Existing Functionality in Pynapple

Oct 13, 2025 by orovajewels.com 65 views

Introduction

Event-related analyses are a crucial aspect of data analysis in various fields, including neuroscience and psychology. In this context, events typically represent repeated stimulus presentations, and one of the basic metrics collected is whether a given cluster or region of interest (ROI) is responsive to it. To assess this, researchers typically define a baseline window (before stimulus onset) and a response window (after onset). This article will delve into the concept of peri-event baseline vs response comparison and provide a comprehensive guide on how to achieve this using Pynapple.

Understanding Peri-Event Baseline vs Response Comparison

Peri-event baseline vs response comparison involves computing a statistic (e.g., mean firing rate) for each window and performing a paired test (e.g., baseline vs response mean). This analysis is essential in understanding the neural response to specific stimuli and identifying responsive clusters or ROIs.

Pynapple: A Powerful Tool for Data Analysis

Pynapple is a Python library designed for data analysis, particularly in the context of neuroscience and psychology. It provides a range of tools and functions for processing and analyzing data, including event-related analyses. In this article, we will explore how to use Pynapple to perform peri-event baseline vs response comparison.

Existing Functionality in Pynapple

Pynapple provides a function called compute_event_trigger_average which seems like it might be useful for this type of analysis. However, the usage of the feature and ep arguments is unclear. In this article, we will explore how to correctly use these arguments to achieve comparable results.

Example Code: Baseline vs Response Comparison

Below is an example code snippet that builds upon the "Example Peri-Event" tutorial to achieve baseline vs response comparison:

# Compare baseline before event onset with response after onset across stimulus repetitions
import pandas as pd
import numpy as np
from scipy import stats
import pynapple as nap

# Include missing stimulus category
indxSB = np.where(stimCategory == 1)  # SB trial indices
SB = nap.Ts(boundary1_time[indxSB])  # SB timings

def compute_perievent_mean(cluster_id, stim_timings, interval, bin_size):
    # I repeat a similar strategy to yours
    peth = nap.compute_perievent(
        spikes[cluster_id], stim_timings, minmax=interval
    )  
    counts = peth.count(bin_size)
    fr = counts / bin_size
    counts = counts.smooth(bin_size*4)
    ## but in my case I want to take the mean over the interval for this given stimulus repetition
    meanfr = fr.mean(axis=0)
    
    return meanfr

# DataFrame to store results
results = pd.DataFrame(columns=["stim_type", "cluster", "responsive"])

for i, cluster in enumerate(spikes):
    for stim, name in zip([NB, SB, HB], ["NB", "SB", "HB"]):
        # Compute mean response and baseline across stimulus repetitions
        response_means = compute_perievent_mean(i, stim, (0.15, 0.3), 0.01)
        baseline_means = compute_perievent_mean(i, stim, (-0.5, 0), 0.01)

        # Store results in a DataFrame, including significance test
        df = pd.DataFrame(
            {
                "stim_type": name,
                "cluster": i, 
                # example responsiveness criteria
                "responsive": stats.ttest_rel(response_means, baseline_means, alternative="greater")[1] < 0.05,
            },
            index=[0]
        )

        results = pd.concat([results, df], ignore_index=True)

This approach allows researchers to use Pandas filtering to identify responsive clusters and stimulus categories efficiently. However, a solution that is fully integrated within Pynapple would be more desirable.

Using `compute_event_trigger_average` Function

The compute_event_trigger_average function in Pynapple seems like it might be useful for this type of analysis. However, the usage of the feature and ep arguments is unclear. To use this function, you need to specify the feature argument, which represents the type of feature to compute (e.g., mean firing rate). The ep argument represents the event period, which is the time window around the event of interest.

Here is an example of how to use the compute_event_trigger_average function:

# Compute event trigger average
feature = "mean_firing_rate"
ep = nap.EventPeriod(stim_timings, (0.15, 0.3), 0.01)

# Compute event trigger average
etav = nap.compute_event_trigger_average(spikes, feature, ep)

In this example, we compute the mean firing rate feature for the event period (0.15, 0.3) with a bin size of 0.01.

Conclusion

Peri-event baseline vs response comparison is a crucial aspect of event-related analyses. Pynapple provides a range of tools and functions for processing and analyzing data, including event-related analyses. In this article, we explored how to use Pynapple to perform peri-event baseline vs response comparison and how to use the compute_event_trigger_average function to achieve comparable results. We also provided an example code snippet that demonstrates how to use Pynapple to perform baseline vs response comparison.

Future Work

In the future, we plan to explore other event-related analyses that can be performed using Pynapple, including event-related spectral perturbation (ERSP) and event-related oscillation (ERO). We also plan to develop new functions and tools for Pynapple to make it more user-friendly and efficient.

References

Pynapple documentation: https://pynapple.readthedocs.io/en/latest/
Example Peri-Event tutorial: https://pynapple.readthedocs.io/en/latest/tutorials/example_peri_event.html
compute_event_trigger_average function documentation: https://pynapple.readthedocs.io/en/latest/api/pynapple.compute_event_trigger_average.html
Peri-Event Baseline vs Response Comparison: A Q&A Article ================================================================

Introduction

In our previous article, we explored the concept of peri-event baseline vs response comparison and how to use Pynapple to perform this analysis. In this article, we will answer some frequently asked questions (FAQs) related to peri-event baseline vs response comparison and Pynapple.

Q: What is peri-event baseline vs response comparison?

A: Peri-event baseline vs response comparison is a type of event-related analysis that involves computing a statistic (e.g., mean firing rate) for each window and performing a paired test (e.g., baseline vs response mean). This analysis is essential in understanding the neural response to specific stimuli and identifying responsive clusters or regions of interest (ROIs).

Q: What is the difference between peri-event baseline vs response comparison and other event-related analyses?

A: Peri-event baseline vs response comparison is a specific type of event-related analysis that focuses on comparing the mean firing rate or other statistics between a baseline window and a response window. Other event-related analyses, such as event-related spectral perturbation (ERSP) and event-related oscillation (ERO), involve analyzing the spectral power or oscillatory activity in response to specific stimuli.

Q: How do I choose the correct window size and bin size for peri-event baseline vs response comparison?

A: The choice of window size and bin size depends on the specific research question and the characteristics of the data. A larger window size may provide more robust results, but may also introduce more noise. A smaller bin size may provide more detailed information, but may also increase the risk of overfitting.

Q: Can I use Pynapple to perform peri-event baseline vs response comparison on other types of data, such as EEG or fMRI?

A: Yes, Pynapple can be used to perform peri-event baseline vs response comparison on other types of data, such as EEG or fMRI. However, the specific functions and tools used may vary depending on the type of data and the research question.

Q: How do I interpret the results of peri-event baseline vs response comparison?

A: The results of peri-event baseline vs response comparison can be interpreted in terms of the statistical significance of the difference between the baseline and response windows. A significant difference may indicate a response to the stimulus, while a non-significant difference may indicate no response.

Q: Can I use Pynapple to perform other types of event-related analyses, such as ERSP or ERO?

A: Yes, Pynapple can be used to perform other types of event-related analyses, such as ERSP or ERO. However, the specific functions and tools used may vary depending on the type of analysis and the research question.

Q: Where can I find more information on Pynapple and event-related analyses?

A: More information on Pynapple and event-related analyses can be found on the Pynapple documentation website: https://pynapple.readthedocs.io/en/latest/

Q: Can I contribute to the development of Pynapple or provide feedback on the library?

A: Yes, you can contribute to the development of Pynapple or provide feedback on the library by submitting issues or pull requests on the Pynapple GitHub repository: https://github.com/pynapple/pynapple

Conclusion

In this article, we answered some frequently asked questions related to peri-event baseline vs response comparison and Pynapple. We hope that this article has provided helpful information and guidance for researchers using Pynapple to perform event-related analyses.

Future Work

In the future, we plan to continue developing and improving Pynapple, including adding new functions and tools for event-related analyses. We also plan to provide more documentation and tutorials on using Pynapple for event-related analyses.

References

Pynapple documentation: https://pynapple.readthedocs.io/en/latest/
Example Peri-Event tutorial: https://pynapple.readthedocs.io/en/latest/tutorials/example_peri_event.html
compute_event_trigger_average function documentation: https://pynapple.readthedocs.io/en/latest/api/pynapple.compute_event_trigger_average.html