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Step 5 : Compute PSTH

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Talia Lerner edited this page Dec 1, 2022 · 9 revisions

a. Click PSTH Computation button to run this step

b. After execution of this step, PSTH will be computed for each event.

  • The transients detection in z-score or/and ΔF/F will also be carried out.

Note:

  • Parameters to adjust for transients detection are Moving Window for transients detection, High Amplitude filtering threshold (HAFT) and Transients detection threshold (TD Thresh).
  • There will always be some false-positives and false-negatives in the transients detection process. Adjust parameters and manually inspect results until you are satisfied, then apply the same parameters to all experiments in a dataset.

  • The peak and area under the curve in the PSTH mean trace for each event will be computed as well.

  • The peak is found between the peak parameters set by the user in the Input Parameters GUI.

  • If Compute Cross-correlation is set to True, it will compute cross-correlation of behavior event PSTHs between signal collected from different brain regions (for example, DLS and DMS) or between different kind of signals (for example, RCamp and GCamp).

  • Area under curve is likewise found between the peak parameters set by the user in the Input Parameters GUI. It is computed using the trapezoidal method, beginning from the user-defined start time and continuing until the user-defined end time.

  • All the outputs in this step are saved in the outputs folder (generated in Step 4 inside the folder of data you are analyzing) in either 'csv' or 'h5' format, which makes it easy for the user to access it in other programming languages (such as MATLAB) if desired.

  • Transients detection plot will pop up after the completion of this step (as shown in the screenshot below).

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  • After viewing this data, you can adjust input parameters in Step 1 (save these parameters to file), then re-run Step 4 to check the results

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