Jihoon Cha

Gus Diggs

Klas Ihme

Jordan Pierce

Brad Rocco

Rick Solis

Jacob Wolfe

Mark Wheeler, Ph.D

Kyle Dunovan

Chris Walker

Analyzing fMRI Data:

Blocked Motor Task

Overview

• Experimental Design

• Data Collection

• Preprocessing

• Slice timing correction

• Motion correction

• Spatial smoothing

• Subject Level GLM

• Registration to Common Space

• Group ANOVA

• Clustering

Finger Tap Task

Block Design

• 2 x 2 Index Finger Tapping

• Hand x Rate

• Left 5 Hz

• Left 1 Hz

• Right 5 Hz

• Right 1 Hz

• Visually cued and auditorily paced

• Hypothesis There will be greater motor cortex activation at 5 Hz

compared to 1 Hz in the contralateral hemisphere.

Experimental Design

4 Conditions

Repeated 8 times =

32 Task blocks

+ 33 Rest blocks =

65 blocks x 16 sec =

17 min 20 sec run

16s

16s

16s

16s

16s

16s

16s

16s

Data Collection • 6 right-handed participants

• Stimuli presented via PsychoPy software

• 3T Siemens Allegra Scanner

• T1 Weighted MPRAGE

• Matrix= 256 x 256, Voxel dimensions= 1mm3

• 522 EPI (T2* weighted) Images (First 2

discarded)

• TR= 2 seconds, TE= 25ms, FA= 70°

• Matrix= 64 x 64, FOV= 200mm x 200mm

• 38 slices

• Voxel dimensions= 3.125mm x 3.125mm x 3.2mm

Overview

• Experimental Design

• Data Collection

• Preprocessing:

• Slice timing correction

• Motion correction

• Spatial smoothing

• Subject Level GLM

• Registration to Common Space

• Group ANOVA

• Clustering

Interleaved Slice Acquisition

ODD SLICES

EVEN SLICES

Signal Shift Due To

Interleaved Slice Acquisition

Signal Intensity for Adjacent Voxels in Adjacent Slices

Left 5Hz Left – 5Hz

Overview

• Experimental Design

• Data Collection

• Preprocessing:

• Slice Timing

• Motion Correction

• Spatial Smoothing

• Subject Level GLM

• Registration to Common Space

• Group ANOVA

• Clustering

Individual Subject Range of Motion

Head Movement: Subject 6

(<6 mm motion) m

otion in d

eg

motion in m

m

pitch roll yaw

z x y

Time in TRs

Effect of Motion at Edge

Time

t = 315 TRs t = 317 TRs

• Intensity in voxels can

change correlated with

head movement

x:

mo

tio

n in

mm

sig

na

l in

ten

sity

70 785

Subject After Rigid Body

Motion Correction

Time

• Motion correction can

correct it to a certain

degree

x:

mo

tio

n in

mm

sig

na

l in

ten

sity

98 (70) 56 (785)

t = 315 TRs t = 317 TRs

Movie Goes Here

Overview

• Experimental Design

• Data Collection

• Preprocessing:

• Slice Timing

• Motion Correction

• Spatial Smoothing

• Subject Level GLM

• Registration to Common Space

• Group ANOVA

• Clustering

Smoothing

• Increases the signal to

noise ratio

• Reduces multiple

comparison problem

• Creates Gaussian

distribution of noise

• Inter-subject anatomical

variability

• Disadvantages

• Decreases spatial

resolution

• Kernel size selection

• Possible reduction of

signal

FWHM 1

FWHM 2

FWHM 4

FWHM 6

FWHM 9

Spatial and time series changes with different smoothing kernel

No smoothing

FWHM 4mm

FWHM 8mm

Effect of Smoothing On

Left Hand 5 Hz Activation

(A.U.)

Left 5Hz

Smoothing Effects on Cluster Size and t-statistic

FWHM of Spatial Smoothing (mm3)

Text

FWHM = 0

FWHM = 4

FWHM = 8

Overview

• Experimental Design

• Data Collection

• Preprocessing:

• Slice Timing

• Motion Correction

• Spatial Smoothing

• Subject Level GLM

• Registration to Common Space

• Group ANOVA

• Clustering

Single Subject Time Series

Volumes

Task Block Left Finger 5 Hz

Task Block Left Finger 1 Hz

Task Block Right Finger 1 Hz

Task Block Right Finger 5 Hz

0 100 200 300 400 500

Subject 1, Right precentral gyrus

Rest Block

Creating the Model Canonical

Hemodynamic

Response Function Block Design "Boxcar" Stimulus

=

Convolved Response Model for

Regression

Temporal Derivative

*FSL "FMRI Pre-processing and Model-based Statistics"

By including the

temporal derivative of

the HRF in the

regression model,

the original model is

effectively shifted to

account for slice

acquisition timing

differences without

interpolating the

data.

Original

Model

Temporal

Derivative

Shifted

Model

-

=

Sample Model Fits

Left 5Hz

Overview

• Experimental Design

• Data Collection

• Preprocessing:

• Slice Timing

• Motion Correction

• Spatial Smoothing

• Subject Level GLM

• Registration to Common Space

• Group ANOVA

• Clustering

Subject 5

Subject 2 Subject 3

Subject 4

Subject 1

Variations in Individual Anatomy

T1 MNI Atlas Registration

1mm3 2mm3

Transformation 1

Transformation 2

T1 MPRAGE T1 MNI Atlas

~3.2mm3

GLM EPI

2mm3

T1 MNI Atlas

After Registration...

Subject 1 Subject 2 Subject 3

Subject 4 Subject 5

Group ANOVA

• 2 x 2 ANOVA

• Hand x Speed

• Post-Hocs:

• Right vs. Baseline

• Left vs. Right

• 5 Hz vs. 1 Hz

• Clustering

• Account for multiple comparisons problem

• Used Monte Carlo simulation to threshold number of

contiguous voxels necessary for significance

L R

Right vs. Baseline, p < 0.01

L R

Left-Right Hand Differences (p<.01)

R L

5 Hz – 1 Hz Difference (p<.01)

Right 5 Hz vs. 1Hz Percent Signal Change

Left Motor Cortex Coordinates:

38.5, 23.5, 69.5

Right Cerebellum Coordinates:

-17.5, 54.5, -20.5

Mean percent signal change calculated across subjects 1-5

Left Motor Cortex Right Cerebellum

0

0.5

1

1.5

2

2.5

3

Right 5Hz Right 1 Hz Right 5 Hz Right 1 Hz

Pe

rce

nt

Sign

al C

han

ge

Conclusions

• Slice timing acquisition differences can be

accounted for by using a temporal derivative

in the GLM

• Motion correction can correct small

movements

• Subjects with movements larger than a voxel

should be excluded.

• Smoothing with a 4 mm FWHM can increase

SNR while maintaining adequate spatial

localization.

We would like to thank…

Dr. Seong-Gi Kim

Dr. William Eddy

Tomika Cohen

Rebecca Clark

Dr. Mark Wheeler

Kyle Dunovan

Chris Walker

Everyone else who made MNTP possible

References

De Martino, B., Camerer, C.F., & Adolphs, R. (2010). Amygdala damage eliminates monetary loss aversion.

PNAS.

Jancke, L., Loose, R., Lutz, K., Specht, K., & Shah, N.J. (2000). Cortical activations during paced finger-

tapping applying visual and auditory pacing stimuli. Cognitive Brain Research, 10: 51-66.

Miezin, F.M., Maccotta, L., Ollinger, J.M., Peters, S.E., & Buckner, R.L. (2000). Characterizing the

hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of

ordering brain activity based on relative timing. NeuroImage, 11: 735-759.

Poldrack, R., Mumford, J., & Nichols, T. (2011). Handbook of functional MRI data analysis. New York, NY:

Cambridge University Press.

Rao, S.M., Bandettini, P.A., Binder, J.R., Bobholz, J.A., Hammeke, T.A., Stein, E.A., & Hyde, J.S. (1996).

Relationship between finger movement rate and functional magnetic resonance signal change in

human primary motor cortex. Journal of Cerebral Blood Flow and Metabolism, 16: 1250-1254.

Skudlarski, P., Constable, R.T., & Gore, J.C. (1997). ROC Analysis of statistical methods used in function

MRI: individual subjects. Neuroimage, 9: 311-329.

Tom, S.M., Fox, C.R., Trepel, C., & Poldrack, R.A. (2007). The neural basis of loss aversion in decision-

making under risk. Science, 315: 515-518.

Subject Level GLM:

Temporal Derivative + Double Gamma

RS

RF

LS

LF

TD

DG

Baselin

e

GLM Modeling Black: Preprocessed Blue: DG + TD, R2= 0.78 Red: DG, R2= 0.57

TD= Temporal Derivative, DG= Double Gamma

Black: Preprocessed Green: Gamma, R2= 0.43 Red: DG, R2=0.43

no STC

STC

Sub 1

Sub 1