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[[FreeSurferWiki|top]] | [[Tutorials|previous]] [[FsFastTutorialV5.1|top]] | [[FsFastTutorialV5.1|previous]]| [[FsFastTutorialV5.1/FsFastDirStruct|next (Directory Structure)]]
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{{attachment:wmparadigm.jpg}}
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= Getting the Data (not necessary for FreeSurfer April 2011 Course) = = Getting the Data (not necessary for the Boston FreeSurfer Course) =
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wget ftp://surfer.nmr.mgh.harvard.edu/pub/data/fsfast-tutorial.tar.gz wget ftp://surfer.nmr.mgh.harvard.edu/pub/data/fsfast-functional.tar.gz
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= Organizing the Tutorial (not necessary for FreeSurfer April 2011 Course) =

= Organizing the Tutorial (not necessary for the Boston FreeSurfer Course) =
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You will need to set the TUTORIAL_DATA environment variable. In tcsh
or csh
You will need to set the TUTORIAL_DATA environment variable. In bash, run:
{{{
export TUTORIAL_DATA=/place/with/space
}}}

In
tcsh or csh
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You should set the FSFAST output format to be compressed NIFTI (nii.gz). In bash, run:
{{{
export FSF_OUTPUT_FORMAT=nii.gz
}}}

In tcsh or csh, run:
{{{
setenv FSF_OUTPUT_FORMAT nii.gz
}}}


[[FsFastTutorialV5.1|top]] | [[FsFastTutorialV5.1|previous]]| [[FsFastTutorialV5.1/FsFastDirStruct|next (Directory Structure)]]

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1. 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

2. 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)
    2. Distractor (16 sec) - 8 distractor images (response whether there is a face in the image)
      1. Emotional - Distractors are emotionally disturbing
      2. Neutral - Distractors are emotionally neutral
    3. 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:

  1. Encode
  2. Emotional Distractor
  3. Neutral Distractor
  4. Probe following Emotional Distractor
  5. Probe following Neutral Distractor

The scrambled image will be modeled as a baseline, not as a condition.

3. 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

4. Anatomical Data

  • FreeSurfer analysis has been run for all 18 subjects

5. 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

6. 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 bash, run:

export TUTORIAL_DATA=/place/with/space

In tcsh or csh

setenv 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). In bash, run:

export FSF_OUTPUT_FORMAT=nii.gz

In tcsh or csh, run:

setenv FSF_OUTPUT_FORMAT nii.gz

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FsFastTutorialV5.1/TutorialData (last edited 2016-09-01 16:37:09 by AllisonMoreau)