Metadata-Version: 2.1
Name: PyMVPD-LITE
Version: 0.0.1
Summary: A python package for multivariate pattern dependence
Home-page: https://github.com/sccnlab/PyMVPD_LITE
Author: Mengting Fang
Author-email: mtfang0707@gmail.com
License: UNKNOWN
Keywords: fMRI,MVPD,machine learning,connectivity
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: nibabel (>=3.2.1)
Requires-Dist: numpy (>=1.19.3)
Requires-Dist: scikit-learn (>=0.20.1)
Requires-Dist: scipy (>=1.1.0)

# 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)

## Workflow
<img src="/PyMVPD_LITE_workflow.png" width="750"/>

## Usage
### Example Dataset
Data of one subject from the [_StudyForrest_](http://studyforrest.org) dataset: FFA - fusiform face area, GM - grey matter.

* Raw data were first preprocessed using [fMRIPrep](https://fmriprep.readthedocs.io/en/latest/index.html) and then denoised by using CompCor (see more details in [Fang et al. 2019](https://doi.org/10.31234/osf.io/qbx4m)).

### 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](mvpd/analysis_spec.py);
2. Run [data_loading.py](mvpd/data_loading.py) to preprocess functional data;
```
python3 data_loading.py
```
3. Run MVPD model: 
```
sh analysis_exec.sh
```

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


