Reference and Reporting Information

This page describes how to reference and report on using this method for parameterizing neural power spectra, and related information.

Table of Contents

Reference

If you use this method, and/or want to refer to our approach, examples, and discussion points, please cite the following paper:

Reference

Donoghue T, Haller M, Peterson EJ, Varma P, Sebastian P, Gao R, Noto T, Lara AH, Wallis JD, Knight RT, Shestyuk A, Voytek B (2020). Parameterizing neural power spectra into periodic and aperiodic components. Nature Neuroscience, 23, 1655-1665. DOI: 10.1038/s41593-020-00744-x

Direct link: https://doi.org/10.1038/s41593-020-00744-x

Example Applications

As well as reporting on the method, the full version of the paper includes simulation work as well as example analyses and applications. The code for the simulations and applications is indexed and available in the Paper repository.

You can also find the full list of articles that cite the Parameterizing Neural Power Spectra paper at this Google scholar link.

Methods Reporting

If you use this module and the power spectrum model in your work, there is some information you should include in the methods section.

Specifically, we recommend including the following information in the methods section:

• The version number of the module that was used (for example 1.0.0)

• The algorithm settings that were used

  • You should report all public settings, even if default values are used

• The frequency range of the data that was fit

In addition, we recommend that reports should include information on:

• Details of the data, including modality and any preprocessing steps applied

• How power spectrum were generated, including the length of segments used to calculate spectra

• If and how model goodness-of-fit measures were assessed

Reporting Template & Example

To assist in reporting on using spectral parameterization, we have created some templates for reporting on spectral parameterization methods. There are also some utilities included in the code to collect the required information.

The following box is an example of what a methods report might look like (where all of the X’s should be filled in with the relevant information).

Methods Report Template

Spectral parameterization, using the specparam Python tool (version X.X.X) was used to parameterize neural power spectra. Settings for the algorithm were: peak width limits : XX; max number of peaks : XX; minimum peak height : XX; peak threshold : XX; and aperiodic mode : XX. Power spectra were parameterized across the frequency range XX to XX Hz.

Checking module version

If you are not sure which version of the module you have installed, you can check the __version__ from Python, using the following code:

# Check the version of the tool
from specparam import __version__ as specparam_version
print('Current specparam version:', specparam_version)

Generating Methods Reports

As of version 1.0.0 there are code utilities to extract all required information for reporting, and for generating methods reports.

These utilities require a defined model object, such as SpectralModel or SpectralGroupModel, assumed to be called ‘model_obj’ in the following examples. This object will be used to extract all the relevant settings and any available meta-data for reporting.

The methods_report_info() function can be used to print out the information you need for reporting:

# Import the utility to print out information for reporting
from specparam.utils.reports import methods_report_info

# Print out all the methods information for reporting
methods_report_info(model_obj)

The methods_report_text() function can be used to print out an auto-generated methods report, like the one demonstrated above, with all available information filled:

# Import the utility to print out information for reporting
from specparam.utils.reports import methods_report_text

# Generate methods text, with methods information inserted
methods_report_text(model_obj)