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

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Functional Neuroimaging of Perceptual Decision Making

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  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 IBLOCK DESIGN To identify areas of perceptual activity of faces and objects

  5. Perception Task To identify areas of perceptual activity of faces and objects every 2s For 30s 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 • 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) • 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 RW Cox. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29:162-173, 1996. Avi Preprocessing Script: http://nrg.wikispaces.com/page/code/4dfp+tools

  7. Block Design: Individual Analysis Face>Object R L Object>Face Consistant with previous findings: e.g. Scherf, S. et al. 2007. Developmental Science, 10(4):F15-F30. 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 S4 S3 S6 S2

  9. Variable FFA Location Across Participants S6 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 IIEVENT 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 Randomized Jitter 0,2,4,6s 100ms 200ms 1600ms 75ms 320 Trials in 2 ER runs, same scanning parameters as BLOCK 5% Visibility 40% Visibility

  13. Optimization of Task Pilot Data: Accuracy as a function of Mask Levels at 100ms Stimulus Percent Accuracy 5 10 20 25 30 35 40 50 Percent Visibility

  14. ResultsBehavioural 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

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