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# Unpack Data into the FSFAST Hierarchy using "unpackscmdir"

  1. QA Check after unpacking
    • A - Check unpacked data (time points, # of slices ..etc)

    • B - Check FSFAST hierarchy in session folder

# Reconstruction – Anatomical using "recon-all –all"

  1. Set SUBJECTS_DIR
  2. QA Check
    • A - Check talairach transformation

    • B - Check skull strip, white matter & pial surface

    • C - Re-run "recon-all" if edits are made

    • D - Check hierarchy of reconstructed anatomical data

# Assembling the Data into the FSFAST Hierarchy

  1. Make FSFAST basic hierarchy (only if data are not unpacked in FSFAST hierarchy)
  2. Link to FreeSurfer anatomical analysis

    • A - Make ‘subjectname’ file in the session directory to link a subject's functional & structural data

  3. Create a sessid file (text file with list of your sessions)in your Study DIR.
  4. Create a Stimulus Schedule (Paradigm file) in bold folder (A "paradigm" file is a record of which stimulus was presented when & for how long. Each paradigm file has four columns:

    • A - Stimulus onset time (sec)

    • B - Condition ID code (0, 1, 2, ...)

    • C - Stimulus Duration (sec)

    • D - Stimulus Weight (usually 1)

# Preprocessing of fMRI Data ()

preproc-sess -s <subjid> -fwhm <#>
  1. By default this will do motion correction, smoothing & brain masking

  2. Quality Check (plot-twf-sess)
  3. Examine additions to FSFAST hierarchy (in each run of bold dir):
    • - f.nii (the raw data)

    • - fmc.nii (motion corrected-MC)

    • - fmcsm5.nii (MC & smoothed)

    • - fmc.mcdat - text file with the MC parameters (AFNI)

    • - mcextreg.bhdr - binary mask of the brain

# Function-Structure Registration

tkregister-sess -s <subjid> -regheader)

spmregister-sess -s <subjid>

tkregister-sess -s <subjid>

tkregister2 --s <subjid> --fstal --surf

# First-Level (Group) Analysis

mkanalysis-sess –gui
  1. Setting up models of the task-related components
  2. Setting up models of the nuisance components
  3. Defining contrasts
  4. Fitting the model
  5. Making inferences
  6. Input files: (*.par & fmcsm#)

# Analyze First Level

selxavg3-sess -s <subjid> -analysis <analysis name>
  1. Examine additions to the FSFAST hierarchy (~bold/analysis):
    • - beta.nii - regression coefficients

    • - rvar.nii - residual error variance

    • - mask.nii - mask (copy of bold/masks/brain.nii)

    • - meanfunc.nii - mean functional image

    • - fsnr.nii - functional SNR map

    • - X.mat - design matrix (in matlab format)

    • - dof - text file with degrees of freedom

    • - fwhm.dat - smoothness estimate (Full-Width/Half-Max)

    • - ~contrast folder/s:

      • - ces.nii - contrast effect size (contrast matrix * regression coef)

      • - cesvar.nii - variance of CES

      • - sig.nii - significance map (-log10(p))

  2. Visualization
    • A - Volume-based visualization using "tkmedit-sess"

    • B - Surface-based visualization using "tksurfer-sess"

# Higher-Level (Group) Analysis--Volume-based (MNI305/fsaverage)

  1. Assemble the contrast Data using "isxconcat-sess"

isxconcat-sess -s <sessid> -analysis <analysis name> -c <contrast name> -o <outdir>
  1. Quality Assurance
    • A - Check registration using "tkregister2"

    • B - Check the individual masks using "tkmedit"

    • C - Look at the functional SNR mapsusing "tkmedit"

  2. Group GLM Analysis using "mri_glmfit"
    • A - Random Effects

mri_glmfit --y tal.ces.nii --osgm --mask ../tal.mask.nii --glmdir tal.rfx.osgm --nii 

mri_glmfit --y tal.ces.nii --osgm --glmdir tal.wrfx.osgm --nii --mask ../tal.mask.nii \
    --wls tal.cesvar.nii

mri_glmfit --y tal.ces.nii --osgm --glmdir tal.ffx.osgm --nii --mask ../tal.mask.nii \
   --yffxvar tal.cesvar.nii --ffxdofdat ../ffxdof.dat
  1. Output & visualization

    • A - First concatenate sig.nii files to a single file using "mri_concat"

    • B - Visualize using "tkmedit"

  2. Correction for Multiple Comparisons/Cluster Analysis using "mri_volcluster"
    • A - Examine additions to the FSFAST hierarchy:

    • - tal.rfx.osgm/osgm/cluster.sum (table shows the size of each cluster in voxels and mm^3, the talairach coordinate, the maximum significance in the cluster, & the clusterwise p-value (CWP))

    • - cwsig.cluster.nii (map of the clusters with the voxel value equal to the -log10(pvalue))

    • - sig.cluster.nii (original sig map with non-cluster voxels removed)

    • - ocn.cluster.nii (map where the value at each voxel is replaced by the number of the cluster that the voxel is associated with)

# Higher-Level (Group) Analysis--Surface-based (MNI305/fsaverage)

  1. Assemble the Data (isxconcat-sess)

isxconcat-sess -s <sessid> -analysis <analysis name> -c <contrast name> -o <outdir> -hemi <?h>
  1. Group GLM Analysis(mri_glmfit)"--surf fsaverage lh" added to volume based.)
    • A - Random Effects

    • B - Weighted Random Effects

    • C - Fixed Effects

  2. Output and visualization (mri_concat and tksurfer)
    • A - Random Effects

    • B - Weighted Random Effects

    • C - Fixed Effects

  3. Correction for Multiple Comparisons/Cluster Analysis (mri_volcluster)

An active e-mail list is available to answer specific questions about processing procedures.

FsFastAnlysisBySteps (last edited 2008-04-29 11:46:14 by localhost)