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

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

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

Blocked Design 10 repetitions Pros. Cons. Stim ON 10s Stim OFF 16s Stim ON 10s Stim ON 10s 15Hz 15Hz 15Hz Stim OFF 16s Stim OFF 16s 10 repetitions 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.

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 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 4s (2TR) 4s Time 2s Jitter 6s Jitter 4s Jitter

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

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

Preprocessing: Slice Time Correction (STC) Huettel, Song, McCarthy 2009 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

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

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

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

Temporal Filtering A highpass filter can remove these unwanted effects 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 Discrete cosine transform Low frequencies 0 – 0.1 Hz 0.1 – 0.5 Hz respiratory 0.6 -1.2 Hz cardiac

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

Smoothing 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 Gaussian Weight

0mm smooth 4mm smooth 8mm smooth 20mm smooth

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

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

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

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

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

Effect of Region of Interest on Task Related Median Percent Signal Change -0.10 0.00 0.10 0.20 0.30 0.40 0.50 15Hz 40Hz 80Hz 80Hz > All* Functionally Defined Structurally Defined Median Percent Signal Change Median percent signal change of the BOLD response to 15, 40, and 80 Hz median nerve stimulation from baseline. The functionally defined ROI was defined as above; the structurally determined ROI was created using the Harvard-Oxford Structural Atlas defined boundaries for the postcentral gyrus, and masked to include only right hemisphere activity. *Note: “80 Hz > All” denotes the contrast of the response to 80 Hz stimulation against both the 15 and 40 Hz responses combined. It has been included to demonstrate the potential to model differences between conditions with the GLM approach. 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

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

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

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

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<109::AID- HBM7>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<106::AID- HBM5>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.

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