niphlem is a toolbox that extracts physiological signals recorded coincidentally with functional MRI data and estimates the signal phases so that they can be used as a covariate in subsequent analyses.
niphlem can generate multiple models of physiological noise to include as regressors from either ECG, pneumatic breathing belt or pulse-oximetry data. These are described in detail in Verstynen and Deshpande (2011).
Briefly, niphlem implements two physiological models for regressors generation:
- RETROICOR: A phasic decomposition method that isolates the fourier series that best describes the spectral properties of the input signal. This was first described by Glover and colleagues (2000).
- Variation Models: For low frequency signals (like the pneumatic belt and low-pass filtered pulse-oximetry) this does the combined respiration variance and response function described by Birn and colleagues (2006, 2008). For high frequency signals (i.e., ECG or high-pass filtered pulse-oximetry), this generates the heart-rate variance and cardiac response function described by Chang and colleagues (2009).
Python 3.6 or greater is required. Any of the below dependencies compatible wth such versions of Python should be OK:
- numpy
- matplotlib
- pandas
- scipy
- scikit_learn
- outlier_utils
pip install - U niphlem
Alternatively, if you are interested in installing the latest version under development, you may clone the github repository and install it from there directly:
git clone https://github.com/CoAxLab/niphlem.git
cd niphlem
pip install -U .
- Verstynen TD, Deshpande V. Using pulse oximetry to account for high and low frequency physiological artifacts in the BOLD signal. Neuroimage. 2011 Apr 15;55(4):1633-44.
- Chang C, Cunningham JP, Glover GH. Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage. 2009 Feb 1;44(3):857-69.
- Birn RM, Smith MA, Jones TB, Bandettini PA. The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage. 2008;40(2):644-654.