Event Tab

NeuroMatic analysis software for detecting spontaneous events. The search algorithm can be either a simple level detector, a threshold-above-baseline detector as described by Kudoh and Taguchi (2002), or a template-matching detector as described by Clements and Bekkers (1997).

If you wish to use the template matching algorithm, download the appropriate XOP (PC | Mac) and place it in your Igor Extensions folder before starting Igor.

• There are now two baseline parameters, avg and dt. Baseline avg specifies the baseline window size. Baseline dt specifies the time after the baseline mid-point to search for an event; this parameter is equivalent to wi of Kudoh and Taguchi. Previously these variables were set equal. You now have the option to use a larger dt without increasing the baseline window. If you do not wish to use a baseline window, set avg = 0.

• The onset search is now a backwards search from event time t_e, rather than a forward search from (t_e - onset limit). Searching backwards gives a more accurate estimate of the onset time since it is less influenced by the baseline noise.

• You now have the option to search without peak or onset criteria. If you choose to search without these criteria, you can save your events to waves (see below) and, using the Stats Tab, compute peak and rise-time measurements, and then sort successes versus failures with the “Sort Wave” Stats2 function.

Event Detection Criteria

Threshold Detection Algorithm. A stepwise search for the first data point that falls above or below y, dt msec after t₀. This algorithm is similar to that of Kudoh and Taguchi (2002), except for the use of a baseline average centered at t₀ rather than a single baseline point at t₀. To use a single baseline point, set baseline avg = 0 msec.

y = th + baseline (threshold > baseline)

y = baseline - th (threshold < baseline)

t₀ = baseline window mid-point

t_e = threshold crossing event time

dt = sliding detection window, t_e - t₀

Level Detection Algorithm. A stepwise search for the first data point that reaches a defined level on either a positive slope deflection (+slope), or negative slope deflection (-slope).

Template Matching Algorithm. In this algorithm, a waveform template with time-course typical of a synaptic event is first “matched” to the current data set as described by Clements and Bekkers (1997). This produces a “detection criterion” wave called EV_MatchTmplt (displayed as a blue wave on top of your original data). An event is detected when EV_MatchTmplt crosses a defined level (Clements and Bekkers suggest a level = 4), using the Level Detection Algorithm. This method requires the MatchTemplate XOP (see above).

To use the MatchTemplate XOP, click the template matching checkbox true. You will be asked to provide parameters for your template, such as rise/decay times, or to provide the name of your predefined template wave. You have the option of specifying an onset window of zero y-value before your waveform; this helps in detecting only those events with a relatively flat baseline. After creating the template, NeuroMatic will compute the matched wave EV_MatchTmplt. If at any time you need to recompute this wave, click the “Match” button. Note, depending on the speed of your computer and the length of your data waves, computing EV_MatchTmplt can take some time.

Backward Onset Search. A sliding-window search backward from t_e for the first occurrence of the right-most window point falling below the window average + N standard deviations (positive events), or falling above the window average - N standard deviations (negative events). Note, this algorithm is slightly different from Kudoh and Taguchi (2002) in that it searches backwards from t_e.

avg = sliding average window

Nsdv = number of standard deviations above/below average to detect

limit = time before t_e to end onset search

Forward Peak Search. A sliding-window search from t_e for the first occurrence of the left-most window point falling above the window average + N standard deviations (positive events), or falling below the window average - N standard deviations (negative events). Note, this algorithm implements only the forward peak search of Kudoh and Taguchi (2002).

avg = sliding average window

Nsdv = number of standard deviations above/below baseline to detect

limit = time after t_e to end peak search

Search Limits. Click this checkbox to set your begin/end search time limits (t_bgn, t_end).

Equivalent Parameters of Kudoh and Taguchi for Threshold Search

wi = dt = sliding detection window (they use 1.6 - 4 msec)

bin = onset avg window = peak avg window (they use 1 - 2 msec)

pp = peak limit (they use 4 - 10 msec)

n = onset Nsdv = peak Nsdv (they use 1 - 1.5)

baseline avg they do not use, in which case avg = 0 msec.

Adjusting Parameters

• Set threshold/level to minimum event amplitude to be detected.

• Set sliding detection window dt such that the mid-point of the baseline average window t₀ rests comfortably before event time t_e. The onset limit value can be set to this same value.

• Set peak limit to approximately half the length of your event.

• Set Nsdv to 1 - 2, depending on the noise levels in your data.

• Adjust peak, onset and baseline avg windows to obtain best results. These values will change depending on the levels of noise in your data.

Event Search Controls

[>] - search for next event.

[<] - set search time and cursors to previous saved event if one exists.

Save - save event times to the current Event Table.

Clear - clear event times from the current Event Table.

Auto - automatic search for events within the current wave, saving results to the current Event Table.

Search Time - set begin search time for next event (automatically set to current t_e).

[t = 0] - reset begin search time to start of search window (t_bgn).

Display Window - set time window of channel display.

Table Menu - use this menu to select the table to which events will be saved, or choose table options New, Clear or Kill. You can have more than one Event table, in which case the tables will be numbered in a sequence starting from zero. To the right of the menu is the number of saved events in the selected table.

Click this button to compute an event interval histogram, or a time histogram across data waves between t_bgn and t_end.

Click this button to copy each event to a new wave, beginning with the prefix “EV_evnt” + event table number, which will automatically be added to NeuroMatic’s wave prefix list. Select the appropriate event prefix to analyze your events using NeuroMatic’s main controls. You can now compute averages of your events, or compute rise/decay times using the Stats Tab, and then, perhaps, perform an x-alignment at the 10% rise-time value (ST_RiseBX).