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MNTP Summer Workshop 2011 - fMRI BOLD Response to Median Nerve Stimulation: A Comparison of Block and Event-Related Design Mark Wheeler Destiny Miller.

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Presentation on theme: "MNTP Summer Workshop 2011 - fMRI BOLD Response to Median Nerve Stimulation: A Comparison of Block and Event-Related Design Mark Wheeler Destiny Miller."— Presentation transcript:

1 MNTP Summer Workshop 2011 - fMRI BOLD Response to Median Nerve Stimulation: A Comparison of Block and Event-Related Design Mark Wheeler Destiny Miller Carly Demopoulos Kyle Dunovan Martin Krönke Todd Monroe Dil Singhabahu Elisa Torres Christopher Walker Funded by: NIH R90DA02342

2 MNTP Workshop: Learning Objectives In-depth understanding of preprocessing of fMRI data –Filtering –Motion correction –Slice Time Correction –Smoothing –Registration Conduct first-level analyses Conduct group-level analyses Investigate two experimental designs

3 The Task: Median Nerve Stimulation Electrical stimulation of the median nerve by applying pulses to the wrist of the non-dominant hand Voltage: motor threshold

4 Blocked Design Pros.Cons. Excellent detection power (knowing which voxels are active) Useful for examining state changes Poor estimation power (knowing the time course of an active voxel) Relatively insensitive to the shape of the hemodynamic response. Stim ON 10s Stim OFF 16s 15Hz Stim ON 10s Stim OFF 16s 10 repetitions

5 Event-Related Design Pros.Cons. Good at estimating shape of hemodynamic response Provides good estimation power (knowing the time course of an active voxel) Can have reduced detection power (knowing which voxels are active) Sensitive to errors in predicted hemodynamic response Event 1Event 2Event 3Event 4

6 Event Related Task Design Three different frequencies: 15Hz, 40Hz, 80Hz (Kampe, Jones & Auer, 2000) Event length: 4s Inter-stimulus jitter – 2, 4, 6 seconds –Exponential distribution (Dale, 1999) 15Hz + 40Hz + 80Hz + 15Hz 4s (2TR) 4s + 40Hz 4s Time 2s Jitter 6s Jitter 2s Jitter 4s Jitter

7 Data Acquisition Scanner: Allegra 3T N=5 Structural Scan –T1 weighted MPRAGE –176 slices –Voxel Size 1mm Functional Scans: Median Nerve Stimulation –Volumes 140 for block 233 for event-related –Voxel Size 3.5mm –Slices 34 –Interleaved –TR 2s –T2* contrast

8 Temporal Filtering Motion correction Slice-timing Smoothing Registration / Normalization Preprocessing Data-conversion Dicom2Nifti Statistical analysis GLM Statistical Parametric Mapping Processing stream Block Design Single Subject Demonstration

9 Preprocessing: Slice Time Correction (STC) Stronger influence of STC for event-related vs. block-designs –sensitivity to timing / shape of HRF Slice acquisition order –interleaved slice acquisition (34 slices in 2s) avoids cross-slice excitation Debate on STC before / after motion correction? –before head motion (interleaved) Temporal non-linear sinc interpolation Huettel, Song, McCarthy 2009

10 Motion correction Due to subject movements inside the scanner, a voxel might represent different parts of the brain across time points, introducing artifacts Huettel, Song, McCarthy, 2004

11 Motion correction 1.Estimation Rigid-body transformation 6 DOF mm 0.2 -0.1 time (TRs) radians 0.003 -0.004 time (TRs) 2. Interpolation trilinear Nearest neighbour (tri-)linear Non-linear (sinc, B-spline)

12 No Motion correction % signal change Crosshair location: Postcentral gyrus Time (TRs) Motion corrected % signal change Time (TRs) Z-Value: 3.9 Z-Value: 3.8

13 Temporal Filtering Artifacts like “slow scanner drift” and changes in basal metabolism can reduce SNR A highpass filter can remove these unwanted effects Do not want to remove task-related signal –Block Design Task: 10s on, 16s rest –Woolrich et al. (2001) recommends filter of at least 2 epochs duration 52s temporal filter .019 Hz Also compared effects of 0 Hz,.038 Hz,.01 Hz Little difference between –.019 Hz –.038 Hz –.01 Hz

14 0Hz / No Temporal Filtering Time (TRs) % Signal Change 52s /.019Hz Temporal Filter % Signal Change Time (TRs)

15 Gaussian Weight Spatially filters data using Gaussian Kernel to remove noise Reduces spatial resolution Improves signal to noise ratio Consider ROI and voxel size in determining the size of the kernel Smoothing

16 0mm smooth4mm smooth 8mm smooth 20mm smooth

17 Registration / Normalization  Group analysis  Compare results in common coordinate system (MNI) Karsten Müller 2. Resample / Transform / Interpolate Nearest neighbour Linear interpolations Bi-, trilinear Non-linear interpolations B-Spline, sinc (Hanning) 1. Estimate transformation Combining affine-linear (12 DOF) subject  standard space (FSL FLIRT) nonlinear methods (> 12 DOF) subject  subject (FSL FNIRT) least squares cost function How? Why?

18 Data-conversion Dicom2Nifti Filtering Highpass (52s /.019Hz) Discrete cosine transform Motion correction Rigid-body, 6DOF Trilinear interpolation Slice-timing Interleaved Sinc interpolation Smoothing FWHM, 8mm Statistical analysis GLM 1st-level Group-analyses Registration / Normalization Affine-linear + Non-linear Block Design Statistical Parametric Mapping Preprocessing Summary

19 Event-related 40Hz 80Hz 15Hz Time Block design 15Hz Design matrix comparison: Block vs. Event-related

20 Block vs. Event-Related Design Block Design 15Hz activation map Modeled with gamma function Event-Related Design 15Hz activation map Modeled with double-gamma function

21 Functionally vs.structurally defined ROIs 21 ROI (structure) ROI (functional 9 mm) ROI (functional 6 mm) ROI (functional 3 mm)

22 -0.10 0.00 0.10 0.20 0.30 0.40 0.50 15Hz40Hz80Hz80Hz > All* Functionally Defined Structurally Defined ROI – F (1, 4) = 6.431, p =.064 Frequency – F (2, 4) = 10.046, p =.007 Frequency * ROI – F (2, 8) = 5.101, p =.037 Effect of Region of Interest on Task Related Median Percent Signal Change Median Percent Signal Change

23 Future Directions: Condition and Subject Timeseries 23 Arbitrary Units Modeled 15 Hz response for 1 subject

24 Event-Related Activation Comparison 15 Hz above baseline 40 Hz above baseline 80 Hz above baseline

25 Future Directions: Overlapping Activation Investigate condition specific differences in activation patterns

26 References Dale, A. M. (1999). Optimal experimental design for event-related fMRI. Human Brain Mapping, 8: 109–114.doi: 10.1002/(SICI)1097-0193(1999)8:2/3 3.0.CO;2-W Huettel, S. A., Song, A. W. and McCarthy, G. (2004). Functional magnetic resonance imaging. Sunderland, MA: Sinauer Associates Kampe, K. K., Jones, R. A. and Auer, D. P. (2000). Frequency dependence of the functional MRI response after electrical median nerve stimulation. Human Brain Mapping, 9: 106–114. doi: 10.1002/(SICI)1097-0193 (200002)9:2 3.0.CO;2-Y Woolrich, M. W., Ripley, B. D., Brady, M., Smith, S. M. (2001). Temporal autocorrelation in univariate linear modeling of FMRI data. NeuroImage, 14, 1370-1386.

27 Thank you Mark Wheeler Destiny Miller Seong-Gi Kim Bill Eddy Tomika Cohen Rebecca Clark Fellow MNTPers! Funded by: NIH R90DA02342

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