Quality Assurance SPM8. Susan Whitfield-Gabrieli MIT

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1 Quality Assurance SPM8 Susan Whitfield-Gabrieli MIT

2 Topics in fmri What makes for a successful fmri experiment? Basic cognitive neuroscience Experimental design Analysis Comparative cognitive neuroscience (development, aging, diseases) Experimental design, Analysis + Normalization, variability in the hrf Quality Assurance (QA) in fmri

3 QA in fmri Before Quality Assurance

4 QA in fmri Before QA After QA

5 QA: Outline EEG fmri QA Analogy fmri quality assurance protocol QA (bottom up) QA (top down)

6 Guidelines for ERP Studies

7 Quality Assurance in EEG/ERP Data inspection essential Artifact detection / rejection essential: artifacts can be orders of magnitude larger than signal EEG (eye blinks) EEG/fMRI (ballistocardiogram) Apparent small differences in data processing may yield large differences in results

8 odd = left even = right z = midline EEG Artifact Detection:

9 Muscle Blinks

10 EEG Data Inspection Epoch Eye correction Baseline

11 Automated Artifact Rejection (e.g., exclude +/- 100 uv) Sort Average

12 µv Final ERP Results NoGo-Go ERP msec Data Inspection Artifact Detection/Rejection

13 EEG - fmri Analogy fmri also has low SNR (A similar level of scrutiny used in EEG processing may be beneficial in fmri processing) Data inspection Artifact detection / rejection Apparent small differences in data processing may yield large differences in results

14 Quality Assurance: Outline EEG fmri Analogy fmri quality assurance protocol QA (bottom up) QA (top down)

15 fmri Processing Overview fmri time-series kernel Design matrix Statistical Parametric Map Motion correction Smoothing General Linear Model Spatial normalisation Parameter Estimates Standard template SPM

16 Quality Assurance: Preprocessing Bottom Up: review data Raw Images Artifact Detection Preprocessing Review Data Check behavior (*clinical populations) Create mean functional image Review time series, movie Interpolate prior to preprocessing

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

18 Quality Assurance: Outline EEG fmri Analogy fmri quality assurance protocol QA (bottom up) Data review Detect outliers Power spectra ( HPF) Residual motion related variance post realignment Stimulus correlated motion (motion covariates?) Stimulus correlation with global signal (global scaling?) Artifact detection / rejection ( interp, deweight, omit ) QA (top down)

19 Data Review Global mean Std. Dev. From mean Movement in mm Movement in radians

20 Data Review Global mean Std. Dev. From mean COMBINED OUTLIERS INTENSITY OUTLIERS Thresholds Movement in mm Movement in radians Outliers MOTION OUTLIERS

21 Artifact Detection Global mean Std. Dev. From mean Movement in mm Scan 79 Scan 95 Movement in radians

22 SAVE ART RESULTS Automatically Saved and shown in workspace: Saving SPM regressor file c:\demo\artifact\art_regression_outliers_sw2brun1_001.mat and c:\demo\artifact\art_regression_outliers_and_movement_sw2brun1_001.mat New analysis mask saved to c:\demo\msit\011\art_mask.img SCM saved in analyses.motion_task_correlation.r Note: May choose to change thresholds and SAVE SPM Regressors to change outliers:

23 SAVE ART OUTPUT

24 Art: Analysis mask

25 DEMO #1 Artifact Detection

26 View in Movie Format art_movie: Each scan is made into a montage of slices. This may show either the raw image data or a contrast the difference between each image and a reference image, amplified so that small data variations are more visible.

27 View in Movie Format Art_movie Spm_movie Scans 79, 83, 86, 95 spm_movie: cycles through all the scans on a particular slice plane.

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

29 DATA REVIEW SCM Power Spectra Show Design

30 SCM: Realignment & Motion parameters Determine the rigid body transformation that minimises the sum of squared difference between images Rigid body transformation is defined by: 3 translations - in X, Y & Z directions 3 rotations - about X, Y & Z axes Operations can be represented as affine transformation matrices: Rigid body transformations parameterised by: Translations Pitch Roll Yaw Xtrans cos( ) 0 sin( ) cos( ) sin( ) Ytrans 0 cos( ) sin( ) sin( ) cos( ) Zt rans 0 sin( ) cos( ) 0 sin( ) 0 cos( ) Nx6 matrix of realignment parameters written to file (N = # of scans) x 1 = m 1,1 x 0 + m 1,2 y 0 + m 1,3 z 0 + m 1,4 y 1 = m 2,1 x 0 + m 2,2 y 0 + m 2,3 z 0 + m 2,4 z 1 = m 3,1 x 0 + m 3,2 y 0 + m 3,3 z 0 + m 3,4

31 Residual movement related variance Sources: i) Movement-x-susceptibility-x-distortion interaction (unwarp) ii) Movement-x-susceptibility-x-dropout interaction iii) Spin-history effects (residual magnetisation effects of previous scans) iv) Inter-volume movement (Rapid movements within a scan) Problem: Residual motion may decrease sensitivity of detecting true activations by adding to the unmodeled error variance or, depending on the timing of the motion/task may also produce artifactual activatons (especially around tissue boundaries) Goal: maximize sensitivity to true activations while minimizing false activations Solutions: 1) Include motion parameters in design which removes variance due to all of these causes and can increase sensitivity.

32 Motion parameters as confounds in GLM Increasing sensitivity by including the motion parameters as covariates in the GLM

33 Stimulus Correlated Motion Issue: If the motion parameters are correlated with the task conditions, including the parameters as covariates could eliminate task activation. No correction Correction by covariation

34 SCM for block and event-related designs Many subjects in block design exhibit stimulus correlated motion Less of a problem in event-related designs Johnstone, HBM 2006

35 Effects of motion correction and covariation Cov Cov Johnstone, HBM 2006

36 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 correlation coefficient (r) between each condition and motion parameter for each session

37 SAVE ART OUTPUT analyses.motion_task_correlation(n).r : matrix of motion/task correlation analyses.motion_task_correlation(n).rows : names of each row of r (task) analyses.motion_task_correlation(n).columns : names of each column of r (motion parameters)

38 DEMO #2 Task Correlated Motion SCM

39 SCM in Clinical Populations - Bullmore

40 Motion parameters as confounds: Conclusion Including parameters as covariates can increase sensitivity, especially in event related designs. However, if the motion parameters are correlated with the task conditions, including the parameters as covariates could eliminate task activation. ** In addition, you must also watch out for between group differences in SCM when comparing groups. (Bullmore 1999) Can result in artifactual between-group differences. Correct analysis results for possible confounding effects via AnCOVA AnCova: Correct for different within-subject levels of stimulus-correlated effects

41 Unwarp Unwarp: Movement-x-susceptibility-x-distortion interaction No correction Correction by covariation Correction by Unwarp t max =13.38 t max =5.06 t max =9.57 Unwarp toolbox: Jesper Anderson & Chloe Hutton

42 Field Maps: EPI sequences are affected by small deviations in the magnetic field and are prone to dropout and distortions. The field map is a 2D gradient echo sequence which acquires an image at 2 different echo times. This sequence generates 2 types of images, a magnitude image and a phase map. The phase map represents the phase differences of the spins which ultimately represent the local field inhomogeneities. You can display this map to see which regions are prone to susceptibility artifacts. SPM5 and SPM8 : Realign and Unwarp option and can incorporate the field map information to perform distortion correction. *Note: use same slice prescription for field map and EPI

43 Distortion Correction Raw EPI Undistorted EPI MRC Cognition and Brain Sciences Unit

44 Distortion Correction: Self Reference Task Base>Sem (Before Correction) Base>Sem (After SPM5 Field Map Correction) p-value = 0.05 (unc) Extend threshold=5 Sheeba Arnold, Whitfield-Gabrieli

45 Power Spectra: HPF Cutoff Selection.01.02

46 QUESTIONS Apparent small differences in data processing may yield large differences in results? Next Topic: Artifact Detection

47 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 Apply user specified analysis mask *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

48 CASE STUDIES: ARTIFACT DETECTION: SINGLE SUBJECT AUDITORY MATCHING TASK>REST

49 Detect Outliers AUDITORY MATCHING TASK>REST Scan 61 Signal Intensity > 3SD Norm of Translation > 3.0mm Norm of Rotation > 0.05mm Scan 62

50 Detect Outliers AUDITORY MATCHING TASK>REST

51 SCANS OMITTED: AUDITORY MATCHING TASK>REST Scan 61 Scan 62 Scan 63

52 SCANS OMITTED: Scan 84 Scan 85 Scan 86 Scan 102 Scan 105 Scan 114 Scan 211

53 T MAPS AUDITORY MATCHING No Scans Omitted Scans Omitted using derivatives of motion parameters

54 Artifact detection using realignment motion parameters

55 Artifact detection using scan to scan motion

56 Time Series Outliers Artifacts not always motion related

57 Example of Time Series Outlier (one scan in one subject)

58 Quality Control: Outline EEG fmri Analogy fmri quality assurance protocol QA (bottom up) Data review Residual motion related variance Stimulus correlated motion Global scaling Artifact detection (detect outliers) QA (top down)

59 1 st Level (GLM) TOP DOWN

60 Top Down view results I) Artifacts in Beta, Contrast, Tmaps and ResMS images ResMS Spm_T II) Missing activation III) Activation cut

61 GLM beta_0001.img beta_0002.img β beta_0003.img 2 n i 1 e i 9.47 ResMS.img Jesper Anderson

62 SPM Mask Image: Binary mask of in brain voxels to be included in statistical analysis View statistic images: example of good data

63 Single Subject, GLM top down CASE I

64 Artifact detection to find offending images Scan 46

65

66 Example of top down artifact detection AUDITORY RHYMING Case II > REST No Scans Omitted Task: Auditory Rhyming and Matching Scan: 2 sessions of 113 scans TR = 2s

67 Mask ResMS Beta1 Beta2 Contrast Tmap

68 Example: Top Down Artifact Detection

69 Outlier Scans

70 Outlier Scans

71 ORIGNAL STATS FINAL STATS Mask ResMS Mask ResMS BETA BETA BETA BETA CON T MAP CON T MAP

72 AUDITORY RHYMING > REST Outlier Scans ResMS T map Before ART ResMS T map After ART

73 Single Subject Artifacts Differ

74 TOP DOWN 2 nd level, RFX

75 Group Stats: Semantic Categorization (N = 14) CASE II Obvious Problem: Language Task 5 th Graders Task Semantic categorization Contrast Task Rest Threshold:.001unc Results No activation in the language area???

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

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

78 Find Offending Subjects: 3 of 50 subjects

79 DEMO #3 Top Down QA Xjview: Xu Cui, Jian Li + =

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

81 Comparison of Group Statistics: Default Network

82 Artifact Detection to detect outliers in times series Scans 79, 83, 86, 95

83 Artifact detection to detect outliers in time series and motion

84 Artifacts in outlier images Scan 79 Scan 86 Scan 83 Scan 95

85 Artifact rejection & Explicit analysis mask

86 32 Channel Data, Explicit Mask SPM SPM +motion reg SPM, Explicit Mask

87 Original mask and epi time point

88 32 Channel improved SNR Christina Triantafyllou, MIT

89 SNR PROFILES Siemens 32Ch 12Ch Matrix Triple Mode Christina Triantafyllou, MIT 0

90 SNR SNR Profiles Distance along profile Christina Triantafyllou, MIT

91 Example Analysis Path with QA 1) Artifact Detection #1 2) Slice timing correction (*optional) 3) Realignment (realign and unwarp using field map) 4) Normalization (*optional) 5) Smooth 6) Artifact Detection #2 / Omit in GLM: Detect outliers line the time series and scan2scan motion Omit these bad time points in the GLM by including a single regressor for each scan you want to remove, with a 1 for the scan you want to remove, and zeros elsewhere. 7) General QA - check for accurate registration, SCM, HPF cutoff 8) Single subject model specification and estimation with user specified analysis mask 9) 1 st level (single subject) "top down QA 10) RFX 11) 2nd Level (RFX) "top down" QA

92 Script it Matlab or Python The artifact script will - automate process of artifact detection of time series and motion parameters given user specified thresholds. - generate regressors for artifactual time points and motion for your single subject GLMs - keep track of # of artifactual time points per condition, per subject, per group. * *important to do QA on artifact rejection

93 Outlier Experiment Data tested: 328 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).

94 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- scores above 3)

95 Removing outliers improves the power Plots show 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 z>3) power improves above.70

96 Outlier Experiment Conclusion The fact that after removing a few scans this signal is approximately normally distributed reassures us that the GLM could be a reasonable model, and justifies thinking of the removed scans as outliers Power analyses indicate that z>3 is a good candidate for outlier detection, since more conservative thresholds start negatively affecting the power (we are starting to remove too many scans)

97 QA and Artifact detection/rejection Differences in pediatric group results Rhyme - Rest Whitfield-Gabrieli

98 Thanks to collaborators!! Alfonso Nieto Castañón Satra Ghosh (python scripts) Sheeba Arnold Tom Zeffiro Robert Savoy Data: Stanford, Yale, MGH, CMU, MIT

99 Guidelines for ERP Studies

100 Guidelines for reporting fmri studies

101 Quality Assurance: Summary EEG fmri Analogy fmri quality assurance protocol QA (bottom up) Residual motion related variance Stimulus correlated motion High Pass filter selection Global scaling Detect outliers QA (top down) Review Statistics REVIEW DATA!!

102 >> guide spm_menu Matlab Guide

103 >>edit spm_spm Matlab Edit

104 QA Software Used in presentation: Susan Whitfield-Gabrieli, Alfonso Nieto Castañón Other related artifact detection and QA software: Wen-Lin Luo, Hui Zhang & Thomas Nichols N. White and Doug. Greve, fbirn Spike detection AFNI 3dDespike - R.W. Cox (NIH), B.D. Ward Paul Mazaika

105 Whole brain to voxel scaling In SPM the last N beta images (where N is the number of sessions) represent the session effects (average within-session signal for each session), Divide the beta images of interest from each session by the corresponding session effects and multiplying by 100. This can be problematic for some designs where the session effects might not be estimable due to over-redundancies in the modeled responses (e.g explicitly modeling the rest condition in a block design), Alternative approach is to divide the betas by the mean volume obtained after realignment (named "mean*.nii", note that these volumes are previous to grand-mean scaling) and multiplied by the grand-mean factor for the corresponding session (obtained as "mean(spm.xgx.rg(spm.sess(n).row))", where n is the session number).

106 References 1. Johnstone T,Ores K.S., Walsh O, Greischar LL, Alexander AL, Fox AS, Davidson RJ, Oakes TR (2006) Motion correction and the use of motion covariates in multiple-subject fmri analysis. Human Brain Mapping 27: Bullmore ET, Brammer MJ, Rabe-Hesketh S, Curtis VA, Morris RG, Williams SCR., Sharma T, McGuire PK (1998) Methods for diagnosis and treatment of stimulus-correlated motion in generic brain activation studies using fmri. Human Brain Mapping 7: Jezzard P and Balaban RS (1995) Correction for geometric distortions in echoplanar images from B0 field variations. Magn Reson Med 34: Andersson JLR, Hutton C, Ashburner J, Turner R, Friston K (2001) Modelling geometric deformations in EPI time series. NeuroImage 13: Hutton C, Bork A, Josephs O, Deichmann R, Ashburner J, Turner R. (2002). Image distortion correction in fmri: A quantitative evaluation. NeuroImage 16: Jenkinson M. (2003). Fast, automated, N-dimensional phase-unwrapping algorithm. MRM 49: Hutton, C., Deichmann, R., Turner R., Andersson, J. L. R., (2004). Combined correction for geometric distortion and its interaction with head motion in fmri. Proceedings of ISMRM 12, Kyoto, Japan

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