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Multimodal Neuroimaging Training Program An fMRI study of visual search

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.

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Multimodal Neuroimaging Training Program An fMRI study of visual search

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  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 -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 anE? Conjunction Feature vs

  4. Treisman & Gelade 1980 Behavioral Results t(6)=3.63, p<.02 t(6)=2.74, p<.04

  5. Design F C Block ER 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 • Post-processing • Individual analysis • GLM analysis • Assumed HRF model • Deconvolution (Finite Impulse Response) • ROI analysis • Group Analysis • Wilcoxon test • Pre-processing • Slice timing correction (Sinc interpolation) • Motion correction • Intensity scaling • Spatial smoothing • Spatial normalization (Talairach atlas transformation)

  7. Block Data Example Conjunction Feature Conj. vs Feat. L L L Feat. > baseline Conj. > baseline Conj. > Feat. Conj. < Feat. Conj. < baseline Feat. < baseline R R R 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 Conj. > Feat. Conj. < Feat. R R 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 R L Smoothed Conj. > Feat. Conj. < Feat. L R Non-smoothed 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 Conj. > Feat. L L Conj. < Feat. Non-Smoothed L L Smoothed

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

  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!!

  15. Deconvolution Assumed HRF

  16. Standardization Subject1 Subject 2 Subject 3

  17. Motor Analysis Left Hand Response Right Hand Response

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