Topological false discovery rates for brain mapping based on signal height

Correcting the effect of multiple testing is important in statistical parametric mapping. If the threshold is too liberal, then spurious claims may flood in; if it is too conservative, then true hints may be overlooked. It is highly desirable to combine random field theory and the false discovery rate (FDR) to achieve more powerful detection under gauged topological errors. However, the current FDR method based on peak height does not fully meet this expectation, and sometimes is more conservative than the traditional family-wise error rate method, for unexplained reasons. In this paper, we introduce a new topological FDR method based on signal height. As analyzed in theory and validated with extensive experiments, it controls error rates much more accurately than the peak FDR method does, and substantially gains detection power. In addition, we discover reasons behind the peak FDR method's under-performance, and formulate equations to predict the two methods' behavior.