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We computed AIC per subject, and used one-sample t tests on the subject-wise AIC differences between two models for model comparison64.
The best separating variable was used to demonstrate subject-wise classification.
The rest experiments are conducted with the subject-wise method.
The alignment then proceeds by sequentially registering the remaining subjects to the group atlas using the pair-wise functional registration algorithm described above.
Using the VESTAL solutions from each subject, two group-wise statistical analyses were performed.
Those scenes were some of the toughest scenes, subject-wise.
Schmithorst and Holland [43] described the methods of subject-wise concatenation, row-wise concatenation, and across-subject averaging.
Additionally, the need for subject-wise baseline correction was checked (no need for subject-wise baseline correction for this data).
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