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The original purpose of AFNI was to
perform the transformation of datasets to Talairach-Tournoux (stereotaxic)
coordinates |
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The transformation is user-controlled,
not automatic (yet) |
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You must mark various anatomical
locations, defined in |
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Jean Talairach and Pierre
Tournoux |
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ÒCo-Planar Stereotaxic Atlas of
the Human BrainÓ |
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Thieme Medical Publishers, New
York, 1988 |
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Marking is best done on a
high-resolution T1-weighted structural MRI volume |
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The transformation defined by the
manually placed markers then carries over to all other datasets in the same
directory |
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This is where the importance of getting
the relative spatial placement of datasets done correctly in to3d really
matters |
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You can then write functional datasets
to disk in Talairach coordinates |
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Purpose: voxel-wise comparison with
other subjects |
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May want to blur functional maps a
little before comparisons, to allow for residual anatomic variability: AFNI
program 3dmerge |
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Transformation proceeds in two stages: |
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Alignment of AC-PC and I-S axes (to +acpc
coordinates) |
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Scaling to Talairach-Tournoux Atlas
brain size (to +tlrc coordinates) |
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Alignment to +acpc coordinates: |
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Anterior commissure (AC) and posterior
commissure (PC) are aligned to be the y-axis |
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The longitudinal (inter-hemispheric or
mid-sagittal) fissure is aligned to be the yz-plane, thus defining the z-axis |
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The axis perpendicular to these is the
x-axis (right-left) |
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Five markers that you must place using
the [Define Markers] control panel: |
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AC superior edge = top middle of anterior commissure |
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AC posterior margin = rear middle of anterior commissure |
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PC inferior edge = bottom middle of posterior
commissure |
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First mid-sag point = some point in the mid-sagittal
plane |
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Another mid-sag point = some other
point in the mid-sagittal plane |
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This procedure tries to follow the
Atlas as precisely as possible |
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Even at the cost of confusion to the
user (e.g., you) |
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First goal is to mark top middle and
rear middle of AC |
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Sagittal: look for AC at bottom level
of corpus callosum, below fornix |
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Coronal: look for ÒmustacheÓ; Axial:
look for inter-hemispheric connection |
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Get AC centered at focus of crosshairs
(in Axial and Coronal) |
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Move superior until AC disappears in
Axial view; then inferior 1 pixel |
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Press IN [AC superior edge] marker
toggle, then [Set] |
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Move focus back to middle of AC |
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Move posterior until AC disappears in
Coronal view; then anterior 1 pixel |
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Press IN [AC posterior margin], then [Set] |
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When [Transform Data] is available,
pressing it will close the
[Define Markers] panel, write marker locations into the dataset
header, and create the +acpc datasets that follow from this one |
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The [AC-PC Aligned] coordinate system
is now enabled in the main AFNI controller window |
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In the future, you could re-edit the
markers, if desired, then re-transform the dataset (but you wouldnÕt make a
mistake, would you?) |
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If you donÕt want to save edited
markers to the dataset header, you must quit AFNI without pressing [Transform
Data] or [Define Markers] |
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ls Þ The newly created ac-pc dataset, anat+acpc.HEAD, is located in our demo_tlrc/ directory |
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At this point, only the header file
exists, which can be viewed when selecting the [AC-PC Aligned] button |
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more on how to create the accompanying .BRIK
file laterÉ |
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Once the quality check is passed, click
on [Transform Data] to save the +tlrc header |
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ls Þ The newly created +tlrc
dataset, anat+tlrc.HEAD, is located in our demo_tlrc/ directory |
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At this point, the following anatomical
datasets should be found in our demo_tlrc/ directory: |
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anat+orig.HEAD anat+orig.BRIK |
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anat+acpc.HEAD |
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anat+tlrc.HEAD |
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In addition, the following functional
dataset (which I -- the instructor -- created earlier) should be stored in
the demo_tlrc/ directory: |
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func_slim+orig.HEAD func_slim+orig.BRIK |
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Note that this functional dataset is in
the +orig format (not +acpc or +tlrc) |
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Automatic creation of Òfollower
datasetsÓ: |
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After the anatomical +orig dataset in a
directory is resampled to +acpc and +tlrc coordinates, all the other datasets
in that directory will automatically get transformed datasets as well |
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These datasets are created
automatically inside the interactive AFNI program, and are not written
(saved) to disk (i.e., only header info exists at this point) |
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How followers are created (arrows show
geometrical relationships): |
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anat+orig ¨ anat+acpc ¨ anat+tlrc |
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ø ø |
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func+orig func+acpc func+tlrc |
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In the class example, func_slim+orig
will automatically be ÒwarpedÓ to our anat datasetÕs ac-pc (anat+acpc) &
Talairach (anat+tlrc) coordinates |
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The result will be func_slim+acpc.HEAD
and func_slim+tlrc.HEAD, located internally in the AFNI program (i.e., you
wonÕt see these files in the demo_tlrc/ directory) |
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To store these files in demo_tlrc/,
they must be written to disk.
More on this laterÉ |
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Writing Òfollower datasetsÓ to disk: |
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Recall that when we created anat+acpc
and anat+tlrc datasets by pressing [Transform Data], only .HEAD files were
written to disk for them |
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In addition, our follower datasets func_slim+acpc
and func_slim+tlrc are not stored in our demo_tlrc/ directory. Currently, they can only be viewed in
the AFNI graphical interface |
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Questions to ask: |
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How do we write our anat .BRIK files to
disk? |
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How do we write our warped follower
datasets to disk? |
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To write a dataset to disk (whether it
be an anat .BRIK file or a follower dataset), use one of the [Define Datamode]
Þ Write buttons: |
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Class exmaple - Writing anat (Underlay)
datasets to disk: |
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You can use [Define Datamode] Þ Write Þ [ULay] to write the
current anatomical dataset .BRIK out at the current grid spacing (cubical
voxels), using the current anatomical interpolation mode |
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After that, [View ULay Data Brick] will
become available |
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ls Þ to view newly created .BRIK
files in the demo_tlrc/ directory: |
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anat+acpc.HEAD anat+acpc.BRIK |
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anat+tlrc.HEAD anat+tlrc.BRIK |
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Class exmaple - Writing func (Overlay)
datasets to disk: |
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You can use [Define Datamode] Þ Write Þ [OLay] to write the
current functional dataset .HEAD and BRIK files into our demo_tlrc/ directory |
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After that, [View OLay Data Brick] will
become available |
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ls Þ to view newly resampled func files in our demo_tlrc/ directory: |
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func_slim+acpc.HEAD func_slim+acpc.BRIK |
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func_slim+tlrc.HEAD func_slim+tlrc.BRIK |
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Command line program adwarp can also be
used to write out .BRIK files for transformed datasets: |
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adwarp -apar anat+tlrc -dpar func+orig |
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The result will be: func+tlrc.HEAD and func+tlrc.BRIK |
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Why bother saving transformed datasets
to disk anyway? |
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Datasets without .BRIK files are of
limited use: |
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You canÕt display 2D slice images from
such a dataset |
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You canÕt use such datasets to graph
time series, do volume rendering, compute statistics, run any command line
analysis program, run any pluginÉ |
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If you plan on doing any of the above
to a dataset, itÕs best to have both a .HEAD and .BRIK files for that dataset |
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You can perform a TLRC transform
automatically using the @auto_tlrc script |
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Differences from Manual Transformation: |
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Instead of setting ac-pc landmarks and
volume boundaries by hand, the anatomical volume is warped (using 12
parameter affine transform) to a template volume in TLRC space. |
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Not quite the transform that Jean
Talairach and Pierre Tournoux specified. (But every body still calls it
Talairach!) |
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AC center no longer at 0,0,0 and size
of brain box is that of the template you use. |
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For reasons that should not be
mentioned in polite company, the various templates adopted by the
neuroimaging community are not of the same size. Be mindful when using
various atlases. |
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Can choose from various templates for
reference but be consistent in your group analysis. |
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Available templates: N27, icbm452,
mni152. |
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Easy, automatic, never needs charging.
Just check final results to make sure nothing went seriously awry. AFNI is
perfect but your data is not. |
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Warping high-res anatomical to template
volume (Usage mode 1): |
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1- Pad the input data set to avoid
clipping errors from shifts and rotations |
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2- Strip skull (if needed) |
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3- Resample to resolution and size of
TLRC template |
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4- Perform 12 parameter affine
registration using 3dWarpDrive |
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Many more steps are performed in
actuality, to fix up various pesky little artifacts. Read the script if you
are interested. |
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Applying high-resÕ transform to
Òfollower datasetsÓ (Usage mode 2): |
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1- Apply high-resÕ transform using
3dWarp |
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