1. Functional Brain Signal Processing: EEG & fMRI Lesson 13
M.Tech. (CS), Semester III, Course B50
Functional Brain Signal Processing: EEG & fMRI Lesson 13
Kaushik Majumdar
Indian Statistical Institute Bangalore Center

2. Different MRI Image Types
Poldrack et al., 2011
Different MRI Image Types

3. Flow Chart of fMRI Processing Steps
Poldrack et al., 2011
Flow Chart of fMRI Processing Steps
Spatial normalization in case of group analysis of fMRI

4. Spatial Smoothing: Filtering out High-Frequency Components
Removal of high-frequency components enhances SNR at the larger spatial scale. Most fMRI analyses are performed across multiple neighboring voxels.
Noisy acquisition in smaller voxels can be smoothed out by spatial smoothing (performed, for example, by convolution with a suitable window function).

5. Spatial Smoothing (cont)
During group analysis of fMRI data spatial smoothing helps even out small individual differences, which interfere with the general (group) trend to be studied. All of these are not taken care of in usual spatial normalization.
Some analysis methods (like, Gaussian random field) require smoothing.

6. Amount of Spatial Smoothing
Spatial smoothing is often achieved by convolution with a Gaussian kernel function with standard deviation σ. In that case the amount of spatial smoothing is “Full width at half maximum” (FWHM) = σ√(2ln2) = 2.55σ. Also FWHM = √(FWHMintrinsic2 + FWHMqpplied2).

7. Effect of Smoothing with Different Applied FWHM Values
Poldrack et al., 2011
Effect of Smoothing with Different Applied FWHM Values

8. Spatial Normalization or Intersubject Registration
There is considerable variation in minute detail, shape and size of the brain across individuals. In order to locate functional activities to specific regions of the brain, irrespective of individual differences, intersubject 3D fMR image registration need to be performed. This is called spatial normalization.
See for detail Chapter 4 of Poldrack et al., 2011.

9. Poldrack et al., 2011
Talairach Coordinate

10.
Anatomical Landmarks

11. Automated Registration
MNI305 template – created by anatomical registration of 305 brains in Talairach atlas and then taking the average across all 305 brains.
MNI305 is the most widely used template in use today. Activities of a brain under study are directly mapped on this template.
This template is based on white Caucasian brains and therefore not ideal in shape and size for many other brains, such as south-east Asian brains.

12. Spatial Normalization Steps
Poldrack et al., 2011
Spatial Normalization Steps

13. Parametric Transformations
Poldrack et al., 2011
Parametric Transformations

14. References
R. A. Poldrack, J. A. Mumford and T. E. Nichols, Handbook of Functional MRI Data Analysis, Cambridge University Press, Cambridge, New York, 2011.

15. THANK YOU This lecture is available at http://www. isibang. ac