We are glad to announce that our new brain network construction & classification toolbox, BrainNetClass v1.0, has been released on Github (https://github.com/zzstefan/BrainNetClass). It is a Matlab-based, open-coded, user-friendly brain functional connectivity network-based disease classification toolbox that automatically conducts functional network construction, network feature extraction and selection, parameter optimization, classification, and result demonstration. It was designed to help neuroscientists, doctors, or researchers in other fields easily and correctly work on brain functional connectome with state-of-the-art algorithms and conduct rigorous individualized disease classification or other machine learning tasks. It is hoped that this toolbox could be of help in standardization the methodology and boost reproducibility, generalizability, and interpretability of the results.
Specifically, BrainNetClass v1.0 provides abundant means of brain functional network construction, including those recently developed for defining “high-order” functional networks and those utilizing sparse representation with biologically meaningful constraint for robust and consistent network construction. What’s more, it provides standard yet rigorous network-based classification with choices for feature extraction, feature reduction, cross-validation, and performance evaluation. Importantly, it does not stop at providing simple numbers like diagnosis accuracy. Instead, BrainNetClass v1.0 offers a comprehensive battery of result evaluation, including the receiver operating characteristic curve, suggestive parameters for future use, and the model robustness test, in addition to a full log of results for a hassle-free report. With a simple configuration on a GUI interface, all these results and reports are a quick click of the “Run” button away. For details, please see the manual; exemplary data are provided for a quick walkthrough. The corresponding paper will be openly accessible soon.
BrainNetClass v1.0 is developed by the Image Display, Enhancement, and Analysis (IDEA) Laboratory, Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill. We would like to thank Xiaobo Chen, Yu Zhang, Lishan Qiao, Renping Yu for their contributions. It is supported by NIH grants EB022880 and AG041721. Please contact Zhen Zhou (zzstefan@email.unc.edu), Han Zhang (hanzhang@med.unc.edu), and Dinggang Shen (dgshen@med.unc.edu) for any correspondence.