fsfast_01 - aboualiaa/freesurfer GitHub Wiki
https://surfer.nmr.mgh.harvard.edu/fswiki/FsFastTutorialV6.0/TutorialData
The functional data were collected as part of the Functional Biomedical Research Network (fBIRN).
Working-memory paradigm with distractors 18 subjects Each subject has 1 run (except sess01 which has 4 runs) Collected at MGH Bay 4 (3T Siemens) FreeSurfer anatomical analyses
- Functional Paradigm The paradigm was designed to study the effects of emotional stimuli on the ability to recall items stored in working memory.
Block design Each block consisted of 3 phases Encode (16 sec) - 8 stick figures to remember (no response) Distractor (16 sec) - 8 distractor images (response whether there is a face in the image) Emotional - Distractors are emotionally disturbing Neutral - Distractors are emotionally neutral Probe (16 sec) - 8 pairs of stick figures. Subject responds as to which of the pair was in the original Encode. Between each block was a 16 sec scrambled image used as baseline. wmparadigm.jpg The above yields 5 conditions:
Encode Emotional Distractor Neutral Distractor Probe following Emotional Distractor Probe following Neutral Distractor The scrambled image will be modeled as a baseline, not as a condition.
- Functional Data Original data: each subject had 8 runs This data: each subject has 1 run (except for sess01 who has 4) Each run lasts 142 time points TR = 2 sec. There is one run of rest data for 13 subjects There is a B0 map for each subject
- Anatomical Data FreeSurfer analysis has been run for all 18 subjects
If you are at a FreeSurfer Course, continue on to the next page now
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- Getting the Data (not necessary for the Boston FreeSurfer Course) You can install the freesurfer tutorial data via instructions here. Afterwards, you will need to set the TUTORIAL_DATA environment variable. In bash:
export TUTORIAL_DATA=/path/to/tutorial_data You will also need to link the FreeSurfer anatomical subjects (data in fsfast-tutorial.subjects) into your $SUBJECTS_DIR. You should set the FSFAST output format to be compressed NIFTI (nii.gz):
export FSF_OUTPUT_FORMAT=nii.gz