bycycle - cycle-by-cycle analysis of neural oscillations

ByCycle is a module for analyzing neural oscillations in a cycle-by-cycle approach.

Overview

bycycle is a tool for quantifying features of neural oscillations in the time domain, as opposed to the frequency domain, using a cycle-by-cycle approach. Rather than applying narrowband filters and other methods that use a sinusoidal basis, this approach segments a recording into individual cycles and directly measures each of their properties including amplitude, period, and symmetry.

This is most advantageous for analyzing the waveform shape properties of neural oscillations. It may also provide advantages for studying traditional amplitude and frequency effects, as well. Using cycle properties can also be used for burst detection.

A full description of the method and approach is available in the paper below.

Documentation

Documentation for bycycle is available on the documentation site.

This documentation includes:

• Tutorials: with a step-by-step guide through the approach and how to apply it
• Examples: demonstrating an example analysis and use case
• API list: which lists and describes all the code and functionality available in the module
• Glossary: which defines key terms used in the module

Dependencies

bycycle is written in Python, and requires >= Python 3.6 to run.

It has the following dependencies:

• neurodsp >= 2.1.0
• numpy >= 1.18.5
• scipy >= 1.4.1
• pandas >= 0.25.3
• matplotlib >= 3.0.3
• pytest (optional)

Install

The current major release is the 1.X.X series, which is a breaking change from the prior 0.X.X series.

Check the changelog for notes on updating to the new version.

Stable Version

To install the latest stable release, you can use pip:

$ pip install bycycle

ByCycle can also be installed with conda, from the conda-forge channel:

$ conda install -c conda-forge bycycle

Development Version

To get the latest, development version, you can get the code using git:

$ git clone https://github.com/bycycle-tools/bycycle

To install this cloned copy, move into the directory you just cloned, and run:

$ pip install .

Editable Version

To install an editable, development version, move into the directory you cloned and install with:

$ pip install -e .

Reference

If you use this code in your project, please cite:

Cole SR & Voytek B (2019) Cycle-by-cycle analysis of neural oscillations. Journal of neurophysiology
122(2), 849-861. DOI: 10.1152/jn.00273.2019

Direct Link: https://doi.org/10.1152/jn.00273.2019

Contribute

This project welcomes and encourages contributions from the community!

To file bug reports and/or ask questions about this project, please use the Github issue tracker.

To see and get involved in discussions about the module, check out:

• the issues board for topics relating to code updates, bugs, and fixes
• the development page for discussion of potential major updates to the module

When interacting with this project, please use the contribution guidelines and follow the code of conduct.

Quickstart

The main function in bycycle is compute_features, which takes a time series and some parameters as inputs, and returns a table of features for each cycle.

For example, consider having a 1-dimensional numpy array, sig, which is a neural signal time series sampled at 1000 Hz (fs) with an alpha (8-12 Hz, f_range) oscillation. We can compute the table of cycle features with the following:

from neurodsp.sim import sim_bursty_oscillation
from bycycle.features import compute_features

fs = 1000
f_range = (8, 12)

sig = sim_bursty_oscillation(10, fs, freq=10)
df_features = compute_features(sig, fs, f_range)

Note that the above compute_features command used default parameters to localize extrema and detect bursts of oscillations. However, it is important to knowledgeably select these parameters, as described in the algorithm tutorial.

The following example introduces some potential parameter changes:

threshold_kwargs = {'amp_fraction_threshold': .2,
                    'amp_consistency_threshold': .5,
                    'period_consistency_threshold': .5,
                    'monotonicity_threshold': .8,
                    'min_n_cycles': 3}

narrowband_kwargs = {'n_seconds': .5}

df = compute_features(sig, fs, f_range, center_extrema='trough',
                      burst_method='cycles', threshold_kwargs=threshold_kwargs,
                      find_extrema_kwargs={'filter_kwargs': narrowband_kwargs})

• center_extrema determines how the cycles are segmented. 'T' indicates the center extrema is a trough, so cycles are segmented peak-to-peak.
• burst_method selects which method to use for burst detection. The 'cycles' option uses features of adjacent cycles in order to detect bursts (e.g. period consistency, see next item). The 'amp' option uses an amplitude threshold to determine the cycles that are part of an oscillatory burst.
• threshold_kwargs sets the keyword arguments for the burst detection functions. For the cycles method, there are 5 keyword arguments (see the end of the algorithm tutorial for advice on choosing these parameters).
• find_extrema_kwargs sets the keyword arguments for the function used to localize peaks and troughs. Most notably, you can change the duration of the bandpass filter (n_seconds) used during extrema localization (see section 1 of the algorithm tutorial)

DataFrame Output

The output of bycycle is a pandas.DataFrame, which is a table, as shown below. There are many columns, so the table is split into two images here.

Each row of this table corresponds to an individual segment of the signal, or a putative cycle of the rhythm of interest.

Columns include:

• sample_peak: the sample of the signal at which the peak of this cycle occurs
• period: period of the cycle
• time_peak: duration of the peak period
• volt_amp: amplitude of this cycle, average of the rise and decay voltage
• time_rdsym: rise-decay symmetry, the fraction of the cycle in the rise period (0.5 is symmetric)
• time_ptsym: peak-trough symmetry, the fraction of the cycle in the peak period (0.5 is symmetric)
• period_consistency: consistency between the periods of the adjacent cycles, used in burst detection
• is_burst: indicator if the cycle is part of an oscillatory burst

The features in this table can be further analyzed, as demonstrated in the resting state data tutorial and the data example. For example, we may be interested in the distribution of rise-decay symmetry values in a resting state recording, shown below.

Burst Detection Results

Funding

Supported by NIH award R01 GM134363 from the NIGMS.