Knowledge Base

The Maximum Distance Moved Smoothing Method

Aim

To remove outliers in the track, due to erratic detection of objects within the arena. For example, in a DanioVision experiment, if detection is not optimal, the margin of the well is sometimes detected as the fish.

Important: If you see many errors, adjust the Arena Settings and/or the Detection Settings.

Procedure

1. Choose Acquisition > Track Smoothing Profile > Open.
2. Select the Maximum Distance Moved check box and click the Edit button.
3. Enter the threshold value of distance moved, above which the sample will be set to missing. If the distance moved from sample 1 to 2 is longer than the threshold, sample 2 is set to missing.
4. Click OK.
5. Plot the tracks to see the result.

Notes

• Tip: To know which value of distance to use as a threshold, plot the values of distance moved in the Integrated Visualization, and look at the spikes corresponding to the outliers. Take a value well below the value of the spikes. Be aware that a threshold being too low can remove good data.
• The Maximum Distance Moved method is applied after data editing, and after the Smoothing (Lowess) and the Minimal Distance Moved methods (when selected). The smoothed data, not the original data, are used for analysis and for export. Applying the Maximum Distance Moved method does not change the original data.

Introduction to Data Analysis

Overview of Data Analysis

Analysis Functions

For data preparation:

• Data Profiles. For choosing the data points subject to analysis. You can filter tracks, create groups, select specific time periods or track segments, etc.
• Analysis Profiles. For choosing the behavioral endpoints, like speed, distance moved, time spent in a zone, etc.

For the analysis results:

• Statistics and Charts. For obtaining a table with summary statistics of the dependent variables per trial and summary statistics and charts per group of trials.
• Track Visualization. For visualizing data as tracks plotted over a static background video image.
• Heatmap Visualization. For visualizing the location where your animals spent most time.
• Integrated Visualization. For visualizing the track together with video, and plotting dependent variables against time.
• Export (Raw Data, Statistics and GLP Log). To export raw data, dependent variables and their summary statistics, and the GLP log (available with the Quality Assurance module).

Note

In general, the analysis results depend on a few factors:

• Whether you applied Noise reduction during acquisition.
• Whether the tracks are smoothed, and how.
• Which tracks and data points are selected in the current Data profile.
• Which dependent variables and statistics are selected in the current Analysis profile.

Important Terms in Data Analysis

Dependent Variables

In EthoVision XT, a dependent variable is a variable that quantifies the behavior of a subject or marks an event occurred in a trial.

• Example 1: To quantify locomotor activity, choose Distance Moved, or Velocity, or Movement as dependent variables.
• Example 2: To calculate how many food pellets were dropped by a pellet dispenser, choose Hardware Continuous as a dependent variable.

Dependent variables are calculated for each sample of the track, when data are available. The table below shows the Distance Moved calculated for a few samples. X and Y are the coordinates of the center of the subject's body. Note that distance moved is not calculated at time = 0, because there is no previous sample to use as a reference.

State Dependent Variables

Some dependent variables are state variables, that is, they have discrete values. For example, In Zone with values 1 (in zone) and 0 (not in zone). The following table shows the In Zone variable calculated for zone A and for a few samples. When the animal's body point is found within zone A, then In Zone = 1, otherwise In Zone = 0. In the example below, the subject's center point enters the zone at 0.16 s.

Statistics

In EthoVision XT, statistics are descriptive statistics of the per-sample values of a dependent variable.

• Example 1: To quantify locomotor activity, choose the Total statistic for the dependent variable Distance Moved. Choose the Mean statistic for the dependent variable Velocity. The total distance moved is the sum of the per-sample values of distance. In the example of the table above, the total is 5.73 cm.
• Example 2: To calculate the time spent in zone, choose the Total statistic for the dependent variable In Zone.

Trial Statistics vs. Group Statistics

For each dependent variable you can specify two types of statistics:

• Trial Statistics. Statistics calculated per trial.
  Example: Your experiment contains 100 trials of one animal in one arena. Choose Total as a Trial Statistic for the dependent variable Distance Moved. You obtain 100 values of Total Distance Moved.
• Group Statistics. Summary statistics of the Trial Statistics, calculated over all the trials specified in the data profile, or in a group.
  Example: You have defined two groups of tracks, Treated and Saline, and you want to calculate the Mean and Standard Deviation of Distance Moved and other variables per group. In the example above with the 100 trials, with Group Statistics you would obtain the average and standard deviation of the 100 values of distance moved.