1. I. Spatial & Temporal Properties (cont.) II. Signal and Noise BIAC Graduate fMRI Course October 11, 2005

3. Spatial and Temporal Properties of BOLD fMRI

4. Why do you need to know? Spatial resolution –Trades off with coverage –Influences viability of preprocessing steps –Influences inferences about distinct ROIs Temporal resolution –Tradeoffs between number of slices and TR –Needed resolution depends upon design

5. Spatial Resolution

6. What spatial resolution do we want? Hemispheric –Lateralization studies –Selective attention studies Systems / lobic –Relation to lesion data Centimeter –Identification of active regions Millimeter –Topographic mapping (e.g., motor, vision) Sub-millimeter –Ocular Dominance Columns –Cortical Layers

7. What determines Spatial Resolution? Voxel Size –In-plane Resolution –Slice thickness Spatial noise –Head motion –Artifacts Spatial blurring –Smoothing (within subject) –Coregistration (within subject) –Normalization (within subject) –Averaging (across subjects) Functional resolution

8. Why can’t we just collect data from more/smaller voxels?

9. ............... A B FOV: 10 cm, Pixel Size: 1 cm FOV: 10cm, Pixel Size: 2 cm K – Space Revisited To increase spatial resolution we need to sample at higher spatial frequencies.

10. Costs of Increased Spatial Resolution Acquisition Time –In-plane Higher resolution takes more time to fill K-space (resolution ~ size of K-space) –#Slices/second –Sample rates for 64*64 images Early Duke fMRI: 2-4 sl/s GE EPI: 12 sl/s Duke Spiral: 14 sl/s Duke Inverse Spiral: 21+ sl/s Reduced signal per voxel –What is our dependent measure?

11. How large are functional voxels?  3.75mm   5.0mm   3.75mm  = ~.08cm 3 Within a typical brain (~1300cm 3 ), there may be about 20,000 functional voxels.

12. How large are anatomical voxels? .9375mm   5.0mm  .9375mm  = ~.004cm 3 Within a typical brain (~1300cm 3 ), there may be about 300,000+ anatomical voxels.

13. T2* Blurring Signal decays over time needed for collection of an image For standard resolution images, this is not a critical issue However, for high-resolution (in-plane) images, the time to acquire an image may be a significant fraction of T2* Under these conditions, multi-shot imaging may be necessary.

14. Partial Volume Effects A single voxel may contain multiple tissue components –Many “gray matter” voxels will contain other tissue types –Large vessels are often present The signal recorded from a voxel is a combination of all components

15. High Spatial Resolution fMRI: Ocular Dominance Columns

17. Early examples of ocular dominance Menon et al., 1997 Red = Left eye Blue = Right eye Pixel size 0.5mm 2

18. Reliability of Ocular Dominance Measurements Cheng et al., 2001 Same subject participated in two sessions –Raw data at left Boundaries of dominance columns match well across sessions

19. Effects of Stimulus Duration on Spatial Extent of Activity

20. Example: Ocular Dominance Goodyear & Menon, 2001

21. 4sec  10sec  Goodyear & Menon, 2001

22. Example: Visual System 100 ms 500 ms 1500 ms

23. Temporal Resolution

24. What temporal resolution do we want? 10,000-30,000ms: Arousal or emotional state 1000-10,000ms: Decisions, recall from memory 500-1000ms: Response time 250ms: Reaction time 10-100ms: –Difference between response times –Initial visual processing 10ms: Neuronal activity in one area

25. Basic Sampling Theory Nyquist Sampling Theorem –To be able to identify changes at frequency X, one must sample the data at (least) 2X. –For example, if your task causes brain changes at 1 Hz (every second), you must take two images per second.

27. Aliasing Mismapping of high frequencies (above the Nyquist limit) to lower frequencies –Results from insufficient sampling –Potential problem for long TRs and/or fast stimulus changes –Also problem when physiological variability is present

28. Sampling Rate in Event-related fMRI

29. Costs of Increased Temporal Resolution Reduced signal amplitude –Shorter flip angles must be used (to allow reaching of steady state), reducing signal Fewer slices acquired –Usually, throughput expressed as slices per unit time

30. Frequency Analyses t < -1.96t < +1.96 McCarthy et al., 1996

31. Phase Analyses Design –Left/right alternating flashes –6.4s for each Task frequency: –1 / 12.8 = 0.078 McCarthy et al., 1996

32. Why do we want to measure differences in timing within a brain region? Determine relative ordering of activity Make inferences about connectivity –Anatomical –Functional Relate activity timing to other measures –Stimulus presentation –Reaction time –Relative amplitude

33. Timing Differences across Regions Menon et al., 1998 Presented left hemifield before right hemifield (0-1000ms delays) Plot of LH signal as function of RH signal fMRI vs RT (LH) fMRI vs. Stimulus

34. Menon et al., 1998 Activation maps Relative onset time differences

35. Timing of mental events measured by fMRI Miezin et al., 2000 Subjects pressed button with one hand at onset of 1.5s stimulus Then, pressed another button at offset of stimulus

36. V1 FFG Huettel et al., 2001

37. Subject 1 4.0s 5.5s Subject 2 Primary Visual Cortex (V1) Secondary Visual Cortex (FFG) Huettel et al., 2001

38. Width of fMRI response increases with duration of mental activity From Menon and Kim, 1999; after Richter et al, 1997

39. Independence of Timing and Amplitude Adapted from Miezin et al. (2000)

40. Linearity of the Hemodynamic Response

41. Linear Systems Scaling –The ratio of inputs determines the ratio of outputs –Example: if Input 1 is twice as large as Input 2, Output 1 will be twice as large as Output 2 Superposition –The response to a sum of inputs is equivalent to the sum of the response to individual inputs –Example: Output 1+2+3 = Output 1 +Output 2 +Output 3

42. Scaling (A) and Superposition (B) B A

43. Linear and Non-linear Systems AB CD

44. Possible Sources of Nonlinearity Stimulus time course  neural activity –Activity not uniform across stimulus (for any stimulus) Neural activity  Vascular changes –Different activity durations may lead to different blood flow or oxygen extraction Minimum bolus size? Minimum activity necessary to trigger? Vascular changes  BOLD measurement –Saturation of BOLD response necessitates nonlinearity –Vascular measures combining to generate BOLD have different time courses From Buxton, 2001

45. Effects of Stimulus Duration Short stimulus durations evoke BOLD responses –Amplitude of BOLD response often depends on duration –Stimuli < 100ms evoke measurable BOLD responses Form of response changes with duration –Latency to peak increases with increasing duration –Onset of rise does not change with duration –Rate of rise increases with duration Key issue: deconfounding duration of stimulus with duration of neuronal activity

46. The fMRI Linear Transform

47. Boynton et al., 1996 Varied contrast of checkerboard bars as well as their interval (B) and duration (C).

48. Boynton, et al, 1996

50. Differences in Nonlinearity across Brain Regions Birn, et al, 2001

51. SMA vs. M1 Birn, et al, 2001

52. Caveat: Stimulus Duration ≠ Neuronal Activity Duration

53. Refractory Periods Definition: a change in the responsiveness to an event based upon the presence or absence of a similar preceding event –Neuronal refractory period –Vascular refractory period

54. Dale & Buckner, 1997 Responses to consecutive presentations of a stimulus add in a “roughly linear” fashion Subtle departures from linearity are evident

55. Intra-Pair Interval (IPI) Inter-Trial Interval (16-20 seconds) 6 sec IPI 4 sec IPI 2 sec IPI 1 sec IPI Single- Stimulus Huettel & McCarthy, 2000 500 ms duration

56. Methods and Analysis 16 male subjects (mean age: 27y) GE 1.5T scanner –CAMRD Gradient-echo EPI –TR : 1 sec –TE : 50 msec –Resolution: 3.125 * 3.125 * 7 mm Analysis –Voxel-based analyses –Waveforms derived from active voxels within anatomical ROI Huettel & McCarthy, 2000

57. Hemodynamic Responses to Closely Spaced Stimuli Huettel & McCarthy, 2000

58. Refractory Effects in the fMRI Hemodynamic Response Huettel & McCarthy, 2000 Time since onset of second stimulus (sec) Signal Change over Baseline(%)

59. Refractory Effects across Visual Regions HDRs to 1 st and 2 nd stimuli in a pair (calcarine cortex) Relative amplitude of 2 nd stimulus in pair across regions

60. Intra-Pair Interval (IPI) Inter-Trial Interval (16-20 seconds) 6 sec IPI 1 sec IPI Single- Stimulus

61. 05 10 15 20 25 30 35 40 45 50 55 60 L R Figure 2 Mean HDRs Single 6s IPI 1s IPI Time since stimulus onset (sec) Signal Change over baseline (%)

62. Refractory Effect Summary Duration –HDR evoked by a long-duration stimulus is less than predicted by convolution of short-duration stimuli –Present for durations < ~6s Interstimulus interval –HDR evoked by a stimulus is reduced by a preceding similar stimulus –Present for intervals < ~6s Differences across brain regions –Some regions show considerable departures from linearity –May result from differences in processing Source of non-linearity not well understood –Neuronal effects comprise at least part of the overall effect –Vascular differences may also contribute

63. Using refractory effects to study cognition: fMRI Adaptation Studies

64. Neuronal Adaptation Several neuronal populations vs. homogeneous population Adaptation If neurons are insensitive to the feature being varied, then their activity will adapt. Viewpoint SensitiveViewpoint Invariant Grill-Spector & Malach, 2001

65. Lateral OccipitalPosterior Fusiform

66. Is the refractory effect attribute specific? Boynton et al., 2003

67. Lateral Temporal-Occipital Peri-Calcarine AB CD Long Short Huettel, Obembe, Song, Woldorff, in preparation

68. Overall Summary Spatial resolution –Advantages (of increasing) Smaller voxels allow distinction among areas –Disadvantages Require more slices, thus longer TR Reduces signal per voxel Temporal resolution –Advantages (of increasing) Improves sampling of hemodynamic response –Disadvantages Reduces # of slices per TR May not be necessary for some designs Non-linearity of hemodynamic response –Advantages (of phenomenon for design) May be used to study adaptation –Disadvantages Reduces power of short interval designs Must be accounted for in analyses