A statistical approach for detecting common features.

Background: With increasing numbers of datasets in neuroimaging studies, it has become an important task to pool information, in order to increase the statistical power of tests and for cross validation. However, no robust global approach unambiguously identifies the common biological abnormalities in, for example, resting-state functional magnetic resonance imaging in a number of mental disorders, where there are multiple datasets/attributes. Here we propose a novel and efficient statistical approach to this problem that finds common features in multiple datasets.

New Method: By collecting the statistics of each dataset into a vector, our method uses a 'multi-dimensional local false discovery' rate to pool information and make full use of the joint distribution of datasets.

Results: We have tested our approach extensively on both simulated and clinical datasets. By conducting simulation studies, we find that our approach has a higher statistical power than existing approaches, especially on correlated datasets. Employing our approach on clinical data yields findings that are consistent with the existing literature.

Comparison With Existing Methods: Conventional methods cannot determine the false discovery rate underlying multiple datasets/attributes. Our approach can effectively handle these datasets. It has a solid Bayesian interpretation, and a higher power than other approaches in numerical simulations. This can be explained by the incorporation of correlations, between different attributes, into the new method.

Conclusions: In this work, we present a natural, novel and powerful statistical approach to tackle situations involving multiple datasets or attributes. This new method has significant advantages over existing approaches and wide applications.