Update README.md

synchronize README with that showing on the webpage

Author: jrasero

README.md

@@ -1,15 +1,15 @@
-# niphlem
 
-*niphlem* is a toolbox that extracts physiological recordings during MRI scanning and estimates the signal phases so that they can be used as a covariate in your general linear model (GLM) with fMRI data.
+*niphlem*: NeuroImaging-oriented Physiological Log Extraction for Modeling
+=====================================================================
 
-*niphlem* can generate multiple models of physiological noise to include as regressors in your GLM model from either ECG, pneumatic breathing belt or pulse-oximetry data.  These are described in Verstynen and Deshpande (2011).
+*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.
 
-Briefly, niphlem implements two physiological models for regressors generation:
+*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).
 
-- *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. 
-- *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 (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).
+Briefly, niphlem implements two physiological models for regressors generation:
 
-*niphlem* can also extract cardiac and respiratory signals from the pulse-oximitry data stream itself, as described in Verstynen and Deshpande (2011).
+- **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).
 
 ## Dependencies
 
@@ -21,19 +21,18 @@ Python 3.6 or greater is required. Any of the below dependencies compatible wth
 - scipy
 - scikit_learn
 - outlier_utils
-    
+
 ## Install
 
     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 .
-    
+
 ## 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.
-