1. I. Improving SNR (cont.) II. Preprocessing BIAC Graduate fMRI Course October 12, 2004

2. Increasing Field Strength

3. Theoretical Effects of Field Strength SNR = signal / noise SNR increases linearly with field strength –Signal increases with square of field strength –Noise increases linearly with field strength –A 4.0T scanner should have 2.7x SNR of 1.5T scanner T 1 and T 2 * both change with field strength –T 1 increases, reducing signal recovery –T 2 * decreases, increasing BOLD contrast

4. Adapted from Turner, et al. (1993)

5. Measured Effects of Field Strength SNR usually increases by less than theoretical prediction –Sub-linear increases in SNR; large vessel effects may be independent of field strength Where tested, clear advantages of higher field have been demonstrated –But, physiological noise may counteract gains at high field ( > ~4.0T) Spatial extent increases with field strength Increased susceptibility artifacts

9. Trial Averaging Static signal, variable noise –Assumes that the MR data recorded on each trial are composed of a signal + (random) noise Effects of averaging –Signal is present on every trial, so it remains constant through averaging –Noise randomly varies across trials, so it decreases with averaging –Thus, SNR increases with averaging

10. Fundamental Rule of SNR For Gaussian noise, experimental power increases with the square root of the number of observations

11. Example of Trial Averaging Average of 16 trials with SNR = 0.6

13. Increasing Power increases Spatial Extent Subject 1Subject 2 Trials Averaged 4 16 36 64 100 144 500 ms 16-20 s 500 ms …

14. AB

15. Number of Trials Averaged Number of Significant Voxels Subject 1 Subject 2 V N = V max [1 - e (-0.016 * N) ] Effects of Signal-Noise Ratio on extent of activation: Empirical Data

16. Active Voxel Simulation Signal + Noise (SNR = 1.0) Noise 1000 Voxels, 100 Active Signal waveform taken from observed data. Signal amplitude distribution: Gamma (observed). Assumed Gaussian white noise.

17. Effects of Signal-Noise Ratio on extent of activation: Simulation Data SNR = 0.10 SNR = 0.15 SNR = 0.25 SNR = 1.00 SNR = 0.52 (Young) SNR = 0.35 (Old) Number of Trials Averaged Number of Activated Voxels

18. Explicit and Implicit Signal Averaging r =.42; t(129) = 5.3; p <.0001 r =.82; t(10) = 4.3; p <.001 A B

19. Caveats Signal averaging is based on assumptions –Data = signal + temporally invariant noise –Noise is uncorrelated over time If assumptions are violated, then averaging ignores potentially valuable information –Amount of noise varies over time –Some noise is temporally correlated (physiology) Nevertheless, averaging provides robust, reliable method for determining brain activity

20. II. Preprocessing of FMRI Data

21. What is preprocessing? Correcting for non-task-related variability in experimental data –Usually done without consideration of experimental design; thus, pre-analysis –Occasionally called post-processing, in reference to being after acquisition Attempts to remove, rather than model, data variability

23. Quality Assurance

26. Tools for Preprocessing SPM Brain Voyager VoxBo AFNI Custom BIAC scripts

27. Slice Timing Correction

28. Why do we correct for slice timing? Corrects for differences in acquisition time within a TR –Especially important for long TRs (where expected HDR amplitude may vary significantly) –Accuracy of interpolation also decreases with increasing TR When should it be done? –Before motion correction: interpolates data from (potentially) different voxels Better for interleaved acquisition –After motion correction: changes in slice of voxels results in changes in time within TR Better for sequential acquisition

30. Effects of uncorrected slice timing Base Hemodynamic Response Base HDR + Noise Base HDR + Slice Timing Errors Base HDR + Noise + Slice Timing Errors

31. Base HDR: 2s TR

32. Base HDR + Noise r = 0.77 r = 0.80 r = 0.81

33. Base HDR + Slice Timing Errors r = 0.85 r = 0.92 r = 0.62

34. HDR + Noise + Slice Timing r = 0.65 r = 0.67 r = 0.19

35. Interpolation Strategies Linear interpolation Spline interpolation Sinc interpolation

36. Motion Correction

37. Head Motion: Good, Bad,…

38. … and catastrophically bad

39. Why does head motion introduce problems? ABC

40. Simulated Head Motion

41. Severe Head Motion: Simulation Two 4s movements of 8mm in -Y direction (during task epochs) Motion

42. Severe Head Motion: Real Data Two 4s movements of 8mm in -Y direction (during task epochs) Motion

43. Correcting Head Motion Rigid body transformation –6 parameters: 3 translation, 3 rotation Minimization of some cost function –E.g., sum of squared differences –Mutual information

44. Effects of Head Motion Correction

45. Limitations of Motion Correction Artifact-related limitations –Loss of data at edges of imaging volume –Ghosts in image do not change in same manner as real data Distortions in fMRI images –Distortions may be dependent on position in field, not position in head Intrinsic problems with correction of both slice timing and head motion

46. What is the best approach for minimizing the influence of head motion on your data?

49. Coregistration

50. Should you Coregister? Advantages –Aids in normalization –Allows display of activation on anatomical images –Allows comparison across modalities –Necessary if no coplanar anatomical images Disadvantages –May severely distort functional data –May reduce correspondence between functional and anatomical images

51. Normalization

53. Standardized Spaces Talairach space (proportional grid system) –From atlas of Talairach and Tournoux (1988) –Based on single subject (60y, Female, Cadaver) –Single hemisphere –Related to Brodmann coordinates Montreal Neurological Institute (MNI) space –Combination of many MRI scans on normal controls All right-handed subjects –Approximated to Talaraich space Slightly larger Taller from AC to top by 5mm; deeper from AC to bottom by 10mm –Used by SPM, fMRI Data Center, International Consortium for Brain Mapping

54. Normalization to Template Normalization TemplateNormalized Data

55. Anterior and Posterior Commissures Anterior Commissure Posterior Commissure

57. Should you normalize? Advantages –Allows generalization of results to larger population –Improves comparison with other studies –Provides coordinate space for reporting results –Enables averaging across subjects Disadvantages –Reduces spatial resolution –May reduce activation strength by subject averaging –Time consuming, potentially problematic Doing bad normalization is much worse than not normalizing (and using another approach)

58. Slice-Based Normalization Before Adjustment (15 Subjects) After Adjustment to Reference Image Registration courtesy Dr. Martin McKeown (BIAC)

59. Spatial Smoothing

60. Techniques for Smoothing Application of Gaussian kernel –Usually expressed in #mm FWHM –“Full Width – Half Maximum” –Typically ~2 times voxel size

61. Effects of Smoothing on Activity Unsmoothed Data Smoothed Data (kernel width 5 voxels)

63. 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

64. Segmentation Classifies voxels within an image into different anatomical divisions –Gray Matter –White Matter –Cerebro-spinal Fluid (CSF) Image courtesy J. Bizzell & A. Belger

65. Histogram of Voxel Intensities

66. Bias Field Correction

69. Temporal Filtering

70. Filtering Approaches Identify unwanted frequency variation –Drift (low-frequency) –Physiology (high-frequency) –Task overlap (high-frequency) Reduce power around those frequencies through application of filters Potential problem: removal of frequencies composing response of interest

71. Power Spectra

72. Region of Interest Drawing

73. Why use an ROI-based approach? Allows direct, unbiased measurement of activity in an anatomical region –Assumes functional divisions tend to follow anatomical divisions Improves ability to identify topographic changes –Motor mapping (central sulcus) –Social perception mapping (superior temporal sulcus) Complements voxel-based analyses

74. Drawing ROIs Drawing Tools –BIAC software (e.g., Overlay2) –Analyze –IRIS/SNAP (G. Gerig from UNC) Reference Works –Print atlases –Online atlases Analysis Tools –roi_analysis_script.m

75. ROI Examples

76. BIAC is studying biological motion and social perception – here by determining how context modulates brain activity in elicited when a subject watches a character shift gaze toward or away from a target.

77. Additional Resources SPM website –http://www.fil.ion.ucl.ac.uk/spm/course/notes01.htmlhttp://www.fil.ion.ucl.ac.uk/spm/course/notes01.html –SPM Manual Brain viewers –http://www.bic.mni.mcgill.ca/cgi/icbm_view/http://www.bic.mni.mcgill.ca/cgi/icbm_view/