1. Peter A. Bandettini, Ph.D. Section on Functional Imaging Methods http://fim.nimh.nih.gov Laboratory of Brain and Cognition & Functional MRI Facility http://fmrif.nimh.nih.gov Contentious Issues in fMRI

2. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

3. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

4. Yacoub E, Le TH, Ugurbil K, Hu X (1999) Magn Res Med 41(3):436-41 Courtesy of Arno Villringer The Undershoots

5. CBF BOLD 0 20 40 60 80 100 0 20 40 60 -20 0.2.4.6 -.2 CBF (% change) BOLD (% change) 20 sec Motor Stimulation Time (sec) A BOLD undershoot without a CBF undershoot could be due to a slow return to baseline of either CBV or CMRO 2 BOLD post-stimulus undershoot

6. BOLD Signal Dynamics Courtesy Rick Buxton

7. “During the poststimulus period, the amplitude and directionality (positive/negative) of the BOLD signal in both contralateral and ipsilateral sensorimotor cortex depends on the poststimulus synchrony of 8–13 Hz EEG neuronal activity, which is often considered to reflect cortical inhibition, along with concordant changes in CBF and metabolism.” “We suggest that the poststimulus EEG and fMRI responses may be required for the resetting of the entire sensory network to enable a return to resting-state activity levels.”

8. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

9. McKiernan, et al (2003), Journ. of Cog. Neurosci. 15 (3), 394-408 Regions showing negative signal changes during cognitive tasks Negative Signal Change

10. Schmuel et al. (2002) Neuron, Vol. 36, 1195–1210

11. B: BOLD C: Volume D: -Volume Goense, J., Merkle, H., and Logothetis, N.K.(2012). Neuron 76, 629–639.

12. Bandettini, PA (2012). Neuron 76, 468-469

14. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

15. Parametric Manipulation Motor Cortex Auditory Cortex S. M. Rao et al, (1996) “Relationship between finger movement rate and functional magnetic resonance signal change in human primary motor cortex.” J. Cereb. Blood Flow and Met. 16, 1250-1254. J. R. Binder, et al, (1994). “Effects of stimulus rate on signal response during functional magnetic resonance imaging of auditory cortex.” Cogn. Brain Res. 2, 31-38

16. fMRI responses in human V1 are proportional to average firing rates in monkey V1 0.4 spikes/sec -> 1% BOLD Rees, G., Friston, K., and Koch, C. 2000. A direct quantitative relationship between the functional properties of human and macaque V5. Nat. Neurosci. 3: 716–723. Heeger, D. J., Huk, A. C., Geisler, W. S., and Albrecht, D. G. 2000.Spikes versus BOLD: What does neuroimaging tell us about neuronal activity? Nat. Neurosci. 3: 631–633. 9 spikes/sec -> 1% BOLD

17. Logothetis et al. (2001) “Neurophysiological investigation of the basis of the fMRI signal” Nature, 412, 150-157

18. Relationship to Neuronal Activity Muthukumaraswamy, S. D., Singh, K. D. (2008) NeuroImage 40 (4), pp. 1552-1560

20. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

21. Logothetis et al. (2001) “Neurophysiological investigation of the basis of the fMRI signal” Nature, 412, 150-157 S. M. Rao et al, (1996) “Relationship between finger movement rate and functional magnetic resonance signal change in human primary motor cortex.” J. Cereb. Blood Flow and Met. 16, 1250-1254. Linearity

22. measured linear Signal (%) -4 -2 0 2 time (s) 05101520 0 0.5 1 1.5 Stimulus OFF duration (s) Linearity linear measured Brief “ off ” periods produce smaller decreases than expected. R.M. Birn, P. A. Bandettini, NeuroImage, 27, 70-82 (2005) measured linear Signal Brief “ on ” periods produce larger increases than expected. Linearity 012345 Stimulus Duration (s) linear 1 2 3 R. M. Birn, Z. Saad, P. A. Bandettini, NeuroImage, 14: 817-826, (2001) time (s) Linearity

23. Varying the Duty Cycle 05 10 15 25% ON 50% ON 75% ON 8% ON time (s) Signal Deconvolved Response 25% 50% 75% Brief ON Brief OFF 00.51 Duty Cycle Linearity 0.5 1 1.5 2 R.M. Birn, P. A. Bandettini, NeuroImage, 27, 70-82 (2005) Linearity

24. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

26. task

27. P. A. Bandettini, Functional MRI temporal resolution in "Functional MRI" (C. Moonen, and P. Bandettini., Eds.), p. 205-220, Springer - Verlag,. 1999.

28. P. A. Bandettini, (1999) "Functional MRI" 205-220. Magnitude Latency + 2 sec - 2 sec Venogram Latency Variation…

31. 051015202530 0 100 200 300 400 500 Number of runs 8 sec on/off 16 sec on/off Smallest latency Variation Detectable (ms) (p < 0.001) t 1 run: -1000-50005001000 Number delay estimate (ms)  delay = 107ms 11 1% Noise 4% BOLD 256 time pts /run 1 second TR

32. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue. How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

33. Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current

34. Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current Yacoub et al. NeuroImage 24 (3), pp. 738-750

35. Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current Yacoub et al. NeuroImage 37 (4), pp. 1161-1177

36. Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current H. Lu et al. Magnetic Resonance in Medicine 50 (2), pp. 263-274

37. Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current K. Miller, et al Proceedings of the National Academy of Sciences of the United States of America 104 (52), pp. 20967-20972 D. Le Bihan, et al Proceedings of the National Academy of Sciences of the United States of America 103 (21), pp. 8263-8268

38. Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current Intracellular Current Magnetic Field Adapted from: J.P. Wikswo Jr et al. J Clin Neurophy 8(2): 170-188, 1991 Surface Field Distribution Across Spatial Scales

39. Power decrease between PRE & TTX EEG : ~ 81% Decrease between PRE & TTX MR phase: ~ 70% Decrease between PRE & TTX MR magnitude: ~ 8% N. Petridou, D. Plenz, A. C. Silva, J. Bodurka, M. Loew, P. A. Bandettini, Proc. Nat'l. Acad. Sci. USA. 103, 16015-16020 (2006). TTX EEG MR (7T)

40. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

41. Over the past year, a heated discussion about ‘circular’ or ‘nonindependent’ analysis in brain imaging has emerged in the literature. An analysis is circular (or nonindependent) if it is based on data that were selected for showing the effect of interest or a related effect. The authors of this paper are researchers who have contributed to the discussion and span a range of viewpoints. To clarify points of agreement and disagreement in the community, we collaboratively assembled a series of questions on circularity herein, to which we provide our individual current answers in <– 100 words per question. Although divergent views remain on some of the questions, there is also a substantial convergence of opinion, which we have summarized in a consensus box. The box provides the best current answers that the five authors could agree upon.

44. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

45. Multi-sensory integration Visual Auditory Multisensory M.S. Beauchamp et al.,

46. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

47. Boynton (2005), News & Views on Kamitani & Tong (2005) and Haynes & Rees (2005)

48. Kamitani & Tong (2005) Lower spatial frequency clumping

49. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

50. Seed Based Correlation from the Posterior Cingulate Cortex (PCC) Greicius M D et al. PNAS 2003;100:253-258 De Luca, Neuroimage 29(4) 2006 Visual Visuospatial Executive Sensory Auditory Dorsal Pathway Ventral Pathway Seed Voxel ICA

51. Identifying the brain’s most globally connected regions, M. W. Cole, S. Pathak, W. Schneider, NeuroImage 49, 2010, 3132-3148 Hub Maps

52. Some Basic Limitations Seed voxel: -Inefficient: Only assesses a fraction of the possible networks. -Subjective: Iterative and user-biased choice of what looks best. ICA: -Random component ranking. Difficult to separate real vs artifactual. Hub Map: - Determines regions that are most globally connected, not specific networks. All of these methods usually only consider time chunks of 5 min or more and do not consider changes in correlation over time.

53. Two other issues with imaging resting state fluctuations: 1.Global signal correction or not? 2.Short range correlations may be scanner-related.

54. The issue of global signal regression K. Murphy, R. M. Birn, D. A. Handwerker, T. B. Jones, P. A. Bandettini, NeuroImage, 44, 893-905 (2009)

55. …the global regression story is not so simple…

56. You can observe a lot by watching --- Yogi Berra

57. He and Liu, NIMG 2012

58. Delta and Theta Alpha = EEG vigilance Klimesch. Brain Res Rev 1999. alpha Delta theta Relative EEG Hz

59. Relation across Subjects and Runs EEG spectra for three representative subjects. Caffeine session pre-dose (blue), control session pre- dose (red) /post-dose sections (green)

60. Relative EEG Higher vigilance -> lower GS -> more anticorrelation Pre Post -8 8 8

61. More diversified neural activity Tononi et al. BMC Neurosci. 2004 Anti-correlation Global signal amplitude Vigilance

62. Summary Global signal amplitude decreases with increases in vigilance. Relationship between vigilance, global signal amplitude, and DMN-TPN anticorrelation may hold across multiple time scales Neural activity may become more spatially diversified and less globally synchronized as we become more alert Preprocessing steps that remove the effects of global signal differences may reduce the effect of connectivity differences that are related to changes in vigilance

64. The issue of correlation across voxels due scanner instabilities N. Kriegeskorte, J. Bodurka, P. Bandettini, International Journal of Imaging Systems and Technology, 18 (5-6), 345-349 (2008)

65. What do these low frequencies really mean? What do the low frequencies “do?” How can we better remove ALL the non-neuronal noise? Other Open Questions on Resting State

66. The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state