Mark Wheeler Destiny Miller Carly Demopoulos Kyle Dunovan Martin Krönke Todd Monroe Dil Singhabahu
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Transcript of Mark Wheeler Destiny Miller Carly Demopoulos Kyle Dunovan Martin Krönke Todd Monroe Dil Singhabahu
MNTP Summer Workshop 2011 - fMRI
BOLD Response to Median Nerve Stimulation: A Comparison of Block and Event-Related Design
Mark WheelerDestiny Miller
Carly DemopoulosKyle DunovanMartin KrönkeTodd Monroe
Dil SinghabahuElisa 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
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 16s15Hz 15Hz 15Hz
Stim ON 10s Stim ON 10s
Stim OFF 16s Stim OFF 16s
10 repetitions
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 1 Event 2 Event 3 Event 4
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
4s
4s
+40Hz
4s
Time
2s Jitter
6s Jitter
2s 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
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
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
Huett
el, S
ong, M
cCart
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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 correction1. Estimation• Rigid-body transformation 6 DOF
mm
0.2
-0.1
time (TRs)
radia
ns 0.003
-0.004 time (TRs)
2. Interpolation• trilinear
Nearest neighbour (tri-)linear Non-linear (sinc, B-spline)
No Motion correction
% s
ign
al ch
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Crosshair location: Postcentral gyrus
Time (TRs)
Motion corrected
% s
ign
al ch
an
ge
Time (TRs)
Z-Value: 3.9
Z-Value: 3.8
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
0Hz / No Temporal Filtering
Time (TRs)
% S
ign
al
Ch
an
ge
52s / .019Hz Temporal Filter
% S
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Time (TRs)
Gau
ssia
n
Weig
ht
• 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
0mm smooth 4mm smooth 8mm smooth 20mm smooth
Registration / Normalization
Group analysis Compare results in common coordinate system (MNI)
Kars
ten M
ülle
r
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?
Data-conversion•Dicom2Nifti
Filtering•Highpass (52s / .019Hz)•Discrete cosine transformMotion 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
Event-related
40Hz
80Hz
15Hz
Time
Block design
15Hz
Design matrix comparison: Block vs. Event-related
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
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ROI (structure)
ROI (functional 9 mm)
ROI (functional 6 mm)
ROI (functional 3 mm)
-0.10
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0.10
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0.40
0.50
15Hz 40Hz 80Hz 80Hz > All*
Functionally DefinedStructurally 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
Med
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Perc
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Future Directions: Condition and Subject Timeseries
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Arb
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Modeled 15 Hz response for 1 subject
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