Functional Brain Signal Processing: EEG & fMRI Lesson 14

1. M.Tech. (CS), Semester III, Course B50 Functional Brain Signal Processing: EEG & fMRILesson 14 Kaushik Majumdar Indian Statistical Institute Bangalore Center kmajumdar@isibang.ac.in

2. http://psuc5d.files.wordpress.com/2012/02/bennett-salmon-2009.jpeghttp://psuc5d.files.wordpress.com/2012/02/bennett-salmon-2009.jpeg Why Statistics in fMRI?

3. Reading Exercise on Multiple Comparison Correction • http://blogs.discovermagazine.com/neuroskeptic/2009/09/16/fmri-gets-slap-in-the-face-with-a-dead-fish/#.UlWjfz_3EdX

4. Step 1: Gaussian Smoothing Gaussian smoothing with 8 mm FWHM. http://blogs.discovermagazine.com/neuroskeptic/2009/09/16/fmri-gets-slap-in-the-face-with-a-dead-fish/#.UlWjfz_3EdX

5. Step 2: Z Score Thresholding Euler characteristics 2 after Z score thresholding. So region of activation is 2 and they are shown in the figure. http://blogs.discovermagazine.com/neuroskeptic/2009/09/16/fmri-gets-slap-in-the-face-with-a-dead-fish/#.UlWjfz_3EdX

6. Buxton, 2009, p. 369 BOLD Activation Detection amidst Noise • During activation, change in BOLD signal is 1% due to a 50% change in cerebral blood flow, when scanned by a 1.5 T scanner. • Noise in the BOLD signal due to blood and CSF motion caused by pulsating heart often causes around 1% fluctuation. • In single shot EPI a large number of images during activation and control are required to average to detect BOLD changes due to activation.

7. Vasomotion • A regular oscillation of blood flow and oxygenation called vasomotion has been observed in numerous optical studies at frequencies around 0.1 Hz. It is significant at high magnetic field, but its origin is not well understood yet.

8. Buxton, 2009 FFT of MR Signal During Activation

9. Buxton, 2009 Noise vs. Activation

10. Buxton, 2009 BOLD Activation Time Course

11. More on BOLD Activation Detection • Subtraction • t – test • Correlation (next slide) • Fourier transform (slide after the next)

12. Noll, 2001 Detection by Subtraction

13. Statistical Parametric Map yij is the response of the ith voxel at the jth time instance, M(i,k) unit kth effect on the ith voxel, akj is intensity of kth effect in jth time instance and eij is error in calculating yij assumed to be independently and identically distributed across all the voxels and time instances. In matrix form:

14. Monti, 2011 GLM in fMRI Time Series

15. Buxton, 2009 Detection by Correlation A simple approximation for the model response to block stimulus pattern is a trapezoid with a 6s ramp delayed by 2s from the onset of the stimulus block. At voxel correlation coefficient between model function and the actual time series at the voxel is calculated the thresholded. 6s 2s

16. Detection by Fourier Transform Poldrack et al., 2011 Buxton, 2009

17. Buxton, 2009 General Linear Model (GLM)

18. GLM – Geometrical Representation

19. GLM – Mathematical Derivation

20. Buxton, 2009, p. 384 Contrast Any linear combination of model amplitudes can be thought of as a contrast of the form c = w1a1 + w2a2. So c = aTw. Since projection of data on the model space, not on the error space, determines magnitude a = LYM.

21. Noise Sensitivity of the fMRI If both YM and YE are independent Gaussian noise, then . The variance is given by So for any contrast of interest defined by a vector of weight w the variance is , which gives noise sensitivity of an fMRI experiment.

22. SNR in fMRI Experiment This is the SNR in an fMRI experiment according to GLM.

23. References • R. B. Buxton, Introduction to Functional Magnetic Resonance Imaging, 2e, Cambridge University Press, Cambridge, UK, 2009. Chapter 15. • M. M. Monti, Statistical analysis of fMRI time series: a critical review of GLM approach, Frontiers in Human Neuroscience, 5: 28, 2011, available online at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3062970/

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