AFNI Jazzercise
Please read the following questions and use your AFNI
know-how to answer them. Hints to
answering these questions are available in the ÒHintsÓ handout. The answers to these questions can be
found in the ÒAnswersÓ handout.
- The
dataset AFNI_data3/afni/func_slim+orig
contains 7 sub-bricks of statistical data. Use 3dbucket to create a smaller
version of this dataset that contains only the sub-bricks: #0, 3-6. Name this new dataset some_stats.
Why:
To understand the layout of AFNI datasets and sub-bricks. YouÕll probably be
interested only in specific sub-bricks and not all the sub-bricks that include
baseline fit statistics.
- In
directory AFNI_data3/afni you
will find three anatomical datasets: anat1+orig, anat2+orig, anat3+orig. These datasets are 3 separate
anatomical scans of a single subject. They have already been aligned. Average them together
into a single dataset called anat_mean+orig.
Notice that the result looks ÔcleanerÕ, since the noise has been
reduced.
Why:
averaging reduces the noise.
- Use
two of AFNIÕs programs that remove non-brain
data, 3dAutomask and 3dSkullStrip, to remove data from
outside of the brain from dataset AFNI_data3/afni/anat+orig. Name the output file from
3dAutomask anat_auto+orig
and the output file from 3dSkullStrip anat_3dSkull+orig. Compare the two output
datasets. Note what
Ònon-brainÓ data was removed by each. Did one program do a better job at
skull stripping or are the results similar? (Note: 3dSkullStrip may take a
few minutes to run so be patient).
Why:
Removing the skull is useful for image registration and creating a
brain-specific mask.
- Creating
and Playing with ROI Masks:
- The
dataset AFNI_data3/afni/func_slim+orig
has beta values and t-stats for 2 stimulus classes, fpos
and fneg. Use 3dcalc to create a mask called PN_mask
that is 1 everywhere that both
the fpos t-stat and the fneg
t-stat values are greater than 4.2, and 0 everywhere else.
Why: Combining the results in a single mask
is useful for a simple conjunction analysis.
- Similar
to part a, create a conjunction mask that is 1 wherever a>4.2 (from fpos
t-stat sub-brick), 2 wherever
b>4.2 (from fneg t-stat sub-brick), 3 wherever both are true, and 0 otherwise. Name this dataset PN_mask_4+orig (since it contains
4 values).
Why: Using masks with values of powers of two are easier to look at all
possible combinations in conjunction analysis.
- Use
the afni GUI to display this mask, PN_mask_4+orig, so that each mask
value gets its own color.
What does each color mean?
Why: The various combinations will each show
a unique color that is easily visible and understood.
- Use 3dROIstats
to store the average time series from epi_r1+orig into the
text file PN_mean.1D, where
the mean is over the voxels in the mask (from part a), PN_mask+orig.
Why: Mean time series curves for each ROI
are useful for further analysis and display with other AFNI programs or with
external applications like Excel and Matlab.
- Fun
with 1D files:
- Create
three 1-column files with the numbers 1-10 in one column of the first file,
11-20 in the second file, and 21-30 in the third file. (note:
you might use 2 different AFNI programs to create each file)
- Catenate
these 3 files into one 3-column file. Call this 1D file 3_cols.1D.
- Create
a new file that contains columns 1, 2, 3, 3, 2,1, from part b (i.e., there will be a total of 6
columns in this new 1D file).
Call this new 1D file 6_cols.1D.
- Now
take the 6 columns from question 7b and average them together to create a
new file with a single column.
Call that new file ex_mean.1D.
Why: AFNI 1D programs can help to combine
and analyze data from multiple voxels or masks.
- Fun
with the AFNI GUI:
- Open
AFNI_data3/afni/anat+orig
and in any one of the views (sagittal, axial,
or coronal), change the gray-scale intensity range to be 500 minimum and 1500 maximum.
- Open
AFNI_data3/afni/func_slim+orig
and set the Full-F as the OLay and
Threshold. Set the Threshold
to F=8.0. Show only Positive
values and set the color scale to show only 8 colors. Edit the color scale so that
F-values between 14 and 28 are shown in lime green.
- View
the above settings you created from part b in a sagittal
slice. Make a jpeg file from
sagittal slice #166 and name it cool_slide.
- Switch
to Talairach view and Talairach to the right
fusiform gyrus.
- Change
the display to show 6 sagittal slices all at
once, in a 3x2 montage.
- Can
you find the AFNI Mission statement hidden in the AFNI GUI?
- Doing
Calculations in AFNI:
- Determine
what type of data (short, float, etc) makes up dataset AFNI_data3/afni/func_slim+orig.
- Calculate
22.3 * 44.5 using the simple calculating program in AFNI.
- Image
Filtering:
- Smooth
AFNI_data3/afni/epi_r1+orig
with a 8mm FWHM filter. Name
the output file ex_blur8.
- Enhance
AFNI_data3/afni/anat+orig
by emphasizing the minimum-valued voxels across +/-3 voxels in the sagittal (x) direction. Name the output dataset ex_minx3. Note
the effects on each slice direction.
- Enhance
dataset ex_minz3+orig from part b by removing the noise with
program 3danisosmooth.
Name the output dataset ex_aniso. Use the -viewer
option in this program to select the number of noise-removing iterations.
Why: Image filtering provides some powerful ways to enhance the data.
Data can be changed in radically different ways. Use this power wisely.
- Random
Exercises with AFNI Datasets:
- Open
dataset AFNI_data3/afni/anat+orig
dataset and find the spatial storage order (i.e., xyz-orientation). Re-orient it to LPI orientation
and name the new output dataset exLPI.
Why: Data may be required by specific programs to match other data or
to match a specific orientation.
- Open
dataset AFNI_data3/afni/func_slim+orig
and create 2 separate datasets:
one with the 3rd sub-brick only and one with the 4th
sub-brick only. Call the former dataset ex_fneg_coef and the
latter ex_fneg_tstat.
Why: Extracting specific data is useful for exporting to other software
or for making the data fit in memory more easily if itÕs a large dataset.
- Combine
ex_fneg_coef+orig and ex_fneg_tstat+orig
from part b into a single dataset called ex_fneg, having the fneg Coef for sub-brick 0,
and fneg t-stat for sub-brick 1.
Why: Datasets can be manipulated to include only what youÕre interested
in.
- Convert
dataset AFNI_data3/afni/func_slim+orig
to Talairach coordinates with a 4mm3
resolution. Use dataset anat+tlrc in
the same directory as the data parent to perform the transformation on func_slim+orig. Name the output file func_slim4mm.
Why: Talairach data will be 1mm3 by
default. This resolution is often not necessary because it doesnÕt reflect the
resolution of the EPI data. It makes processing slower and taxes memory too.
- Locate
the maximum ÒFull-FÓ stat voxel value in dataset func_slim4mm+tlrc
and find the name of the Talairach atlas region
that corresponds to that voxelÕs position.
Why: Finding maximum activation can be scripted or searched
interactively. Atlas regions should be used as a guide. The AFNI GUI includes
various atlases that cite the regions associated with any specific voxel.
- Dataset
AFNI_data3/afni/anat+orig
was acquired sagittally and contains 124
slices. Create a new dataset
that contains only slices 40-90 of anat+orig. Provide the new dataset with the
prefix name anat_40_90.
Why: If memory or processing speed is a constraint, you can work with
only part of the data.