analyzing fmri data: blocked motor taskanalyzing fmri data: ... *fsl "fmri pre-processing and...
TRANSCRIPT
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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
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Overview
• Experimental Design
• Data Collection
• Preprocessing
• Slice timing correction
• Motion correction
• Spatial smoothing
• Subject Level GLM
• Registration to Common Space
• Group ANOVA
• Clustering
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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.
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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
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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
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Overview
• Experimental Design
• Data Collection
• Preprocessing:
• Slice timing correction
• Motion correction
• Spatial smoothing
• Subject Level GLM
• Registration to Common Space
• Group ANOVA
• Clustering
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Interleaved Slice Acquisition
ODD SLICES
EVEN SLICES
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Signal Shift Due To
Interleaved Slice Acquisition
Signal Intensity for Adjacent Voxels in Adjacent Slices
Left 5Hz Left – 5Hz
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Overview
• Experimental Design
• Data Collection
• Preprocessing:
• Slice Timing
• Motion Correction
• Spatial Smoothing
• Subject Level GLM
• Registration to Common Space
• Group ANOVA
• Clustering
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Individual Subject Range of Motion
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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
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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
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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
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Movie Goes Here
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Overview
• Experimental Design
• Data Collection
• Preprocessing:
• Slice Timing
• Motion Correction
• Spatial Smoothing
• Subject Level GLM
• Registration to Common Space
• Group ANOVA
• Clustering
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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
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Spatial and time series changes with different smoothing kernel
No smoothing
FWHM 4mm
FWHM 8mm
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Effect of Smoothing On
Left Hand 5 Hz Activation
(A.U.)
Left 5Hz
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Smoothing Effects on Cluster Size and t-statistic
FWHM of Spatial Smoothing (mm3)
Text
FWHM = 0
FWHM = 4
FWHM = 8
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Overview
• Experimental Design
• Data Collection
• Preprocessing:
• Slice Timing
• Motion Correction
• Spatial Smoothing
• Subject Level GLM
• Registration to Common Space
• Group ANOVA
• Clustering
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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
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Creating the Model Canonical
Hemodynamic
Response Function Block Design "Boxcar" Stimulus
=
Convolved Response Model for
Regression
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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
-
=
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Sample Model Fits
Left 5Hz
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Overview
• Experimental Design
• Data Collection
• Preprocessing:
• Slice Timing
• Motion Correction
• Spatial Smoothing
• Subject Level GLM
• Registration to Common Space
• Group ANOVA
• Clustering
![Page 26: Analyzing fMRI Data: Blocked Motor TaskAnalyzing fMRI Data: ... *FSL "FMRI Pre-processing and Model-based Statistics" By including the temporal derivative of the HRF in the regression](https://reader035.vdocuments.us/reader035/viewer/2022062603/5f02e33a7e708231d406815e/html5/thumbnails/26.jpg)
Subject 5
Subject 2 Subject 3
Subject 4
Subject 1
Variations in Individual Anatomy
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T1 MNI Atlas Registration
1mm3 2mm3
Transformation 1
Transformation 2
T1 MPRAGE T1 MNI Atlas
~3.2mm3
GLM EPI
2mm3
T1 MNI Atlas
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After Registration...
Subject 1 Subject 2 Subject 3
Subject 4 Subject 5
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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
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L R
Right vs. Baseline, p < 0.01
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L R
Left-Right Hand Differences (p<.01)
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R L
5 Hz – 1 Hz Difference (p<.01)
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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
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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.
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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
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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.
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Subject Level GLM:
Temporal Derivative + Double Gamma
RS
RF
LS
LF
TD
DG
Baselin
e
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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