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Enhancing fMRI Analysis Through Spatial Smoothing

Learn the importance of spatial smoothing in fMRI analysis for noise reduction and data enhancement in group studies. Understand techniques like convolution with Gaussian kernel and spatial normalization.

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Enhancing fMRI Analysis Through Spatial Smoothing

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  1. M.Tech. (CS), Semester III, Course B50 Functional Brain Signal Processing: EEG & fMRILesson 13 Kaushik Majumdar Indian Statistical Institute Bangalore Center kmajumdar@isibang.ac.in

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

  3. 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. 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. http://ja.m.wikipedia.org/wiki/%E3%83%95%E3%82%A1%E3%82%A4%E3%83%AB:Gray726_central_sulcus.svghttp://ja.m.wikipedia.org/wiki/%E3%83%95%E3%82%A1%E3%82%A4%E3%83%AB:Gray726_central_sulcus.svg 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. Poldrack et al., 2011 Spatial Normalization Steps

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

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