PyMVPD: MultiVariate Pattern Dependence in Python
- Linear Regression Models
- L2_LR: linear regression model with L2 regularization
- PCA_LR: linear regression model with no regularization after principal component analysis (PCA)
- 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
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).
- Choose one MVPD model, set model parameters, input functional data and ROI masks, set output directory in analysis_spec.py;
- Run data_loading.py to preprocess functional data;
python3 data_prep.py
- Run MVPD model:
sh analysis_exec.sh
Reach out to [email protected] for questions, suggestions and feedback.