deliver stimuli and to collect data with minimal restrictions on the speed and memory of the computer operating Discovery. This configuration was easily integrated with our current software system and obviated the need to program clock sequences. This configuration also permitted data to be collected continuously for the duration of the experiment. The continuous collection of data, especially slowwave data, obviously increases the amount of disk space required to store the data, but it enables the analysis of long duration changes in activity. Storage space can be minimized by reducing the sampling frequency of the continuous data channel. Alternatively, data storage can be manually paused and resumed, or clock sequences can be used to turn data storage on and off.
2. Hardwarer r software requirements 4. File manipulation on the PC Neural and behavioral data are collected using the Discovery acquisition software ŽDataWave Technologies, Longmont, CO. for PC computers. In our laboratories, we have used Discovery v3.2 extensively in a DOS environment on both a PC 80x486 DX-33 and DX-50. We have analyzed recordings from both single electrodes and stereotrodes, and have successfully tested the routines with files generated by the current version of Discovery Žv5.1. which includes data collected with a tetrode configuration. The Macintosh based routines described here require the IgorPro software package ŽWaveMetrics, Lake Oswego, OR.. This program is ideal for graphing and analyz-
Discovery acquires and saves data into a ‘UFF’ file ŽUniversal File Format.. A common processing module is then used to separate the activity from channels with multiple-unit activity into the activity of single units. A ‘cut’ file is generated after separating the waveforms into single units. This process is referred to as cluster cutting. The first routines described in this paper are run from a master program and operate on the UFF or CUT files. The program was written in C language for use with PC computers. It runs well on a 80x486 DX-50 computer, but Pentium-based PCs are recommended for faster process-
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ing. The program reads the header of the UFF or CUT files, deletes unnecessary intertrial intervals, and translates the CUT file into the Igor file format. This program is available from the first author and the routines are described in the following sections. 4.1. File information The file information option reads the header of the cut file and indicates the channels that are active, the number of spikes detected for each identified neuron on each probe, i.e., single electrode, stereotrode or tetrode, the number and type of event flags, and the duration of time included in the file. This routine is useful to remind the
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user of the active data channels and to determine the total number of spikes for each single neuron that was identified for a given channel. This routine will also work on a UFF file since that file format is identical to the format of the cut file. The output from an analysis of a UFF file would indicate the total number of events detected rather than the number of spikes per identified neuron. 4.2. Chopping and splicing of data The ‘chop’ option may be used to delete data that does not fall into a window of time surrounding an event, i.e., a TTL pulse read in from a spike channel, or an event flag generated from either a clock sequence or a keystroke.
Fig. 1. ŽA. A ‘screen dump’ of the Session View window. The coding system for the data is indicated along the left margin. CR0 the first continuous record channel. In this example the data represents nictitating membrane extension. SE1.1–SE4.3 represent data from 4 single electrodes. The second digit identifies a single neuron that was discriminated from each electrode with the cluster cutting routine. SE5.1 represents the onset of the tone CS; SE6.1 represents the onset of the airpuff US. The trials appear rather evenly spaced in time because much of the intertrial interval was ‘chopped’ out of the file. The x-axis indicates the amount of time represented after the file was chopped. ŽB. A screen dump of the dialogue box that is presented along with the session view. Similar type boxes are presented with each macro. This box provides interactive menus to control the display of the Trial View Žsee Fig. 2.. The ‘main event’ represents the first synchronization signal Žtone CS in our case.. The ‘second event’ represents another event that is time locked to the main event Žairpuff US in our case.. The information in this dialogue box will produce a trial view with the appropriate cursors for the two events.
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This operation is ideal for deleting unnecessary intertrial intervals and reducing the size of a large data set into discrete regions of interest. It generates a new file, i.e., those with a ‘.chp’ extension, and preserves the original data. The user indicates the channel that has the trigger signal and the time window around the trigger that is to be preserved. The chop option then reads the data file and determines which blocks of continuous data need to be saved in order to splice together a new seamless data file. This routine is ideal for those who want to add the power of these routines to an existing computer system. We typically have about a 1 s trial and random intertrial intervals of 30–60 s for our eyeblink conditioning experiments. Continuous data collection during these intervals is important because occasional neurons have exhibited response patterns lasting as long as 10 s, and other neurons respond so infrequently that they might not be detected without continuous data collection. The chop option is then used to reduce the data set into a more manageable size, and it is repeated with longer analysis windows if the data suggest that the activity has not returned to baseline. Collecting data for the entire experiment obviates the need to program and compile clock sequences for Discovery and facilitates the integration of Discovery with existing behavioral control systems. This strategy of data collection also allows one to examine long latencyrlong duration events by rechopping the UFF file with longer time windows, or by not chopping the file at all. The probability of detecting ‘silent cells’ ŽThompson and Best, 1989. is also increased with continuous data collection. The chop routine then allocates space within the displays for silent cells Že.g. spike SE1.2 in Fig. 1. and it enables the user to reduce the size of the cut file when long duration or long latency events are not present. If one is not concerned about silent cells, the chop option can also be used before cluster cutting to reduce the size of the file that needs to be sorted into single units. This may substantially reduce the processing time required for cluster cutting. 4.3. Format conÕersion The PC routines convert Discovery files into the binary Igor file format. This conversion is required even if the file is not chopped. In this format the timestamp of each waveform is treated as an independent event This format is efficient at representing the data, and hence, the translated files are significantly smaller in size than those generated by Discovery. These files are then transferred to a Macintosh computer with a 1.4 MB floppy disk. Almost all of our files are small enough to transport by floppy disk once they have been chopped. Larger files can be transferred to the Macintosh by other means such as ethernet communication or direct cable connection and MacLink software ŽDataViz, Inc., Trumbull, CT..
5. Igor on the Macintosh The Igor software package has many built-in analysis and graphing routines, and it can be programmed for customized macro routines. Users can write their own code with the built-in programming language and develop other macros for specific needs. The macros we developed for IgorPro v2.04 ŽFPU. will be provided and can serve as a template for other macros. These macros are always available for viewing in Igor’s ‘procedure’ window. The ‘help’ window also provides excellent on-line documentation for the available commands. The routines described here are selected from a pop-up menu using the point and click method with a mouse. All the routines start by reading in a file that was translated on the PC. This routine uses very little computer RAM to store a large amount of data which can then be manipulated by the other macros and other built-in Igor functions. 5.1. Trial Õiewing and sorting After the file is loaded into memory the entire data set is displayed in a window on the monitor. An example of the display for this Session View is shown in Fig. 1. This data represents an eyeblink conditioning session with multi-channel single-neuron recordings from the rabbit hippocampus. The data was acquired with Discovery and then chopped to remove most of the long intertrial intervals Ž30–60 s.. This display provides an ideal opportunity to detect artifacts, discrepancies in the collection or manipulation of data, channels with or without data that are worth analyzing, and repeating changes in activity. The continuous data is displayed above the spike data and represents the voltage from an infrared eyeblink detector ŽThompson et al., 1994.. The different recording probes are coded by their electrode type and sequence number, e.g., SE1 Žsingle electrode 1., ST1 Žstereotrode 1. and QT1 Žtetrode 1.. Different neurons from a given probe are indicated by different numbers e.g., SE1.1, SE1.2, SE1.3. Action potentials for each single neuron on any given channel are indicated by vertical tick marks. We also typically have two channels that contain TTL pulses for marking the onset of our tone conditioning stimulus and airpuff unconditioned stimulus Žlabeled Event1 and Event2, respectively.. At this point, a menu is also presented to define the channelŽs. that contain the events for synchronizing the data, i.e., TTL pulses or event flags, and the amount of time to be displayed about the events. After the channelŽs. are defined, the trials are displayed individually and fitted to the display window as shown in Fig. 2. 5.2. Separate trials All of the trials are initially unclassified, but they can be classified and separated by various methods. In our
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eyeblink conditioning experiments, we are interested primarily in classifying trials as those with either conditioned responses ŽCR., unconditioned responses ŽUCR., or neither Že.g., trials with spontaneous blinks.. Although the trials can be classified into these categories by visual inspection, we utilized the criteria of Akase et al. Ž1994. to program an effective algorithm for the automatic classification of eyeblink responses. This algorithm determines the start of an eyeblink Žactually nictitating membrane, NM, extension for our data., that exceeds the mean baseline position by 4 SD for a minimum of 10 ms. After the Separate Trials macro is implemented an arrow appears at the onset of each response, and a mark appears in the appropriate box to indicate the type of response emitted during the trial. An example of such a trial is shown in Fig. 2. In this figure the trial was classified as a CR and the arrow is placed at the onset of the response, i.e., where the signal is 4 SD above the baseline. In this example the arrow appears after the apparent onset of the response because of the high baseline at the start of the trial. If the user does not agree with the automated classification, individual trials can be reclassified according to user defined criteria. Regardless of any secondary reclassification by the user, the onset time generated by the Separate macro will be used for sorting purposes in other macros. 5.3. Sort by latency This macro creates a new display window that presents a stacked plot of the eyeblink data above a similarly ordered display of neural data in a raster format. An example of such a display is shown in Fig. 3. A dialogue box prompts the user for the neural channels of interest, and the trial type of interest, e.g. CRs, UCRs or both. This
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Fig. 3. ŽA. Example of the display generated by the Sort by Latency macro. Each behavioral response is stacked one above the other in order of increasing response latency. The raster of neural data is rearranged in the same order as the behavioral data. The trial number of each behavioral trace can be identified by clicking on the trace. ŽB. Example of the display generated by the Sort by Amplitude macro. Each behavioral response is stacked one above the other in order of increasing amplitude. The raster of neural data is similarly ordered. The x-axis indicates the time in seconds relative to the onset of the tone CS.
selection process enables the analysis of specific trials after using the Unseparate macro Žsee below.. Responses with the shortest latency are presented above responses with longer latencies. Thus, CRs appear above the UCRs when the ‘both’ trial type is selected. A useful feature of Igor is that the actual trial number of each waveform Žeyeblink response. can be identified by positioning the cursor on the waveform of interest and then using the mouse to click and hold. Built-in Igor functions will display a wave number that corresponds to the trial number. This feature can be used to compile a list of trial
Fig. 2. An example of a trial view window after the Separate macro has been utilized. ŽA. The behavioral data ŽNM extension. is indicated by the top trace. The arrow indicates the onset of response according to the algorithm described in the text. The onset appears to be late because of the high baseline at the start of the trial. ŽB. The activity of each neuron is represented by vertical tick marks in each row. The two channels of synch pulse markers are in the last two rows and are masked by the cursors. This trial Žno. 20. was classified as a CR. The x-axis indicates time in s.
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numbers that need to be selected or reassigned as described below. A line in the command window Ža window that Igor generates below the displays. for this and many other macros also confirms the number of trials that were included in the analysis. We routinely use the Separate macro to identify representative CRs and UCRs, then use Unseparate to unclassify all trials. Finally, the trials of interest are displayed Žby typing in the trial number. and manually reclassified appropriately. This processing is all very fast.
Fig. 5. ŽA,B. Display generated by the Show Average macro. The behavioral trace ŽA. represents the average of all trials of the type selected by the dialogue box. The raster ŽB. is organized in trial sequence with the earliest trials on top. ŽC. An example of the display generated by the Show Histogram macro for the same set of data as in B. The dialogue box for this macro includes a section to indicate the bin width of the histogram. Tone CS onset is at time zero.
5.4. Sort by amplitude This macro is very similar to Sort by Latency. The difference is that the stacked plot of the eyeblink data is in order of smallest peak response to largest peak response. Thus, when CR trials are selected through the dialogue box, trials with the largest CRs can be identified. Similarly, when UCRs are selected through the dialogue box, UCR trials with the largest unconditioned response can easily be identified. An example of the output from this macro is also shown in Fig. 3. These data are the same as those shown for Sort by Amplitude, but the eyeblinks and raster display have been resorted in order of the amplitude of response. Fig. 4. The Show Statistics macro produces one page of output for each neuron that is entered in the dialogue box. Data from conditioning sessions Žthose with both a ‘main event’ and a ‘second event’. or pseudoconditioning control sessions Žmain event or second event. can be generated. A histogram of neural activity and the superimposed average behavior is presented with a ‘bird’s eye’ view ŽA. and a trial view ŽB.. The bird’s eye view includes data from 2 s prior to tone CS onset and 3.1 s after tone CS onset. The trial view includes the same data from 0.2 s prior to tone CS onset and 1.0 s after tone CS onset. The dark horizontal bars along the x-axis in the trial view represent the timing of the tone CS and airpuff US. ŽC. The statistics for the unit activity for each of the trial periods Žtone, trace, UR. and the pretrial period. The statistics include the mean, SD, and the number of bins with activity. If five or fewer bins had activity a Z score was calculated instead of a t score. ŽD. The dialogue box that controls execution of the statistics.
5.5. Unseparate trials This macro erases the response classification of all trials while leaving the onset arrow in place on the display and the onset time in memory. This is an extremely useful macro when the user needs to select a small set of specific trials with user defined criteria for an analysis, e.g. the 10 largest CRs or 10 largest nonCRs. Trial selection would occur by sorting the responses by latency or amplitude, noting which trials were best suited for analysis, invoking the Unseparate macro, and then manually recoding response types for the specified trials.
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5.6. Show statistics This macro superimposes the aÕeraged eyeblink response upon a histogram of the activity for each selected neuron. The histogram is presented with an ordinate of spikesrbin and the NM extension is plotted in terms of ArD volts. Our analysis is configured to present the data twice, once as a ‘bird’s eye’ view Žy2 to 3 s. and once as a trial view Žy200 to 1000 ms.. The views are relative to the tone CS onset and they permit the observation of long latency or long duration changes as well as the more detailed response during presentation of the stimuli. An example of the hardcopy output from this macro is shown in Fig. 4. A statistical summary is presented below the histograms. The statistics are generated for several analysis windows. We routinely analyze a window for the 100 ms tone conditioning stimulus ŽCS., the 500 ms trace period ŽTrace., a 500 ms unconditioned response window ŽUR. that commences at the onset of the unconditioned stimulus,
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and a 2 s post trial period ŽPT. that commences at the end of the UR period. The statistics include the mean and SD for each window after dividing the window into 20 bins to get a measure of variance. The number of bins with activity greater than zero are also indicated. This is needed to determine if a parametric t-test, or a nonparametric binomial test is to be used for statistical analysis. A two-tailed, paired t-test is performed for most comparisons. The nonparametric binomial test is used for cells that fired so slowly that five or fewer of the 20 bins in each window had activity ŽSiegel and Castellan, 1988.. We use data from a 2 s preCS window to determine the baseline firing rate. The means and SEs for all windows are used to perform the t-test. A two-tailed paired t-test is used since either excitation or inhibition may occur. In this test the difference between the means is related to the SE of the baseline activity, i.e., the test is essentially a one sample t-test which compares the window of interest against the expected data gathered during the baseline period. Tests which yield a p value F 0.01 are deemed
Fig. 6. Example of the output generated by the show multi-correlation macro. Several graphs are presented according to the number of data channels that are to be cross correlated with each other. In this example the average behavioral response ŽA. and the summated activity, i.e., histograms of two different neurons ŽB,C. were correlated. The arrows in D–G indicate the maximum correlation for each pair of data and the latency at which it occurred. ŽD. The autocorrelation of the average behavior. As expected, this correlation was 1.0 at a lag time of 0.0. ŽE. The correlation of B and A. This result indicates that the CR0 had a maximal correlation of 0.68 with ‘spikes7’ at a lag time of q94 ms, i.e., the behavior lags behind the neural activity. ŽF. The correlation of C and A. ŽG. The correlation of C and B. This resulted in a negative lag time, i.e., ‘spikes7’ precedes ‘spikes6’.
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statistically significant. These routines are extremely fast, e.g., statistics for a population of single neurons recorded from several wires during a session may have taken several hours to process and document with DataWave routines, but are now processed and documented in a few minutes with Igor routines. 5.7. Show aÕerage This macro provides a dialogue box to select either CR trials or UCR trials. The output is displayed in a new graph which includes a raster display for each neuron Žearliest trial is on top. and a single trace that represents the
average NM response. An example of this display is shown in Fig. 5. This display is similar to that generated by Show Statistics, but it is more flexible in terms of the time window for analysis and the ability to stack timealigned rasters for each neuron one above the other. This macro also operates only upon the trial type that is selected. The display presents an informative summary for all neurons in relation to each other’s activity and to the averaged behavior. 5.8. Show histogramr show frequency plot These two macros produce a histogram for the neurons that are specified in the dialogue box. The bin width for
Fig. 7. Example of data collected with Discovery during an experiment that examined the neural control of vocalization ŽYajima and Larson, 1993. by synchronizing the data to a response on a continuous slow-wave channel. The top trace ŽA. displays an average of the rectified and smoothed vocal signal. The next three traces ŽB–D. are EMG signals collected on continuous data channels from the posterior cricoarytenoid, throarytenoid, and criothyroid muscles, respectively. ŽE–G. Rasters of the activity from three different neurons within the nucleus ambiguus during the vocalizations. ŽH–J. Histograms of the activity from each neuron in E–G. Some basic statistics for each neuron are indicated below the rasters.
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the display is also defined through this window. The Show Histogram macro produces a graph with an ordinate that has the units of spikesrbin. The Show Frequency Plot has an ordinate with units of mean spikesrbin, i.e. it is the spikesrbin per trial. This scale can also be multiplied by the binwidth to convert the scale into rate, i.e., mean spikesrs per bin. An example of the Show Histogram macro is also presented in Fig. 5. 5.9. Show cross correlationr multi-cross correlation This macro presents a menu to identify two or more neural or behavioral channels of data that are to be crosscorrelated with each other. We use this macro to examine time-amplitude relations among the different neurons and between the behavior and neural activity of individual single neurons. An example of the output is presented in Fig. 6. The macro generates graphs of the cross correlation function for all the pairs of data. The title of each graph indicates the two data sets that were correlated with each other. This macro also includes a routine to indicate the maximal correlation and the time shift at which it occurred. In the example for Fig. 6E, the maximal correlation was 0.68 at a time shift of 94 ms. 5.10. Show ANOVA This macro is designed to generate the data necessary to perform a repeated measures ANOVA of trial type ŽCRrnonCR. by analysis window ŽpreCS, CS, trace, UR, PT.. The macro creates a new file that includes the response rate for each of the 20 bins in each of the five analysis windows. The array includes a code for the spike number, and the mean rate for each of the 20 bins in each analysis window. This array is then used as input to commercially available statistical software Žthis was more efficient than programming a repeated measures ANOVA.. We use SuperAnova ŽAbacus Concepts, Berkeley, CA. which we configured with a template to perform the ANOVA. 5.11. Hardcopy output The Igor routines are designed to generate publication quality histograms, raster plots, frequency plots, and cross correlations for each neuron. All of the macro generated graphs can be copied and pasted into Igor ‘layouts’ Žthe graphs from Show Stats are pasted automatically.. Publication quality printouts of these layouts can be directly printed from Igor. The graphics can also be stored and opened by other graphic programs. 5.12. Processing of multiple slow channels The Igor based macros can be used to process multiple slow wave channels and to synchronize displays about an
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event detected on a slow wave channel. This greatly expands the ability of these macros to analyze data from a variety of experiments. One such example is from the neural control of vocalization Že.g., Yajima and Larson, 1993.. This type of experiment is better analyzed by synchronizing the data to a vocalization that is recorded on a slow channel. The data presented in Fig. 7 are from an experiment designed to analyze the neural control of vocalization. The figure displays an average vocalization signal, EMG activity from three different laryngeal muscles, and the activity of three neurons discriminated from a single electrode in the nucleus ambiguus. A similar analysis could be used to analyze responses related to spontaneous eyeblinks.
6. Discussion A suite of integrated software routines was developed to manipulate, analyze and display data collected with the Discovery software system. The procedures described here operate on UFF andror cut files generated by the Discovery and Common Processing programs of DataWave Technologies. The routines are designed to easily integrate data from trial based experiments that collect data from spike channels and behavioral channels. File manipulation and translation on the PC are described, as are the Macintosh based analysis routines that we most often use. These routines were used to generate the results described in a recent paper on the activity of hippocampal neurons during eyeblink conditioning ŽWeiss et al., 1996.. This software should work well for those who find that Discovery is easily prepared and reliably acquires high frequency data. Stimulus presentations can then be implemented with existing hardware and software arrangements, a second computer, or a stimulator. The two component system works well to deliver stimuli and to collect both neural and behavioral data. The problem of time-aligning fast and slow data is solved by using IgorPro to read, display and analyze the data after translating the file into a compact binary format on the PC. Until the routines described here were developed we were able to get statistical results for the neural data through DataWave’s Common Processing modules, but only after a process that took hours of time. We used ‘setup’ files to run a preset configuration of histogram analyses and then printed out the results for hardcopy documentation. The statistics Žmeans and SDs. were then entered into a spread sheet program ŽStatview, Abacus Concepts, Berkeley, CA. for the calculation of t-tests and binomial tests. An analysis of the corresponding behavior was, however, missing from the analysis and is still difficult to accomplish even with more current versions of software from DataWave. The Igor macros described here quickly and efficiently analyze data that originate from Discovery and are cut, i.e.,
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separated into the activity of single neurons, using a Common Processing module. We are also in the process of developing routines to cut the waveforms in an Igor based environment on the Macintosh computer. We should point out that, in spite of the analysis limitations, the DataWave system is an excellent data acquisition tool. The ability to collect fast and slow data asynchronously, i.e., at different sampling frequencies, to replay the data from disk, and to incorporate stereotrode and tetrode routines ŽMcNaughton et al., 1983; Gray et al., 1996. makes the system a worthwhile investment. Currently available alternative acquisition systems, of similar or higher cost, do not yet incorporate the collection and analysis of both slow-wave and fast data, or the ability to replay digitized data. Now, with the addition of the analysis routines described here, the Discovery system becomes a more powerful tool for the collection and analysis of behavioral plus multi-channel single neuron data. These routines enable both types of data to be quickly and easily reduced, manipulated, and statistically and graphically summarized. This work was supported by grants RO1MH47340 and R01AG06796 to J.F.D. and DC00207 to C.R.L. We thank Mr. Martin Wilde for help with software development, and R. Kettner for his help and comments. References Akase, E., Thompson, L.T. and Disterhoft, J.F. Ž1994. A system for quantitative analysis of associative learning. 2. Real-time software for MS-DOS microcomputers, J. Neurosci. Methods., 54: 119–130.
Bak, M.J. and Schmidt, E.M. Ž1977. An improved time-amplitude window discriminator, IEEE Trans. Biomed. Eng., 24: 486–489. Gray, C.M., Maldonado, P.E., Wilson, M. and McNaughton, B. Ž1996. Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex, J. Neurosci. Methods., in press. McNaughton, B.L., Barnes, C.A. and O’Keefe, J. Ž1983. The stereotrode: A new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records, J. Neurosci. Methods, 8: 391–397. Olds, J. Ž1973. Multiple unit recordings from behaving rats. In R.F. Thompson and M. Patterson ŽEds.., Bioelectric Recording Tech. Part A, Cellular Processes and Brain Potentials, Academic Press, New York, pp. 165–198. Schmidt, E.M. Ž1984a. Instruments for sorting neuroelectric data: a review, J. Neurosci. Methods, 12: 1–24. Schmidt, E.M. Ž1984b. Computer separation of multi-unit neuroelectric data: a review, J. Neurosci. Methods, 12: 95–111. Siegel, S. and Castellan, N.J., Jr. Ž1988. Nonparametric Statistics for the Behavioral Sciences, 2nd Edn., McGraw-Hill, St. Louis. Thompson, L.T. and Best, P.J. Ž1989. Place cells and silent cells in the hippocampus of freely-behaving rats, J. Neurosci., 9: 2382–2390. Thompson, L.T., Moyer, J.R., Akase, E. and Disterhoft, J.F. Ž1994. A system for quantitative analysis of associative learning. 1. Hardware interfaces with cross-species applications, J. Neurosci. Methods, 54: 109–117. Weiss, C., Kronforst, M.A. and Disterhoft, J.F. Ž1996. Activity of hippocampal pyramidal neurons during trace eyeblink conditioning, Hippocampus, 1000000. Yajima, Y. and Larson, C.R. Ž1993. Multifunctional properties of ambiguous motoneurons identified electrophysiologically during vocalization in the awake monkey, J. Neurophysiol., 70: 529–540.
Analysis of neuro-behavioral Discoverye data on the Macintosh computer Rokny Akhavein b
a,1
, Craig Weiss
a,)
, Charles R. Larson
b,2
, John F. Disterhoft
a
a Department of Cell and Molecular Biology, Northwestern UniÕersity, Chicago, IL 60611 USA Department of Communication Sciences and Disorders, Northwestern UniÕersity, Chicago, IL 60611 USA
Received 9 November 1995; revised 20 June 1996; accepted 20 June 1996
Abstract This paper describes a suite of routines using IgorPro, a powerful analysis and graphing software package for the Macintosh computer, to enhance the ability to analyze, manipulate, and display data recorded with the Discoverye acquisition software marketed by DataWave Technologies. The routines are able to time-align fast and slow data channels, and are especially useful for analyses that involve both neural and behavioral data. The software was designed for eyeblink conditioning and vocalization experiments, but it can easily be used for analyzing other types of neurobehavioral data. The data are first prepared on the PC with routines that inspect the header of the data file and translate the data file into a compact binary format that can be read by IgorPro. An option is also available to splice out data from unnecessary portions of an intertrial interval. The new file is then put on the Macintosh computer for display and analysis by IgorPro. These routines enable both neural and behavioral data to be quickly and easily reduced, manipulated, and statistically and graphically summarized. Keywords: BrainWave; DataWave; Electrophysiological instrumentation; Eyeblink conditioning; Hippocampus; Neurophysiology; On line system; Single neuron recording; Vocalization
1. Introduction A goal of laboratories in many different fields of neurobiology is to relate changes in behavior to changes in neural activity. A wide variety of transducers are available to convert behavior into an analog signal that can be used as input to a polygraph for a hardcopy record, or into a computer for collection and analysis. A variety of electrodes and microwires are also available to detect neural activity. Activity from an electrode is then amplified and filtered before being sent to the same computer that collects the behavioral data. If behavior in response to a stimulus is of interest, a stimulus marker is also needed for input to the computer. This signal is used to time-align the neural and behavioral data.
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Corresponding author. Tel.: Ž312. 503-3112; Fax: Ž312. 503-7912; E-mail: [email protected] 1 Present address: Biomedical Engineering Department, Northwestern University, Evanston, IL 60208, USA. 2 Present address: Dept. of Communication Sciences & Disorders, Northwestern University, Francis Searle Bld, Evanston, IL 60208, USA. 0165-0270r96r$15.00 Published by Elsevier Science B.V. PII S 0 1 6 5 - 0 2 7 0 Ž 9 6 . 0 0 1 1 0 - 0
The relation of behavior to single-unit activity becomes more difficult to analyze since the activity of one or a few neurons needs to be separated from a background of noise. The earliest discriminators of neural activity were based on delivering a short pulse whenever the signal exceeded a designated voltage level. This Schmitt trigger based discriminator was the standard for many years, and is still in use today. The simplest discriminators have one level to include activity that is above a critical threshold. This works well for multiple-unit recordings, or for well isolated single neurons. More advanced discriminators include a second threshold to exclude signals that are too large, e.g. stimulus artifacts. The best of these hardware based discriminators also include a time window to separate waveforms according to spike width Že.g., Olds, 1973; Bak and Schmidt, 1977.. These issues are discussed in a previous review of the instrumentation ŽSchmidt, 1984a. and algorithms ŽSchmidt, 1984b. used for spike separation. A disadvantage of most discriminators and separation systems is that the waveform of the spike is not saved unless the data is also stored on analog tape. The tape then needs to be replayed to recapture wave shape information for the single neuron. One computerized system that col-
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lects and replays waveforms is the Discoverye software package that is marketed by DataWave Technologies. It enables the asynchronous collection of both voltage-discriminated and continuous recordings. Voltage-discriminated data collection, referred to here as fast recording, is used to record brief, discrete events, such as action potentials and TTL pulses that exceed a user-defined voltage. The analog waveform of each event is digitized and timestamped. The duration and sampling frequency for the waveform is dependent upon the speed of the ArD board and the number of channels being sampled. Continuous data collection, referred to here as slow recording, may be used to monitor relatively long, sustained events, such as eyeblinks, vocalizations, or other behavioral and lowfrequency measurements such as EEG and EMG activity. The continuous analog signal is digitized and stored with one timestamp for the start of each block of data. The ability to digitize data asynchronously among fast and slow channels, and to replay and analyze the data off-line, has made the Discovery system a popular collection tool among neuroscientists. Our laboratories, for example, need to compare directly single-neuron activity collected on fast channels with eyeblink activity or vocalizations collected on slow channels. We facilitated these types of analyses by transferring the Discovery data file into a wave-based analysis system known as IgorPro ŽWaveMetrics, Inc., Lake Oswego, OR.. This software package provides a rich and flexible environment on Macintosh computers for the analysis, display and output of publication quality results. The suite of IgorPro routines described here provide a generalized analysis framework to enhance the ability to analyze, manipulate, and display data that was recorded with the Discovery acquisition software and separated into single-neuron activity with DataWave routines. The Igor routines were designed for eyeblink conditioning and vocalization experiments, but they can easily be used for analyzing other types of neurobehavioral data. The routines also work with the built-in analysis functions of Igor, which are not explicitly described here.
ing wave-type data. It operates with Macintosh 68030 and 68040 computers, and is available in native code for the PowerMac line of computers. A floating point processor is recommended for maximum processing speed on the 68030 and 68040 based Macintoshes.
3. Discovery collection parameters The setup procedure for collecting data with Discovery is dependent mostly upon two sets of parameters. The first set describes the collection of spike data; the second set describes the collection of the slow-wave data. Configuring these two sets of parameters does not require any programming experience and is accomplished easily. We also use one or more fast channels to collect TTL pulses that mark the start of individual trials or the delivery of stimuli. Those who are adept at programming can write ‘clock sequences’ to modify Discovery in several ways, e.g. event flags can be generated and collection can be automatically paused and re-started. We found that experiments were conducted most reliably when we had one computer controlling the behavioral experiment ŽAkase et al., 1994. and another operating Discovery to collect the single neuron and behavioral data. This configuration provided an excellent environment to deliver stimuli and to collect data with minimal restrictions on the speed and memory of the computer operating Discovery. This configuration was easily integrated with our current software system and obviated the need to program clock sequences. This configuration also permitted data to be collected continuously for the duration of the experiment. The continuous collection of data, especially slowwave data, obviously increases the amount of disk space required to store the data, but it enables the analysis of long duration changes in activity. Storage space can be minimized by reducing the sampling frequency of the continuous data channel. Alternatively, data storage can be manually paused and resumed, or clock sequences can be used to turn data storage on and off.
2. Hardwarer r software requirements 4. File manipulation on the PC Neural and behavioral data are collected using the Discovery acquisition software ŽDataWave Technologies, Longmont, CO. for PC computers. In our laboratories, we have used Discovery v3.2 extensively in a DOS environment on both a PC 80x486 DX-33 and DX-50. We have analyzed recordings from both single electrodes and stereotrodes, and have successfully tested the routines with files generated by the current version of Discovery Žv5.1. which includes data collected with a tetrode configuration. The Macintosh based routines described here require the IgorPro software package ŽWaveMetrics, Lake Oswego, OR.. This program is ideal for graphing and analyz-
Discovery acquires and saves data into a ‘UFF’ file ŽUniversal File Format.. A common processing module is then used to separate the activity from channels with multiple-unit activity into the activity of single units. A ‘cut’ file is generated after separating the waveforms into single units. This process is referred to as cluster cutting. The first routines described in this paper are run from a master program and operate on the UFF or CUT files. The program was written in C language for use with PC computers. It runs well on a 80x486 DX-50 computer, but Pentium-based PCs are recommended for faster process-
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ing. The program reads the header of the UFF or CUT files, deletes unnecessary intertrial intervals, and translates the CUT file into the Igor file format. This program is available from the first author and the routines are described in the following sections. 4.1. File information The file information option reads the header of the cut file and indicates the channels that are active, the number of spikes detected for each identified neuron on each probe, i.e., single electrode, stereotrode or tetrode, the number and type of event flags, and the duration of time included in the file. This routine is useful to remind the
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user of the active data channels and to determine the total number of spikes for each single neuron that was identified for a given channel. This routine will also work on a UFF file since that file format is identical to the format of the cut file. The output from an analysis of a UFF file would indicate the total number of events detected rather than the number of spikes per identified neuron. 4.2. Chopping and splicing of data The ‘chop’ option may be used to delete data that does not fall into a window of time surrounding an event, i.e., a TTL pulse read in from a spike channel, or an event flag generated from either a clock sequence or a keystroke.
Fig. 1. ŽA. A ‘screen dump’ of the Session View window. The coding system for the data is indicated along the left margin. CR0 the first continuous record channel. In this example the data represents nictitating membrane extension. SE1.1–SE4.3 represent data from 4 single electrodes. The second digit identifies a single neuron that was discriminated from each electrode with the cluster cutting routine. SE5.1 represents the onset of the tone CS; SE6.1 represents the onset of the airpuff US. The trials appear rather evenly spaced in time because much of the intertrial interval was ‘chopped’ out of the file. The x-axis indicates the amount of time represented after the file was chopped. ŽB. A screen dump of the dialogue box that is presented along with the session view. Similar type boxes are presented with each macro. This box provides interactive menus to control the display of the Trial View Žsee Fig. 2.. The ‘main event’ represents the first synchronization signal Žtone CS in our case.. The ‘second event’ represents another event that is time locked to the main event Žairpuff US in our case.. The information in this dialogue box will produce a trial view with the appropriate cursors for the two events.
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This operation is ideal for deleting unnecessary intertrial intervals and reducing the size of a large data set into discrete regions of interest. It generates a new file, i.e., those with a ‘.chp’ extension, and preserves the original data. The user indicates the channel that has the trigger signal and the time window around the trigger that is to be preserved. The chop option then reads the data file and determines which blocks of continuous data need to be saved in order to splice together a new seamless data file. This routine is ideal for those who want to add the power of these routines to an existing computer system. We typically have about a 1 s trial and random intertrial intervals of 30–60 s for our eyeblink conditioning experiments. Continuous data collection during these intervals is important because occasional neurons have exhibited response patterns lasting as long as 10 s, and other neurons respond so infrequently that they might not be detected without continuous data collection. The chop option is then used to reduce the data set into a more manageable size, and it is repeated with longer analysis windows if the data suggest that the activity has not returned to baseline. Collecting data for the entire experiment obviates the need to program and compile clock sequences for Discovery and facilitates the integration of Discovery with existing behavioral control systems. This strategy of data collection also allows one to examine long latencyrlong duration events by rechopping the UFF file with longer time windows, or by not chopping the file at all. The probability of detecting ‘silent cells’ ŽThompson and Best, 1989. is also increased with continuous data collection. The chop routine then allocates space within the displays for silent cells Že.g. spike SE1.2 in Fig. 1. and it enables the user to reduce the size of the cut file when long duration or long latency events are not present. If one is not concerned about silent cells, the chop option can also be used before cluster cutting to reduce the size of the file that needs to be sorted into single units. This may substantially reduce the processing time required for cluster cutting. 4.3. Format conÕersion The PC routines convert Discovery files into the binary Igor file format. This conversion is required even if the file is not chopped. In this format the timestamp of each waveform is treated as an independent event This format is efficient at representing the data, and hence, the translated files are significantly smaller in size than those generated by Discovery. These files are then transferred to a Macintosh computer with a 1.4 MB floppy disk. Almost all of our files are small enough to transport by floppy disk once they have been chopped. Larger files can be transferred to the Macintosh by other means such as ethernet communication or direct cable connection and MacLink software ŽDataViz, Inc., Trumbull, CT..
5. Igor on the Macintosh The Igor software package has many built-in analysis and graphing routines, and it can be programmed for customized macro routines. Users can write their own code with the built-in programming language and develop other macros for specific needs. The macros we developed for IgorPro v2.04 ŽFPU. will be provided and can serve as a template for other macros. These macros are always available for viewing in Igor’s ‘procedure’ window. The ‘help’ window also provides excellent on-line documentation for the available commands. The routines described here are selected from a pop-up menu using the point and click method with a mouse. All the routines start by reading in a file that was translated on the PC. This routine uses very little computer RAM to store a large amount of data which can then be manipulated by the other macros and other built-in Igor functions. 5.1. Trial Õiewing and sorting After the file is loaded into memory the entire data set is displayed in a window on the monitor. An example of the display for this Session View is shown in Fig. 1. This data represents an eyeblink conditioning session with multi-channel single-neuron recordings from the rabbit hippocampus. The data was acquired with Discovery and then chopped to remove most of the long intertrial intervals Ž30–60 s.. This display provides an ideal opportunity to detect artifacts, discrepancies in the collection or manipulation of data, channels with or without data that are worth analyzing, and repeating changes in activity. The continuous data is displayed above the spike data and represents the voltage from an infrared eyeblink detector ŽThompson et al., 1994.. The different recording probes are coded by their electrode type and sequence number, e.g., SE1 Žsingle electrode 1., ST1 Žstereotrode 1. and QT1 Žtetrode 1.. Different neurons from a given probe are indicated by different numbers e.g., SE1.1, SE1.2, SE1.3. Action potentials for each single neuron on any given channel are indicated by vertical tick marks. We also typically have two channels that contain TTL pulses for marking the onset of our tone conditioning stimulus and airpuff unconditioned stimulus Žlabeled Event1 and Event2, respectively.. At this point, a menu is also presented to define the channelŽs. that contain the events for synchronizing the data, i.e., TTL pulses or event flags, and the amount of time to be displayed about the events. After the channelŽs. are defined, the trials are displayed individually and fitted to the display window as shown in Fig. 2. 5.2. Separate trials All of the trials are initially unclassified, but they can be classified and separated by various methods. In our
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eyeblink conditioning experiments, we are interested primarily in classifying trials as those with either conditioned responses ŽCR., unconditioned responses ŽUCR., or neither Že.g., trials with spontaneous blinks.. Although the trials can be classified into these categories by visual inspection, we utilized the criteria of Akase et al. Ž1994. to program an effective algorithm for the automatic classification of eyeblink responses. This algorithm determines the start of an eyeblink Žactually nictitating membrane, NM, extension for our data., that exceeds the mean baseline position by 4 SD for a minimum of 10 ms. After the Separate Trials macro is implemented an arrow appears at the onset of each response, and a mark appears in the appropriate box to indicate the type of response emitted during the trial. An example of such a trial is shown in Fig. 2. In this figure the trial was classified as a CR and the arrow is placed at the onset of the response, i.e., where the signal is 4 SD above the baseline. In this example the arrow appears after the apparent onset of the response because of the high baseline at the start of the trial. If the user does not agree with the automated classification, individual trials can be reclassified according to user defined criteria. Regardless of any secondary reclassification by the user, the onset time generated by the Separate macro will be used for sorting purposes in other macros. 5.3. Sort by latency This macro creates a new display window that presents a stacked plot of the eyeblink data above a similarly ordered display of neural data in a raster format. An example of such a display is shown in Fig. 3. A dialogue box prompts the user for the neural channels of interest, and the trial type of interest, e.g. CRs, UCRs or both. This
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Fig. 3. ŽA. Example of the display generated by the Sort by Latency macro. Each behavioral response is stacked one above the other in order of increasing response latency. The raster of neural data is rearranged in the same order as the behavioral data. The trial number of each behavioral trace can be identified by clicking on the trace. ŽB. Example of the display generated by the Sort by Amplitude macro. Each behavioral response is stacked one above the other in order of increasing amplitude. The raster of neural data is similarly ordered. The x-axis indicates the time in seconds relative to the onset of the tone CS.
selection process enables the analysis of specific trials after using the Unseparate macro Žsee below.. Responses with the shortest latency are presented above responses with longer latencies. Thus, CRs appear above the UCRs when the ‘both’ trial type is selected. A useful feature of Igor is that the actual trial number of each waveform Žeyeblink response. can be identified by positioning the cursor on the waveform of interest and then using the mouse to click and hold. Built-in Igor functions will display a wave number that corresponds to the trial number. This feature can be used to compile a list of trial
Fig. 2. An example of a trial view window after the Separate macro has been utilized. ŽA. The behavioral data ŽNM extension. is indicated by the top trace. The arrow indicates the onset of response according to the algorithm described in the text. The onset appears to be late because of the high baseline at the start of the trial. ŽB. The activity of each neuron is represented by vertical tick marks in each row. The two channels of synch pulse markers are in the last two rows and are masked by the cursors. This trial Žno. 20. was classified as a CR. The x-axis indicates time in s.
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numbers that need to be selected or reassigned as described below. A line in the command window Ža window that Igor generates below the displays. for this and many other macros also confirms the number of trials that were included in the analysis. We routinely use the Separate macro to identify representative CRs and UCRs, then use Unseparate to unclassify all trials. Finally, the trials of interest are displayed Žby typing in the trial number. and manually reclassified appropriately. This processing is all very fast.
Fig. 5. ŽA,B. Display generated by the Show Average macro. The behavioral trace ŽA. represents the average of all trials of the type selected by the dialogue box. The raster ŽB. is organized in trial sequence with the earliest trials on top. ŽC. An example of the display generated by the Show Histogram macro for the same set of data as in B. The dialogue box for this macro includes a section to indicate the bin width of the histogram. Tone CS onset is at time zero.
5.4. Sort by amplitude This macro is very similar to Sort by Latency. The difference is that the stacked plot of the eyeblink data is in order of smallest peak response to largest peak response. Thus, when CR trials are selected through the dialogue box, trials with the largest CRs can be identified. Similarly, when UCRs are selected through the dialogue box, UCR trials with the largest unconditioned response can easily be identified. An example of the output from this macro is also shown in Fig. 3. These data are the same as those shown for Sort by Amplitude, but the eyeblinks and raster display have been resorted in order of the amplitude of response. Fig. 4. The Show Statistics macro produces one page of output for each neuron that is entered in the dialogue box. Data from conditioning sessions Žthose with both a ‘main event’ and a ‘second event’. or pseudoconditioning control sessions Žmain event or second event. can be generated. A histogram of neural activity and the superimposed average behavior is presented with a ‘bird’s eye’ view ŽA. and a trial view ŽB.. The bird’s eye view includes data from 2 s prior to tone CS onset and 3.1 s after tone CS onset. The trial view includes the same data from 0.2 s prior to tone CS onset and 1.0 s after tone CS onset. The dark horizontal bars along the x-axis in the trial view represent the timing of the tone CS and airpuff US. ŽC. The statistics for the unit activity for each of the trial periods Žtone, trace, UR. and the pretrial period. The statistics include the mean, SD, and the number of bins with activity. If five or fewer bins had activity a Z score was calculated instead of a t score. ŽD. The dialogue box that controls execution of the statistics.
5.5. Unseparate trials This macro erases the response classification of all trials while leaving the onset arrow in place on the display and the onset time in memory. This is an extremely useful macro when the user needs to select a small set of specific trials with user defined criteria for an analysis, e.g. the 10 largest CRs or 10 largest nonCRs. Trial selection would occur by sorting the responses by latency or amplitude, noting which trials were best suited for analysis, invoking the Unseparate macro, and then manually recoding response types for the specified trials.
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5.6. Show statistics This macro superimposes the aÕeraged eyeblink response upon a histogram of the activity for each selected neuron. The histogram is presented with an ordinate of spikesrbin and the NM extension is plotted in terms of ArD volts. Our analysis is configured to present the data twice, once as a ‘bird’s eye’ view Žy2 to 3 s. and once as a trial view Žy200 to 1000 ms.. The views are relative to the tone CS onset and they permit the observation of long latency or long duration changes as well as the more detailed response during presentation of the stimuli. An example of the hardcopy output from this macro is shown in Fig. 4. A statistical summary is presented below the histograms. The statistics are generated for several analysis windows. We routinely analyze a window for the 100 ms tone conditioning stimulus ŽCS., the 500 ms trace period ŽTrace., a 500 ms unconditioned response window ŽUR. that commences at the onset of the unconditioned stimulus,
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and a 2 s post trial period ŽPT. that commences at the end of the UR period. The statistics include the mean and SD for each window after dividing the window into 20 bins to get a measure of variance. The number of bins with activity greater than zero are also indicated. This is needed to determine if a parametric t-test, or a nonparametric binomial test is to be used for statistical analysis. A two-tailed, paired t-test is performed for most comparisons. The nonparametric binomial test is used for cells that fired so slowly that five or fewer of the 20 bins in each window had activity ŽSiegel and Castellan, 1988.. We use data from a 2 s preCS window to determine the baseline firing rate. The means and SEs for all windows are used to perform the t-test. A two-tailed paired t-test is used since either excitation or inhibition may occur. In this test the difference between the means is related to the SE of the baseline activity, i.e., the test is essentially a one sample t-test which compares the window of interest against the expected data gathered during the baseline period. Tests which yield a p value F 0.01 are deemed
Fig. 6. Example of the output generated by the show multi-correlation macro. Several graphs are presented according to the number of data channels that are to be cross correlated with each other. In this example the average behavioral response ŽA. and the summated activity, i.e., histograms of two different neurons ŽB,C. were correlated. The arrows in D–G indicate the maximum correlation for each pair of data and the latency at which it occurred. ŽD. The autocorrelation of the average behavior. As expected, this correlation was 1.0 at a lag time of 0.0. ŽE. The correlation of B and A. This result indicates that the CR0 had a maximal correlation of 0.68 with ‘spikes7’ at a lag time of q94 ms, i.e., the behavior lags behind the neural activity. ŽF. The correlation of C and A. ŽG. The correlation of C and B. This resulted in a negative lag time, i.e., ‘spikes7’ precedes ‘spikes6’.
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statistically significant. These routines are extremely fast, e.g., statistics for a population of single neurons recorded from several wires during a session may have taken several hours to process and document with DataWave routines, but are now processed and documented in a few minutes with Igor routines. 5.7. Show aÕerage This macro provides a dialogue box to select either CR trials or UCR trials. The output is displayed in a new graph which includes a raster display for each neuron Žearliest trial is on top. and a single trace that represents the
average NM response. An example of this display is shown in Fig. 5. This display is similar to that generated by Show Statistics, but it is more flexible in terms of the time window for analysis and the ability to stack timealigned rasters for each neuron one above the other. This macro also operates only upon the trial type that is selected. The display presents an informative summary for all neurons in relation to each other’s activity and to the averaged behavior. 5.8. Show histogramr show frequency plot These two macros produce a histogram for the neurons that are specified in the dialogue box. The bin width for
Fig. 7. Example of data collected with Discovery during an experiment that examined the neural control of vocalization ŽYajima and Larson, 1993. by synchronizing the data to a response on a continuous slow-wave channel. The top trace ŽA. displays an average of the rectified and smoothed vocal signal. The next three traces ŽB–D. are EMG signals collected on continuous data channels from the posterior cricoarytenoid, throarytenoid, and criothyroid muscles, respectively. ŽE–G. Rasters of the activity from three different neurons within the nucleus ambiguus during the vocalizations. ŽH–J. Histograms of the activity from each neuron in E–G. Some basic statistics for each neuron are indicated below the rasters.
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the display is also defined through this window. The Show Histogram macro produces a graph with an ordinate that has the units of spikesrbin. The Show Frequency Plot has an ordinate with units of mean spikesrbin, i.e. it is the spikesrbin per trial. This scale can also be multiplied by the binwidth to convert the scale into rate, i.e., mean spikesrs per bin. An example of the Show Histogram macro is also presented in Fig. 5. 5.9. Show cross correlationr multi-cross correlation This macro presents a menu to identify two or more neural or behavioral channels of data that are to be crosscorrelated with each other. We use this macro to examine time-amplitude relations among the different neurons and between the behavior and neural activity of individual single neurons. An example of the output is presented in Fig. 6. The macro generates graphs of the cross correlation function for all the pairs of data. The title of each graph indicates the two data sets that were correlated with each other. This macro also includes a routine to indicate the maximal correlation and the time shift at which it occurred. In the example for Fig. 6E, the maximal correlation was 0.68 at a time shift of 94 ms. 5.10. Show ANOVA This macro is designed to generate the data necessary to perform a repeated measures ANOVA of trial type ŽCRrnonCR. by analysis window ŽpreCS, CS, trace, UR, PT.. The macro creates a new file that includes the response rate for each of the 20 bins in each of the five analysis windows. The array includes a code for the spike number, and the mean rate for each of the 20 bins in each analysis window. This array is then used as input to commercially available statistical software Žthis was more efficient than programming a repeated measures ANOVA.. We use SuperAnova ŽAbacus Concepts, Berkeley, CA. which we configured with a template to perform the ANOVA. 5.11. Hardcopy output The Igor routines are designed to generate publication quality histograms, raster plots, frequency plots, and cross correlations for each neuron. All of the macro generated graphs can be copied and pasted into Igor ‘layouts’ Žthe graphs from Show Stats are pasted automatically.. Publication quality printouts of these layouts can be directly printed from Igor. The graphics can also be stored and opened by other graphic programs. 5.12. Processing of multiple slow channels The Igor based macros can be used to process multiple slow wave channels and to synchronize displays about an
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event detected on a slow wave channel. This greatly expands the ability of these macros to analyze data from a variety of experiments. One such example is from the neural control of vocalization Že.g., Yajima and Larson, 1993.. This type of experiment is better analyzed by synchronizing the data to a vocalization that is recorded on a slow channel. The data presented in Fig. 7 are from an experiment designed to analyze the neural control of vocalization. The figure displays an average vocalization signal, EMG activity from three different laryngeal muscles, and the activity of three neurons discriminated from a single electrode in the nucleus ambiguus. A similar analysis could be used to analyze responses related to spontaneous eyeblinks.
6. Discussion A suite of integrated software routines was developed to manipulate, analyze and display data collected with the Discovery software system. The procedures described here operate on UFF andror cut files generated by the Discovery and Common Processing programs of DataWave Technologies. The routines are designed to easily integrate data from trial based experiments that collect data from spike channels and behavioral channels. File manipulation and translation on the PC are described, as are the Macintosh based analysis routines that we most often use. These routines were used to generate the results described in a recent paper on the activity of hippocampal neurons during eyeblink conditioning ŽWeiss et al., 1996.. This software should work well for those who find that Discovery is easily prepared and reliably acquires high frequency data. Stimulus presentations can then be implemented with existing hardware and software arrangements, a second computer, or a stimulator. The two component system works well to deliver stimuli and to collect both neural and behavioral data. The problem of time-aligning fast and slow data is solved by using IgorPro to read, display and analyze the data after translating the file into a compact binary format on the PC. Until the routines described here were developed we were able to get statistical results for the neural data through DataWave’s Common Processing modules, but only after a process that took hours of time. We used ‘setup’ files to run a preset configuration of histogram analyses and then printed out the results for hardcopy documentation. The statistics Žmeans and SDs. were then entered into a spread sheet program ŽStatview, Abacus Concepts, Berkeley, CA. for the calculation of t-tests and binomial tests. An analysis of the corresponding behavior was, however, missing from the analysis and is still difficult to accomplish even with more current versions of software from DataWave. The Igor macros described here quickly and efficiently analyze data that originate from Discovery and are cut, i.e.,
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separated into the activity of single neurons, using a Common Processing module. We are also in the process of developing routines to cut the waveforms in an Igor based environment on the Macintosh computer. We should point out that, in spite of the analysis limitations, the DataWave system is an excellent data acquisition tool. The ability to collect fast and slow data asynchronously, i.e., at different sampling frequencies, to replay the data from disk, and to incorporate stereotrode and tetrode routines ŽMcNaughton et al., 1983; Gray et al., 1996. makes the system a worthwhile investment. Currently available alternative acquisition systems, of similar or higher cost, do not yet incorporate the collection and analysis of both slow-wave and fast data, or the ability to replay digitized data. Now, with the addition of the analysis routines described here, the Discovery system becomes a more powerful tool for the collection and analysis of behavioral plus multi-channel single neuron data. These routines enable both types of data to be quickly and easily reduced, manipulated, and statistically and graphically summarized. This work was supported by grants RO1MH47340 and R01AG06796 to J.F.D. and DC00207 to C.R.L. We thank Mr. Martin Wilde for help with software development, and R. Kettner for his help and comments. References Akase, E., Thompson, L.T. and Disterhoft, J.F. Ž1994. A system for quantitative analysis of associative learning. 2. Real-time software for MS-DOS microcomputers, J. Neurosci. Methods., 54: 119–130.
Bak, M.J. and Schmidt, E.M. Ž1977. An improved time-amplitude window discriminator, IEEE Trans. Biomed. Eng., 24: 486–489. Gray, C.M., Maldonado, P.E., Wilson, M. and McNaughton, B. Ž1996. Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex, J. Neurosci. Methods., in press. McNaughton, B.L., Barnes, C.A. and O’Keefe, J. Ž1983. The stereotrode: A new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records, J. Neurosci. Methods, 8: 391–397. Olds, J. Ž1973. Multiple unit recordings from behaving rats. In R.F. Thompson and M. Patterson ŽEds.., Bioelectric Recording Tech. Part A, Cellular Processes and Brain Potentials, Academic Press, New York, pp. 165–198. Schmidt, E.M. Ž1984a. Instruments for sorting neuroelectric data: a review, J. Neurosci. Methods, 12: 1–24. Schmidt, E.M. Ž1984b. Computer separation of multi-unit neuroelectric data: a review, J. Neurosci. Methods, 12: 95–111. Siegel, S. and Castellan, N.J., Jr. Ž1988. Nonparametric Statistics for the Behavioral Sciences, 2nd Edn., McGraw-Hill, St. Louis. Thompson, L.T. and Best, P.J. Ž1989. Place cells and silent cells in the hippocampus of freely-behaving rats, J. Neurosci., 9: 2382–2390. Thompson, L.T., Moyer, J.R., Akase, E. and Disterhoft, J.F. Ž1994. A system for quantitative analysis of associative learning. 1. Hardware interfaces with cross-species applications, J. Neurosci. Methods, 54: 109–117. Weiss, C., Kronforst, M.A. and Disterhoft, J.F. Ž1996. Activity of hippocampal pyramidal neurons during trace eyeblink conditioning, Hippocampus, 1000000. Yajima, Y. and Larson, C.R. Ž1993. Multifunctional properties of ambiguous motoneurons identified electrophysiologically during vocalization in the awake monkey, J. Neurophysiol., 70: 529–540.