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

hy 2

00

9

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

an

ge

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

ign

al C

han

ge

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

21

ROI (structure)

ROI (functional 9 mm)

ROI (functional 6 mm)

ROI (functional 3 mm)

-0.10

0.00

0.10

0.20

0.30

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

ian

Perc

en

t S

ign

al C

han

ge

Future Directions: Condition and Subject Timeseries

23

Arb

itra

ry U

nit

s

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