One of my current projects involves working with some data that I did not collect. I have the smoothed and normalized NIfTI images and the first-level stats images, including SPM.mat and the con_*, beta_*, and spmT_* files. Generating the second-level images is straight-forward, but when I visualized simple effects, there was larger than expected signal dropout in vmPFC and temporal pole around the sinuses. It seemed plausible that across the 80+ subjects in the dataset, there might be a handful of subjects with abnormally high dropout. Losing one or two individual subjects seems like a reasonable price to pay to recover vmPFC signal, since the task involves social decision-making. Rather…
-
-
Utilities: Python and xjView reports
I use xjView to visualize statistical maps most of the time. One reason why, is that it's report function generates a lot of useful information, including not only the peak MNI coordinates and peak intensity, but also a breakdown of the number of voxels within each cluster than within specific AAL labels (is that parahippocampal or amgydala?). I also use these reports when I need to generate tables for manuscripts. The processes isn't overly elegant, but it's functional: Load the stats image. Generate the xjView report. Paste the report from the MATLAB command window into a *.txt document. Convert the *.txt document into a comma-separated table. Open the CSV tables…