PyMVPD

PyMVPD: MultiVariate Pattern Dependence 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)

2. Neural Network Models

• NN_1layer: 1-layer fully-connected linear neural network model
• NN_5layer: 5-Layer fully-connected linear neural network model
• NN_5layer_dense: 5-Layer fully-connected linear neural network model with dense connections

Workflow

Usage

Example Dataset

Data of one subject from the StudyForrest dataset: FFA - fusiform face area, GM - grey 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

1. Choose one MVPD model, set model parameters, input functional data and ROI masks, set output directory in analysis_spec.py;
2. Run data_loading.py to preprocess functional data;

python3 data_prep.py

3. Run MVPD model:

sh analysis_exec.sh

Contact

Reach out to mtfang0707@gmail.com for questions, suggestions and feedback.