  For large-scale testing with graph-associated data, we present an empirical
Bayes mixture technique to score local false discovery rates. Compared to
empirical Bayes procedures that ignore the graph, the proposed 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 false discovery rate in a variety of settings. On
magnetic resonance imaging data from a study of brain changes associated with
the onset of Alzheimer's disease, GraphMM produces substantially greater yield
than conventional large-scale testing procedures.
