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Quality Assurance

Quality Assurance. NITRC Enhancement Grantee Meeting June 18, 2009. Susan Whitfield-Gabrieli & Satrajit Ghosh RapidArt MIT. Acknowledgements. THANKS! Collaborators: Alfonso Nieto Castañón Shay Mozes Data:

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Quality Assurance

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  1. Quality Assurance NITRC Enhancement Grantee Meeting June 18, 2009 Susan Whitfield-Gabrieli & Satrajit Ghosh RapidArt MIT

  2. Acknowledgements THANKS! Collaborators: • Alfonso Nieto Castañón • Shay Mozes Data: • Stanford, Yale, MGH, CMU, MIT Funding: • R03 EB008673: PIs: SatrajitGhosh, Susan Whitfield-Gabrieli, MIT

  3. fMRI QA • Data inspection as well as artifact detection and rejection routines are essential steps to ensure valid imaging results. • Apparent small differences in data processing may yield large differences in results

  4. QA in fMRI Before Quality Assurance

  5. QA in fMRI Before QA After QA

  6. QA: Outline • fMRI quality assurance protocol • QA (bottom up) • QA (top down)

  7. Quality Assurance: Preprocessing Bottom Up: review data Raw Images Artifact Detection Preprocessing Review Data Check behavior Create mean functional image Review time series, movie Interpolate prior to preprocessing

  8. Quality Assurance: PostPreprocessing Top Down: review stats Bottom Up: review functional images GLM PreProc Artifact Check RFX Artifact Check Artifact Check - Check registration - Check motion parameters - Generate design matrix template - Check for stimulus corr motion - Check global signal corr with task - Review power spectra - Detect outliers in time series, motion: determine scans to omit /interp or deweight Data Review - time series - movie Review Statisitcs Mask/ResMS/RPV Beta/Con/Tmap

  9. Data Review Global mean COMBINED OUTLIERS Thresholds INTENSITY OUTLIERS Deviation From mean Over time MOTION OUTLIERS Realign Param Outliers Data Exploration

  10. Including motion parameters as covariates • Eliminates (to first order) all motion related residual variance. • If motion is correlated with the task, this will remove your task activation. • Check SCM: If there exists between group differences in SCM, AnCova

  11. Power Spectra: HPF Cutoff Selection .01 .02

  12. Artifact Detection Scan 79 Scan 95

  13. Artifact Detection/Rejection Artifact Sources: Head motion * Physiological : respiration and cardiac effects Scanner noise Solutions: Review data Apply artifact detection routines Omit*, interpolate or deweight outliers *Include a single regressor for each scan you want to remove, with a 1 for the scan you want to remove, and zeros elsewhere. *Note # of scan omissions per condition and between groups Correct analysis for possible confounding effects: AnCova : use # outliers as a within subject covariate

  14. BOTTOM UPAUDITORY RHYMING > REST Outlier Scans T map ResMS Before ART ResMS T map After ART

  15. “TOP DOWN” 2nd level, RFX

  16. Group Stats ( N = 50 ) Working Memory Task Not an obvious problem: Frontal and parietal activation for a working memory task.

  17. Group Stats (N=50) 2B Working Memory Task

  18. Find Offending Subjects: 2 of 50 subjects

  19. Artifacts in outlier images Scan 86 Scan 79 Scan 95 Scan 83

  20. Comparison of Group Stats:Working Memory (2B>X) ORIGINAL FINAL

  21. Comparison of Group Statistics: Default Network

  22. Method Validation Experiment • Data analyzed: 312 subjects, 3 sessions per subject • Outlier detection based on global signal and movement • Normality: tests on the scan-to-scan change in global BOLD signal after regressing out the task and motion parameters. Normally-distributed residuals is a basic assumption of the general linear model. Departures from normality would affect the validity of our analyses (resulting p- values could not be trusted) If all is well, we should expect this global BOLD signal change to be normally distributed because: average of many sources (central limit theorem ) • Power: the probability of finding a significant effect if one truly exists. Here it represents the probability of finding a significant (at a level of p<.001 uncorrected) activation at any given voxel if in fact the voxel is being modulated by the task (by an amount of 1% percent signal change).

  23. Outlier Experiment • Global signal is not normally distributed In 48% of the sessions the scan-to-scan change in average BOLD signal is not normally distributed. This percentage drops to 4% when removing an average of 8 scans per session (those with z score threshold = 3)

  24. Removing outliers improves the power • Plot shows the average power to detect a task effect (effect size = 1% percent signal change, alpha = .001) • Before outlier removal the power is .29 ( 29% chance of finding a significant effect at any of these voxels) After removing an average of 8 scans per session (based on global signal threshold z=3) power improves above .70

  25. THANKS! Dissemination (NITRC) - International visiting fMRI fellowships @ MGH - 2 week MMSC @ MGH - SPM8 Courses (local/remote) -Visiting programs at MIT Documentation • Manuals, Demos, Tutorials • Scripts

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