PyMVPD-LITE 0.0.3

A python package for multivariate pattern dependence

PyMVPD_LITE

This is a lite version of PyMVPD to model the multivariate interactions between brain regions using fMRI data. You can find a description of the MVPD method in this article.

[NEW!] We added a preprint with detailed descriptions about the toolbox and example applications. Check it out here!

MVPD Model Family

1. Linear Regression (LR) Models

Available built-in model components:

• Dimensionality reduction: principal component analysis (PCA), independent component analysis (ICA)

• Regularization: Lasso (L1), Ridge (L2), RidgeCV (L2 with built-in cross-validation)

• Cross validation: leave k run out

  Example LR models:

  • L2_LR: linear regression model with L2 regularization
  • PCA_LR: linear regression model with PCA but no regularization

In addition to the above built-in functions, you can also customize your own functions by adding scripts under mvpdlite/custom_func.

Workflow

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 or other pre-implemented models under exp/ (e.g. run_MVPD_xxx.py):

cd exp/
python3 run_MVPD.py

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

Customization

To customize and run your own MVPD model, please follow the three steps:

import os
from mvpdlite import data_loading, model_exec

Step 1 - Analysis Specification

# Model Input Info
inputinfo=data_loading.structtype()
inputinfo.sub='sub-01' # subject whose data are to be analyzed
filepath_func=[] # input functional Data
filepath_func+=['path/to/functional/data/run1.nii.gz']
filepath_func+=['path/to/functional/data/run2.nii.gz']
......

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

inputinfo.roidata_save_dir='path/to/save/roidata/' # output data directory
inputinfo.results_save_dir='path/to/save/results/' # output model results directory
inputinfo.save_prediction=False # whether to save predicted timecourses in the target ROI

# MVPD Model Parameters
params=data_loading.structtype()
params.leave_k=1 # cross validation: leave k run out, default=1

### LR model parameters
......

Step 2 - Data Loading

data_loading.load_data(inputinfo)

Step 3 - Analysis Execution

model_exec.MVPD_exec(inputinfo, params)

Required Input Information

• inputinfo.sub
  • This variable specifies the subject whose data are to be analyzed.
• input.filepath_func
  • This variable specifies the list of paths to the directories containing processed functional data.
• inputinfo.filepath_mask1
  • This variable specifies the path to the directory containing the predictor ROI mask.
• inputinfo.filepath_mask2
  • This variable specifies the path to the directory containing the target ROI mask.
• inputinfo.roidata_save_dir
  • This variable specifies the path to the directory where the extracted functional data will be saved.
• inputinfo.results_save_dir
  • This variable specifies the path to the directory where the results will be saved.
• inputinfo.save_prediction
  • This variable specifies whether to save predicted timecourses in the target ROI.

List of Model Parameters

NOTICE: Remember to set the value of the parameter manually if you do not want to use the default value.

• General model parameters

  • params.leave_k
    • This parameter determines the number of leave out runs in cross-validation.
    • The default value is 1 (leave-one-run-out procedure).

• LR model parameters

  • params.dim_reduction:

    • This parameter determines whether dimensionality reduction is applied to the input data.
    • It is only used if you are using a linear regression model by setting params.mode_class='LR'
    • The default value is false.

  • params.dim_type:

    • This parameter determines the type of the dimensionality reduction.
    • It is only used if you are using a linear regression model and you set "params.dim_reduction=True".
    • The available values are 'pca', 'ica', or the name of your custom dimensionality reduction method.
    • The default value is 'pca'.

  • params.num_dim:

    • This parameter determines the number of dimensions to keep after dimensionality reduction.
    • It is only used if you are using a linear regression model and you set "params.dim_reduction=True".
    • The default value is 3.

  • params.lin_reg:

    • This parameter determines whether to add a regularization term to the linear regression model.
    • It is only used if you are using a linear regression model by setting params.mode_class='LR'.
    • The default value is false.

  • params.reg_type

    • This parameter determines the type of regularization term that you want to add to the linear regression model.
    • It is only used if you are using a linear regression model with regularization by setting "params.mode_class='LR', params.lin_reg=True".
    • The available values are 'Ridge', 'Lasso', and 'RidgeCV'.
    • The default value is 'Ridge'.

  • params.reg_strength

    • This parameter determines the regularization strength of the chosen regularization term.
    • It is only used if you are using a linear regression model with regularization by setting "params.mode_class='LR', params.lin_reg=True".
    • The default value is '0.001'.

  • params.reg_strength_list

    • This parameter determines the array of regularization strength values to try in the cross-validation for Ridge regression.
    • It is only used if you are using a linear RidgeCV regression model by setting "params.mode_class='LR', params.lin_reg=True, params.reg_type='RidgeCV'".
    • The default array is [0.001, 0.01, 0.1].

Citation

PyMVPD has been used in:

• PyMVPD: A toolbox for multivariate pattern dependence. PDF
  Fang, M., Poskanzer, C., Anzellotti, S.

• Identifying hubs that integrate responses across multiple category-selective regions.
  Fang, M., Aglinskas, A., Li, Y., Anzellotti, S.

If you plan to use the toolbox, please consider citing this.

@article{fang2021pymvpd,
  title={PyMVPD: A toolbox for multivariate pattern dependence},
  author={Fang, Mengting and Poskanzer, Craig and Anzellotti, Stefano},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}

Contact

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