Main content

Home

Menu

Loading wiki pages...

View
Wiki Version:
## **Connectome-based Machine Learning using NBS-Predict** ## **Emin Serin, MSc**, Charite Berlin **Relevant work:** Serin et al. (2021) [NBS paper][1] in NeuroImage and the book chapter [preprint][2] **Softwares/programs requirements:** [MATLAB][3] (R2016b or newer) including Statistics and Machine Learning Toolbox, Parallel Computing Toolbox (optional) as well as [NBS-Predict][4] **AOMICS dataset:** [ID1000][5] with some modifications (see 'files' section below for data used in this presentation). **Modalities:** fMRI **Abstract:** In this tutorial, I will demonstrate NBS-Predict, 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 crossvalidation structure, it provides a fast and convenient tool to identify neuroimaging-based biomarkers with high generalizability. Unlike generic machine leaming algorithms, results derived from the toolbox are straightforwardly interpretable. NBS-Predict comes with a user-friendly graphical user interface (GUI) developed on MATLAB. Thu 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. [1]: https://doi.org/10.1016/j.neuroimage.2021.118625 [2]: https://osf.io/4ghpm/ [3]: https://uk.mathworks.com/products/matlab.html [4]: https://github.com/eminSerin/NBS-Predict [5]: https://openneuro.org/datasets/ds003097
OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
Accept
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.
Accept
×

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.