functional neuroimaging of perceptual decision making

Functional Neuroimaging of Perceptual Decision Making

Group E:Elia Abi-Jaoude, Seung Hee Won,

Sukru Demiral, Angelique Blackburn

Faculty: Mark Wheeler

TA: Elisabeth Ploran

Background

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

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

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

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.

PART IBLOCK DESIGN

To identify areas of perceptual activity of faces and objects

Perception TaskTo identify areas of perceptual activity of faces and objects

30s every 2s For 30s 30s 30s

2 runs each with 4 blocks

Run 1: Face/Object/Face/ObjectRun 2: Object/Face/Object/Face

Run order counterbalanced across participants15 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

Scan Parameters

every 2s For 30s

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: http://nrg.wikispaces.com/page/code/4dfp+tools

RW Cox. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29:162-173, 1996.

Block Design: Individual Analysis

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Consistant with previous findings: e.g.Scherf, S. et al. 2007. Developmental Science, 10(4):F15-F30.

RL

QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.

P<0.01

Face>Object

Object>Face

Block Design: Group AnalysisAs 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 mmY=55.0 mmZ=-14.0 mm QuickTime™ and a

TIFF (Uncompressed) decompressorare needed to see this picture.

S6

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

S4

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

S3

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

S2

P<0.01

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

S4X=-1mmY=38mmZ=4mm

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

S6X=49mmY=55mmZ=-14mm

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

S3X=41mmY=37mmZ=-29mm

Variable FFA Location Across Participants

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

PART II

EVENT RELATED DESIGN

Determine how decision making varies with perceptual difficulty.

Determine face and object differences as a result of perceptibilityusing ROIs defined in the Block Design and comparing to ACC differences due to difficulty.

Discrimination Task: Face vs. Object

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

To determine how decision making varies with perceptual difficulty

200ms 75ms 1600ms

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

100ms RandomizedJitter

0,2,4,6s

Visibility (%)

Face Object

5 60 60

10 60 60

40 40 40

320 Trials in 2 ER runs, same scanning parameters as BLOCK

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

5% Visibility 40% Visibility

Optimization of Task

Percent Visibility

Percent A

ccuracy

Pilot Data: Accuracy as a function of Mask Levels at 100ms Stimulus

5 10 20 25 30 35 40 50

ResultsBehavioural Data

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

Low Medium High

Accuracy across Visibility Levels

*

*

*

Visibility Level

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.

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Future Expectations: ROI analysis of ER

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

For Face Presentation:5% low predicted activity40% high predicted activity

Object Presentation:5% low predicted activity40% high predicted activity

ACC: 40% low predicted activity5% high predicted activity

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.

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.

Acknowledgments

• The MNTP Program

• Seong-Gi Kim

• Bill Eddy

• Mark Wheeler

• Elisabeth Ploran and Jeff Phillips

• Tomika Cohen and Bec Clark

• NIH