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FreeSurfer Slides

Functional Analysis with FS-FAST

This tutorial steps you through the analysis of an fMRI data set with the FreeSurfer Functional Analysis Stream (FSFAST) version 5.1, from organizing the data to group analysis.

Contents

FreeSurfer Slides
Tutorial Data Description
Getting and Organizing the Tutorial
Understanding the FS-FAST Directory Structure

Tutorial Data Description

The data being analyzed 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

Getting and Organizing the Tutorial

If you do not have the tutorial data set up, then consult the FsFastTutorialData page. You will need to set the FSFTUTDIR environment variable. NOTE: if you are taking a class at MGH, the data have already been set up on your computer.

cd into the tutorial data directory and run ls:

cd $FSFTUTDIR
ls

You will see 18 folders with names like "sess09". These are the 18 subjects. There are some other files and folders there but don't worry about them now.

Understanding the FS-FAST Directory Structure

All of these sessions have been analyzed with the exception of sess01.noproc. This session is what the directory structure should look like immediately prior to beginning analysis. This includes:

Directory stucture
Raw data
subjectname file
Parasigm files for each run

The directory structure and raw data are usually created by "unpacking" the data with dcmunpack or unpacksdcmdir, but it could also be done by hand. The subjectname file and paradigm files must be added manually.

The 'Session' Folder

The folder/directory where all the data for a session are stored is called the 'session' or the 'sessid'. There may be more than one session for a given subject (eg, in a longitudinal analysis). Go into the sess01.noproc folder and run 'ls':

cd sess01.noproc
ls

You will see see two folders ('bold' and 'rest') and a file called 'subjectname'.

The 'subjectname' File

subjectname is a text file with the name of the FreeSurfer subject as found in $SUBJECTS_DIR (ie, the location of the anatomical analysis).

View the contents of the text file (with 'cat', 'more', 'less', 'gedit', or 'emacs') and verify that this subject is in the $SUBJECTS_DIR.

NOTE: it is important that the anatomical data and the functional data be from the same subject. The contents of the subjectname file is the only link! Make sure that it is right! There is a check for this below.

Functional Subdirectories (FSDs)

The other two directories (bold and rest) are 'functional subdirectories' (FSDs) and contain functional data. If you 'ls rest' you will see '001'. If you 'ls bold' you will see '001 002 003 004'. Each of these is a 'run' of fMRI data, ie, all the data collected from a start and stop of the scanner.

Raw Data

Go into the first run of the bold directory with 'cd bold/001' and run ls. You will see 'f.nii.gz wmfir.par workmem.par'. The raw data is stored in f.nii.gz (compressed NIFTI) and is directly converted from the DICOM file. Examine this file with mri_info:

mri_info --dim f.nii.gz
mri_info --res f.nii.gz

The first command results in '64 64 30 142'. This is the dimension of the functional data. Since it is functional, it has 4 dimensions: 3 spatial and one temporal (ie, 64 rows, 64 cols, 30 slices, and 142 time points or TRs or frames). The second command results in '3.438 3.437 5.000 2000.000'. This is the resolution of the data, ie, each voxel is 3.438mm by 3.437mm by 5.000mm and the TR is 2000ms (2 sec).

Paradigm Files

The workmem.par and wmfir.par files are paradigm files. They are text files that you create that indicate the stimulus schedule (ie, which stimulus was presented when).

View workmem.par. Each row indicates a stimulus presentation. You will see that each row has 5 columns. The columns are:

Stimulus Onset Time (sec)
Numeric Stimulus Identifier
Stimulus Duration (sec)
Weight (usually 1)
Text Stimulus Identifier (redundant with Numeric Stimulus Identifier)

The Stimulus Onset Time is the onset relative to the acquiistion time of first time point in f.nii.gz. The Numeric and Text Stimulus Identifiers indicate which stimulus was presented. The Stimulus Duration is the amount of time the stimulus was presented.

In this case, there are 5 event types:

Encode - encode phase
EDistrator - emotional distractor
EProbe - probe after emotional distractor
NDistrator - neutral distractor
NProbe - probe after neutral distractor

Note two things: (1) Not all the time is taken up, and (2) Basline/Fixation is not explicitly represented. By default, any time not covered by stimuluation is assumed to be baseline.

What time was the third Encode presented?
What time was the last Neutral Distractor presented?
Is this timing for all runs the same?

-Deletions are marked like this.
+Additions are marked like this.
 Line 22:
+ * Each subject has 1 run (except sess01 which has 4 runs)
-Line 23:
+Line 24:
- * TR = 3 sec, 85 time points/run (run length = 255 sec)
 Line 35:
-Cd into the tutorial data directory and set SUBJECTS_DIR to use this data:
+cd into the tutorial data directory and run ls:
 Line 46:
-All of these subjects have been analyzed with the exception of

sess01.noproc.
+= Understanding the FS-FAST Directory Structure =
-Line 49:
+Line 48:
+All of these sessions have been analyzed with the exception of

sess01.noproc. This session is what the directory structure 

should look like immediately prior to beginning analysis. This

includes:
-Line 50:
+Line 53:
+  * Directory stucture 

  * Raw data

  * subjectname file

  * Parasigm files for each run
-Line 51:
+Line 58:
+The directory structure and raw data are usually created by

"unpacking" the data with dcmunpack or unpacksdcmdir, but it could

also be done by hand. The subjectname file and paradigm files must be

added manually.
-Line 52:
+Line 63:
+== The 'Session' Folder ==
-Line 53:
+Line 65:
-== Viewing multiple functional overlays and time courses in the volume ==



Exit from your current tkmedit window. Run tkmedit-sess (this is an FSFAST wrapper for tkmedit):
+The folder/directory where all the data for a session are stored is

called the 'session' or the 'sessid'. There may be more than one

session for a given subject (eg, in a longitudinal analysis).  Go into

the sess01.noproc folder and run 'ls':
-Line 58:
+Line 71:
-cd $FSFTUTDIR

setenv SUBJECTS_DIR $PWD

cd $FSFTUTDIR/fb1-analysis-study

tkmedit-sess -s mgh-101.1 -a sm-fir-fwhm5 -c odd-v-0 -aparc+aseg
+cd sess01.noproc

ls
-Line 64:
+Line 75:
-This will bring up the two windows as you saw before (the brain image

window will have a different overlay). It will also bring up a window

called "Time Course". Click anywhere in the volume, and you will see a

time course associated. If you happened to have clicked in some activation, then 

the time course will look similar to the one below.
+You will see see two folders ('bold' and 'rest') and a file called

'subjectname'.
-Line 70:
+Line 78:
-{{attachment:tkm-101-fir.gif}}
+== The 'subjectname' File ==
-Line 72:
+Line 80:
-The meaning of the time course depends upon the nature of the time

course loaded. In this case it is the hemodynamic response to the task

block averaged over all blocks over all runs (this is an "FIR"

model). The horizontal axis is time (0 means the onset of the

block). You can visualize the values of any multi-frame data set in

this way (it does not have to be a "time" course).<<BR>>
+subjectname is a text file with the name of the FreeSurfer subject as

found in $SUBJECTS_DIR (ie, the location of the anatomical analysis).
-Line 79:
+Line 83:
-Notice that there is not much activation in the functional

overlay. This is good! You are looking at an overlay that corresponds

3 seconds prior to stimulus onset, so there should be no

activation.
+View the contents of the text file (with 'cat', 'more', 'less',

'gedit', or 'emacs') and verify that this subject is in the

$SUBJECTS_DIR.



NOTE: it is important that the anatomical data and the functional data

be from the same subject. The contents of the subjectname file is

the only link! Make sure that it is right! There is a check for this

below. 

    

== Functional Subdirectories (FSDs) ==



The other two directories (bold and rest) are 'functional

subdirectories' (FSDs) and contain functional data. If you 'ls rest'

you will see '001'. If you 'ls bold' you will see '001 002 003

004'. Each of these is a 'run' of fMRI data, ie, all the data

collected from a start and stop of the scanner.



=== Raw Data ===



Go into the first run of the bold directory with 'cd bold/001' and run

ls. You will see 'f.nii.gz  wmfir.par  workmem.par'. The raw data is

stored in f.nii.gz (compressed NIFTI) and is directly converted from

the DICOM file.  Examine this file with mri_info:
-Line 84:
+Line 108:
-To see the other time points, bring up the functional overlay configuration window 

(View->Configure->Functional Overlay). Notice that the "Time Point" field now has a 

range of 0-8 indicating the 9 time points in the Time Course window. If you hit the

+ button next to "Time Point" then hit Apply, you will see the overlay change.
+mri_info --dim f.nii.gz

mri_info --res f.nii.gz
-Line 90:
+Line 112:
-You will also see a vertical dashed line in the Time Course

window. You are now looking at the map associated with the time between 0

and 3 sec after stimulus onset.
+The first command results in '64 64 30 142'. This is the dimension of

the functional data. Since it is functional, it has 4 dimensions: 3

spatial and one temporal (ie, 64 rows, 64 cols, 30 slices, and 142

time points or TRs or frames). The second command results in '3.438

3.437 5.000 2000.000'. This is the resolution of the data, ie, each

voxel is 3.438mm by 3.437mm by 5.000mm and the TR is 2000ms (2 sec).
-Line 94:
+Line 119:
-{{{

Keep hitting the +/Apply buttons to see different time points. You can

hit the "|>" button to start a  movie of the activation (then "[]" to stop it).

}}}
+=== Paradigm Files  ===
-Line 99:
+Line 121:
-== Viewing a single functional overlay on the surface ==
+The workmem.par and wmfir.par files are paradigm files. They are text

files that you create that indicate the stimulus schedule (ie, which

stimulus was presented when).
-Line 101:
+Line 125:
-To view the same data on the surface, start a new sheel and run:
+View workmem.par. Each row indicates a stimulus presentation. You will

see that each row has 5 columns. The columns are:

  

  * Stimulus Onset Time (sec)

  * Numeric Stimulus Identifier

  * Stimulus Duration (sec)

  * Weight (usually 1)

  * Text Stimulus Identifier (redundant with Numeric Stimulus Identifier)
-Line 103:
+Line 134:
-{{{

cd $FSFTUTDIR/fb1-analysis-study

tksurfer-sess -s mgh-101.1 -a sm-gamma-fwhm5 -c odd-v-0 -hemi lh -aparc

}}}
+The Stimulus Onset Time is the onset relative to the acquiistion time

of first time point in f.nii.gz. The Numeric and Text Stimulus

Identifiers indicate which stimulus was presented. The Stimulus

Duration is the amount of time the stimulus was presented.
-Line 108:
+Line 139:
-Again you will see two windows, "Tksurfer Tools" and a surface image

window like the one below. As with tkmedit, you can configure the

overlay with View->Configure...->Overlay. The second command will load

the surface parcellation which will hide the overlay. Change it to

outline mode with: View->Label Style->Outline.
+In this case, there are 5 event types:
-Line 114:
+Line 141:
-{{attachment:tks-101-gam-lat.2-4.gif}}
+  * Encode - encode phase

  * EDistrator - emotional distractor

  * EProbe - probe after emotional distractor

  * NDistrator - neutral distractor

  * NProbe - probe after neutral distractor
-Line 116:
+Line 147:
-You can view the time course with:

{{{

tksurfer-sess -s mgh-101.1 -a sm-fir-fwhm5 -c odd-v-0 -hemi lh -aparc

}}}
+Note two things: (1) Not all the time is taken up, and (2)

Basline/Fixation is not explicitly represented. By default, any time

not covered by stimuluation is assumed to be baseline.
-Line 121:
+Line 151:
--------------------------------------------------------------------

-------------------------------------------------------------------

= Assembling the Data into the FSFAST Hierarchy =



Before analyzing data in FSFAST, it needs to be in a "Hierarchy", i.e., a

certain directory structure and naming convention. By adhering to a

hierarchy, you allow FSFAST to analyze large amounts of data

automatically because it knows where to find the input and where to

put the output. 



 * Each step in the analysis modifies the hierarchy in some way. 

 * All the data collected in one functional session (or visit) are placed under the folder (the "session directory"). 

 * All the sessions are arranged under one folder (called the "Study Directory").



The full hierarchy is shown in the image below:



{{attachment:fsfast-hierarchy.jpg}}



The red boxes indicate a directory with raw or preprocssed functional

data. E.g., f.nii is a raw fMRI 4D file, and fmc.nii is motion

corrected.



== Creating a Hierarchy from the Tutorial Data ==



There are five subjects of data, each with four runs. We will create a

an FSFAST session from one of the subjects. A properly created

hierarchy can be found in $FSFTUTDIR/fb1-raw-study.



Create a Study Directory and cd into it:

{{{

cd $FSFTUTDIR

mkdir -p my-study

cd my-study

}}}



Create a session directory for the first subject:

{{{

mkdir -p mgh-101.1

}}}



Create a bold and four run directories:

{{{

mkdir -p mgh-101.1/bold

mkdir -p mgh-101.1/bold/003

mkdir -p mgh-101.1/bold/005

mkdir -p mgh-101.1/bold/007

mkdir -p mgh-101.1/bold/010

}}}



Copy the raw data:

{{{

cp ../fb1-raw/f.mgh-101.1-r003.nii mgh-101.1/bold/003/f.nii

cp ../fb1-raw/f.mgh-101.1-r005.nii mgh-101.1/bold/005/f.nii

cp ../fb1-raw/f.mgh-101.1-r007.nii mgh-101.1/bold/007/f.nii

cp ../fb1-raw/f.mgh-101.1-r010.nii mgh-101.1/bold/010/f.nii

}}}

Note that they are all named "f.nii" but are distinguished by the

directory they are in.



== Link to FreeSurfer Anatomical Analysis ==



FSFAST makes extensive use of the FreeSurfer anatomical analysis.  To

link a subject's functional and structural data, create a "subjectname"

file in the session directory. This file is literally called

"subjectname" and its contents have the FreeSurfer subject name

assigned during reconstruction and found in $SUBJECTS_DIR. These

subjects have already been reconstructed; this subject's name is

"fbph1-101". You can create this file with any text editor (emacs, vi,

pico), or just:



{{{

cd $FSFTUTDIR/my-study

echo fbph1-101 > mgh-101.1/subjectname

}}}



== Create a Stimulus Schedule (Paradigm File) ==



A "paradigm" file is a record of which stimulus was presented when and

for how long. Each paradigm file has four columns (extras are

ignored). The columns are:



 * 1. Stimulus onset time (sec)

 * 2. Condition ID code (0, 1, 2, ...)

 * 3. Stimulus Duration (sec)

 * 4. Stimulus Weight (usually 1)



The Stimulus onset is with respect to the time of the first image. The

Condition is a unique number that identifies the event or block type

that the stimulus belongs to. They must be contiguous, and Condition 0

is reserved for the Null Event (usually Fixation). The weight can be

used to model various effects of learning or adaptation (just set them

to 1 for now).



For this exercise, you will create two paradigm files based on the

fBIRN sensory-motor experiment. The timing for the experiment was

described above. The task timing is shown graphically below.

 

{{attachment:fbirn1-taskplot.gif}}



{{{

Using a text editor (emacs, vi, pico, etc), create a paradigm file called "sensory-motor0.par". Eg:

cd $FSFTUTDIR/fb1-raw-study

emacs sensory-motor0.par&

}}}

The correct paradigm file is here SensMotPar (This example file has a

5th column with the condition name. This is ignored in the analysis).



{{{

To make things a little more interesting, we are going to create

another paradigm file pretending that the odd and even blocks are

different. Assign Odd blocks Condition 1 and Even blocks Condition 2.

cd $FSFTUTDIR/fb1-raw-study

emacs sensory-motor.par&

}}}

Call this paradigm file "sensory-motor.par". The correct paradigm file

here is SensMotOddEvenPar.



Now copy this file into each run directory:

{{{

cd $FSFTUTDIR/my-study

cp ../fb1-raw-study/sensory-motor.par mgh-101.1/bold/003/sensory-motor.par

cp ../fb1-raw-study/sensory-motor.par mgh-101.1/bold/005/sensory-motor.par

cp ../fb1-raw-study/sensory-motor.par mgh-101.1/bold/007/sensory-motor.par

cp ../fb1-raw-study/sensory-motor.par mgh-101.1/bold/010/sensory-motor.par

}}}



While each of these have the same contents, they can just as easily be

different. 



== Create a Session ID File ==



You would normally do the above steps for each of the subjects, but

you've probably got the point by now. If you were to create a session

for each, you would then have five sessions: mgh-101.1, mgh-103.1,

mgh-104.1, mgh-105.1, mgh-106.1. To help keep track of your sessions,

you can create a session ID file with your favorite text editor.  This

is just a simple file with a list of your sessions. The contents should

look like (the order is unimportant):



{{{

mgh-101.1

mgh-103.1

mgh-104.1

mgh-105.1

mgh-106.1

}}}



This file can be passed to many of the FSFAST commands with the -sf

argument. Individual sessions can be passed with -s, as you will see

below. 



---------------------------------------------------------------------

---------------------------------------------------------------------

= Preprocessing of fMRI Data (preproc-sess) =

== What preprocessing stages do you want to run? ==



There are at least seven types of preprocessing that are run on fMRI

data: 



 * 1. motion correction (MC)

 * 2. spatial smoothing 

 * 3. brain masking  

 * 4. slice-timing correction (STC)

 * 5. intensity normalization

 * 6. resampling to common space

 * 7. B0 distortion correction. 



Not all packages run all of these seven, and they are not always run

in the same order, and some stages are sometimes run as

post-processing. In FSFAST, we can run 1. MC, 2. smoothing 3. STC (optional),

and 4. intensity normalization (optional). We create a brain mask,

but we do not mask the functional data.



For these exercises, cd into the fb1-raw-study directory:

{{{

cd $FSFTUTDIR/my-study

}}}

Note that the fully preprocessed data can be found in fb1-preproc-raw.



== Run preprocessing ==



To run MC and spatial smoothing by 5 mm FWHM along with brain mask

creation on one session run: 



{{{

cd $FSFTUTDIR/my-study

preproc-sess -s mgh-101.1 -fwhm 5

}}}



Note that this has already been done with all subjects in the

fb1-preproc-study directory. To preprocess all of the session, you

would run "preproc-sess -sf sessid -fwhm 5". Also note that if

preprocessing has already been performed on a session, it will

automatically skip it and move on to the next (unless you add -force

to the command-line).



== Examine additions to FSFAST hierarchy ==



As mentioned above, each processing step adds something to the FSFAST

hierarchy. To see what has been created, run:



{{{

cd $FSFTUTDIR/my-study

ls mgh-101.1/bold/003

}}}



You will see:

 * f.nii (the raw data)

 * fmc.nii (motion corrected)

 * fmcsm5.nii (motion corrected and smoothed). 

 * fmc.mcdat - text file with the motion correction parameters (AFNI)



You will also see mcextreg.bhdr. This is a binary file with the

orthogonalized motion correction parameters which can later be used as

nuisance regressors when analyzing the data. These files will exist in

each of the runs (i.e., 005, 007, 010). 



{{{

cd $FSFTUTDIR/my-study

ls mgh-101.1/bold/masks

}}}



You will see a brain.nii volume in the masks folder. This is a binary

mask of the brain as found by FSL's BET program. The functional data

themselves are not masked.<<BR>>



To view the translation components, run

{{{

cd $FSFTUTDIR/my-study

plot-twf-sess -s mgh-101.1 -mc

}}}

This will bring up a plot with the translations for each of the

runs. Notice that run 007 has a head jerk at about time point 11.



---------------------------------------------------------------------

---------------------------------------------------------------------

= Function-Structure Registration =



In order to render the functional results on the anatomical background

as well as to map the functional results into a common space for group

analysis, it is necessary to register/align the functional volume with

a structural volume. In FSFAST, we:



 * First register to the same-subject FreeSurfer anatomical with a 6 DOF registration. 

 * Map the functional to Talairach/MNI305/fsaverage space by concatenating the within-subject function/structure registration with the Talairach registration (talairach.xfm) created when the subject was reconstructed. 

 * For mapping to surface-based space, we concatenate the within-subject function/structure registration with the surface-based registration. 



Since we are only dealing with the functional analysis here, we will

just consider the within-subject function/structure registration. 



For these series of exercises, cd into fb1-preproc-study:

{{{

cd $FSFTUTDIR/fb1-preproc-study

}}}



== View unregistered (tkregister-sess) ==



Run the following command to see how the functional and structural are

aligned prior to performing any automatic registration (delete the old one, if there).



{{{

cd $FSFTUTDIR/fb1-preproc-study

rm -f mgh-101.1/bold/register.dat

tkregister-sess -s mgh-101.1 -regheader -inorm

}}}



You should see the anatomical image (left, below). The green line is

the cortical surface for that subject. Hit the compare button to see

the functional middle image. The cortical surface shows that the

volumes are not in line.



{{attachment:tkregister-anat.gif}}

{{attachment:tkregister-before.gif}}

{{attachment:tkregister-after.gif}}



== Run automatic registration (spmregister-sess) ==

{{{

cd $FSFTUTDIR/fb1-preproc-study

fslregister-sess -s mgh-101.1

}}}

When this command is complete, you will see a register.dat file in

mgh-101.1/bold. This is the only change. The functional data are not

resampled! Note that this command requires FSL. You can also run:

{{{

cd $FSFTUTDIR/fb1-preproc-study

spmregister-sess -s mgh-101.1

}}}

This does the same thing but requires matlab and spm in

your matlab path (in startup.m), otherwise use fslregister-sess.



== Check automatic registration (tkregister-sess) ==

{{{

cd $FSFTUTDIR/fb1-preproc-study

tkregister-sess -s mgh-101.1 -inorm

}}}

When you hit the Compare button, you should see the image on the

right, above. 



== Check talairach registration ==



When performing a volume-based group analysis, it is important that

the talairach transform be accurate (or as accurate as talairach can

be). You should have done this during the FreeSurfer reconstruction

for this subject. The command below is just a reminder of how to do

it.



{{{

tkregister2 --s fbph1-101 --fstal --surf

}}}



---------------------------------------------------------------------

---------------------------------------------------------------------

= First-Level Analysis =



The First-Level Analysis (FLA) consists of:



 * Setting up models of the task-related components

 * Setting up models of the nuisance components

 * Defining contrasts

 * Fitting the model

 * Making inferences.  



In FSFAST, the FLA is done in two stages. In the first stage, the

FLA is configured (with mkanalysis-sess). This is done ONCE regardless

of how many data sets you have (you do not even need to have any data

to run the configuration). 



In the second stage, you actually perform the analysis/inference with

selxavg3-sess by passing it the configuration and the session that you

want to analyze. selxavg3-sess customizes the analysis for that

session based on what it finds in the hierarchy, builds the design

matrix and performs the analysis. Breaking the FLA up into these two

stages assures that all sessions are analyzed in the same way.



In this exercise, we will configure two analyses, one assuming an HRF

and the other using a Finite Impulse Response (FIR) model. You will

then launch the analysis and view the results. The fully analyzed data

can be found in fb1-analysis-study. These exercises will be done in

fb1-preproc-study:



{{{

cd $FSFTUTDIR/fb1-preproc-study

}}}



== Configure Analysis and Contrasts I: Gamma HRF Model  ==



Configuring the FLA is performed with mkanalysis-sess. When you run:

{{{

cd $FSFTUTDIR/fb1-preproc-study

mkanalysis-sess -gui

}}}



You will see the following window:



{{attachment:mkana.init.gif}}



You will use this window to specify the input of the analysis, the

hemodynamic response model, contrasts and nuisance regressors. 

The red fields are fields that you must enter before you can save

the analysis.  There is a lot going on with this GUI, so we'll break it

down. Note that many of the components have "tooltips" that will show

when you pause the mouse pointer over them.



In the upper left corner is a panel called "FS-FAST Hierarchy". The

"Func Stem" is the input to the analysis. You should specify the

output from the preprocessing. For this exercise, we are going to use

the motion corrected and 5mm smoothed data. This functional volume is

called fmcsm5.nii in the hierarchy which makes its stem "fmcsm5" (i.e.,

just strip off the nii). Enter "fmcsm5" into the field. When you hit

return, it changes from red to white. Next, enter the TR (sec). For

this experiment it was 3 sec. This will be checked against the TR

found in the input nifti file. Leave INorm checked.

{{{

Func Stem = fmcsm5

TR = 3

}}}



Turn your attention to the "Noise and Nuisance Variables" panel. Low

frequency noise so prevalent in fMRI is compensated for in a

combination of three ways. Drift components are modeled with

polynomial regressors. The order can be adjusted, but leave it at 2

for now. The motion correction parameters can be used as regressors by

checking the "MC Regressors" box (do so now). Finally, the remaining noise is

modeled as time-invariant linear AR1 process when the "Temporal

Whitening" box is checked (leave it so). There is one additionally way

to compensate for noise through the use of a "Time Point Exclude

File", but we will not consider that here.

{{{

Check "MC Regressors" Box

}}}



You will specify the model of the task-related signal in the "Event

Related/Block Design" panel (leave that box checked). Choose the

number of conditions by clicking on the "NConditions" slider. This is

the number of TASK conditions (do not include the Null/Fixation

condition). In this example, we have two conditions (Odd and Even), so

set this to 2. To the right of this is the "Paradigm File". Enter

"sensory-motor.par". Note that the number of task conditions in the

paradigm file must match that specified with "NConditions". Below, you

will specify the Hemodynamic Response Model. There are three choices:

Gamma, SPM HRF, and FIR. Choose Gamma for now. If you hit the "Plot"

button it will show the Gamma and SPM HRF. As you change the Gamma

parameters (Delay, Dispersion (Tau), and Exponent (Alpha)), the Gamma

plot will change. Make sure that they are at Delay=2.25, Tau=1.25, and

Alpha=2.

{{{

Set NConditions to 2

Paradigm File = sensory-motor.par

Press "Plot" button

}}}



At this point, you have specified the model of the BOLD signal

including HRF, nuisance, and noise. The GUI should look like the image

below:



{{attachment:mkana-gamma-precon.gif}}



Now you are ready to specify contrasts. A contrast is an instantiation

of a hypothesis and is represented by a contrast matrix (i.e., a linear

summation of the regression coefficients). Contrasts are managed

through a separate GUI accessed through the "Contrast" list box. When

you click on "Add Contrast", you will see the following screen:

{{{

Click on "Add Contrast"

}}}



{{attachment:mkcon.init.gif}}



There are several things going on here, but the most important is the

list of conditions in the middle of the GUI (i.e., "Condition 1",

"Condition 2") shown as green, red, and black radio buttons. 



 * Green indicates an "active" condition.

 * Red means a "control" condition

 * Black means to ignore the condition in the contrast. 



Active conditions are given a weight of +1; controls are given -1;

ignores get 0. The weight is given to the right of the buttons.  All

contrasts are implicitly computed against the Null or Fixation

condition. If you want to test the null hypothesis that Condition 1 is

no different than the Null condition, then you would make Condition 1

active and ignore the rest. To test the null hypothesis that Condition

1 is no different than Condition 2, then you would make Condition 1

active and Condition 2 control.



For this exercise, we are going to test four NULL hypotheses:



 * Odd == Fixation (odd-v-0)

 * Even == Fixation (even-v-0)

 * Odd == Even (odd-v-even)

 * Odd+Even == Fixation (odd+even)



The last one tests whether the average of the responses to odd and

even are different than fixation. Remember that, according to the

Paradigm File, Condition 1 is Odd, and Condition 2 is Even.  



{{{

Click on the green button next to "Condition 1". (see its weight change from 0 to 1).

}}}



Condition 2 is already black (ignored). This our first contrast, so there is

nothing more we need to do except give the contrast a name. You should give

your contrasts meaningful but terse names. 

{{{

Specify "odd-v-0" for the name of this contrast.

Hit the "Done/Save" button.

}}}

You will now see "odd-v-0" appear in the Contrast list box in the mkanalyiss GUI.



{{{

Click on "Add Contrast" again to bring up the contrast GUI again.

Click on the green button next to Condition 2 (see its weight change from 0 to 1).

}}}



This activates condition 2. Condition 1 is still black (ignored). 

{{{

Change the name to "even-v-0"

Click Done/Save

}}}

"even-v-0" will appear in the list box.



{{{

Click on "Add Contrast" again

Click the green button next to Condition 1 (weight changes to 1.0).

Click the red button next to Condition 2 (weight changes to -1.0).

Change the name to "odd-v-even"

Click Done/Save.

}}}



{{{

Click on "Add Contrast" one more time

Click the green button next to Condition 1. Weight changes to 1.0.

Click the green button next to Condition 2. Both weights change to 0.5.

Change the name to "odd+even"

Click Done/Save.

}}}



You can go back and view and/or edit an contrast by clicking on it in

the list box.



The last thing you have to do is to give your analysis a name. Like

the contrasts, it should be terse but descriptive.  It cannot have any

spaces or blanks.  

{{{

Specify "sm-gamma-fwhm5" as the analysis name.

}}}

sm = sensory-motor, gamma = Gamma HRF, and fwhm5 for the input. The interface should now look



{{attachment:mkana.final.gif}}



{{{

Hit the "Save" button

Hit the "Quit" button

}}}



After you hit Quit, control will be returned to the shell that you ran

mkanalysis-sess from. If you type "ls", you will see a new folder

called "sm-gamma-fwhm5". If you "ls sm-gamma-fwhm5", you will see

analysis.info, analysis.cfg, odd-v-0.mat, even-v-0.mat,

odd-v-even.mat, odd+even.mat. Your configuration is stored in these

files. You can browse/edit your configuration by running:



{{{

mkanalysis-sess -gui -analysis sm-gamma-fwhm5

}}}



== Configure Analysis and Contrasts II: FIR HRF Model  ==



Now we are going to use a Finit Impulse Response (FIR) to model the

hemodynamic response. The FIR does not make any assumptions about the

shape of the HRF but is also less interpretable. Again, run



{{{

mkanalysis-sess -gui

}}}



Set the Func Stem, TR, NConditions, and Paradigm File as above, but

now click on the "FIR" checkbox. This will enable the "Total Time

Window", "PreStim", and "TER" entry boxes. The Time Window is the

window within which we will estimate the HRF. Given that the task is

15 sec long and the rest is 15 sec, let's choose 27 sec. The PreStim

is the amount of time before stimulus onset to start estimating the

HRF. A non-zero PreStim gives us an idea of what the baseline is at

stimulus onset. Set it to 6. 



Set up the same contrasts as you did above, then name the analysis

"sm-fir-fwhm5", hit Save, then Quit.



== Analyze First Level (selxavg3-sess) ==



You are now ready to analyze some data! Note that the fully analyzed

data (along with correctly configured analyses) can be found in

fb1-analysis-study. To analyze the data for session mgh-101.1 with the

sm-gamma-fwhm5 analysis, run:



{{{

selxavg3-sess -s mgh-101.1 -analysis sm-gamma-fwhm5

}}}



Note that if you want to analyze all the sessions, you can run

"selxavg3-sess -sf sessid -analysis sm-gamma-fwhm5", but this might

take a while.



To analyze the data for session mgh-101.1 with the sm-fir-fwhm5

analysis, run:

{{{

selxavg3-sess -s mgh-101.1 -analysis sm-fir-fwhm5

}}}



== Examine additions to the FSFAST hierarchy ==



{{{

ls mgh-101.1/bold

ls mgh-101.1/bold/sm-gamma-fwhm5

}}}



The bold folder now has sm-gamma-fwhm5 (as well as the fir too). This

folder has all of the results for this analysis, including: 



 * 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 - text file with smoothness estimate (Full-Width/Half-Max)

 * contrast folders: odd-v-0, even-v-0, odd+even, odd-v-even



Look in one of the contrasts:



{{{

ls mgh-101.1/bold/sm-gamma-fwhm5/odd-v-0

}}}



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

 * cesvar.nii - variance of CES

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



== Visualize ==



Visualization of the first-level analysis will be done in the folder

with the fully analyzed data set:



{{{

cd $FSFTUTDIR/fb1-analysis-study

}}}



=== Volume-based visualization (tkmedit-sess) ===



View the result of the Gamma HRF analysis on the FreeSurfer anatomical

volume for mgh-101.1 with:

{{{

tkmedit-sess -s mgh-101.1 -aparc+aseg -analysis sm-gamma-fwhm5 \

  -c odd-v-0 -c even-v-0 -c odd+even -c odd-v-even 

}}}

This will automatically load sig.nii.

When you configure the functional overlay with

View->Configure->Functional Overlay, you will see that there are 4

"Time Points". Each point is different contrast (i.e., 0 is odd-v-0, 1

is even-v-0, etc). Scroll through each one.



View the result of the HRF HRF analysis for mgh-101.1 with:

{{{

tkmedit-sess -s mgh-101.1 -aparc+aseg -analysis sm-fir-fwhm5 -c odd-v-0 

}}}



Here we view only one contrast at a time because each contrast has

multiple time points for each point in the FIR time window. When you

click on a point, you will see the HRF for both Condition 1 (Odd) and

Condition 2 (Even) blocks.



Finally, view the overlay maps from the Gamma with the HRF from the FIR:

{{{

tkmedit-sess -s mgh-101.1 -aparc+aseg -analysis sm-fir-fwhm5 \

  -mapanalysis sm-gamma-fwhm5 -c odd-v-0 -c even-v-0 -c odd+even -c odd-v-even 

}}}

When you configure the overlay, you will see that there are 4 "Time

Points" -- these correspond to the 4 contrasts from the Gamma analysis.



=== Surface-based visualization (tksurfer-sess) ===

View the result of the Gamma HRF analysis on the FreeSurfer anatomical

surface for mgh-101.1 with:

{{{

tksurfer-sess -hemi lh -aparc -s mgh-101.1 -analysis sm-gamma-fwhm5 \

  -c odd-v-0 -c even-v-0 -c odd+even -c odd-v-even

}}}

Note that the command-line is nearly identical to that of tkmedit

above. The difference is that the hemisphere is specified ("-hemi

lh"), and "-aparc+aseg" is replaced with "-aparc" to load the

surface-based segmentation.



---------------------------------------------------------------------

---------------------------------------------------------------------



= Higher-Level (Group) Analysis =



Higher-Level is where you make inferences about the population that

your subjects are drawn from. It is a bit confusing at times because

both use GLMs, so at both levels you are constructing design matrices,

contrasts, etc. 



Traditionally, fMRI group analysis has been done in a standard volume

space (i.e., Talairach/MNI152/MNI305). With FreeSurfer, we also have the

option to analyze group data in the surface space. Volume-based

analyses are done in MNI305 space (which is the same as the fsaverage

subject).



For the next exercises, we will work in the fb1-analysis-study directory

{{{

cd $FSFTUTDIR/fb1-analysis-study

}}}

All the first level analyses have been done here. The group-level

analyses have been done in the group-analysis-tut directory.



== Volume-based (MNI305/fsaverage) Group Analysis ==



=== Assemble the Data (isxconcat-sess) (Volume) ===



The first step in the group analysis is to "assemble" the data. This

means creating a single 4D file with where the 4th "time" dimension is

actual all the subjects concatenated together in a common space. There is a

different command, depending upon whether the common space is volume-

or surface-based.



To run the volume-based concatenation, run the command below. Note

that the data from this command already exist in the

group-analysis-tut directory.



{{{

isxconcat-sess -sf sessid -analysis sm-gamma-fwhm5 -c odd-v-0 -o group-analysis

}}}



This command will:

 * Go through each session in the sessid file

 * Find the odd-v-0 contrast in the sm-gamma-fwhm5 analysis

 * Use the register.dat for that session to resample to MNI305/fsaverage space. 

 * Concatenate all subjects' data into one file



The concatenated data are saved in

group-analysis/sm-gamma-fwhm5/odd-v-0/tal.ces.nii file. You can run

other contrasts by adding -c arguments. In addition to this output,

several other files are created. To see them,



{{{

cd $FSFTUTDIR/fb1-analysis-study

ls group-analysis-tut/sm-gamma-fwhm5

cat group-analysis-tut/sm-gamma-fwhm5/sessid.txt

cat group-analysis-tut/sm-gamma-fwhm5/ffxdof.dat

}}}



In the output directory, you will see a series of files that start

with "tal":

 * tal.meanfunc.nii is a stack where each "time point" is the mean functional image of each subject sampled in the MNI305 space.

 * tal.masks.nii are the binary masks for all the subjects

 * tal.fsnr.nii are the functional SNR maps from each subject. 

 * tal.mask.nii is a single binary mask made from the intersection of the individuals. 

 * ffxdof.dat is the fixed-effects DOF across all subjects. 

 * sessid.txt is the list of sessions, the corresponding freesurfer subject name, and the DOF contributed by each subject.



You will also see some files that being with "lh". These are the same

thing in the surface-based space (see below).



Now look in the directory for odd-v-0 the contrast

{{{

cd $FSFTUTDIR/fb1-analysis-study

ls group-analysis-tut/sm-gamma-fwhm5/odd-v-0

}}}



You will see:

 * tal.ces.nii - the contrast maps for each of the subjects

 * tal.cesvar.nii are the variance of the contrast for each subject (i.e., the square of the standard error). This variance is needed for fixed-effects and weighted random-effects analysis. 





=== Quality Assurance (Volume) ===



There are three important quality assurance steps that can be performed

here. First, view the mean functionals to make sure that all are

registered together properly. To do this run,



{{{

tkregister2 --s fsaverage --surf --regheader --check-reg \

  --mov group-analysis-tut/sm-gamma-fwhm5/tal.meanfunc.nii 

}}}

The image window will show the MNI305 brain. Hit the "Compare" button

to show the average functional of the first session. Click in the

image window, then hit the 'a' key. Each time you hit the 'a' key, it

will advance to the next subject.



The next QA step is to check the individual masks. This can be done

with:

{{{

tkmedit fsaverage orig.mgz \

  -overlay group-analysis-tut/sm-gamma-fwhm5/tal.masks.nii -fthresh 0.5

}}}

The threshold of 0.5 is appropriate because these masks are binary 

(ie 0-1). When you View->Configure->Functional Overlay, you will see 

that there are 5 "Time Points" (0-4) corresponding to the 5 subjects. 

Advance through each one to assure that the masks are in the proper place.



The final step is to look at the functional SNR maps with

{{{

tkmedit fsaverage orig.mgz \

  -overlay group-analysis-tut/sm-gamma-fwhm5/tal.fsnr.nii \

  -timecourse group-analysis-tut/sm-gamma-fwhm5/tal.fsnr.nii \

  -fthresh 50 -fmax 250

}}}



{{{

When you View->Configure->Functional Overlay, you will see 

that there are 5 "Time Points" (0-4) corresponding to the 5 subjects. 

Advance through each one to assure that the SNR maps are "consistent".

}}}



When you click on a voxel, you will see the SNR for each subject

plotted in the "Time Course" window. The actual value of the FSNR will

vary depending upon how much smoothing you've done and the details of

the acquisition. You are looking for outliers here.



=== Group GLM Analysis (Volume) ===



When you perform a group analysis, you are looking for effects of the

task across the population. In this example, we are only going to test

whether the mean effect of Odd vs Fixation (odd-v-0) is different than

0. This is known as a one-sample-group-mean (OSGM) and corresponds to

a group design matrix that is simply a column of 1s. One can perform

considerably more elaborate tests (e.g., differences in groups). Now, cd

to where the data are:



{{{

cd $FSFTUTDIR/fb1-analysis-study/group-analysis-tut/sm-gamma-fwhm5/odd-v-0

}}}



==== Random Effects (RFx, OLS) (Volume) ====

Random effects is a test of whether the mean of the population that

the subjects were drawn from is 0. It is done with mri_glmfit:



{{{

cd $FSFTUTDIR/fb1-analysis-study/group-analysis-tut/sm-gamma-fwhm5/odd-v-0

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

}}}



 * tal.ces.nii are the inputs for this contrast for all subjects. 

 * --osgm tells it to create the simple OSGM design matrix and to create a group contrast to test the group mean against 0. 

 * The mask is used to exclude extra-brain voxels.  



The results will be stored in tal.rfx.osgm:

{{{

cd $FSFTUTDIR/fb1-analysis-study/group-analysis-tut/sm-gamma-fwhm5/odd-v-0

ls tal.rfx.osgm

ls tal.rfx.osgm/osgm

}}}



You will see several files in the output. beta.nii is the map of

regression coefficients. There is also an "osgm" folder with

sig.nii. This is a map of the significances of the test. We will view

it below.



==== Weighted Random Effects (WRFx, WLS) (Volume) ====

In a real experiment, some subjects are noisier than others, and it is

a good idea to take this into account since we have information about

how noisy a subject is through the lower-level analysis. In weighted

least squares (WLS), this is handled by weighting each subject by the

inverse of their noise (i.e., noisier subjects get lower weight). To do

this with mri_glmfit, run:



{{{

cd $FSFTUTDIR/fb1-analysis-study/group-analysis-tut/sm-gamma-fwhm5/odd-v-0

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

    --wls tal.cesvar.nii

}}}



The command-line is almost the same as the RFx except that the

first-level noise variances (tal.cesvar.nii) are passed with the --wls

option. The results are saved in tal.wrfx.osgm and have the same

naming convention as the RFx.



==== Fixed Effects (FFx) (Volume) ====

A fixed effects analysis tests whether an effect exists within the

given subject pool (as opposed to the population that the subjects

were pulled from). It is like doing one big fist-level analysis. The

big difference with RFx or WRFx is that the variance is computed from

the lower level analysis variance. This is done in mri_glmfit with:



{{{

cd $FSFTUTDIR/fb1-analysis-study/group-analysis-tut/sm-gamma-fwhm5/odd-v-0

mri_glmfit --y tal.ces.nii --osgm --glmdir tal.ffx.osgm --nii --mask ../tal.mask.nii \

   --yffxvar tal.cesvar.nii --ffxdofdat ../ffxdof.dat

}}}



Again, the command-line is similar to above, except that the lower

level variances are passed with --yffxvar, and the total lower level

DOFs are passed with --ffxdofdat.



=== Output and visualization (Volume) ===



To visualize these three analyses, we will first concatenate them into

one file to make it easier to view in tkmedit:



{{{

cd $FSFTUTDIR/fb1-analysis-study/group-analysis-tut/sm-gamma-fwhm5/odd-v-0

mri_concat tal.rfx.osgm/osgm/sig.nii \

           tal.wrfx.osgm/osgm/sig.nii \

           tal.ffx.osgm/osgm/sig.nii  \

        --o all.sig.nii

tkmedit fsaverage orig.mgz -aux brain.mgz \

     -overlay all.sig.nii -fthresh 2 -fmax 4

}}}

Configure the overlay with View->Configure->Functional Overlay. Scroll

through the "time" points to see the differences in the analyses.



=== Correction for Multiple Comparisons/Cluster Analysis (Volume) ===



With so many voxels in fMRI maps, it is very likely that many voxels

will appear to be active purely by random chance (ie, a false

positive). The is known as the "Problem of Multiple Comparisons". One

way around this is to do a cluster analysis in which active voxels are

eliminated unless they appear in a cluster, the idea being that false

positives will not appear next to each other. To do this in FSFAST,

run the following command on the RFx analysis:



{{{

cd $FSFTUTDIR/fb1-analysis-study/group-analysis-tut/sm-gamma-fwhm5/odd-v-0

mri_volcluster --in tal.rfx.osgm/osgm/sig.nii --thmin 2 \

   --fwhmdat tal.rfx.osgm/fwhm.dat --cwpvalthresh 0.1 \

   --mask tal.rfx.osgm/mask.nii --fsaverage \

   --sum tal.rfx.osgm/osgm/cluster.sum --sign pos\

   --cwsig tal.rfx.osgm/osgm/cwsig.cluster.nii \

   --out tal.rfx.osgm/osgm/sig.cluster.nii \

   --ocn tal.rfx.osgm/osgm/ocn.cluster.nii 

}}}



In this command we use the significance map as input thresholded at 2,

where 2 is -log10(p), so p < .01. The --fwhmdat option tells

mri_volcluster to use the spatial smoothness estimate from GLM

analysis (saved in tal.rfx.osgm/fwhm.dat). The "--cwpvalthresh 0.1"

tells it to ignore clusters unless their p-value is less than 0.1 (you

can threshold more stringently later on). 



One of the outputs is a cluster table (cluster.sum). This is a text

file with the list of clusters. View it with:



{{{ 

more tal.rfx.osgm/osgm/cluster.sum 

}}} 



You can also see it here: FsFastTutorial/ClusterSummary. The table

shows the size of each cluster in voxels (mm^3), the Talairach

coordinate, the maximum significance in the cluster, and GRFCWP (the

clusterwise p-value computed using gaussian random field). Some of them are 0, meaning that the

p-value was so low that it could not be computed (remember, this is a

good thing!).



The other outputs are volumes. cwsig.cluster.nii is a map of the

clusters with the voxel value equal to the -log10(pvalue) of the

cluster that the voxel is associated with. sig.cluster.nii is the

original sig map with non-cluster voxels removed. ocn.cluster.nii is a

map where the value at each voxel is replaced by the number of the

cluster that the voxel is associated with. View the clusterwise

significance: 



{{{

tkmedit fsaverage orig.mgz -aux brain.mgz  \

     -overlay tal.rfx.osgm/osgm/cwsig.cluster.nii -fthresh 2 -fmax 4

}}}



--------------------------------------------------------------------

--------------------------------------------------------------------



== Surface-based Group Analysis ==



=== Assemble the Data (isxconcat-sess) (Surface) ===



The surface-based analysis proceeds in much the same way as the

volume-based. The assembly command is the same except you add "-hemi

lh" (or "-hemi rh") to the command:

{{{

cd $FSFTUTDIR/fb1-analysis-study

isxconcat-sess -sf sessid -analysis sm-gamma-fwhm5 -c odd-v-0 -o group-analysis -hemi lh

}}}



This creates the same files as the volume-based command, except that

they have "lh" or "rh" preprepended instead of "tal". 

{{{

ls group-analysis/sm-gamma-fwhm5

ls group-analysis/sm-gamma-fwhm5/odd-v-0

}}}



You should see the following files: lh.meanfunc.nii, lh.mask.nii,

lh.fsnr.nii, lh.ces.nii, lh.cesvar.nii.



=== Group GLM Analysis (Surface) ===



The commands for the GLM analysis are the same as for the volume-based

except that "--surf fsaverage lh" is added to the command line:



Cd into the directory with the assembled data:



{{{

cd $FSFTUTDIR/fb1-analysis-study/group-analysis-tut/sm-gamma-fwhm5/odd-v-0

}}}



==== Random Effects (RFx, OLS) (Surface) ====

{{{

mri_glmfit --y lh.ces.nii --osgm --mask ../lh.mask.nii --glmdir lh.rfx.osgm --nii \

  --surf fsaverage lh

}}}



==== Weighted Random Effects (WRFx, WLS) (Surface) ====

{{{

mri_glmfit --y lh.ces.nii --osgm --glmdir lh.wrfx.osgm --nii --mask ../lh.mask.nii \

    --wls lh.cesvar.nii --surf fsaverage lh

}}}



==== Fixed Effects (FFx) (Surface) ====

{{{

mri_glmfit --y lh.ces.nii --osgm --glmdir lh.ffx.osgm --nii --mask ../lh.mask.nii \

   --yffxvar lh.cesvar.nii --ffxdofdat ../ffxdof.dat --surf fsaverage lh

}}}



=== Output and visualization (Surface) ===



For convenience, we concatenate the different analyses into a single

file, then visualize with tksurfer:



{{{

mri_concat lh.rfx.osgm/osgm/sig.nii lh.wrfx.osgm/osgm/sig.nii lh.ffx.osgm/osgm/sig.nii  \

        --o lh.all.sig.nii

tksurfer fsaverage lh inflated -annot aparc -overlay lh.all.sig.nii -fthresh 2 

}}}





=== Correction for Multiple Comparisons/Cluster Analysis (Surface) ===



In principle, correction for multiple comparisons on the surface is

done in the same way as volume-based, but in implementation is

considerably more difficult. We have several methods for correcting on

the surface, including False Discovery Rate (FDR), Monte-Carlo

Simulation, and Permutation Simulation. The Guassian Random Field

(GRF) is still being developed (as are the tutorials for it).





= Play with Your Data =



In this section, you are encouraged to experiment with the data. These

analyses have not already been pre-computed, but they should not take

long to run.



== Change the smoothing level ==



Use this series of commands to analyze the data smoothed by 10 mm

FWHM. The preproc-sess command will create fmcsm10.nii.  In

mkanalysis-sess, use the -clone option to copy the parameters from an

existing analysis, then just change the Func Stem to fmcsm10.



{{{

cd $FSFUTDIR/fb1-analysis-study

preproc-sess -s mgh-101.1 -fwhm 10

mkanalysis-sess -gui -clone sm-gamma-fwhm5 -analysis sm-gamma-fwhm10

selxavg3-sess -s mgh-101.1 -a sm-gamma-fwhm10

tkmedit-sess -s mgh-101.1 -analysis sm-gamma-fwhm10 \

  -c odd-v-0 -c even-v-0 -c odd+even -c odd-v-even -aparc+aseg 



}}}



== Change noise/nuisance model ==



Use this series of commands to analyze the data with little

compensation for noise and nuisance variables. Change the analysis by

turning off whitening, set the Polynomial Order to 0, and uncheck the

MC Regressors.



{{{

cd $FSFUTDIR/fb1-analysis-study

mkanalysis-sess -gui -clone sm-gamma-fwhm5 -analysis sm-gamma-fwhm5B

selxavg3-sess -s mgh-101.1 -a sm-gamma-fwhm5B

tkmedit-sess -s mgh-101.1 -analysis sm-gamma-fwhm5B \

  -c odd-v-0 -c even-v-0 -c odd+even -c odd-v-even -aparc+aseg 



}}}
+  * What time was the third Encode presented? 

  * What time was the last Neutral Distractor presented? 

  * Is this timing for all runs the same?