1. fMRI Data Quality Assurance and Preprocessing http://www.fmri4newbies.com/ Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University of Western Ontario Jody Culham Brain and Mind Institute Department of Psychology University of Western Ontario

2. The Black Box The danger of automated processing and fancy images is that you can get blobs without every really looking at the real data The more steps done at without quality assurance, the greater the chance of wonky results Raw Data Big Black Box of automated software Pretty pictures

3. Culham’s First Commandment: Know Thy Data Look at raw functional images –Where are the artifacts and distortions? –How well do the functionals and anatomicals correspond? Look at the movies –Is there any evidence of head motion? –Is there any evidence of scanner artifacts (e.g., spikes)? Look at the time courses –Is there anything unexpected (e.g., abrupt signal changes at the start of the run)? –What do the time courses look like in the unactivatable areas (ventricles, white matter, outside head)? Look at individual subjects Double check effects of various transformations –Make sure left and right didn’t get reversed –Make sure functionals line up well with anatomicals following all transformations Think as you go. Investigate suspicious patterns

4. Sample Artifacts Ghosts Spikes Metallic Objects (e.g., hair tie) Hardware Malfunctions

5. Contrast and Contrast:Noise T1 T2 High Contrast: Noise Low Contrast: Noise

6. Why SNR Matters Note: This SNR level is not based on the formula given Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

7. Sources of Noise Physical noise “Blame the magnet, the physicist, or the laws of physics” Physiological noise “Blame the subject”

8. How Can You Tell the Difference? Test a phantom -- No physiological noise!

9. A Map of Noise voxels with high variability shown in white Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

10. Effect of Field Strength on Signal and Noise Although raw SNR goes up with field strength, so does thermal and physiological noise Thus there are diminishing returns for increases in field strength Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

11. Effect of Field Strength on Signal

12. Effect of Field Strength on Vascular Signals

13. Effect of Field Strength on Susceptibility 1.5 T 4.0 T

14. Coils Head coil homogenous signal moderate SNR Surface coil highest signal at hotspot high SNR at hotspot Photo source: Joe Gati

15. Phased Array Coils SNR of surface coils with the coverage of head coils OR… faster parallel imaging modern scanners come standard with 8- or 12-channel head coils and capability for up to 32 channels Photo Source: Technology Review 90-channel prototype Mass. General Hospital Wiggins & Wald 12-channel coil32-channel coil 32-channel head coil Siemens

16. Phased Array Coils Source: Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

17. Voxel Size Bigger is better… to a point Increasing voxel size  signals summate, noise cancels out “Partial voluming”: If tissue is of different types, then increasing voxel size waters down differences –e.g., gray and white matter in an anatomical –e.g., activated and unactivated tissue in a functional

18. Sampling Time More samples  More confidence effects are real

19. Head Motion: Main Artifacts Head motion Problems time1time2  1)Rim artifacts hard to tell activation from artifacts artifacts can work against activation 2) Region of interest moves lose effects because you’re sampling outside ROI Looking at the negative tail can help you identify artifacts Playing a movie of slices over time helps you detect head motion

20. Head Motion: Good, Bad,… Slide from Duke course

21. … and really, really ugly! Slide from Duke course

22. Motion Correction Algorithms Most algorithms assume a rigid body (i.e., that brain doesn’t deform with movement) Align each volume of the brain to a target volume using six parameters: three translations and three rotations Target volume: the functional volume that is closest in time to the anatomical image x translation z translation y translation pitchrollyaw

23. BVQX Motion Correction Options Align each volume to the volume closest in time to the anatomical –Why? Analysis/fMRI 2D data preprocessing menu

24. Mass Motion Artifacts motion of any mass in the magnetic field, including the head, is a problem head coil arm brace gaze grasparatus brace

25. Grasping and reaching data from block designs circa 1998 Mass Motion Artifacts Time Course: % Signal Change -4 0 7 Time (seconds) 1503060901200 LeftRight LeftRightLeft.60 -.60 1.0 r value -0.4 0 0.6 Time (seconds) 1503060901200 Motion Detected (mm or degrees) Motion Correction Parameters Even in the absence of head motion, mass motion creates huge problems 30 s Where is the signal correlated with the mass position? phantom (fluid-filled sphere) Culham, chapter in Cabeza & Kingstone, Handbook of Functional Neuroimaging of Cognition (2 nd ed.), 2006 0 0 900

26. Mass Motion Distort Magnetic Field Barry et al., in press, Magnetic Resonance Imaging

27. Motion Correction Algorithms Existing algorithms correct two of our three problems: 1.Head motion leads to spurious activation 2.Regions of interest move over time 3.Motion of head (or any other large mass) leads to changes to field map Sometimes algorithms can introduce artifacts that weren’t there in the first place (Friere & Mangin, 2001, NeuroImage) √ √ X

28. The Fridge Rule When it doubt, throw it out!

29. Head Restraint Head Vise (more comfortable than it sounds!) Bite Bar Often a whack of foam padding works as well as anything Vacuum Pack Thermoplastic mask

30. Prospective Motion Correction Siemens Prospective Acquisition CorrEction (PACE) shifts slices on-the-fly so that slice planes follow motion Siemens claims it improves data quality Caution: unlike retrospective motion correction algorithms, you can never get “raw” data Source: SiemensSiemens

31. Prevention is the Best Remedy Tell your subjects how to be good subjects –“Don’t move” is too vague Make sure the subject is comfy going in –avoid “princess and the pea” phenomenon Emphasize importance of not moving at all during beeping –do not change posture –if possible, do not swallow –do not change posture –do not change mouth position –do not tense up at start of scan Discourage any movements that would displace the head between scans Do not use compressible head support For a summary of info to give first-time subjects, see http://defiant.ssc.uwo.ca/Jody_web/Subject_Info/firsttime_subjects.htm

32. Mock “0 T” Scanners

33. Disdaqs Discarded data acquisitions: trashed volumes at the beginning of a run before the magnet has reached a steady state Sometimes it can take awhile for the subject to reach a steady state too -- Startle response!

34. BV Preprocessing Options

35. Slice Scan Time Correction The first slice is collected almost a full TR (e.g., 3 s) before the last slice Source: Brain Voyager documentation Non- Interleaved

36. Slice Scan Time Correction Interleaved Source: Brain Voyager documentation

37. Slice Scan Time Correction

38. Source: Brain Voyager documentation interpolates the data from each slice such that is is as if each slice had been acquired at the same time

39. BV Preprocessing Options

40. Spatial Smoothing Gaussian kernel smooth each voxel by a Gaussian or normal function, such that the nearest neighboring voxels have the strongest weighting Maximum Half-Maximum Full Width at Half-Maximum (FWHM) FWHM Values some smoothing: 4 mm typically smoothing: 6-8 mm heavy duty smoothing: 10 mm 3D Gaussian smoothing kernel

41. Effects of Spatial Smoothing on Activity No smoothing 4 mm FWHM7 mm FWHM10 mm FWHM

42. Should you spatially smooth? Advantages –Increases Signal to Noise Ratio (SNR) Matched Filter Theorem: Maximum increase in SNR by filter with same shape/size as signal –Reduces number of comparisons Allows application of Gaussian Field Theory –May improve comparisons across subjects Signal may be spread widely across cortex, due to intersubject variability Disadvantages –Reduces spatial resolution –Challenging to smooth accurately if size/shape of signal is not known Slide from Duke course “Why would you spend $4 million to buy an MRI scanner and then blur the data till it looked like PET?” -- Ravi Menon

43. BV Preprocessing Options

44. Linear Drift Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

45. Components of Time Course Data Source: Smith chapter in Functional MRI: An Introduction to Methods

46. BV Preprocessing Options Before LTR: After LTR:

47. BV Preprocessing Options High pass filter pass the high frequencies, block the low frequencies a linear trend is really just a very very low frequency so LTR may not be strictly necessary if HP filtering is performed (though it doesn’t hurt) Before High-pass linear drift ~1/2 cycle/time course ~2 cycles/time course After High-pass

48. BV Preprocessing Options Gaussian filtering –each time point gets averaged with adjacent time points –has the effect of being a low pass filter passes the low frequencies, blocks the high frequencies –for reasons we will discuss later, I recommend AGAINST doing this After Gaussian (Low Pass) filteringBefore Gaussian (Low Pass) filtering

49. Find the “Sweet Spots” Respiration every 4-10 sec (0.3 Hz) moving chest distorts susceptibility Cardiac Cycle every ~1 sec (0.9 Hz) pulsing motion, blood changes Solutions gating avoiding paradigms at those frequencies You want your paradigm frequency to be in a “sweet spot” away from the noise

50. Macro- vs. micro- vasculature Macrovasculature: vessels > 25  m radius (cortical and pial veins)  linear and oriented  cause both magnitude and phase changes Microvasculature: vessels < 25  m radius (venuoles and capillaries)  randomly oriented  cause only magnitude changes Capillary beds within the cortex Source: Duvernoy, Delon & Vannson, 1981, Brain Research Bulletin

51. “Vein, vein, go away” Source: Menon, 2002, Magn Reson Med large vessels tend to be consistently oriented (with respect to the cortex) whereas capillaries are randomly oriented Ravi’s algorithm uses this fact to estimate and remove the contribution of large vessels in the signal this was verified by examining the time course of a voxel in a vein and a voxel in gray matter, with and without vessel suppression voxel in vein voxel in gray matter raw datavessel suppressionvessel selection

52. Order of Preprocessing Steps is Important Thought question: Why should you run motion correction before temporal preprocessing (e.g., linear trend removal)? If you execute all the steps together, software like Brain Voyager will execute the steps in the appropriate order Be careful if you decide to manually run the steps sequentially. Some steps should be done before others.

53. Take-Home Messages Look at your data Work with your physicist to minimize physical noise Design your experiments to minimize physiological noise Motion is the worst problem: When in doubt, throw it out Preprocessing is not always a “one size fits all” exercise

54. EXTRA SLIDES

55. What affects SNR? Physical factors PHYSICAL FACTORSSOLUTION & TRADEOFF Thermal Noise (body & system)Inherent – can’t change Magnet Strength e.g. 1.5T  4T gives 2-4X increase in SNR Use higher field magnet – additional cost and maintenance – physiological noise may increase Coil e.g., head  surface coil gives ~2+X increase in SNR Use surface coil – Lose other brain areas – Lose homogeneity Voxel size e.g., doubling slice thickness increases SNR by root- 2 Use larger voxel size – Lose resolution Sampling timeLonger scan sessions – additional time, money and subject discomfort Source: Doug Noll’s online tutorialDoug Noll’s online tutorial

56. Head Motion: Main Artifacts 1.Head motion can lead to spurious activations or can hinder the ability to find real activations. Severity of problem depends on correlation between motion and paradigm 2.Head motion increases residuals, making statistical effects weaker. 3.Regions move over time –ROI analysis: ROI may shift –Voxelwise analyses: averages activated and nonactivated voxels 4.Motion of the head (or any other large mass) leads to changes to field map 5.Spin history effects Voxel may move between excitation pulse and readout

57. ABC Motion  Intensity Changes Slide modified from Duke course

58. Motion  Spurious Activation at Edges time1time2  lateral motion in x direction motion in z direction (e.g., padding sinks) time 1 > time 2 time 1 < time 2 brain position stat map

59. Spurious Activation at Edges spurious activation is a problem for head motion during a run but not for motion between runs

60. Motion  Increased Residuals fMRI Signal × 1× 1 × 2× 2 = ResidualsDesign Matrix ++ “what we CAN explain” “what we CANNOT explain” = + Betasx “how much of it we CAN explain” “our data” = + x Statistical significance is basically a ratio of explained to unexplained variance

61. Regions Shift Over Time A time course from a selected region will sample a different part of the brain over time if the head shifts For example, if we define a ROI in run 1 but the head moves between runs 1 and 2, our defined ROI is now sampling less of the area we wanted and more of adjacent space This is a problem for motion between runs as well as within runs time1time2 

62. Problems with Motion Correction lose information from top and bottom of image –possible solution: prospective motion correction calculate motion prior to volume collection and change slice plan accordingly we’re missing data here we have extra data here Time 1 Time 2

63. Why Motion Correction Can Be Suboptimal 1.Parts of brain (top or bottom slices) may move out of scanned volume (with z-direction motion or rotations) 2.Motion correction requires spatial interpolation, leads to blurring –fast algorithms (trilinear interpolation) aren’t as good as slow ones (sinc interpolation) –Motion correction

64. Why Motion Correction Algorithms Can Fail Activation can be misinterpreted as motion –particularly problematic for least squares algorithms (Friere & Mangin, 2001) Field distortions associated with moving mass (including mass of the head) can be misinterpreted as motion Simulated activation Spurious activation created by motion correction in SPM (least squares) Mutual information algorithm in SPM has fewer problems Friere & Mangin, 2001

65. Head Motion: Solution to Susceptibility Solution: one trial every 10 or 20 sec fMRI signal is delayed ~5 sec distinguish true activity from artifacts IMPORTANT: Subject must remain in constant configuration between trials 0510 Time (Sec) fMRI Signal action activityartifact

66. Different motions; different effects Drift within runMovement between runs Uncorrelated abrupt movement within run Correlated abrupt movement within a run Motion correction Spurious activationsokay, corrected by LTR okayminor problemhuge problemcan reduce problems Increased residualsokay, corrected by LTR okayproblem can reduce problems; may be improved by including motion parameters as predictors of no interest Regions moveproblemminor-major problem depending on size of movement problem can reduce problems; if algorithm is fooled by physics artifacts, problem can be made worse by MC Physics artifactsnot such a problem because effects are gradual okayproblemhuge problemcan’t fix problem; may be misled by artifacts

67. Motion Correction Output gradual motions are usually well-corrected abrupt motions are more of a problem (esp if related to paradigm SPM output raw data linear trend removal motion corrected in SPM Caveat: Motion correction can cause artifacts where there were none

68. Effect of Temporal Filtering before after Source: Brain Voyager course slides

69. Trial-to-trial variability Single trials Average of all trials from 2 runs

70. Time Course Filtering

71. Spatial Distortions Isocentre Isocentre + 12 cm Lengthwise Cross-section Before Correction Lengthwise Cross-Section After Correction Core Cross-section Before Correction Top Bottom ABC DEF

72. Homogeneity Correction

73. Data Preprocessing Options reconstruction from raw k-space data frequency space  real space artifact screening ensure the data is free from scanner and subject artifacts vessel suppression reduce the effects of large vessels (which are further away from activation than capillaries) slice scan time correction correct for sampling of different slices at different times motion correction correct for sampling of different slices at different times spatial filtering smooth the spatial data temporal filtering remove low frequency drifts (e.g., linear trends) remove high frequency noise (not recommended because it increases temporal autocorrelation and artificially inflates statistics) spatial normalization put data in standard space (Talairach or MNI Space)

74. A Brief Primer on Fourier Analysis Sine waves can be characterized by frequency and amplitude peak: high point trough: low point frequency: number of cycles within a certain time or space (e.g., cycles per sec = Hz, cycles per cm) amplitude: height of wave phase: starting point (b) has same frequency as (a) but lower amplitude (c) has lower frequency than (a) and (b) (d) has same frequency and amplitude as (c) but different phase peak trough amplitude Source: DeValois & DeValois, Spatial Vision, 1990

75. Fourier Decomposition Any wave form can be decomposed into a series of sine waves Frequency spectrum Source: DeValois & DeValois, Spatial Vision, 1990

76. Temporal and Spatial Analysis Temporal waveforms e.g., sound waves e.g., fMRI time courses Spatial waveforms can be one dimensional (e.g., sine wave gratings in vision) or two dimensional (e.g., a 2D image) e.g., image analysis e.g., an fMRI slice (k- space) Source: DeValois & DeValois, Spatial Vision, 1990

77. Fourier Synthesis centre = low frequencies periphery = high frequencies You can see how the image quality grows as we add more frequency information Source: DeValois & DeValois, Spatial Vision, 1990

78. K-Space Source: Traveler’s Guide to K-space (C.A. Mistretta)Traveler’s Guide to K-space

79. What affects SNR? Physiological factors Source: Doug Noll’s online tutorialDoug Noll’s online tutorial PHYSIOLOGICAL FACTORSSOLUTION & TRADEOFF Head (and body) motionUse experienced or well-warned subjects – limits useable subjects Use head-restraint system – possible subject discomfort Post-processing correction – often incompletely effective – 2 nd order effects – can introduce other artifacts Single trials to avoid body motion Cardiac and respiratory noiseMonitor and compensate – hassle Low frequency noiseUse smart design Perform post-processing filtering BOLD noise (neural and vascular fluctuations)Use many trials to average out variability Behavioral variationsUse well-controlled paradigm Use many trials to average out variability

80. Slice Scan Time Correction Slice scan time correction adjusts the timing of a slice corrected at the end of the volume so that it is as if it had been collected simultaneously with the first slice original time course shifted time course Source: Brain Voyager documentation

81. Calculating Signal:Noise Ratio Pick a region of interest (ROI) outside the brain free from artifacts (no ghosts, susceptibility artifacts). Find mean (  ) and standard deviation (SD). Pick an ROI inside the brain in the area you care about. Find  and SD. SNR =  brain /  outside = 200/4 = 50 [Alternatively SNR =  brain / SD outside = 200/2.1 = 95 (should be 1/1.91 of above because  /SD ~ 1.91)] Head coil should have SNR > 50:1 Surface coil should have SNR > 100:1 When citing SNR, state which denominator you used. Source: Joe Gati, personal communication e.g.,  =4, SD=2.1 e.g.,  = 200 WARNING!: computation of SNR is complicated for phased array coils WARNING!: some software might recalibrate intensities so it’s best to do computations on raw data