Functional Brain Signal Processing: EEG & fMRI Lesson 13 Kaushik Majumdar Indian Statistical Institute Bangalore Center [email protected] M.Tech.

Functional Brain Signal Processing: EEG & fMRI

Lesson 13

Kaushik Majumdar

Indian Statistical Institute Bangalore Center

[email protected]

M.Tech. (CS), Semester III, Course B50

Different MRI Image Types

Poldrack et al., 2011

Flow Chart of fMRI Processing Steps

Poldrack et al., 2011

Spatial normalization in case of group analysis of fMRI

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).

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.

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 + FWHMqpplied

2).

Effect of Smoothing with Different Applied FWHM Values

Poldrack et al., 2011

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.

Talairach Coordinate

Poldrack et al., 2011

Anatomical Landmarks

http://ja.m.wikipedia.org/wiki/%E3%83%95%E3%82%A1%E3%82%A4%E3%83%AB:Gray726_central_sulcus.svg

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.

Spatial Normalization Steps

Poldrack et al., 2011

Parametric Transformations

Poldrack et al., 2011

References

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

THANK YOU

This lecture is available at http://www.isibang.ac.in/~kaushik