1. 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
David Roalf, BS
Graduate Student
Behavioral Neuroscience
Oregon Health Science Univ.
Xingchen Wu, MD & PhD
DRCMR, MR Dept. Copenhagen University Hospital Hvidovre Denmark

2. Aims and Methods Aims Methods
-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

3. Task and Hypotheses
-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)
Is there an E?
Conjunction
Feature vs

4. Behavioral Results Treisman & Gelade 1980 t(6)=3.63, p<.02

5. Design F C Block ER 4 runs X 6 blocks X 10 trials
Wager, 2007
4 runs X 6 blocks X 10 trials
4 runs X 4 same task sets X 12 trials
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.

6. Pre/Post Processing Pre-processing Post-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

7. Block Data Example Conjunction Feature Conj. vs Feat. L L L R R R
Conj. > baseline
Conj. < baseline
Feat. > baseline
Feat. < baseline
Conj. > Feat.
Conj. < Feat.
q = 0.05

8. 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. L L R R
Conj. > Feat.
Conj. < Feat.
q = 0.05
Conjunction HRF
Feature HRF

9. Spatial Smoothing 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 L R
Smoothed L R
Non-smoothed
Conj. > Feat.
Conj. < Feat.
FDR q=0.05

10. 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. L
Wilcoxon Statistical map, |Z|>1.964, n=6 L L
Conj. > Feat.
Conj. < Feat.
Non-Smoothed
Smoothed L L

11. ROI Timecourse Data Block onset Block offset n=6 n=6
Conjunction
Feature
n=6
Left Occipital Lobe
(2096 mm3) TR
n=6
Right Parietal Lobe
(1263 mm3) TR

12. What we have learned
We learned the details of fMRI pre-processing steps. This course allowed for discussion and understanding of slice-time correction, motion correction, spatial smoothing
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.
AFNI- A good tool for understanding the complicated steps of analysis.
There is no recipe for fMRI analysis. Each study design and each analysis is unique which requires detailed understanding of the processing steps.

13. Acknowledgements Seong-Gi Kim William Eddy Mark E. Wheeler
Jeff Phillips
Elisabeth Ploran
Denise Davis
Tomika Cohen
Rebecca Clark

14. 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!!

16. Deconvolution
Assumed HRF

17. Standardization
Subject1
Subject 2
Subject 3

18. Motor Analysis
Left Hand
Response
Right Hand
Response