## page was renamed from FsTutorial/MultiModalFmriGroup
[[FsTutorial|Back to Top Tutorial Page]] <<BR>>
[[FsTutorial/MultiModal_tktools|Back to Multimodal Top]]

Other multimodal tutorials: [[FsTutorial/MultiModalRegistration_tktools|A. Multimodal Registration]], [[FsTutorial/MultiModalDtiIndividual_tktools|B. Individual DTI Integration]], [[FsTutorial/MultiModalFmriIndividual_tktools|C. Individual fMRI Integration]]

The purpose of this tutorial is to get you acquainted with the
concepts need to perform fMRI integration in FreeSurfer by interacting
with the data from several subjects in a group analysis. You will not
learn how to perform fMRI analysis here; that knowledge is already
assumed. This tutorial also does not assume any particular directory
structure (as would happen in FS-FAST). This tutorial makes use of
data from the Functional Biomedical Informatics Research Network
(fBIRN, [[http://www.nbirn.net|www.nbirn.net]]).

<<TableOfContents>>

= fMRI Basics =

In fMRI, stimuli are presented to a subject, which creates a BOLD
hemodynamic response function (HRF) in certain areas of the brain. The
analysis is performed by first performing motion correction, then
correlating each voxel's time course with the stimulus schedule
convolved with an assumed HRF shape. The result is an estimate of the
HRF amplitude for each condition at each voxel, contrasts of the HRF
amplitudes of various conditions, the variance of this
contrast, and some measure of the significance (eg, p, t, F, or z)
map. All these maps are aligned with the motion correction template,
which should be used as the registration template.

== Preparations ==
=== If You're at an Organized Course ===
If you are taking one of the formally organized courses, everything has been set up for you on the provided laptop. The only thing you will need to do is run the following commands in ''every'' '''new''' terminal window (aka shell) you open throughout this  tutorial. Copy and paste the commands below to get started:

{{{
setenv SUBJECTS_DIR $TUTORIAL_DATA/buckner_data/tutorial_subjs
cd $SUBJECTS_DIR/multimodal/fmri
}}}
''To copy: Highlight the command in the box above, right click and select copy (or use keyboard shortcut Ctrl+c), then use the middle button of your mouse to click inside the terminal window (this will paste the command). Press enter to run the command.''

These two commands set the SUBJECTS_DIR variable to the directory where the data is stored and then navigates into this directory. You can now skip ahead to the tutorial (below the gray line).

=== If You're not at an Organized Course ===
If you are '''NOT''' taking one of the formally organized courses, then to follow this exercise exactly be sure you've downloaded the [[FsTutorial/Data|tutorial data set]] before you begin. If you choose not to download the data set you can follow these instructions on your own data, but you will have to substitute your own specific paths and subject names. These are the commands that you need to run before getting started:

{{{
tcsh
source your_freesurfer_dir/SetUpFreeSurfer.csh
setenv SUBJECTS_DIR $TUTORIAL_DATA/buckner_data/tutorial_subjs
cd $SUBJECTS_DIR/multimodal/fmri
}}}
Notice the command to open tcsh.  If you are already running the tcsh command shell, then the 'tcsh' command is not necessary. If you are not using the tutorial data you should set your {{{SUBJECTS_DIR}}} to the directory in which the recon(s) of the subject(s) you will use for this tutorial are located.

--------


There are five subjects from the fBIRN Phase I acquisition. They are
fbirn-10?, where "?" is 1, 3, 4, 5, 6 (note that #2 is missing).  Each
has a FreeSurfer reconstruction by the name fbirn-anat-10?.v4. The
[[FsTutorial/MultiModalRegistration| individual registration]] 
[[FsTutorial/MultiModalFmriIndividual|ROI Analysis]] have already been
performed on all subjects.

The data are the results from a sensorimotor paradigm (flashing
checkerboard, audible tone, and finger tapping). The raw fMRI data
were motion corrected but not smoothed. Each subject has four
volumes:

{{{
template.nii - motion correction template
ces.nii - contrast effect size
cesvar.nii - variance of contrast effect size
sig.nii - signed significance of contrast (-log10(p))
}}}

The contrast is the contrast between the ON and the OFF (ie, a
comparison against baseline). The sig.nii volume has signed -log10(p)
values. So, if the p-value = .01, -log10(p) = 2. If the contrast was
positive, then the value would be +2, if negative (ie, ON<OFF), then
the value would be -2.

= Check Registration =

First (and always), check the registration (see the
[[FsTutorial/MulitModalRegistration|Registration Tutorial]] for more
information). 

----
{{{
tkregister2 --mov fbirn-101/template.nii \
  --reg fbirn-101/bb.register.dat --surf
}}}
----

This registration should already be good, so there is no need to make
any modifications. In a real analysis, you should check the
registrations for all subjects, but that is not necessary here.

= Surface-based Group fMRI Analysis =

The group analysis is done in three stages:
  1. Assemble data
  1. Spatially smooth data on surface (if desired)
  1. GLM Analysis 

== Assemble Data ==

Only the first step is different than an anatomical (eg, thickness)
analysis. Still, you run mri_preproc, but with different arguments:

----
{{{
mris_preproc --target fsaverage --hemi lh \
  --iv  fbirn-101/ces.nii fbirn-101/bb.register.dat \
  --iv  fbirn-103/ces.nii fbirn-103/bb.register.dat \
  --iv  fbirn-104/ces.nii fbirn-104/bb.register.dat \
  --iv  fbirn-105/ces.nii fbirn-105/bb.register.dat \
  --iv  fbirn-106/ces.nii fbirn-106/bb.register.dat \
  --projfrac 0.5 \
  --out lh.ces.mgh 
}}}
----


Notes:
  1. The target surface is the left hemisphere of fsaverage
  1. Each input volume and corresponding registration is listed separately
  1. The fMRI data are sampled half-way between the white and pial surfaces ("--projfrac 0.5"). 

The output is lh.ces.mgh. It has the same size as any other surface overlay for fsaverage
eg, lh.thickness. To see it's dimensions, run:
----
{{{
mri_info lh.ces.mgh
}}}
----

You will see "dimensions: 163842 x 1 x 1 x 5", indicating that there are
163842 "columns" (ie, vertices), 1 "row", and 1 "slice". There are 5
frames, one for each subject.

== Surface Smoothing ==

----
{{{
mri_surf2surf --hemi lh --s fsaverage --fwhm 5 --cortex\
  --sval lh.ces.mgh --tval lh.ces.sm05.mgh 
}}}
----

Notes:
  1. Full-Width/Half-Max (fwhm) is 5 mm 
  1. The smoothing is only perform in regions of legitimate cortex (--cortex, which uses the lh.cortex.label).

== GLM Fit ==

This performs a one-sample, group mean (OSGM) just looking for places where the CES is different than 0.

----
{{{
mri_glmfit --y lh.ces.sm05.mgh --surf fsaverage lh \
  --osgm --glmdir lh.ces.sm05.osgm --cortex
}}}
----

----
{{{
tksurfer fsaverage lh inflated -annot aparc -ov lh.ces.sm05.osgm/osgm/sig.mgh
}}}
----

{{attachment:glmfit.jpg}} {{attachment:glmfitmedial.jpg}}

Note that the activation is light because there are only five
subjects, but the activation that is there is in the right place.

= Group ROI fMRI Analysis =

The purpose of this tutorial is to combine individual stats files into one summary table file that can be loaded into a spreadsheet program such as excel or oocalc.

The individual ROI analyses were performed as described in the
[[FsTutorial/MulitModalFmriIndividual|Individual fMRI Integration Tutorial]]. Namely, stats files were created by mri_segstats using no
functional contraint, an unsigned (abs) constraint, and a positive
(pos) constraint to create stats files in each directory. Furthermore,
only 4 segmentations were summarized:
  1. 1021=ctx-lh-pericalcarine
  1. 1022=ctx-lh-postcentral 
  1. 1030=ctx-lh-superiortemporal
  1. 17=Left-Hippocampus

== Measure Volume of Positive Activation in Each ROI ==

----
{{{
asegstats2table \
  --meas volume \
  --tablefile ces.pos-masked.vol.stats \
  --i fbirn-101/ces.pos-masked.bb.stats \
  fbirn-103/ces.pos-masked.bb.stats \
  fbirn-104/ces.pos-masked.bb.stats \
  fbirn-105/ces.pos-masked.bb.stats \
  fbirn-106/ces.pos-masked.bb.stats  
}}}
----

Click [[FsTutorial/MultiModalFmriIndividual/BbStats#head-ddb7dd6be9b64b6a490cc20e619cb715e59f4721|here]] to see the output (ces.pos-masked.vol.stats).
Notes:
  1. The values are the volume in mm3 of the ON>OFF voxels whose pvalues < .01.
  1. The value first column is just the row number (starting from 0). Normally, the subject name would be here, but the subject name is not input to the command above.

== Measure Mean HRF Contrast of Unsigned Activation in Each ROI ==

----
{{{
asegstats2table \
  --meas mean \
  --tablefile ces.abs-masked.mean.stats \
  --i fbirn-101/ces.abs-masked.bb.stats \
  fbirn-103/ces.abs-masked.bb.stats \
  fbirn-104/ces.abs-masked.bb.stats \
  fbirn-105/ces.abs-masked.bb.stats \
  fbirn-106/ces.abs-masked.bb.stats  
}}}
----

Click [[FsTutorial/MultiModalFmriIndividual/BbStats#head-ddb7dd6be9b64b6a490cc20e619cb715e59f4721|here]] to see the output (ces.abs-masked.mean.stats).
Notes:
  1. The values are the mean HRF values (raw MR units) of the ON>OFF or ON<OFF voxels whose pvalues < .01.
  1. Some of the values are negative. This is possible because the mask is unsigned (abs).
  1. In particular, the cross-subject average of Left-Hippocampus is near 0. This is expected because the sensorimotor task should not activate hippocampus.

== Measure Mean HRF Contrast of Postiive Activation in Each ROI ==

----
{{{
asegstats2table \
  --meas mean \
  --tablefile ces.pos-masked.mean.stats \
  --i fbirn-101/ces.pos-masked.bb.stats \
  fbirn-103/ces.pos-masked.bb.stats \
  fbirn-104/ces.pos-masked.bb.stats \
  fbirn-105/ces.pos-masked.bb.stats \
  fbirn-106/ces.pos-masked.bb.stats 
}}}
----

Click [[FsTutorial/MultiModalFmriIndividual/BbStats#head-ddb7dd6be9b64b6a490cc20e619cb715e59f4721|here]] to see the output (ces.pos-masked.mean.stats).
Notes:
  1. The values are the mean HRF values (raw MR units) of the ON>OFF voxels whose pvalues < .01.
  1. NONE of the values are negative. This is because the mask is positive (pos).
  1. In particular, the cross-subject average of Left-Hippocampus is NOT close to 0 because only positive values were chosen. So, one must be careful how to interpret and draw conclusions from these numbers.

Other multimodal tutorials: 
[[FsTutorial/MultiModalRegistration_tktools|A. Multimodal Registration]],
[[FsTutorial/MultiModalDtiIndividual_tktools|B. Individual DTI Integration]],
[[FsTutorial/MultiModalFmriIndividual_tktools|C. Individual fMRI Integration]]