# Brain Network Construction and Classification Toolbox (BrainNetClass)

## TL;DR

BrainNetClass is an open-source MATLAB toolbox that offers advanced methods for constructing and classifying brain functional networks, facilitating neuroimaging analysis and diagnosis without requiring extensive machine learning expertise.

## Contribution

This work introduces BrainNetClass, a comprehensive toolbox integrating state-of-the-art brain network construction and classification methods for the neuroscience community.

## Key findings

- Demonstrated on real resting-state fMRI datasets.
- Provides interpretable results for clinical and cognitive studies.
- Supports advanced, complex interaction modeling among brain regions.

## Abstract

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.

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Source: https://tomesphere.com/paper/1906.09908