Hi Johnson,

There are two thresholds typically used when looking at voxel-level results, the first at the voxel-level (height threshold, limiting which voxels are considered in the results) and the second at the cluster-level (extent threshold, limiting which clusters are considered in the results). The values considered acceptable are a matter of convention, but typically you will find:

   a) height-threshold: either uncorrected p-values<.001 or FDR- or FWE-corrected p-values<.05 
   b) extent-threshold: FWE-corrected or FDR-corrected p-values<.05

Any combination of threshold values that combines both height-threshold and extent-thresholds among the values above is considered acceptable (false positive control is appropriately corrected for multiple comparisons), but the sensitivity/specificity of the results will vary. As long as one uses a FWE- or FDR- corrected 'cluster-level' threshold, it is possible to use uncorrected 'voxel-level' thresholds and still the results will be appropriately corrected for multiple comparisons. A rule of thumb is to use more conservative height thresholds (e.g. FDR- or FWE-corrected p-values<.05) when the expected effects are focal (i.e. strong effects over small areas), while using more liberal height threshold (e.g. uncorrected p-value<.001 or higher) when the expected effects are broad but weak. Still I would be wary of using too liberal height thresholds (e.g. uncorrected p-value<.01 or higher) because some of the approximations behind the cluster-level statistics might not be appropriate for very liberal height thresholds (you can check Friston et al. 1994 'assessing the significance of focal activatios using their spatial extent' for additional info, and perhaps the spm list might also be a good resource to find out more details about these issues). Of course there are other approaches to inferences that go beyond those implemented in the conn toolbox. For example if you explore the results with SPM8 you may want to use 'peak-level' FDR- or FWE- corrected 'height threshold' (and disregard the 'cluster-level' stats) and that would also be considered acceptable (e.g. Chumbley et al. 'topological FDR for neuroimagig'). There are also all sorts of additional methods to perform inferences from voxel-level imaging data (e.g. permutation tests, non-homogeneity corrections for cluster-level stats, etc.) and many of these are implemented in SPM or using additional toolboxes so feel free to explore. 

Hope this helps
Alfonso


 
Originally posted by Johnson H: