Translational Neuromodeling Unit Preprocessing of fMRI data (basic) Practical session SPM Course 2016, Zurich Andreea Diaconescu, Maya Schneebeli, Jakob Heinzle, Lars Kasper, and Jakob Siemerkus Translational Neuromodeling Unit (TNU) Institute for Biomedical Engineering (IBT) University and ETH Zürich
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Preprocessing of fMRI data (basic) - TNU · Preprocessing of fMRI data (basic) Practical session SPM Course 2016, Zurich. Andreea Diaconescu, ... How: Rigid-body transformation ...
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Translational Neuromodeling Unit
Preprocessing of fMRI data (basic)
Practical sessionSPM Course 2016, Zurich
Andreea Diaconescu, Maya Schneebeli, Jakob Heinzle,
Lars Kasper, and Jakob SiemerkusTranslational Neuromodeling Unit (TNU) Institute for Biomedical Engineering (IBT)University and ETH Zürich
Goals of this session
• Go through a preprocessing pipeline in SPM.
• Learn how to check whether some basic stepsworked.
• Some basic file operations in SPM.
• Save, load and modify batches
• How to make your own preprocessing script.
• Answers to "all" your questions.
Preprocessing tools on the SPM GUI and Batch Editor
SPM course 2016, University and ETH Zurich, Switzerland3
The Dataset: Event-related fMRI
SPM course 2016, University and ETH Zurich, Switzerland
Goal: Investigate Repetition Suppression
How: Each face presented twice during
the session, 26 famous and 26 non-
famous faces 2x2 factorial design Factor Fam(iliarity): long-term
memory, Level: Famous or Unfamous
Factor Rep(itition) Level: 1 or 2
Task: Button press to decide fame
0 50 100 150 200 250 300 3500
0.5
1
1.5N1
time (scans)
0 50 100 150 200 250 300 3500
0.5
1
1.5N2
time (scans)
0 50 100 150 200 250 300 3500
0.5
1
1.5F1
time (scans)
0 50 100 150 200 250 300 3500
0.5
1
1.5F2
time (scans)
all_conditions.mat
Stimulus Onsets R. Henson et al., Cereb Cortex 2002
4
fMRI time-series
Slice-timing corrected images
Slice-Timing Correction
Slice-Timing Correction (Temporal Preproc)
SPM course 2016, University and ETH Zurich, Switzerland
Goal: Correct for different acquisition time of
each slice within an image volume
How: All voxel time series are aligned to
acquisition time of 1 slice via Sinc-
interpolation of each voxel’s time series
fmri.nii
afmri.nii
5
fMRI time-series
Motion corrected Mean functional
REALIGN COREG
Anatomical MRI
SEGMENT NORM WRITE SMOOTH
TPMs
100034333231
24232221
14131211
mmmmmmmmmmmm
GLM
Input
Output
Segmentation
Deformation Field
y_*.nii
Kernel
(Headers changed) MNI Space
Spatial Preprocessing
SPM course 2016, University and ETH Zurich, Switzerland 6
fMRI time-series
Motion corrected
Mean functional
REALIGN
Realignment
SPM course 2016, University and ETH Zurich, Switzerland
Goal: Correct for subject motion between volumes
by minimising mean-squared difference
How: Rigid-body transformation
Note: Realignment improves if images are reoriented in
advance (find the origin, change header, use check reg with
the canonical image)
(Headers changed)
fmri.nii
fmri.mat meanfmri.niifmri.nii
(unchanged)
Realignment parameters
rp_fmri.txt
100034333231
24232221
14131211
mmmmmmmmmmmm
7
Mean functional
COREG
Anatomical MRI
Co-registration
SPM course 2016, University and ETH Zurich, Switzerland