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

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

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

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

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.

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

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

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

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

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

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

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.

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

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!!

Deconvolution Assumed HRF

Standardization Subject1 Subject 2 Subject 3

Motor Analysis Left Hand Response Right Hand Response