multimodal neuroimaging training program an fmri study of visual search
DESCRIPTION
Multimodal Neuroimaging Training Program An fMRI study of visual search Functional Magnetic Resonance Imaging: Group J. Wenzhu Bi, MS Graduate Student Biostatistics, CNBC University of Pittsburgh. Yanni Liu, PhD Graduate Student/Post-doc Psychology University of Michigan. - PowerPoint PPT PresentationTRANSCRIPT
Multimodal Neuroimaging Training ProgramAn fMRI study of visual search
Functional Magnetic Resonance Imaging: Group J
Wenzhu Bi, MS
Graduate Student
Biostatistics, CNBC
University of Pittsburgh
David Roalf, BS
Graduate Student
Behavioral Neuroscience
Oregon Health Science Univ.
Yanni Liu, PhD
Graduate Student/Post-doc
Psychology
University of Michigan
Xingchen Wu, MD & PhD
DRCMR, MR Dept. Copenhagen University Hospital Hvidovre Denmark
Aims and MethodsAims-Learn to implement block and event-related fMRI experimental designs
-Learn fMRI data pre-processing steps
-Learn fMRI data post-processing: GLM and group analysis
Methods-Subjects scanned: n=6 (3 males, 3 females)
-Scanner: Siemens 3T
-Images collected: MPRAGE(T1), In-Plane(T2 anatomical), EPI-BOLD(T2*,interleaved acquisition, TR=2s, voxel size 3.2mm3)
- Block Design: 166 volumes X 4 runs
- Event-Related Design: 159 volumes X 4 runs
-Functional analysis: WashU pre-processing script, AFNI
-Visual Search attention task (feature vs. conjunction search)
-More demanding attention task will elicit larger RT/Lower Accuracy
-More demanding attention task result in greater activation of attention network (parietal regions)
Task and Hypotheses
FeatureConjunction
vs
Is there an E?
Treisman & Gelade 1980
Behavioral Results
Reaction Time
0
200
400
600
800
1000
1200
1400
Conjunction Feature
Tim
e (
ms
)
t(6)=3.63, p<.02
Accuracy
0.820.840.860.88
0.90.920.940.960.98
11.02
Conjuction Feature
% C
orr
ec
t
t(6)=2.74, p<.04
Block ER
Design
Pros:
High detection power due to response summation.
Simple analysis
Con:
Can’t look at effects of single events (e.g., correct vs. incorrect trials; target present vs. absent)
Pros:
Good estimation of time courses and reasonable detection
Enables post hoc sorting (e.g., correct vs. incorrect; target present vs. absent)
Con:
Some loss of power for the contrast between trial types.
4 runs X 6 blocks X 10 trials 4 runs X 4 same task sets X 12 trialsWager, 2007
F C
Pre/Post Processing
• Pre-processing– Slice timing correction (Sinc interpolation)
– Motion correction– Intensity scaling– Spatial smoothing
– Spatial normalization (Talairach atlas transformation)
• Post-processing– Individual analysis
• GLM analysis– Assumed HRF model– Deconvolution (Finite
Impulse Response)
• ROI analysis
– Group Analysis• Wilcoxon test
Block Data Example
Conj. vs Feat.Conjunction Feature
q = 0.05
Conj. > Feat.Conj. < Feat.
Conj. > baselineConj. < baseline
Feat. > baselineFeat. < baseline
L L L
R R R
Block vs. ER Data
Block design ER design Results: Block design is more powerful to detect cerebral activation than ER design.
ER design allows us to examine individual trial responses.
Conj. > Feat.Conj. < Feat.
q = 0.05
L L
R R
Conjunction HRFFeature HRF
Spatial Smoothing
Smoothed
Non-smoothed
A Gaussian filter with FWHM (full-width-half-max) = 6.4mm (i.e., twice the voxel width).
Pros:-Smoothing resulted in greater areas of activation.
-Increased signal to noise ratio
Cons:-Reduced spatial precision
-Introduce statistical interdependence among voxels
FDR q=0.05Conj. > Feat.Conj. < Feat.
R
RL
L
Group Analysis: Block Design-Individual subject data was transformed to a standard space (Talairach).
-A non-parametric Wilcoxon Signed Rank test was used to test for difference in visual search.
Non-Smoothed
Smoothed
Wilcoxon Statistical map, |Z|>1.964, n=6
Conj. > Feat.Conj. < Feat.
L
L
L
L
L
ROI Timecourse Data
ROI1
1315
1320
1325
1330
1335
1340
1345
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Time
BO
LD
Inte
ns
ity
Conjunction
Feature
n=6
n=6
Right Parietal Lobe
(1263 mm3)
Left Occipital Lobe
(2096 mm3)
Block onset Block offset
TR
TR
What we have learned
1) We learned the details of fMRI pre-processing steps. This course allowed for discussion and understanding of slice-time correction, motion correction, spatial smoothing
2) We learned the details of post-processing including the use of the GLM for modeling our fMRI experiment. We also learned the analysis of individual and group level data.
3) AFNI- A good tool for understanding the complicated steps of analysis.
4) There is no recipe for fMRI analysis. Each study design and each analysis is unique which requires detailed understanding of the processing steps.
• Seong-Gi Kim
• William Eddy
• Mark E. Wheeler
• Jeff Phillips
• Elisabeth Ploran
• Denise Davis
• Tomika Cohen
• Rebecca Clark
Acknowledgements
How much movement is too much?
Depends on many things:
-the type of movement (sharp movement vs. drift)
-timing of the movement (during a trial vs. during a break period)
-the resolution of your data:
3 mm movement may be okay if you are collecting 3.2 X 3.2 X 3.2 mm3 resolution but may not if you are collecting 1.0 X 1.0 X 1.0 mm3
No specific criteria, the investigator must decide!!
Assumed HRF Deconvolution
Standardization
Subject1
Subject 2
Subject 3
Left HandResponse
Right HandResponse
Motor Analysis