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