1. Functional Neuroimaging of Perceptual Decision Making
Group E:
Elia Abi-Jaoude, Seung Hee Won,
Sukru Demiral, Angelique Blackburn
Faculty: Mark Wheeler
TA: Elisabeth Ploran

2. Background http://whyfiles.org/209autism/images/slide3.gif
Philiastides and Sajda, 2007

3. Objective
Does perceptibility (visibility) affect decision making?
Does activity in the FFA predict decision making activity?
Hypothesis
Relative activity in areas identified in facial processing will vary proportionately with visibility of face images; likewise with object activity in those areas identified in object perception.
As difficulty increases, activity in the ACC, AI, and DLPFC will increase. This will vary inversely with perceptual activity.

4. PART I BLOCK DESIGN
To identify areas of perceptual activity of faces and objects

5. Perception Task
To identify areas of perceptual activity of faces and objects
30s
every 2s
For 30s
every 2s
For 30s
30s
30s
Scan Parameters
2 runs each with 4 blocks
Run 1: Face/Object/Face/Object
Run 2: Object/Face/Object/Face
Run order counterbalanced across participants
15 images per block, random presentation order
3T Siemens scanner
TR: 2s
TE: 40ms
Voxel Size:
3.2 x 3.2 x 3.2mm
Flip angle: 70 degrees
Slices: 38
Structural: MP-RAGE

6. Data Processing Slice Time Correction
To compensate for slices taken over 2s interval, used sinc function to time correct all slices to first slice
Motion Correction
In 6 directions: x, y, z rotational and translational
Intensity Normalisation
Set most frequent intensity in each subject to 1000 to normalise intensities across participants
Structural/Functional Alignment
All functional scans were aligned to the MP-RAGE structural scan
Talairach Transformation
Reconstructed images were transformed into Talairach space
Smoothing
Smoothed to 6.4 x 6.4 x 6.4mm (2 voxels)
Avi Preprocessing Script:
RW Cox. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29: , 1996.

7. Block Design: Individual Analysis
Face>Object L R
Consistant with previous findings: e.g.
Scherf, S. et al Developmental Science, 10(4):F15-F30.
Object>Face
P<0.01

8. Block Design: Group Analysis
As FFA is highly variable across individuals, we were unable to localize the FFA in the group analysis. This is a common problem with small sample sizes and could be ameliorated with a larger sample size.
All Images at Talairach Coordinates:
X=49.0 mm
Y=55.0 mm
Z=-14.0 mm
P<0.01 S6 S4 S3 S2

9. Variable FFA Location Across Participants
X=49mm
Y=55mm
Z=-14mm S4
X=-1mm
Y=38mm
Z=4mm S3
X=41mm
Y=37mm
Z=-29mm

10. Block Design Summary
We were able to localize face and object areas in the individual analysis – which conformed to previous findings
Our group analysis did not have enough power to identify the FFA

11. PART II EVENT RELATED DESIGN
Determine how decision making varies with perceptual difficulty.
Determine face and object differences as a result of perceptibility
using ROIs defined in the Block Design and comparing to ACC differences due to difficulty.

12. Discrimination Task: Face vs. Object
To determine how decision making varies with perceptual difficulty
200ms
75ms
1600ms
100ms
Randomized
Jitter
0,2,4,6s
320 Trials in 2 ER runs,
same scanning parameters as BLOCK
Visibility (%)
Face
Object 5 60 10 40
5% Visibility
40% Visibility

13. Pilot Data: Accuracy as a function of Mask Levels at 100ms Stimulus
Optimization of Task
Pilot Data: Accuracy as a function of Mask Levels at 100ms Stimulus
Percent Accuracy
Percent Visibility

14. Results Behavioural Data* * *
Visibility Level

15. ER: Individual Analysis
Markers for each stimulus type
3 visibility levels (Low, Med, High)
2 stimulus types (Face and Object)
2 Accuracy (Correct and Incorrect)
Due to time constraints we were unable to adjust our analysis to fix the Signal to Noise.

16. Future Expectations: ROI analysis of ER
Object Presentation:
5% low predicted activity
40% high predicted activity
For Face Presentation:
5% low predicted activity
40% high predicted activity
ACC: 40% low predicted activity
5% high predicted activity

17. Summary
Using a block design, we were able to identify face and object areas in our population.
We would like to use these regions to identify relative changes in these areas and the ACC, DLPFC, and AI at an individual level during our event related design.

18. We have learned How to design an fMRI experiment
About the steps in data preprocessing
How to do individual subject analysis using the GLM
Reasonable data at an individual level becomes less reasonable once averaging starts, need a larger sample size.
Ideas about how to incorporate fMRI into research using our current modalities (EEG, NIRS) when we return home.

19. Acknowledgments The MNTP Program Seong-Gi Kim Bill Eddy Mark Wheeler
Elisabeth Ploran and Jeff Phillips
Tomika Cohen and Bec Clark
NIH