# Brain Network Construction and Classification Toolbox (BrainNetClass)

arXiv: https://arxiv.org/abs/1906.09908 · HTML: https://mlsanity.com/paper/1906.09908/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 8 of 8)

- [CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis](https://mlsanity.com/paper/2301.01642/) (2023)
- [PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis](https://mlsanity.com/paper/2305.14376/) (2023)
- [Principle of Relevant Information for Graph Sparsification](https://mlsanity.com/paper/2206.00118/) (2022)
- [MvHo-IB: Multi-View Higher-Order Information Bottleneck for Brain Disorder Diagnosis](https://mlsanity.com/paper/2507.02847/) (2025)
- [ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRI](https://mlsanity.com/paper/2405.06178/) (2024)
- [Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks](https://mlsanity.com/paper/2403.20058/) (2024)
- [InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis](https://mlsanity.com/paper/2506.08884/) (2025)
- [BrainNet Studio: A Unified Toolkit for Brain Network Construction, Intelligent Analysis, and Visualization](https://mlsanity.com/paper/2609.37956/) (2026)

## Abstract (arXiv)

Brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods. It comprises various brain network construction methods, including some state-of-the-art methods that were recently developed to capture more complex interactions among brain regions along with connectome feature extraction, reduction, parameter optimization towards network-based individualized classification. BrainNetClass is a MATLAB-based, open-source, cross-platform toolbox with graphical user-friendly interfaces for cognitive and clinical neuroscientists to perform rigorous computer-aided diagnosis with interpretable result presentations even though they do not possess neuroimage computing and machine learning knowledge. We demonstrate the implementations of this toolbox on real resting-state functional MRI datasets. BrainNetClass (v1.0) can be downloaded from https://github.com/zzstefan/BrainNetClass.