#pragma section-numbers on
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<<TableOfContents>>
= FSFAST Tutorial Data Description =
The functional data were collected as part of the Functional Biomedical Research Network (fBIRN, www.nbirn.net).
 * 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.
 * Blocked 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}}
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

'''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 get the analyzed functional data (10G) from:
{{{
wget ftp://surfer.nmr.mgh.harvard.edu/pub/data/fsfast-functional.tar.gz
}}}
You can get the structural data  (5G) from:
{{{
wget ftp://surfer.nmr.mgh.harvard.edu/pub/data/fsfast-tutorial.subjects.tar.gz
}}}
= Organizing the Tutorial (not necessary for the Boston FreeSurfer Course) =
cd to a place on your network where you have enough space to unpack
the tutorial data.
{{{
cd /place/with/space
}}}
Untar the data
{{{
tar xvfz fsfast-tutorial.tar.gz
tar xvfz fsfast-tutorial.subjects.tar.gz
}}}
You will need to set the TUTORIAL_DATA environment variable. In tcsh
or csh
{{{
export TUTORIAL_DATA=/place/with/space
}}}
You will also need to link the FreeSurfer anatomical subjeccts (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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