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Dear Tal, Hi all. Here is a very basic question: all the non-TBSS whole brain FA literature in my area involves picking p<0.001 (UNCORRECTED FOR MULIPLE COMPARISON) voxels and picking out clusters > 100 contiguous voxels. Is there some reason I cannot do the same style analysis on a tbss skeleton? If I did, what cluster size (roughly) would be acceptable? The brain imaging literature, especially in the early days, was rife with publications which made no attempt to formally control for multiple comparisons. "P<0.001 uncorrected" (or P<0.005, or even P< 0.01) with a cluster size requirement is an example of such ad hoc criterion. A very simple argument shows that such heuristics cannot uniformly control false positives: Say that Author A published a 1995 paper showing that P< 0.001 with 100 voxel cluster size threshold controlled the chance of one or more false positives anywhere in the brain, controlling the familywise error rate (though good luck finding such careful papers in practice!). In order to be confident that Author A's work applies to your data, the following factors must be identical: - smoothness - As smoothness increases, you have more large clusters just by chance - search volume - More voxels, greater risk of false positives - degrees-of-freedom - Distribution of cluster size changes with DF - statistic type - Distribution of cluster size differs with T, F, Z, etc What's more, just matching up search volumes doesn't guarantee anything: A 10,000-voxel TBSS skeleton (which is somewhere between 2D and 3D) will have dramatically different cluster size characteristics than a 10,000-voxel 3D volume. In short, to be confident that you are controlling the risk of false positives, you need methods that adapt to the smoothness, the size and topology of the search volume, and the type and DF of statistic used. FEAT uses random field theory to get this adaptiveness; randomise (with TBSS) uses permutation methods implicitly adapt to the data via empirically-determined distributions, and FDR, though the observed distribution of P-values, also adapts to the data. It's tempting to be pulled to the lowest common denominator (i.e. most lenient statistical method used in the peer-reviewed literature), but readers do know that "corrected inferences" can be trusted and while ad hoc methods cannot. Hope this helps. -Tom ____________________________________________ Thomas Nichols, PhD Director, Modelling & Genetics GlaxoSmithKline Clinical Imaging Centre Senior Research Fellow Oxford University FMRIB Centre