¥ STEP 3: Normalizing the
Data - Calculating Percent Change
G
GThis particular step is a bit more involved, because it
is comprised of three parts. Each part will be described in
detail:
G
A.Set
all background values (outside of the volume) to zero with 3dAutomask
B.Do
a voxel-by-voxel calculation of the mean intensity value with 3dTstat
C.Do
a voxel-by-voxel calculation of the percent signal change with 3dcalc
D.
GWhy should we normalize our data?
åNormalization becomes an important issue when comparing
data across subjects, because baseline/rest
states will vary from subject to subject
åThe amount of activation in response to a stimulus event
will also vary from subject to
subject
åAs a result, the baseline Ideal Response Function (IRF)
and the stimulus IRF will vary from subject
to subject -- we must account for this variability
åBy converting to percent change, we can compare the
activation calibrated with the relative change
of signal, instead of the arbitrary baseline of
FMRI signal