Hi,
I'm performing my first FEAT analysis and your guidance is greatly
appreciated!
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My Experimental Design
11 subjects;
All subjects have the same design:
4 runs ;
run1 : cond1 - on/off
run2 : cond2 - on/off
run3 : cond3 - on/off
run4 : cond4 - on/off
behavioral measures: RT and Accuracy
I'm thinking to do the following for first level analysis:
# Preprocessing . - MC, Normalization, Smoothing, registration for each
session (4X11).
Because I have only 1 condition/run do you think that is it a good idea to
concatenate all the runs in a single run after preprocessing is done?
.........................................
# First level analysis.
If I won't concatenate the runs, then
Analyze each of the 4X11 sessions separately.
EV's:
I would like to include the baseline as a condition ==> 2EV's , one for the
condition, one for the rest period.
Basic shape: Custom 1 entry
Convolution: double gamma function
COntrasts: 1 Contrast
EV1 EV2
contrast (cond1-rest): 1 0
......................................
If I concatenate all 4 runs,then
EV's:
4EV's one for each condition
Basic shape: Custom 1 entry (for each EV)
Convolution: double gamma function
Contrasts: 4 Contrasts
EV1 EV2 EV3 EV4
cond1 1 0 0 0
cond2 0 1 0 0
cond3 0 0 1 0
cond4 0 0 0 1
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#Post-stats
pre-treshold masking - mask.hdr (generate by pre-stats);
Tresholding :Uncorrected 0.01
Use actual Z min/max
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So far , so good?
Regarding the behavioral measures (RT and Accuracy), how can I incorporate
them in my analysis? How can be formulated a hypothese with behavioral
measures?
Should I define an EV for each behavioral measures at the first level ?
Thank you.
Regards,
Anda
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