PyMVPD-LITE 0.0.2

A python package for multivariate pattern dependence

PyMVPD LITE

PyMVPD LITE: A LITE version of MultiVariate Pattern Dependence (MVPD) analysis in Python

MVPD Model Family

1. Linear Regression Models

• L2_LR: linear regression model with L2 regularization
• PCA_LR: linear regression model with no regularization after principal component analysis (PCA)

Installation & Dependencies

The easiest way to install the package is to execute (possibly in a new virtual environment) the following command:

pip install PyMVPD-LITE

You can also install from the GitHub repository to get the most up-to-date version.

git clone https://github.com/sccnlab/PyMVPD_LITE.git
pip install -r requirements.txt

The following packages need to be installed to use PyMVPD LITE:

• python >= 3.6
• nibabel>=3.2.1
• numpy>=1.19.3
• scikit-learn>=0.20.1
• scipy>=1.1.0

Tutorial

Test Dataset

Data of one subject from the StudyForrest dataset.

Predictor ROI: FFA - fusiform face area,

Target ROI: GM - gray matter.

• Raw data were first preprocessed using fMRIPrep and then denoised by using CompCor (see more details in Fang et al. 2019).

Example Analyses and Scripts

To give a quick try for MVPD analysis, you can directly run our example script run_MVPD.py:

cd exp/
python3 run_MVPD.py

We have also provided a tutorial in jupyter notebook. Feel free to check it out!

Customization

To generate your own scripts, please follow the three steps:

import os
from mvpdlite import data_loading, model_exec

Step 1 - Analysis Specification

sub='sub-01' # subject whose data are to be analyzed
total_run=XX # total number of experimental runs

# Input functional Data
filepath_func=[]
filepath_func+=['path/to/functional/data/run1.nii.gz']
filepath_func+=['path/to/functional/data/run2.nii.gz']
......

# Input predictor ROI mask and target ROI mask
filepath_mask1='path/to/predictor/ROI/mask.nii.gz'
filepath_mask2='path/to/target/ROI/mask.nii.gz'

base1=os.path.basename(filepath_mask1)
base2=os.path.basename(filepath_mask2)
roi_1_name=base1.split('.nii')[0]
roi_2_name=base2.split('.nii')[0]

# Output Directory
roidata_save_dir='path/to/save/roidata/'
results_save_dir='path/to/save/results/'

# Choose MVPD model
model_type='L2_LR' # ['PCA_LR', 'L2_LR']

# Set model parameters
# Only for PCA_LR
num_pc=3 # number of principal components used 
# Only for L2_LR
alpha=0.01 

# Save predicted timecourses
save_prediction=False # default

Step 2 - Data Loading

data_loading.load_data(sub, total_run, roi_1_name, roi_2_name, filepath_func, filepath_mask1, filepath_mask2, roidata_save_dir)

Step 3 - Analysis Execution

model_exec.MVPD_exec(model_type, sub, total_run, alpha, num_pc, roidata_save_dir, roi_1_name, roi_2_name, filepath_func, filepath_mask1, filepath_mask2, results_save_dir, save_prediction)

Contact

Reach out to Mengting Fang ([e-mail]) for questions, suggestions and feedback!