NBS-Predict is a prediction-based extension of the Network-based Statistic (Zalesky et al., 2010). NBS-Predict aims to alleviate the curse of dimensionality, lack of interpretability, and problem of generalizability. By combining the powerful features of machine learning and graph theory in a cross-validation (CV) structure, it provides a fast and convenient tool to identify neuroimaging-based biomarkers with high generalizability. Unlike generic machine learning algorithms, results derived from the toolbox are straightforwardly interpretable.

NBS-Predict comes with a user-friendly graphical user interface (GUI) developed on MATLAB. Thus, it does not require any programming expertise. The toolbox provides an interactive viewer to visualize the results. The extensive user manual and usage tutorials are included in the toolbox.

Reference:
Serin, E., Zalesky, A., Matory, A., Walter, H., & Kruschwitz, J. D. (2021). NBS-Predict: A Prediction-based Extension of the Network-based Statistic. NeuroImage, 118625.