STEP 1 (tshift): Check for possible “outliers” in each of the 9 time series datasets using 3dToutcount . Then perform temporal alignment using 3dTshift.

• STEP 1 (tshift): Check for possible “outliers” in each of the 9 time series datasets using 3dToutcount . Then perform temporal alignment using 3dTshift.

• • An outlier is usually seen as an isolated spike in the data, which may be due to a number of factors, such as subject head motion or scanner irregularities.
  • The outlier is not a true signal that results from presentation of a stimulus event, but rather, an artifact from something else -- it is noise.
  • • foreach run (01 02 03 04 05 06 07 08 09)
    • 3dToutcount -automask pb00.$subj.r$run.tcat+orig \
    • > outcount_r$run.1D
    • end
  • How does this program work? For each time series, the trend and Median Absolute Deviation are calculated. Points far away from the trend are considered outliers.
  • • "far away" is defined as at least 5.219*MAD (for a time series of 64 TRs)
    • • see 3dToutcount -help for specifics
    • -automask: does the outlier check only on voxels within the brain and ignores background voxels (which are detected by the program because of their smaller intensity values)
    • > : redirects output to the text file outcount_r01.1D (for example), instead of sending it to the terminal window.