  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.
