1. Research course on functional magnetic resonance imaging Lecture 2
Juha Salmitaival

2. Today’s lecture Preprocessing Registration FSL demo Motion correction
Slice timing correction
Spatial filtering
Temporal filtering
ICA denoising
Global intensity correction
Registration
FSL demo
Things we have learned so far

3. Preprocessing – general things
Signal changes in BOLD are typically somewhere between 0.1% and 5%
To enhance the signal and reduce the noise
To prepare the data for statistical analysis
Learn to know your data!

4. Preprocessing – motion correction
Padding around the head to avoid movement!
Head movements -> different tissue in same voxel and artefactual signal changes
Rigid body = 3 rotations 3 translations
8 mm search, 4 mm search, 4 mm search with tighter criteria

5. Preprocessing – motion correction
How much motion is too much?
Large jumps are more serious than slow drifts
Exclusion: outlier?, 1mm?
If you have stimulus correlated motion, you probably need other methods (e.g., INRIAlign)

6. Preprocessing – slice timing correction
Slices are scanned at a slightly different time (0,2,4,…1,3,5…)
Slice timing correction: fourier transform for the voxel time-series -> phase-correction -> inverse fourier transform

7. Preprocessing – spatial filtering
How big are your blobs?
-> increases SNR
-> Gaussian distribution (thresholding)
Typically 3-10 mm
Local averaging – adds the signal and cancels the noise

8. Preprocessing – temporal filtering
Scanner-related and physiological drifts
HP filter - usually, LP filter if needed (MELODIC?)
Cycle length x 1.5
(also delete first volumes)
FSL uses standard FIR (finite impulse response) filter (stable, no feedback)

9. Preprocessing – ICA denoising
Need to know what the signal should look!
Non gray-matter?, weird time-series/frequency spectrum?
Individual/group analysis?
If one of these ”fail” you can say that it is an artefact. However, is it safe to remove this? Does it have anything that could be signal?

10. Preprocessing – global intensity normalization

11. Registration of images – whole brain
Standard spaces: MNI space, Talairach space/atlas (
fMRI space -> perform analysis here if possible
fMRI to structural -> anatomical localization
fMRI to standard -> comparison of results
(between subjects and datasets)
Step 1 estimating transformation (transformation matrix)
Step 2 resampling (modified image)

12. Registration of images – parameters
FNIRT
- Same modality
- High quality
DOFS
Cost function
correlation ratio (same session T1)
mutual info (T2 anatomical)
Interpolation
Cost function = goodness of alignment, seek the minimum value

13. Registration of images
Always check the results visually!
Two stage registration
Field map correction

14. Registration of gyri and sulci
Individual differences in cortical folding are huge!

15. Preprocessing & Registration demo
1. Motion correction (fMRI image)
2. Brain extraction (manual check!)
3. FEAT preprocessing
(4. fMRI modeling/statistics (next weeks topic))
5. FLIRT registration (manual check!)

16. Groups 1 GLM and ICA: music vs. speech, audiovisual interaction
Jussi, Onerva, Hanna, Olli-Pekka
2 artifacts and signals (ICA/GLM)
Dinos, Jari T., Juha P., Eero K, Timo
3 cross-sensory coherence (ISC)
Alexander, Anne, Jonathan, Jaakko
Passwords / Computers

17. About the dataset
The data is not only for this course, but also for scientific purposes
Original plan is not to use any of your work in publication
If you think that your contribution is enough to be author in the publication, please discuss with me!
If you want to publish something out of the data, come to discuss with me!

18. References & Images FSL-course SPM-course
SPM-course