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

2. AcknowledgementsAcknowledgements THANKS!Collaborators: Alfonso Nieto Castañón Alfonso Nieto Castañón Shay Mozes Shay Mozes Data: Data: Stanford, Yale, MGH, CMU, MIT Stanford, Yale, MGH, CMU, MIT Funding: Funding: R03 EB008673: PIs: Satrajit Ghosh, Susan Whitfield-Gabrieli, MITR03 EB008673: PIs: Satrajit Ghosh, Susan Whitfield-Gabrieli, MITTHANKS!Collaborators: Alfonso Nieto Castañón Alfonso Nieto Castañón Shay Mozes Shay Mozes Data: Data: Stanford, Yale, MGH, CMU, MIT Stanford, Yale, MGH, CMU, MIT Funding: Funding: R03 EB008673: PIs: Satrajit Ghosh, Susan Whitfield-Gabrieli, MITR03 EB008673: PIs: Satrajit Ghosh, 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.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 resultsApparent small differences in data processing may yield large differences in results Data inspection as well as artifact detection and rejection routines are essential steps to ensure valid imaging results.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 resultsApparent 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 protocolfMRI quality assurance protocol QA (bottom up)QA (bottom up) QA (top down)QA (top down) fMRI quality assurance protocolfMRI quality assurance protocol QA (bottom up)QA (bottom up) QA (top down)QA (top down)

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

8. GLM 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 Quality Assurance: Post Preprocessing Artifact Check Review Statisitcs Mask/ResMS/RPV Beta/Con/Tmap Data Review - time series - movie Artifact Check RFX PreProc Top Down: review stats Bottom Up: review functional images

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

10. Including motion parameters as covariates 1.Eliminates (to first order) all motion related residual variance. 2. If motion is correlated with the task, this will remove your task activation. 3.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 * Head motion * Physiological : respiration and cardiac effects Physiological : respiration and cardiac effects Scanner noise Scanner noiseSolutions: Review data Review data Apply artifact detection routines Apply artifact detection routines Omit*, interpolate or deweight outliers Omit*, interpolate or deweight outliers * Include a single regressor for each scan you want to remove, with a 1 * Include a single regressor for each scan you want to remove, with a 1 for the scan you want to remove, and zeros elsewhere. 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 Artifact Sources: Head motion * Head motion * Physiological : respiration and cardiac effects Physiological : respiration and cardiac effects Scanner noise Scanner noiseSolutions: Review data Review data Apply artifact detection routines Apply artifact detection routines Omit*, interpolate or deweight outliers Omit*, interpolate or deweight outliers * Include a single regressor for each scan you want to remove, with a 1 * Include a single regressor for each scan you want to remove, with a 1 for the scan you want to remove, and zeros elsewhere. 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 UP AUDITORY RHYMING > REST T map ResMS Outlier Scans ResMS Before ART After ART T map

15. “TOP DOWN” 2 nd level, RFX

16. Group Stats ( N = 50 ) Working Memory Task 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 79 Scan 83 Scan 86 Scan 95

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 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)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). Data analyzed: 312 subjects, 3 sessions per subject Outlier detection based on global signal and movement 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)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. 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) 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) Outlier Experiment

24. Plot shows the average power to detect a task effect (effect size = 1% percent signal change, alpha =.001)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 Plot shows the average power to detect a task effect (effect size = 1% percent signal change, alpha =.001)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 Removing outliers improves the power

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