Collecting physiological data during fMRI experiments can improve fMRI data cleaning and contribute to our understanding of psychophysiological processes; however, these recordings are frequently fraught with artifacts from the MRI pulse sequence. Here, we look at manufacturer recommendations for filtering such artifacts from physiological data collected during single-band, single-echo fMRI sequences and extend these recommendations to address artifacts associated with multiband, multi-echo fMRI sequences. While the magnitude and frequencies of artifacts differ with these aspects of pulse sequences, their effects can be mitigated via application of digital filters focused on the slice collection and repetition time.
The main program in this package is physioComb.py , which is a BIDS-friendly command-line Python script that performs digital filtering of MR-related
artifacts from eletrophysiological data (i.e., electrocardiogram and electrodermal activity), using IIR notch filters centered at the slice collection frequency and (optionally) TR frequency. NOTE: current BIDS specifications recommend using cardiac to name columns in physio containing cardiac data, regardless of recording source (i.e., ECG or PPG) and physioComb.py just looks for a cardiac column. Proceed with caution and/or discard cardiac_filtered* columns if your cardiac data is from a photoplethysmogram.
usage: physioComb.py [-h] [--slices SLICES] [--mb MB] [--tr TR] [--multiecho MULTIECHO] [--biopac] [--progress] [--multicomb MULTICOMB]
[--verbose]
dset
Accept BIDS directory, specify # slices if slice timing isn't specified in BOLD sidecar.
positional arguments:
dset Valid BIDS dataset containing physiological data.
optional arguments:
-h, --help show this help message and exit
--slices SLICES The number of slices acquired in BOLD sequences concurrent with physiological data acquisition. If multiple BOLD
sequences were used with different numbers of slices, this option is invalid, please make sure slice timing is
specified in the json sidecar for each BOLD sequence.
--mb MB Multiband factor of fMRI scan sequence (if single band, --mb=1).
--tr TR Repetition time of fMRI scan sequence.
--multiecho MULTIECHO
Does the fMRI scan sequence collect multiple echoes.
--biopac Run only BIOPAC-recommended filtering at single-band slice collection frequency.
--progress Display progress bar (note: requires enligten).
--multicomb MULTICOMB
Run multiple comb notch filtering strategies and save all outputs.
--verbose Print information as the cleaning script runs.
To compare signal quality indices across signals (i.e., filtered and raw), signalQuality.py reads in a BIDS dataset (with PhysioComb derivatives) and calculates kurtosis, signal-to-noise ratio, and an integrated signal quality heuristic based on work from Zhao & Zhang (2018).
usage: signal_quality.py [-h] [--verbose] dset
Accept BIDS directory, specify # slices if slice timing isn't specified in BOLD sidecar.
positional arguments:
dset Valid BIDS dataset containing physiological data with PhysioComb derivatives (i.e., run physioComb.py first).
optional arguments:
-h, --help show this help message and exit
--verbose Print filename as the script runs.
These scripts were written for a previous version of a manuscript-in-progress comparing comb filters for MR-related noise removal.
ephys_filtering.py is a command-line Python script that performs digital filtering of MR-related artifacts from eletrophysiological data,
using IIR notch filters centered at slice collection and TR frequencies.
usage: ephys_filtering.py [-h] [--mb MB] [--verbose] in_file out_dir tr slices
Accept physio + sidecar input for cleaning EDA/ECG collected simultaneously
with fMRI.
positional arguments:
in_file AcqKnowledge file containing physio measurements from a single
scan session.
out_dir Absolute or relative path to directory where output (figures and
cleaned data) will be saved.
tr The TR (repetition time) in seconds of the fMRI scan sequence
used during colleciton of these data.
slices Number of slices collected by fMRI scan sequence.
optional arguments:
-h, --help show this help message and exit
--mb MB Multiband factor of fMRI scan sequence (if single band, --mb=1).
--verbose Print information as the cleaning script runs.
biopac_to_csv.py is a command-line Python script that converts BIOPAC AcqKnowledge files (extension .acq) to comma-separated value files (.csv)
with a common time index and standardized headers (currently only supports ECG, EDA, and Respiration recordings).
usage: biopac_to_csv.py [-h] in_files [in_files ...] out_dir
Accept BIOPAC .acq files as input, save them as .csv files.
positional arguments:
in_files AcqKnowledge file(s) containing physio measurements from a
single scan session.
out_dir Absolute or relative path to directory where output (figures and
cleaned data) will be saved.
optional arguments:
-h, --help show this help message and exit
requirements.txt specifies Python packages (and versions thereof) used in the creation and application of these filters.
Contains interactive Jupyter notebooks (denoising_ecg.ipynb and denoising_eda.ipynb) that apply the filters to electrocardiogram and electrodermal
activity data, respectively, collected during multiband, single-echo and multi-echo EPI sequences, plots raw and cleaned data,
and compares ferquency spectra across the filtering strategies using magnitude squared coherence.
To perform the analyses detailed in Bottenhorn et al., (preprint), the following workflow was run:
physioComb.py --multicomb=Trueto filter ECG and EDA signals according to BIOPAC recommendations and updated filtersmsc_and_plot.pyto calculates magnitude squared coherence, pairwise, between signals per supported modality (i.e., ECG, EDA)signal_quality.pyto calculate signal quality indices across filtering strategies.