Thomas Nichols, GlaxoSmithKline Clinical Imaging Centre
Cluster mass inference - a new random field theory method for neuroimaging
Functional and structural brain imaging analyses typically fit univariate models at each volume element, or voxel, producing images of T-statistics. These statistics images must be threshold or assessed insome way to identify significant regions while controlling some specified false positive rate. A widely used method for assessing signficance is the cluster size test, where an arbitary threshold is applied to the statistic image and contiguous suprathreshold voxels are grouped into clusters. The size of a cluster forms a test statistic on the null hypothesis of no activation anywhere within the cluster, and P-values are found with Random Field Theory (RFT). Various authors have reported on improved sensitivity with a modified version of the cluster size test, the cluster mass test. Cluster mass is the integral of suprathreshold intensities within the cluster, and captures information about both the extent and magnitude of the effect. However, all previous work has relied on permutation inference as no distributional results have been available. I will show recent work on deriving a null distribution for cluster mass using RFT. Using the approximate parabolic shape about local maxima and distributional results for the cluster extent and peak curvature, we produce a joint distribution for mass and peak height which is marginalized to produce a P-value for the mass statistic. We show results on simulated and real data which demonstrate the tests validity and power.