ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRI

TL;DR

ACTION is an open-source, user-friendly toolbox that enhances brain network analysis from fMRI data by integrating data augmentation, deep learning, and federated learning techniques, thereby improving analysis accuracy and utility.

Contribution

The paper introduces ACTION, a comprehensive Python toolbox that uniquely combines fMRI data augmentation, deep learning model support, and federated learning for brain network analysis.

Findings

Effective augmentation of BOLD signals and brain networks.

Supports deep learning models with large-scale pretraining.

Facilitates multi-site fMRI studies with federated learning.

Abstract

Functional magnetic resonance imaging (fMRI) has been increasingly employed to investigate functional brain activity. Many fMRI-related software/toolboxes have been developed, providing specialized algorithms for fMRI analysis. However, existing toolboxes seldom consider fMRI data augmentation, which is quite useful, especially in studies with limited or imbalanced data. Moreover, current studies usually focus on analyzing fMRI using conventional machine learning models that rely on human-engineered fMRI features, without investigating deep learning models that can automatically learn data-driven fMRI representations. In this work, we develop an open-source toolbox, called Augmentation and Computation Toolbox for braIn netwOrk aNalysis (ACTION), offering comprehensive functions to streamline fMRI analysis. The ACTION is a Python-based and cross-platform toolbox with graphical user-friendly interfaces. It enables automatic fMRI augmentation, covering blood-oxygen-level-dependent (BOLD) signal augmentation and brain network augmentation. Many popular methods for brain network construction and network feature extraction are included. In particular, it supports constructing deep learning models, which leverage large-scale auxiliary unlabeled data (3,800+ resting-state fMRI scans) for model pretraining to enhance model performance for downstream tasks. To facilitate multi-site fMRI studies, it is also equipped with several popular federated learning strategies. Furthermore, it enables users to design and test custom algorithms through scripting, greatly improving its utility and extensibility. We demonstrate the effectiveness and user-friendliness of ACTION on real fMRI data and present the experimental results. The software, along with its source code and manual, can be accessed online.