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Hi - if I understand correctly, your data and data-dervied model have not been high-pass filtered before feeding into your second analysis. Hence you should be able to either turn on high-pass filtering (or add the equivalent model confounds) for the second analysis, and that would be ok? Cheers. On 20 Sep 2010, at 09:56, Andrea L. Gold wrote: > Hi FSLers, > > I have a question re: deciding whether to use temporal filtering for a first-level analysis for PPI analysis (i.e., for the Pre-stats tab in FEAT -- not for the regressors in the GLM). We are using filtered_func_data as the 4D data input (from a first level analysis in which we used highpass temporal filtering in "Pre-stats" in FEAT), and I initially did not use the highpass temporal filtering option for the data. In the GLM, I applied highpass filtering to the task/psychological regressor but not to the seed timecourse or the interaction (PPI) regressor. > > The task regressor for this analysis has very long task blocks (i.e., 45s). When looking at the activation map (thresh_zstat1.nii.gz) for the physiological regressor (i.e., seed timecourse) and we see a "halo" of voxels around the brain. Given drift that may be associated with these very long task blocks, would applying highpass temporal filtering to the data in the first level analysis for the PPI model be recommended in this case? > > Any feedback/advice would be appreciated! > Thanks, > Andrea > --------------------------------------------------------------------------- Stephen M. Smith, Professor of Biomedical Engineering Associate Director, Oxford University FMRIB Centre FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK +44 (0) 1865 222726 (fax 222717) [log in to unmask] http://www.fmrib.ox.ac.uk/~steve ---------------------------------------------------------------------------