Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data

For large-scale testing with graph-associated data, we present an empirical Bayes mixture technique to score local false-discovery rates (FDRs). Compared to procedures that ignore the graph, the proposed Graph-based Mixture Model (GraphMM) method gains power in settings where non-null cases form connected subgraphs, and it does so by regularizing parameter contrasts between testing units. Simulations show that GraphMM controls the FDR in a variety of settings, though it may lose control with excessive regularization. On magnetic resonance imaging data from a study of brain changes associated with the onset of Alzheimer's disease, GraphMM produces greater yield than conventional large-scale testing procedures.

Automated summary

The Graph-based Mixture Model (GraphMM) is proposed for large-scale testing with graph-associated data to control local false-discovery rates. Compared to procedures that ignore the graph, GraphMM performs better when non-null cases form connected subgraphs. In a study of brain changes associated with Alzheimer's disease onset, GraphMM produces greater yield than conventional large-scale testing procedures.