XBAM Tutorial (June 08)

XBAM tutorial
June 08

1
http://brainmap.it

About the tutorial and XBAM 3

Downloading fMRI data 13

Data conversion 18

Motion correction 25

First level analysis in native space (IBAM) 32

First level analysis in Talairach space (TBAM) 46

Second level: group mapping (GBAM) 60

Second level: analysis of variance (ABAM) 75

Second level: correlation analysis (BBAM) 93

Extracting average time series / BOLD response 110

Extracting statistical values from ROI 121

TABLE OF CONTENTS XBAM tutorial June 2008 2

XBAM tutorial
About the tutorial and XBAM

http://brainmap.it

XBAM tutorial June 2008 3

This document is a self-paced tutorial to XBAM

This tutorial is a walkthrough aiming to teach you
how to analyse fMRI data from pre-processing raw
data to extracting statistical values from XBAM
result maps

This tutorial will show you one of the many ways to
analyse your data with XBAM.

If a feature/method that you are interested in is not
in the tutorial, it does not mean that XBAM cannot
do it!

Please ask us if you have any questions/requests!

ABOUT THIS TUTORIAL XBAM tutorial June 2008 4

Unix-like operating system (e.g. Solaris, Linux, Mac OS
X) or PC Unix emulator (e.g. Cygwin)

Basic knowledge of Unix (you need to know how to
create files and folders, copy, rename, move, edit files…)

If you are using your own computer: you need at least
1 GB of free disk space

If you are using a centralised setup, like at the IoP: you
need a Unix/Linux account with at least 1 GB of free disk
quota

The latest version of XBAM_v4, which you can download
from http://brainmap.it (no need if you are at the IoP)

The tutorial dataset, which you can download at http://
brainmap.it/XBAM_DATA.tar.gz (no need if you are at the
IoP)

PREREQUISITES XBAM tutorial June 2008 5

Keywords, keyboard keys, program & command
names are shown in bold and italic in the text

Everything that appears in a Terminal window is
shown in the Courier font within a light gray box

Things that programs and the Unix shell return
appear in bold

Things that you need to type in the Terminal
window appear in bordeaux and bold

Slides in which you need to type in something
have a PINK ITALIC bottom title rather than a
STRAIGHT WHITE one

XBAM_v4 > 1 > 3 means “start XBAM_v4,
choose option 1 and then option 3”

TEXT CONVENTIONS XBAM tutorial June 2008 6

Default values for the various analysis parameters
are shown between square brackets [ ], e.g.:
Number of randomisations ? [20]

To accept the default value, simply press the Return
key on your keyboard

If you do not want to use the default, type in your
desired value before pressing the Return key on your
keyboard

During the conversion, pre-processing and first
level analysis steps you can process several
subjects at once by entering the folder names
separated by a space, e.g.:
Directory containing the images to convert ? DIR1 DIR2

XBAM CONVENTIONS XBAM tutorial June 2008 7

XBAM generates plain text .log files

Those can be viewed

In a Terminal window with the more or less commands

In a separate popup window with nedit

In real-time in a Terminal window with tail -f (type CTRL+C to quit
this mode)

The log file of a successful analysis always indicates that
the analysis is finished
Finished conversion at Thu Jan 24 15:32:34 GMT 2008

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

The log file of an unsuccessful analysis contains ERROR or
WARNING messages ERROR ERROR ERROR ERROR ERROR ERROR

No FEAR01_REGISTRATION.log in FEAR01

Make sure that you have registered the

dataset

ERROR ERROR ERROR ERROR ERROR ERROR

XBAM LOGS XBAM tutorial June 2008 8

At the IoP, XBAM analysis jobs are placed in a
queuing system and run in the background
(i.e. you can log off once you have started the
analysis)
##### BACKGROUND PROCESSING #####



This job is number 42 and it has been placed in a batch queue.

To check the state of the queue, type: atq

If job 42 does not appear on the queue, DO NOT RESTART IT, it is
probably already running

To remove this job from the queue, type: atrm 42

To check the progress of this job, type:

tail -f EMOTIVE03/EMOTIVE03_CONVERSION.log

XBAM JOB QUEUE XBAM tutorial June 2008 9

If you are running XBAM on your own computer,
the analysis jobs will by default run in the
foreground (i.e. you should not log off while an
analysis is running)
Started conversion at Thu Jan 24 15:24:05 GMT 2008

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

Ask us about what to type to enable the queuing
system in XBAM

If you submit multiple analysis jobs at the same
time from the same Terminal window, they will
run one after another

If you submit multiple analysis jobs at the same
time from different Terminal windows, they will
run at the same time, but will share the available
resources
XBAM JOB QUEUE XBAM tutorial June 2008 10

The brain maps produced by XBAM are available in
several formats:

Blob-only files in Analyze format that can be used as an overlay,
for example in MRIcro (e.g FEAR01_MASS_POSa2.img )

Blobs overlayed on a background brain files in Analyze format
(e.g. FEAR01_MASS_POSa2out.img )

.gif or .ppm pictures that can be opened with any graphic
program (e.g. FEAR01_MASS_POSa2out.gif )

postscript files that have be used when printing from the
command line (e.g. FEAR01_MASS_POSa2out.ps )

The text files produced by XBAM are plain text .txt
or .dat files that can be opened with any text editor (e.g.
FEAR01_allclusters_POSa2.dat ).
These can be printed directly from the command line and
prettified (for printing) versions are also available as .ps
postscript files

XBAM OUTPUT FILES XBAM tutorial June 2008 11

3 subjects: EMOTIVE01 to 03

1 event-related task: fearful Ekman faces

3 active conditions: neutral, 50% and 100% fear

Baseline: fixation cross

TR: 2 s

180 T2* volumes

16 slices, matrix size 64x64

In-plane voxel size 3.75 mm

Slice thickness 7.7 mm (including slice gap)

Interleaved acquisition, starting from bottom

2 high resolution (GE T2* and IR) structural
volumes to use for Talairach normalisation
Thank you to Simon Surguladze for the data
THE EXPERIMENT TO ANALYZE XBAM tutorial June 2008 12

XBAM tutorial
Downloading fMRI data

http://brainmap.it


Check the size of the files to download
[mirabelle% du -hs /home/mri_data/EMOTIVE0[1-3]

15M /home/mri_data/EMOTIVE01



Check how much space is left in your IoP Unix account
mirabelle% pquota

Biostats:

---------

Total: 50.00G

Used: 8.71G

Free: 41.29G

If you are using your own computer for analysis, find out
how much space is left on the hard drive
mirabelle% df -h ~

Filesystem Size Used Avail Capacity Mounted on

/dev/disk0s2 149G 130G 19G 87% /

DOWNLOADING FMRI DATA XBAM tutorial June 2008 14

In this case the combined size of the three raw datasets
is 45 MB and you have only used about 9 GB out of a 50
GB quota limit

There is therefore ample space to download the files
(remember that 1 GB = 1024 MB)

Create a new folder to store the tutorial data
mirabelle% mkdir ~/EMOTIVE

Download the fMRI data to this new folder
mirabelle% cp -r /home/mri_data/EMOTIVE0[1-3] ~/EMOTIVE

Check that the files have been successfully copied
mirabelle% diff -r /home/mri_data/EMOTIVE01 ~/EMOTIVE/EMOTIVE01




The fMRI images are in UNC (University of North Carolina)
format

Typical UNC file name: S_K.06441.008.s000.Z where:

S_K are the subject’s initials for Mapother House data (CNSA
for CNS 1.5 T data, CNSB for CNS 3 T)

06441 is the unique experiment ID

008 is the series number (e.g. 001 localiser, 002 high resolution
structural image, 008 n-back fMRI…)

s000 means slice #0 of fMRI data (structural, localiser and
template images do not have this s??? field)

When present, the last name field indicates which kind of
compression was used on the image:
Z means that the file has been compressed with the Unix
compress program, gz with gzip, and bz2 with bzip2

For each subject, check (using your logbook) that all the
scanned series are there and verify that you have the correct
number of fMRI slices for each experiment (everything is fine
with the tutorial data)


Look at some of the images to make sure that they are
not distorted and that there is no missing data (e.g.
missing time point)
mirabelle% cd ~/EMOTIVE/EMOTIVE01

mirabelle% ls

S_K.06441.001.Z S_K.06441.008.s003.Z S_K.06441.008.s010.Z

S_K.06441.003.Z S_K.06441.008.s004.Z S_K.06441.008.s011.Z

S_K.06441.004.Z S_K.06441.008.s005.Z S_K.06441.008.s012.Z

S_K.06441.008.Z S_K.06441.008.s006.Z S_K.06441.008.s013.Z

S_K.06441.008.s000.Z S_K.06441.008.s007.Z S_K.06441.008.s014.Z

S_K.06441.008.s001.Z S_K.06441.008.s008.Z S_K.06441.008.s015.Z

S_K.06441.008.s002.Z S_K.06441.008.s009.Z

mirabelle% gunzip S_K.06441.008.s007.Z

mirabelle% imagej S_K.06441.008.s007


XBAM tutorial
Data conversion

http://brainmap.it


XBAM requires the fMRI images to be in the Analyze
7.5 format, created by the Mayo Foundation (for the
rest of the tutorial, this will simply be called Analyze)

The first thing that XBAM needs to do is to convert the
fMRI files from the raw UNC to the Analyze format

In the raw UNC format, each file contains both the
header and the data part of the image

In Analyze format, the header and the data come as
two separate files:

a .hdr file for the header

a .img file for the data

For example, once converted to Analyze the UNC file
S_K.06441.008.s000 becomes sl0.hdr and sl0.img

The conversion is lossless for image data, but the MR
specific header information is lost

DATA CONVERSION XBAM tutorial June 2008 19

XBAM requires the following directory/file structure:

Each subject fMRI experiment should be in its own folder

Each slice of data should be in its own folder, called sl?, in
which the Analyze image file has to be called sl?.img and
sl?.hdr

The first slice is number 0 (i.e. the Analyze files for this slice
are sl0.img and sl0.hdr in the sl0 folder)

The structural image has to be called struct.img & struct.hdr
and should be placed within the experiment folder

The model file used for statistical analysis should also be
placed in the experiment folder and has to be called either
infile.dat (for block design or TR-locked event-related design),
newstarts.dat (for event-related design), or model.dat (for
correlation analysis) EMOTIVE01

struct.img struct.hdr newstarts.dat sl0 sl1 sl...

sl0.hdr sl0.img sl1.hdr sl1.img


Run XBAM_v4 > 1 > 1 to automatically convert your
images from the UNC to the Analyze format
mirabelle% cd ~/EMOTIVE

mirabelle% ls

EMOTIVE01 EMOTIVE02 EMOTIVE03

mirabelle% XBAM_v4

…

##### MAIN MENU #####

Choose from the following options:

1 - Data conversion...

2 - Preprocessing...

3 - First level analysis (subject level)...

4 - Second level analysis (group level)...

5 - Region of interest (ROI) analysis tools...

6 - Model free analysis...

7 - Utilities...

8 - About XBAM

9 - Quit

What is your choice? 1


##### DATA CONVERSION #####


1 - UNC to XBAM data format & file hierarchy

2 - SPM/XYT-Z/4D/REC to XBAM data format & file hierarchy

3 - Quit


------------------oOo------------------

XBAM - Conversion from UNC to Analyze

------------------oOo------------------

What is the name of the directory containing the images to convert ?
EMOTIVE01 EMOTIVE02 EMOTIVE03

Number of subjects -> 3

The conversion log file is called _CONVERSION.log
and it can be found within each subject folder, e.g.
mirabelle% nedit ~/EMOTIVE/EMOTIVE01/EMOTIVE01_CONVERSION.log


After conversion, each fMRI experiment is in its own folder
named experimentID.seriesNb (e.g. 06441.008)

Give this folder a new short and easily recognisable name
mirabelle% cd ~/EMOTIVE/EMOTIVE01

mirabelle% ls

06441.008 OTHER_FILES

EMOTIVE01_CONVERSION.log struct

mirabelle% mv 06441.008 FEAR01

mirabelle% ls FEAR01

S_K.06441.008.s000 S_K.06441.008.s013 sl3

S_K.06441.008.s001 S_K.06441.008.s014 sl4

S_K.06441.008.s002 S_K.06441.008.s015 sl5

S_K.06441.008.s003 TR.dat sl6

S_K.06441.008.s004 sl0 sl7

S_K.06441.008.s005 sl1 sl8

S_K.06441.008.s006 sl10 sl9

S_K.06441.008.s007 sl11 struct.hdr

S_K.06441.008.s008 sl12 struct.img

S_K.06441.008.s009 sl13 struct_003_GE.hdr

S_K.06441.008.s010 sl14 struct_003_GE.img

S_K.06441.008.s011 sl15 struct_004_IR.hdr

S_K.06441.008.s012 sl2 struct_004_IR.img

mirabelle% ls FEAR01/sl0

sl0.hdr sl0.img


Rename the fMRI experiments of the two other subjects
mirabelle% cd ../EMOTIVE02

mirabelle% ls

06504.005 OTHER_FILES EMOTIVE02_CONVERSION.log struct


mirabelle% cd ../EMOTIVE03

mirabelle% ls

07460.007 OTHER_FILES EMOTIVE03_CONVERSION.log struct


Create a new folder called FEAR to hold the fMRI experiments

Move the three renamed fMRI experiments to this new folder in
order to have all the data at the same level
mirabelle% cd ~/EMOTIVE

mirabelle% mkdir FEAR

mirabelle% ls

EMOTIVE01 EMOTIVE02 EMOTIVE03 FEAR

mirabelle% mv EMOTIVE0*/FEAR* FEAR

mirabelle% ls FEAR

FEAR01 FEAR02 FEAR03


XBAM tutorial
Motion correction

http://brainmap.it


Run XBAM_v4 > 2 > 1 to start the preprocessing: motion correction,
spin excitation history correction, and linear trend removal
mirabelle% cd ~/EMOTIVE/FEAR/

mirabelle% ls


mirabelle% XBAM_v4

…

##### MAIN MENU #####








7 - Utilities...

8 - About XBAM

9 - Quit


MOTION CORRECTION XBAM tutorial June 2008 26

##### PREPROCESSING #####


1 - Motion & linear trend & spin excitation history correction

2 - Preprocessing utilities...

3 - Quit


------------------------------------oOo------------------------------

XBAM - Co-registration, intensity and spin excitation history
correction

------------------------------------oOo------------------------------

What is the name of the directory containing the images to co-
register ? FEAR01


Please only analyse FEAR01 during this
tutorial to avoid drowning the servers!
Checking FEAR01 :

Process FEAR02 and FEAR03 later on

Image size: 64 X 64 X 180

Voxel size: 3.750000 X 3.750000 X 7.700000

Number of slices: 16


Calculate brain/non-brain threshold by:

1 - Histogram

Nothing to type in if you want
2 - 10 % of maximum value in 3D average

to accept the default options.
3 - Manual threshold

Just press the Return key
4 - Use unthresholded images

What is your choice (enter 1,2,3, or 4)? [4]

Co-register the images by:

1 - Rigid body minimising the differences

2 - Rigid body maximising correlation

3 - Quadratic maximising correlation (sometimes more accurate, but
always takes longer)

What is your choice of registration (enter 1,2 or 3)? [2]

Spin excitation history correction:

1 - Full (spatial and temporal autocorrelation correction)

2 - Partial (spatial correction only - to use for example if your
time-series have been chopped off or are concatenated)

3 - No correction at all

What is your choice of correction (enter 1,2 or 3)? [1]


The preprocessing log file is called
_REGISTRATION.log and it can be found
within the subject folder, e.g.
mirabelle% nedit FEAR01/FEAR01_REGISTRATION.log

The preprocessed images are stored in
the slice folders and are called sl?reg.img
(and sl?reg.hdr)
mirabelle% ls -l FEAR01/sl4

total 5776

-rw-r--r-- 1 spakvig spakvig 348 Jan 24 15:32 sl4.hdr

-rw-r--r-- 1 spakvig spakvig 1474560 Jan 24 15:32 sl4.img

-rw-r--r-- 1 spakvig spakvig 348 Jan 25 12:16 sl4reg.hdr

-rw-r--r-- 1 spakvig spakvig 1474560 Jan 25 12:16 sl4reg.img


The motion parameters can be found in a file called
reg.dat within the subject folder

The first three columns show the X, Y, and Z rotations
(in radian), and the second three columns represent the
X, Y, and Z translations (in voxel)

These can be plotted, for example in Excel
FEAR01 - Motion Parameters

0.15
radian (rotation) or voxel (translation)

0.1

0.05 X rotation
Y Rotation
Z Rotation
0
X Translation
1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181
Y Translation
-0.05 Z Translation

-0.1

-0.15
TR


The end of the _REGISTRATION.log file contains further
information about head movement during the experiment

This can be used, for example, to test for differences in
motion between subject groups with a series of t-tests
Largest displacement in X is 0.153702 voxels

Largest displacement in Y is 0.124358 voxels

Largest displacement in Z is 0.416319 voxels

The motion parameters can be found in reg.dat (for plotting)

The columns are: xRotation yRotation zRotation xTranslation
yTranslation zTranslation

Translations are in voxel and rotations in radian.

-----> xRotation

mean 0.035241

standard deviation 0.025459

maximum 0.122385

minimum 0.000001

median 0.035767

…


XBAM tutorial
First level analysis in native space (IBAM)

http://brainmap.it


To run a first level analysis (subject level), you
need to produce a model file describing your
experiment

This file should be a plain text file, placed within
the experiment folder

Create the file in Unix/Linux with a text editor
such as nedit

If you create the file in Windows, for example
using Excel, you will need to convert it to Unix/
Linux format before using it (as Windows adds
control characters at the end of each line)

To convert the file, use the dos2unix command
(native2ascii for Mac OS X), e.g.
dos2unix infile.dat infile.dat

FIRST LEVEL ANALYSIS IN NATIVE SPACE (IBAM) XBAM tutorial June 2008 33

For a block design (or TR-locked event-related design)

The file should be called infile.dat

One line per TR

One column per active condition, separated by a blank Space

For each TR (line) and for each column (condition), write 1 when
there is an event, 0 otherwise (parametric design possible)

The baseline condition is not coded as such, but is made up of all
the lines where there are only 0 in the column(s)

Sample beginning of an ABC design (with A the baseline)
mirabelle% more infile.dat

1st TR 0 0

0 0

event of type A
1 0

(baseline)
4th TR 1 0

1 1

event of type B 1 1

0 1

event of type C


For an event-related design

The file should be called newstarts.dat

One column per condition (including one for the baseline),
separated by a blank Space

In each line and for each column (condition), write the
timings in second of the events, counting from the
beginning of the experiment

The time matrix needs to be square: if some conditions
have less events than others, complete the lines with X

Sample beginning of an ABC design (with A the
baseline) mirabelle% more newstarts.dat

5.3 23.2 0

events of type A 2 7.4 11

15.2

X 17.5

X

X 23.2

events of type B events of type C


For a correlation analysis

The file should be called model.dat

One line per TR

A single column describing the time series to
correlate with all the voxel time series in the brain

The numbers can be integer (e.g. 2) or floating point
(e.g. 3.4)

It is the shape of the model time series which counts,
not its amplitude (it will be zero-meaned)

Sample beginning of a correlation model file
mirabelle% more model.dat

5.3

events of type A 2

15.2

4.5


The experiment to analyse in this tutorial is
an event-related design, therefore requiring
a newstarts.dat model file

Copy the pre-prepared model files to your
account
mirabelle% cd ~spakvig/data/MODELS
mirabelle% ls
S_K.06441.008_newstarts.dat U_R.07460.007_newstarts.dat
T_K.06504.005_newstarts.dat
mirabelle% cp S_K.06441.008_newstarts.dat ~/EMOTIVE/FEAR/FEAR01/newstarts.dat
mirabelle% cp T_K.06504.005_newstarts.dat ~/EMOTIVE/FEAR/FEAR02/newstarts.dat
mirabelle% cp U_R.07460.007_newstarts.dat ~/EMOTIVE/FEAR/FEAR03/newstarts.dat
mirabelle% cd ~/EMOTIVE/FEAR

If you have downloaded the tutorial data
from the web, you will find the model files in
the EMOTIVE/MODELS folder

Run XBAM_v4 > 3 > 1 to start the single subject analysis in
native space (IBAM)

mirabelle% ls


mirabelle% XBAM_v4

…

##### MAIN MENU #####








7 - Utilities...

8 - About XBAM

9 - Quit



##### FIRST LEVEL ANALYSIS (SUBJECT LEVEL) #####


1 - Single subject analysis in native space - Block /

Event Related (IBAM)

2 - Single subject analysis in native space - Correlation

3 - Talairach normalisation of a native space analysis (TBAM)

4 - First level analysis utilities...

5 - Quit


------------------------oOo-------------------------

XBAM - Individual subject analysis in native space

------------------------oOo-------------------------

What is the name of the directory containing the images to analyse?
FEAR01


Checking FEAR01 :





Local or global analysis (loc/glo)? [glo]

Do you want to smooth (spatial smoothing) the images (y/n)? [y]

What is the filter kernel size in voxels (choose from 3, 5, 7,
9,...)? [5]

Filter kernel = 5 --> filter standard deviation = 1.00000

What is your allowed number of error pixels per slice (Type I errors)?
[5]

Enter the 3D voxel level p-value: [0.05]

Enter the 3D cluster level p-value: [0.01]


Number of randomisations? [20]

What is the acquisition time, i.e. <TR - silent_period> (in seconds)?
[2.00]

What is the time before slice collection for compressed sequences (in
seconds)? [0]


Select type of slice timing correction:

1 - Non interleaved slices starting from bottom of the brain

(automatic calculation)

2 - Interleaved slices starting from bottom of the brain (automatic

calculation)

3 - Specify your own timing information with a text file called

timings.dat (include the time before slice collection)

Enter the number corresponding to the chosen correction: [2]

Skip slice timing correction (y/n)? [n]

Select the model file that you want to use:

1 - Ordinary infile.dat (TR locked) with standard Poisson model 4s

and 8s

2 - Ordinary newstarts.dat (arbitrary timings) with standard Poisson

model 4s and 8s

3 - Cannonical infile.dat (TR locked) with 4s Poisson model and its

first derivative

4 - Sinusoidal modeling (old style analysis as in v1.3 and v1.31)

Enter the number corresponding to the chosen model: [2]

Do you want to use piecewise linear baseline correction (BLOCK DESIGN
ONLY - FOR EVENT RELATED USE HI-PASS DETRENDING BELOW ) (y/n)? [n]


Do you want to detrend your data (y/n)? [y]

Filters available for detrending:

1 - Hi-pass

2 - Band-pass

3 - Lo_pass

Enter the number corresponding to the chosen filter: [1]

Select the type of analysis you wish to use:

1 - Wavelet ( non-cylic) permutation:

2 - Cyclic permutation of the original data

3 - Wavelet Cyclic permutation of time series with Donoho

denoising

4 - Cochrane_Orcutt AR1 regression with basic randomisation ( non

wavelet)

Enter the number corresponding to the chosen method of analysis: [4]

Number of ACTIVE conditions (i.e. number of columns in your model
file)? [3]

Number of Basis conditions functions to use ( 1 (4 sec), 2 (4,8 sec)
or 3 ( 4,8,16 sec) )? [2]


Number of covariates to be included in the analysis? [0]

Multiply models by continuous behaviour file (continuous.txt in
the subject directory) (y/n)? [n]

The first level analysis log file is called _IBAM.log
and it can be found within the experiment folder, e.g.

mirabelle% nedit FEAR01/FEAR01_IBAM.log

The analysis results (pictures) can be found in the
OUTPUT directory within the experiment folder, e.g.
mirabelle% ls FEAR01/OUTPUT/*gif

FEAR01/OUTPUT/FEAR01massa2.gif FEAR01/OUTPUT/FEAR01outvoltFBAM.gif

FEAR01/OUTPUT/FEAR01massa3.gif FEAR01/OUTPUT/FEAR01outvolta2.gif

FEAR01/OUTPUT/FEAR01massa4.gif FEAR01/OUTPUT/FEAR01outvolta3.gif

FEAR01/OUTPUT/FEAR01massvoltF.gif FEAR01/OUTPUT/FEAR01outvolta4.gif

mirabelle%


The IBAM output files are numbered in the following way:

voltF is the result of collapsing all the columns of the model file vs.
baseline

a1 is a noise map

a2 is first active condition (first column of the model file) vs.
baseline

a3 is second active condition (second column of the model file) vs.
baseline

a4 is third active condition (third column of the model file) vs.
baseline

…

The unthresholded statistical maps are called obs (observed), the
effect size maps are called effect, the randomised files are called
ran and they can all be found in the experiment folder
mirabelle% ls FEAR01/*obs* FEAR01/*ran* FEAR01/*effect*

FEAR01/FEAR01obsa2.img FEAR01/FEAR01rana2.img FEAR01/FEAR01effecta2.img



FEAR01/FEAR01obsvoltF.img FEAR01/FEAR01ranvoltF.img FEAR01/FEAR01effecta1.img


At the IBAM level, only results in phase with the stimuli
are shown in the .gif pictures (i.e. when the active condition
activates more than the baseline)

2D and 3D cluster analyses are performed independently
on the maps generated by the voxel based statistical analysis

The pictures from the 2D analysis are called outvolt

The pictures from the 3D analysis are called mass

The background in the pictures is the 3D average brain
volume (over the whole experiment) which is used as co-
registration template
mirabelle% ls FEAR01/OUTPUT/*gif

FEAR01/OUTPUT/FEAR01massa2.gif FEAR01/OUTPUT/
FEAR01outvoltFBAM.gif

FEAR01outvolta2.gif

FEAR01outvolta3.gif

FEAR01/OUTPUT/FEAR01massvoltF.gif FEAR01/OUTPUT/
FEAR01outvolta4.gif

mirabelle% xv FEAR01/OUTPUT/FEAR01massa4.gif &


XBAM tutorial
First level analysis in Talairach space (TBAM)

http://brainmap.it


The final step of the first level analysis process normalises
the native space data into Talairach space

In XBAM, this is done in two steps:

Mapping of the fMRI data to the subject’s own high resolution
structural image

Mapping of the data in structural space to Talairach space

For TBAM to run, you need a structural image called
struct.img (and.hdr) within the experiment folder

If you have acquired only one GE/IR structural image during
your session, it will be automatically selected

If you have acquired multiple GE/IR structural images, they will
all be copied to the experiment folder.
If there is only one GE image, it will be automatically selected.
If there are several GE images, you will have to choose the
one to use (and rename it to struct.img and struct.hdr).

If you do not have a structural image, you can create one from
your fMRI data using XBAM_v4 > 3 > 4 > 1

FIRST LEVEL ANALYSIS IN TALAIRACH SPACE (TBAM) XBAM tutorial June 2008 47

Run XBAM_v4 > 3 > 3 to start the Talairach normalisation of a
native space analysis (TBAM)

mirabelle% ls


mirabelle% XBAM_v4

…

##### MAIN MENU #####








7 - Utilities...

8 - About XBAM

9 - Quit



##### FIRST LEVEL ANALYSIS (SUBJECT LEVEL) #####


1 - Single subject analysis in native space - Block / Event

Related (IBAM)

2 - Single subject analysis in native space - Correlation

3 - Talairach normalisation of a native space analysis (TBAM)

4 - First level analysis utilities...

5 - Quit


---------------oOo--------------

XBAM - Talairach normalisation

---------------oOo--------------

What is the name of the directory containing the images to map onto

Talairach space? FEAR01


Checking FEAR01 :




Voxel level P-value for 3D clustering? [0.05]

Cluster level P-value for 3D clustering? [0.01]

Number of ACTIVE conditions (i.e. number of columns in your model
file) : [3]

Slice thickness of the FUNCTIONAL images in mm (including gap) :
[7.700000]

Slice thickness of the STRUCTURAL image in mm (including gap) :
[3.300000]

Has your structural image been processed from an SPGR (y/n)? [n]

What is your allowed number of error pixels (Type I errors) per
volume? [50]

Number of randomisations (has to be the same as for the IBAM)? [20]

Do you want to smooth (spatial smoothing) the images (y/n)? [n]

Images previously mapped to Talairach space (y/n)? [n]


IMPORTANT: You can only run a local analysis if you have done the

required number of permutations at the IBAM level (more than 100).

Local or global cluster analysis (loc/glo)? [glo]

Mapping of Correlational data (y/n)? [n]

Max probability (e.g. 0.99999) of CSF allowed in voxels (choose
default to use the normal template)? [n/a]

Max probability (e.g. 0.95) of white matter allowed in voxels (choose

Register to Template using correlation, Mutual information steps 1
and 2 or Mutual information step 2 only (cor,mut12,mut2)? [cor]

Starting point offset ( in slices ) for first registration step (func
to struct)? [0]

Use image mask (mask.img) for small volume correction (y/n)? [n]

Correct regave and struct images for intensity drift (y/n)? [n]

Use difference maps if they are present (y/n)? [n]

Use BABY template (y/n)? [n]

Do you want to use your own template (y/n)? [n]


The normalisation log file is called _TBAM.log and it can be
found at the same level as the experiment folder, e.g.
mirabelle% nedit FEAR01_TBAM.log

All the TBAM output files are in a new folder called
<experiment>_TBAM, e.g. FEAR01_TBAM

The analysis results (pictures and text files) can be found in
the _TBAM/OUTPUT_VPV_CPV directory, where VPV is the
chosen voxel P value, and CPV is the chosen cluster P value
(OUTPUT_0.05_0.01 by default)
mirabelle% ls


FEAR01_TBAM FEAR02_TBAM FEAR03_TBAM

FEAR01_TBAM.log FEAR02_TBAM.log FEAR03_TBAM.log

FEAR01_TBAM.parameters FEAR02_TBAM.parameters FEAR03_TBAM.parameters

mirabelle% ls FEAR01_TBAM/OUTPUT_0.05_0.01/*MASS*a2*gif

FEAR01_TBAM/OUTPUT_0.05_0.01/FEAR01_MASS_NEGa2out.gif

FEAR01_TBAM/OUTPUT_0.05_0.01/FEAR01_MASS_POSa2out.gif

mirabelle% ls FEAR01_TBAM/OUTPUT_0.05_0.01/*MASS*Effect*a2*ppm

FEAR01_TBAM/OUTPUT_0.05_0.01/FEAR01_MASS_MeanEffectoutNEGa2.ppm

FEAR01_TBAM/OUTPUT_0.05_0.01/FEAR01_MASS_MeanEffectoutPOSa2.ppm


The TBAM output files are numbered in the following way:

a1 is a noise map

a2 is first active condition (first column of the model file) vs.
baseline

baseline

a4 is third active condition (third column of the model file) vs.
baseline

…

The normalised unthresholded statistical maps are called talobs
(observed), the normalised effect size maps are called taleffect
and the normalised randomised files are called talran.
They can all be found in the _TBAM folder
mirabelle% ls FEAR01_TBAM/*talobs* FEAR01_TBAM/*talran* FEAR01_TBAM/*taleffect*

FEAR01_TBAM/FEAR01taleffecta1.img FEAR01_TBAM/FEAR01talobsa3.img

FEAR01_TBAM/FEAR01taleffecta2.img FEAR01_TBAM/FEAR01talobsa4.img

FEAR01_TBAM/FEAR01taleffecta3.img FEAR01_TBAM/FEAR01talrana2.img

FEAR01_TBAM/FEAR01taleffecta4.img FEAR01_TBAM/FEAR01talrana3.img

FEAR01_TBAM/FEAR01talobsa2.img FEAR01_TBAM/FEAR01talrana4.img


TBAM results are independent from the IBAM results, i.e. the p
values you set at the IBAM level have no impact on TBAM

The background in the pictures is the Talairach template

2D and 3D cluster analyses are performed independently on the
maps generated by the voxel based statistical analysis

The file called _Overlap_Mask.gif in the OUTPUT_???_???
folder shows the extent of the Talairach coverage, i.e. it shows how
much of the Talairach template is covered by the normalised fMRI
data.

A binary version of this file is provided,
called _Overlap.gif

This map should always be near perfect.
If it has a lot of holes or if large parts of
the brain are missing, this could indicate
a misregistration problem.
mirabelle% ls FEAR01_TBAM/OUTPUT_0.05_0.01/*Overlap*gif

FEAR01_TBAM/OUTPUT_0.05_0.01/FEAR01_Overlap.gif

FEAR01_TBAM/OUTPUT_0.05_0.01/FEAR01_Overlap_Mask.gif

mirabelle% xv FEAR01_TBAM/OUTPUT_0.05_0.01/FEAR01_Overlap_Mask.gif


For the 2D cluster analysis

The pictures are called _TBAMa?out (e.g. FEAR01_TBAMa2out.gif)
for the SSQ maps and MeanEffectouta? (e.g.
FEAR01MeanEffectouta2.ppm) for the effect size maps

They contain both positive (active condition > baseline, shown in
an orange to yellow colour scale) and negative (baseline > active
condition, shown in dark to light blue colour scale) contrasts

Text files containing information about the activated 2D regions are
called _TBAM_BAIDa? (e.g. FEAR01_TBAM_BAIDa2.dat) and
_TBAM_BAIDa?_effects (e.g. FEAR01_TBAM_BAIDa2_effects.dat)

mirabelle% xv FEAR01_TBAM/OUTPUT_0.05_0.01/FEAR01_TBAMa4out.gif &

mirabelle% nedit FEAR01_TBAM/OUTPUT_0.05_0.01/FEAR01_TBAM_BAIDa4.dat &



The pictures are called _MASS_POSa?out (e.g. FEAR01_MASS_POSa2out.gif)
for the SSQ maps and MASS_MeanEffectoutPOSa? (e.g.
FEAR01_MASS_MeanEffectoutPOSa2.ppm) for the effect size maps

The positive (active condition > baseline) contrasts are called POS and the
negative (baseline > active condition) contrasts are NEG. In both images, the
colour scale goes form dark red to light yellow

Text files containing information about the activated 3D regions (including
Talairach Daemon labels) are called _allclusters_POSa? and
_allclusters_NEGa? (e.g. FEAR01_allclusters_POSa2.dat).
These files report both the SSQ and effect size of the activation peak

Slice-by-slice (2D) split of the 3D clusters can be found in the
_allclusters_2D_POSa? and _allclusters_2D_NEGa? files (e.g.
FEAR01_allclusters_2D_POSa2.dat)

mirabelle% xv FEAR01_TBAM/OUTPUT_0.05_0.01/FEAR01_MASS_POSa4out.gif &

mirabelle% nedit FEAR01_TBAM/OUTPUT_0.05_0.01/FEAR01_allclusters_POSa4.dat &


XBAM allows you to control the Type I error rate of your 3D
cluster results.

For each contrast, you can adjust the voxel and cluster P values
of the 3D cluster analysis with the aim of obtaining less than one
false positive cluster over the whole map

Once you have reached less than one false positive cluster per
map, you can be assured that all the remaining clusters are
significant

The file containing a table of the Type I error rates is called
_massOI_POSa? or _massOI_NEGa?, depending on the direction
of the contrast (e.g. FEAR01_massOI_POSa4.dat)

The _massOI file is generated for the chosen voxel P value.

If you re-run the analysis, but keep the same voxel P value, you
will get the same file (regardless of what happens to the cluster P
value)

If you re-run the analysis and change the voxel P value, you will
get a different _massOI file.


Each line of the _massOI table corresponds to a cluster P value (2nd
column)

The 3rd column shows the number of false positive clusters expected for
each cluster P value

The 4th column shows the number of actual clusters present in the
image (observed) for each cluster P value

You should run the analysis once with the default voxel and cluster P
values (respectively 0.05 and 0.01), before checking the _massOI file of
your contrast of interest to find out which cluster P value gives less than 1
false positive cluster. Then run the analysis a second time, with the
adjusted P value, to get only significant blobs on the map

It usually isn’t necessary to adjust the voxel P value, but you may want to
do so if your clusters are too big and encompass several brain structures.
In this case, reducing the voxel P value may break up the clusters

All in all, it isn’t important to spend ages finding out the optimal P values
for each subject and for each contrast: whatever you do at the TBAM level
has no impact on the second level analyses which use the unthresholded
normalised statistical maps (the talobs and talran images)
2.727632 0.010000 0.820000 2

1.880617 0.020000 1.640000 2


The _TBAM.log file contains the exact cluster p
values (for each POS and NEG contrast) producing
1 false positive cluster per map, and 0.5 false
positive cluster per map

Use these to adjust the cluster P value for a quick
re-analysis generating your desired number of Type
I error clusters per map

To quickly find these p values in the log, open the
file in a text editor and search for the word yielding

---> P-value yielding 1 false positive cluster per map is 0.005780 <---

---> P-value yielding 0.5 false positive cluster per map is 0.002890 <---


XBAM tutorial
Second level: group mapping (GBAM)

http://brainmap.it


By the end of the first level analysis, the statistical maps of all your
subjects are normalised to standard space

This makes it possible to compute a group map using the first of the
second level analysis modules: GBAM

To use GBAM, you need to have all your subject _TBAM folders at the
same level

You do not need the individual subject folder for this analysis, but having
them there will allow you to extract the average time-series and BOLD
response later on

Create a text file called subjects.txt containing the names of the
subjects to analyse (without the _TBAM bit)
mirabelle% cd ~/EMOTIVE/FEAR/

mirabelle% ls


FEAR01_TBAM FEAR02_TBAM FEAR03_TBAM

FEAR01_TBAM.log FEAR02_TBAM.log FEAR03_TBAM.log

FEAR01_TBAM.parameters FEAR02_TBAM.parameters FEAR03_TBAM.parameters

mirabelle% echo FEAR01 FEAR02 FEAR03 > subjects.txt

mirabelle% cat subjects.txt


SECOND LEVEL: GROUP MAPPING (GBAM) XBAM tutorial June 2008 61

Run XBAM_v4 > 4 > 1 to start generating a group activation
map (GBAM)


mirabelle% XBAM_v4

…

##### MAIN MENU #####








7 - Utilities...

8 - About XBAM

9 - Quit



##### SECOND LEVEL ANALYSIS (GROUP LEVEL) #####


1 - Group activation map (GBAM)

2 - Analysis of variance / covariance of statistical maps

(ABAM)

3 - Correlation of behavioural data with statistical maps

(BBAM)

4 - Conjunction analysis

5 - Group level cluster analysis (Klustakwik)

6 - Calculate group level mixed/fixed/random effects

permutation maps

7 - Second level analysis utilities...

8 - Quit


-----------oOo-----------

XBAM - Group Mapping

-----------oOo-----------

Subjects -> FEAR01 FEAR02 FEAR03


Checking FEAR01_TBAM :






Voxel level P-value for 3D clustering? [0.05]

Cluster level P-value for 3D clustering? [0.01]

Do you just want to re-run the analysis with different voxel/
cluster-wise probabilities (y/n)? [n]

Number of ACTIVE conditions (i.e. number of columns in your
model file) : [3]

What is your allowed number of error pixels (Type I errors)
per volume ? [50]


Number of randomisations (has to be the same as for the

IBAM/TBAM)? [20]


IMPORTANT: You can only run a local analysis if you have done the

required number of permutations at the IBAM level (more than 100).

Local or global cluster analysis (loc/glo)? [glo]

Mapping of Correlational data (y/n)? [n]



Max probability (e.g. 0.95) of white matter allowed in voxels

(choose default to use the normal template)? [n/a]

Allow statistics at voxels where at least half the group has data

present (y/n)? [n]


Use difference maps if they are present (y/n)? [n]




The GBAM log file is called _GBAM.log and it can be
found at the same level as the _TBAM folders

All the GBAM output files are in a new folder called
_GBAM, e.g. FEAR_GBAM where FEAR is the name of
the folder you started XBAM_v4 from

mirabelle% pwd

/Users/spakvig/EMOTIVE/FEAR

mirabelle% ls

FEAR01 FEAR02_TBAM.log FEAR_GBAM

FEAR01_TBAM FEAR02_TBAM.parameters FEAR_GBAM.log

FEAR01_TBAM.log FEAR03 FEAR_GBAM.parameters

FEAR01_TBAM.parameters FEAR03_TBAM subjects.txt

FEAR02 FEAR03_TBAM.log

FEAR02_TBAM FEAR03_TBAM.parameters


The analysis results (pictures and text files) can
be found in the _GBAM/OUTPUT_VPV_CPV
directory, where VPV is the chosen voxel P
value, and CPV is the chosen cluster P value
(OUTPUT_0.05_0.01 by default)
mirabelle% ls FEAR_GBAM/OUTPUT_0.05_0.01/*gif

FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_GBAMa2out.gif



FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_MASS_NEGa2out.gif



FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_MASS_POSa2out.gif



FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_Overlap.gif

FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_Overlap_Mask.gif


The GBAM output files are numbered in the following way:

a1 is a noise map

a2 is first active condition (first column of the model file) vs. baseline

baseline

a4 is third active condition (third column of the model file) vs. baseline

…

GBAM calculates at each voxel the median SSQ of the group and
checks by permutation if this median is significant against the null
distribution of median values generated from the randomised time
series

The file containing the observed (non-randomised) median values
for each voxel and for each condition is called TSSQa?.img and it
can be found in the _GBAM folder
mirabelle% ls FEAR_GBAM/*TSSQ*

FEAR_GBAM/FEARTSSQa2.img FEAR_GBAM/FEARTSSQa4.img

FEAR_GBAM/FEARTSSQa3.img


The GBAM results are independent from the IBAM & TBAM ones, i.e.
whatever p values you used for IBAM & TBAM has no impact on GBAM


2D and 3D cluster analyses are performed independently on the maps
generated by the voxel based statistical analysis

The file called _Overlap_Mask.gif in the OUTPUT_???_??? folder
shows the extent of the group Talairach coverage, i.e. it shows those
voxels where all the subjects map into Talairach template.
If at least one subject has no coverage for a specific voxel, this voxel is
dismissed from the rest of the analysis and appears in black (i.e. a hole)

A grey level version of this file is provided,
called _Overlap.gif in which black shows
0% coverage and white 100% coverage

mirabelle% ls FEAR_GBAM/OUTPUT_0.05_0.01/*Overlap*gif

FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_Overlap.gif

FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_Overlap_Mask.gif

mirabelle% xv FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_Overlap.gif &



The pictures are called _GBAMa?out (e.g. FEAR_GBAMa2out.gif)
for the SSQ maps and MeanEffectouta? (e.g.
FEARMeanEffectouta2.ppm) for the effect size maps

They contain both positive (active condition > baseline, shown in
an orange to yellow colour scale) and negative (baseline > active
condition, shown in dark to light blue colour scale) contrasts

Text files containing information about the activated 2D regions are
called _GBAM_BAIDa? (e.g. FEAR_GBAM_BAIDa2.dat) and
_GBAM_BAIDa?_effects (e.g. FEAR_GBAM_BAIDa2_effects.dat)

mirabelle% xv FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_GBAMa4out.gif &

mirabelle% nedit FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_GBAM_BAIDa4.dat &



The pictures are called _MASS_POSa?out (e.g. FEAR_MASS_POSa2out.gif)
for the SSQ maps and MASS_MeanEffectoutPOSa? (e.g.
FEAR_MASS_MeanEffectoutPOSa2.ppm) for the effect size maps

The positive (active condition > baseline) contrasts are called POS and the
negative (baseline > active condition) contrasts are NEG. In both images, the
colour scale goes form dark red to light yellow

Talairach Daemon labels) are called _allclusters_POSa? and
_allclusters_NEGa? (e.g. FEAR_allclusters_POSa2.dat).
These files report both the SSQ and effect size of the activation peak

_allclusters_2D_POSa? and _allclusters_2D_NEGa? files (e.g.
FEAR_allclusters_2D_POSa2.dat)

mirabelle% xv FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_MASS_POSa4out.gif &

mirabelle% nedit FEAR_GBAM/OUTPUT_0.05_0.01/FEAR_allclusters_POSa4.dat &


cluster results.

For each contrast, you can adjust the voxel and cluster P
values of the 3D cluster analysis with the aim of obtaining less
than one false positive cluster over the whole map

significant

_massOI_POSa? or _massOI_NEGa?, depending on the
direction of the contrast (e.g. FEAR_massOI_POSa4.dat)

The _massOI file is generated for the chosen voxel P value

If you re-run the analysis, but keep the same voxel P value,
you will get the same file (regardless of what happens to the
cluster P value)

If you re-run the analysis and change the voxel P value, you
will get a different _massOI file.


column)




1.034411 0.005000 0.885000 5

0.859139 0.006000 1.062000 5

0.787644 0.007000 1.239000 5

0.702908 0.008000 1.416000 8

0.687216 0.009000 1.593000 8

0.638748 0.010000 1.770000 8


The _GBAM.log file contains the exact cluster p






XBAM tutorial
Second level: analysis of variance (ABAM)

http://brainmap.it


By the end of the first level analysis, the statistical maps of all your
subjects have been normalised to standard space

This makes it possible to perform a analysis of variance between two
or more groups/experiments using the second level analysis module
called ABAM

As ABAM is a really versatile program, it has its own manual which can
be found online at:
http://www.brainmap.co.uk/Documents/ABAM%20manual.doc

To use ABAM, you need to create a new folder to hold your analysis
data and results

Copy into it the normalised statistical maps of the subjects that you want
to compare. These can be found in the group _GBAM folder(s)

If you want to compare SSQ statistical maps, copy the talobs files (e.g.
FEAR_GBAM/FEAR01talobsa2.img)

If you want to compare effect size maps, copy the taleffect files (e.g.
FEAR_GBAM/FEAR01taleffecta2.img)

Also copy the file called DoNotDelete.hdr from one of your _GBAM
folder. It will be used to automatically get the image header information.
If you do not include this file, you will be asked for image dimensions.

SECOND LEVEL: ANALYSIS OF VARIANCE (ABAM) XBAM tutorial June 2008 76

In the folder where you have copied the files, create a plain
text file called subjects.txt which should contain the name of
the images to analyse, in one column (use nedit to create it).

The order of the files is important and depends on the
type of analysis that you want to do

In the case of this tutorial, we want to compare the three
emotional intensities and we have an hypothesis about the
direction of change: we want to find out those brain regions
where 100% fear > 50% fear > neutral

This is going to be a repeated-measure analysis, with one
group of subjects and three samples per subject

The subjects.txt file should therefore contain first all the
neutral images, then all the 50% images, and finally the
100% images (or the other way around). The subject order
has to be the same for the three repeats, e.g. FEAR01,
FEAR02, FEAR03



mirabelle% mkdir TREND

mirabelle% cp FEAR_GBAM/*talobsa[2-4]* FEAR_GBAM/DoNotDelete.hdr TREND

mirabelle% ls TREND

DoNotDelete.hdr FEAR02talobsa2.img FEAR03talobsa3.img

FEAR01talobsa2.img FEAR02talobsa3.img

FEAR03talobsa4.img


FEAR01talobsa4.img

FEAR03talobsa2.img

mirabelle% nedit TREND/subjects.txt &

neutral

50% fear

100% fear


The traditional module of ABAM should be used for main effect
analyses (group or condition), trend analyses (e.g. 3x1) and 2x2
interactions

Be aware that if you are doing a trend analysis on more than two
groups/conditions, it is always a good idea to test both linear and
quadratic trends to cover all possibilities

For more complicated designs (e.g. 3x3, 3x4) you need to use the
complex module of ABAM which has been specifically written to
handle more complex analyses

Please be aware that this latter module will show you which
regions are different, but it will not look at the direction of the
changes. You will need to extract and plot the statistical values and
may need to perform pairwise comparisons to fully understand
your results

The first question asked by the ABAM program allows you to
choose either the traditional (trad) or the complex (comp) module

You can also add up to two covariates to regress in a file called
covar.dat (two columns separated by a Space)


In this tutorial, we want to identify those parts of the brain where
100% fear activates more than 50% fear which activates
more than neutral faces

We have an hypothesis and this is a trend analysis, so the
traditional ABAM module will be used

To use this module, you need to create a plain text file called
DesignMatrix which should contains the ANOVA factors to use

There should be as many factors as there are subject files in the
subjects.txt file

If you have an hypothesis about the direction of the change,
the factors will be used to describe the changes

If you do not have an hypothesis about the direction of the
change, the factors will just be used as dummy variables to
indicate which subject is in which group

It is always a good idea to verify side by side your subjects.txt
and DesignMatrix files before starting the analysis


As we are interested in 100% (talobsa4) > 50%
(talobsa3) > neutral (talobsa2), the corresponding
ANOVA factors are 1 > 0 > -1
mirabelle% cd ~/EMOTIVE/FEAR/TREND

mirabelle% ls


FEAR01talobsa2.img FEAR02talobsa3.img FEAR03talobsa4.img

FEAR01talobsa3.img FEAR02talobsa4.img subjects.txt


mirabelle% nedit DesignMatrix &

[1] 499

mirabelle% paste subjects.txt DesignMatrix

FEAR01talobsa2.img -1

neutral


50% fear
FEAR01talobsa3.img 0


100% fear





Run XBAM_v4 > 4 > 2 to perform an ANOVA between groups/
conditions (ABAM)


mirabelle% XBAM_v4

…

##### MAIN MENU #####








7 - Utilities...

8 - About XBAM

9 - Quit





1 - Group activation map (GBAM)

2 - Analysis of variance / covariance of statistical maps

(ABAM)

3 - Correlation of behavioural data with statistical maps

(BBAM)

4 - Conjunction analysis

5 - Group level cluster analysis (Klustakwik)

6 - Calculate group level mixed/fixed/random effects

permutation maps


8 - Quit


-----------oOo-----------

XBAM - Group Comparison

-----------oOo-----------


Use traditional or complex module (used to be v3.4-dev=6 for
3x2, 3x3, 3x3x...) ABAM (trad/comp)? [trad]


Enter the name of the directory containing the files to study: TREND

Enter the voxel-wise probability of Type I error: [0.05]

Enter the cluster-wise probability of Type I error: [0.01]

Do you just want to re-run the analysis with different voxel/cluster-
wise probabilities (y/n)? [n]


You can test the significance of a voxel either against a null
distribution formed from all the voxels

in the brain (global) or alternatively from a distribution created by
permuting only this voxel (local)

Local permutation may be more sensitive but takes longer and requires
more storage space.


You have only one column in your DesignMatrix file.

Is this multiple observations on the same group of subjects, i.e.
repeated measures, or 1 observation per subject in different groups
(rep/norep)? [norep] rep


How many observations for each subject? [1] 3

Maximum number of randomisations allowed? [50]

There are 2 possible types of analysis:

*) You have an hypothesis about the way that the activations in the
different groups are related

OR

*) You think that the responses may differ across groups but you don't
know how.

PLEASE NOTE THAT YOU NEED TO ANSWER y FOR ANY KIND OF TREND ANALYSIS

Do you have an hypothesis (y/n)? [y]

Please note that the next question only applies to the covariate in the
case of a repeated measure design.

In the case of a non repeated measure design, it applies to the whole
model (variate + covariate).

Use least squares or absolute deviation minimisation (lsq/abs)? [abs]

Max probability (e.g. 0.99999) of CSF allowed in voxels (choose default
to use the normal template)? [n/a]





present (y/n)? [n]





The ABAM log file is called _ABAM.log and it can be found at the same level as
the files to analyse and the subjects.txt file

The analysis results can be found in the OUTPUT_VPV_CPV directory, in the
ABAM analysis folder, where VPV is the chosen voxel P value, and CPV is the
chosen cluster P value (by default OUTPUT_0.05_0.01)

mirabelle% ls TREND/OUTPUT_0.05_0.01/*gif

TREND/OUTPUT_0.05_0.01/ANCOVA_BAMout.gif

TREND/OUTPUT_0.05_0.01/TREND_MASSout_NEG.gif

TREND/OUTPUT_0.05_0.01/TREND_MASSout_POS.gif

TREND/OUTPUT_0.05_0.01/TREND_Overlap.gif

TREND/OUTPUT_0.05_0.01/TREND_Overlap_Mask.gif


The ABAM results are independent from the IBAM & TBAM results, i.e.
whatever p value you used at for IBAM & TBAM has no impact on the ABAM



The file called _Overlap_Mask.gif in the OUTPUT_???_??? folder shows the
extent of the group Talairach coverage, i.e. it shows those voxels where all the
subjects map into Talairach template.
If at least one subject has no coverage for a specific voxel, this voxel is dismissed
from the rest of the analysis and appears in black (i.e. a hole)



mirabelle% ls TREND/OUTPUT_0.05_0.01/*Overlap*gif

TREND/OUTPUT_0.05_0.01/TREND_Overlap.gif

TREND/OUTPUT_0.05_0.01/TREND_Overlap_Mask.gif

mirabelle% xv TREND/OUTPUT_0.05_0.01/TREND_Overlap_Mask.gif &



The picture is always called ANCOVA_BAMout.gif

It contains both positive (1 > 0 > -1, i.e. 100% > 50% > neutral,
shown in orange to yellow colour scale) and negative (-1 > 0 > 1, i.e.
neutral > 50% > 100%, shown in dark to light blue colour scale)
contrasts

The text files containing information about the activated 2D regions is
always called ANCOVA_BAM.dat

mirabelle% xv TREND/OUTPUT_0.05_0.01/ANCOVA_BAMout.gif &

mirabelle% nedit TREND/OUTPUT_0.05_0.01/ANCOVA_BAM.dat &



The pictures are called _MASSout_POS and _MASSout_NEG (e.g.
TREND_MASSout_POS.gif)

The positive (1 > 0 > -1, i,.e. 100% > 50% > neutral) contrast is called POS
and the negative (-1 > 0 > 1, i.e. neutral > 50% > 100%) contrast is called
NEG. In both images, the colour scale goes form dark red to light yellow

Talairach Daemon labels) are called _allclusters_POS and
_allclusters_NEG (e.g. TREND_allclusters_POS.dat).

_allclusters_2D_POS and _allclusters_2D_NEG files (e.g.
TREND_allclusters_2D_POS.dat)

mirabelle% xv TREND/OUTPUT_0.05_0.01/TREND_MASSout_POS.gif &

mirabelle% nedit TREND/OUTPUT_0.05_0.01/TREND_allclusters_POS.dat &


cluster results.

For each contrast, you can adjust the voxel and cluster P values
of the 3D cluster analysis with the aim of obtaining less than one
false positive cluster over the whole map

significant

_massOI_POSa? or _massOI_NEGa? ,depending on the direction
of the contrast (e.g. TREND_massOI_POSa4.dat)

The _massOI file is generated for the chosen voxel P value.

If you re-run the analysis, but keep the same voxel P value, you
will get the same file (regardless of what happens to the cluster P
value)

If you re-run the analysis and change the voxel P value, you will
get a different _massOI file.


column)




1.868958 0.006000 0.714000 3

1.440363 0.007000 0.833000 3

1.258579 0.008000 0.952000 3

0.984080 0.009000 1.071000 3

0.954444 0.010000 1.190000 3

0.541551 0.020000 2.380000 6


The _ABAM.log file contains the exact cluster p






XBAM tutorial
Second level: correlation analysis (BBAM)

http://brainmap.it


By the end of the first level analysis, the statistical maps of all your subjects have
been normalised to standard space

This makes it possible to perform a correlation analysis between the statistical
maps and a behavioural measure (e.g. IQ, age…) using the second level analysis
module called BBAM

BBAM can compute either the correlation coefficient or the regression
coefficient (slope)

You can use a parametric Pearson or a non-parametric (and outlier resistant)
Kendall correlation coefficient

BBAM can also be used to compare the regression slope/correlation coefficient
between 2 groups, i.e. it will show those regions of the brains where one group is
significantly more correlated to the behavioural variable than the other group

To use BBAM, you need to create a new folder to hold your data and results

Copy into it the normalised statistical maps of the subjects that you want to
compare. These can be found in the group _GBAM folder(s)

If you want to correlate SSQ statistical maps, copy the talobs files (e.g.
FEAR_GBAM/FEAR01talobsa2.img)

If you want to correlate effect size maps, copy the taleffect files (e.g.
FEAR_GBAM/FEAR01taleffecta2.img)

Also copy the file called DoNotDelete.hdr from one of your _GBAM folder. It will be
used to automatically get the image header information.
If you do not include it, you will be asked for the image dimensions.

SECOND LEVEL: CORRELATION ANALYSIS (BBAM) XBAM tutorial June 2008 94

In the folder where you have copied the files, create a plain
text file called subjects.txt which should contain the name of
the images to analyse, in one column (use nedit to create it)

The order of the files is not important, as long as you use
the same order for the behavioural variable

In the case of this tutorial, we do not actually have relevant
data to run a BBAM, but we will nevertheless run a BBAM
using the same files as those used previously in ABAM.

Please do not try to interpret the final results!

You need to create a text file called behaviour.dat, at the
same level as the statistical maps, which should contain, in a
single column, the behavioural value, one per subject, in the
same order as subjects.txt

You can also add up to two covariates to regress in a file
called covar.dat (two columns separated by a Space)



mirabelle% mkdir BEHAV

mirabelle% cp FEAR_GBAM/*talobsa[2-4]* FEAR_GBAM/DoNotDelete.hdr BEHAV

mirabelle% ls BEHAV



FEAR03talobsa4.img


FEAR01talobsa4.img

FEAR03talobsa2.img

mirabelle% nedit BEHAV/subjects.txt &

neutral

50% fear

100% fear


In this tutorial, the BBAM analysis is simulated and the
behaviour.dat contain values which are on average
following the increase in stimulation intensity
mirabelle% cd ~/EMOTIVE/FEAR/BEHAV

mirabelle% ls



FEAR01talobsa3.img FEAR02talobsa4.img subjects.txt


mirabelle% nedit behaviour.dat &

[1] 499

mirabelle% paste subjects.txt behaviour.dat


neutral
FEAR02talobsa2.img 3.4


50% fear


100% fear





Run XBAM_v4 > 4 > 3 to perform a correlation analysis (BBAM)


mirabelle% XBAM_v4

…

##### MAIN MENU #####








7 - Utilities...

8 - About XBAM

9 - Quit





1 -
Group activation map (GBAM)

2 -
Analysis of variance / covariance of statistical maps (ABAM)

3 -
Correlation of behavioural data with statistical maps (BBAM)

4 -
Conjunction analysis

5 -
Group level cluster analysis (Klustakwik)

6 -
Calculate group level mixed/fixed/random effects permutation

maps


8 - Quit


-----------------oOo------------------

XBAM - Group behavioural correlation

-----------------oOo------------------

Enter the name of the directory containing the files to BBAM: BEHAV


Subjects -> FEAR01talobsa2.img FEAR02talobsa2.img FEAR03talobsa2.img


Enter the 3D voxel level p-value: [0.05]

Enter the 3D cluster level p-value: [0.01]

Do you just want to re-run the analysis with different voxel/cluster-
wise probabilities (y/n)? [n]


What is your allowed number of error pixels per volume (Type I
errors)? [5]

You can test the significance of a voxel either against a null
distribution formed from all the voxels in the brain (global) or
alternatively from a distribution created by permuting only this voxel
(local) Local permutation may be more sensitive but takes longer and
requires more storage space.



Number of randomisations? [50]

Remove covariate? (y/n) [n]

Single group correlation or group difference in correlation

(single/difference)? [single]

Use Pearson's or Kendall's correlation coefficient (p/k)? [p]


present (y/n)? [n]










The BBAM log file is called _BBAM.log and it can
be found at the same level as the files to analyse
and the subjects.txt file

The analysis results can be found in the
OUTPUT_VPV_CPV directory, in the BBAM
analysis folder, where VPV is the chosen voxel P
value, and CPV is the chosen cluster P value (by
default OUTPUT_0.05_0.01)

mirabelle% ls BEHAV/OUTPUT_0.05_0.01/*gif

BEHAV/OUTPUT_0.05_0.01/BEHAV_MASSout_NEG.gif

BEHAV/OUTPUT_0.05_0.01/BEHAV_MASSout_POS.gif

BEHAV/OUTPUT_0.05_0.01/BEHAV_Overlap.gif

BEHAV/OUTPUT_0.05_0.01/BEHAV_Overlap_Mask.gif

BEHAV/OUTPUT_0.05_0.01/BEHAV_correlbam.gif


The BBAM results are independent from the IBAM & TBAM results, i.e.
whatever p values you used at the IBAM & TBAM level has no impact on BBAM



The file called _Overlap_Mask.gif in the OUTPUT_???_??? folder shows the
extent of the group Talairach coverage, i.e. it shows those voxels where all the
subjects map into Talairach template.
If at least one subject has no coverage for a specific voxel, this voxel is dismissed
from the rest of the analysis and appears in black (i.e. a hole)



mirabelle% ls BEHAV/OUTPUT_0.05_0.01/*Overlap*gif

BEHAV/OUTPUT_0.05_0.01/BEHAV_Overlap.gif

BEHAV/OUTPUT_0.05_0.01/BEHAV_Overlap_Mask.gif

mirabelle% xv BEHAV/OUTPUT_0.05_0.01/BEHAV_Overlap_Mask.gif &



The picture is called _correlbam.gif (e.g. BEHAV_correlbam.gif)

It contains both positive (positive correlation between statistics
and behaviour, shown in orange to yellow colour scale) and
negative (negative correlation between statistics and behaviour,
shown in dark to light blue colour scale) contrasts

The text files containing information about the activated 2D regions is
always called CORRELATION_BAM.dat

Not surprisingly in this simulated case, no much is happening…

mirabelle% xv BEHAV/OUTPUT_0.05_0.01/BEHAV_correlbam.gif &

mirabelle% nedit BEHAV/OUTPUT_0.05_0.01/CORRELATION_BAM.dat &



The pictures are called _MASSout_POS and _MASSout_NEG (e.g.
BEHAV_MASSout_POS.gif)

The positive (positive correlation between statistics and behaviour)
contrast is called POS and the negative (negative correlation between
statistics and behaviour) contrast is called NEG.
In both images, the colour scale goes form dark red to light yellow

Talairach Daemon labels) are called _allclusters_POS and
_allclusters_NEG (e.g. BEHAV_allclusters_POS.dat).

_allclusters_2D_POS and _allclusters_2D_NEG files (e.g.
BEHAV_allclusters_2D_POS.dat)

mirabelle% xv BEHAV/OUTPUT_0.05_0.01/BEHAV_MASSout_POS.gif &

mirabelle% nedit BEHAV/OUTPUT_0.05_0.01/BEHAV_allclusters_POS.dat &


cluster results.

For each contrast, you can adjust the voxel and cluster P
values of the 3D cluster analysis with the aim of obtaining less
than one false positive cluster over the whole map

significant

_massOI_POSa? or _massOI_NEGa?, depending on the
direction of the contrast (e.g. BEHAV_massOI_POSa4.dat)

The _massOI file is generated for the chosen voxel P value

If you re-run the analysis, but keep the same voxel P value,
you will get the same file (regardless of what happens to the
cluster P value)

If you re-run the analysis and change the voxel P value, you
will get a different _massOI file.


column)




0.358633 0.007000 0.469000 5

0.349716 0.008000 0.536000 5

0.340029 0.009000 0.603000 5

0.329725 0.010000 0.670000 6

0.284620 0.020000 1.340000 8

0.264354 0.030000 2.010000 8


The _BBAM.log file contains the exact cluster p






To discard or not to discard: the neural basis of hoarding symptoms in obsessive-compulsive disorder.
An SK ,Mataix-Cols D, Lawrence N et al., Mol Psychiatry. 2008 Jan 8; [Epub ahead of print]

hoarding score


XBAM tutorial
Extracting average time series / BOLD response

http://brainmap.it


XBAM Tutorial (June 08)

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