#pragma section-numbers on
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= FSFAST Tutorial Data Description =
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
  1. Encode (16 sec) - 8 stick figures to remember (no response)
  1. Distractor (16 sec) - 8 distractor images (response whether there is a face in the image)
   a. Emotional - Distractors are emotionally disturbing
   a. Neutral - Distractors are emotionally neutral
  1. 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.

{{attachment:wmparadigm.jpg}} <<BR>> The above yields 5 conditions:

 1. Encode
 1. Emotional Distractor
 1. Neutral Distractor
 1. Probe following Emotional Distractor
 1. 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

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= Getting the Data (not necessary for the Boston FreeSurfer Course) =

You can install the freesurfer tutorial data via instructions [[https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/Data|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
}}}
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