niphlem: NeuroImaging-oriented Physiological Log Extraction for Modeling
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).
Installation
niphlem can be easily installed through pypi as follows:
pip install -U niphlem
References:
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.