Journal article

NBS-Predict: A prediction-based extension of the network-based statistic

Emin Serin, Andrew Zalesky, Adu Matory, Henrik Walter, Johann D Kruschwitz



Graph models of the brain hold great promise as a framework to study functional and structural brain connectivity across scales and species. The network-based statistic (NBS) is a well-known tool for performing statistical inference on brain graphs, which controls the family-wise error rate in a mass univariate analysis by combining the cluster-based permutation technique and the graph-theoretical concept of connected components. As the NBS is based on group-level inference statistics, it does not inherently enable informed decisions at the level of individuals, which is, however, necessary for the realm of precision medicine. Here we introduce NBS-Predict, a new approach that combines the p..

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University of Melbourne Researchers


Awarded by NIH

Funding Acknowledgements

This study was funded by the Melbourne/Berlin Research Partner-ship (MEL-BER) in the context of the Berlin University Alliance. We would like to thank Tim Hahn of the University of Munster for his help-ful comments on NBS-Predict during our meeting. Data used in the third application of NBS-Predict were provided [in part] by the Human Con-nectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.