Real-time fMRI

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In neuroimaging neuroscience, real-time fMRI, broadly speaking, is a type of functional magnetic resonance imaging (fMRI) in which reconstruction of the raw data obtained by the brain scanner is done while the scan is happening. In brain scanning, this allows individuals who are undergoing the scan to see the patterns of brain activation which they are generating in one or more regions of interest. The theory behind this practice is that since areas of the brain are active when a person does, thinks, or experiences particular activities, and relatively inactive when they are doing, thinking or experiencing unrelated activities, the signals from those areas can be used like the global signals of biofeedback to target specific symptoms. The awareness of localized brain activation patterns made possible by this technology have been used in clinical populations by researchers including Christopher deCharms and others at Omneuron, Inc. and at Stanford University to investigate whether patients can use these signals to decrease the symptoms of social anxiety disorder and chronic pain, with some success reported at neuroscience meetings.[1][1] Other research groups, notably the creators of TurboBrainVoyager, a commercial software package which allows this real-time reconstruction of data, have used it in proof of concept applications to play pong using only their brains.[2]

To date, only blood-oxygen-level dependent (BOLD) fMRI imaging has been used in real-time applications, which adds a delay of approximately 2-5 seconds to the signal due to the physiological delay of the hemodynamic response. In the future, other methods of fMRI which do not rely on a secondary messenger like blood flow may reduce the delay and allow truly instantaneous signal generation.


  1. R. A. Adcock el al. (2005). "Real time fMRI during the psychotherapy session: toward a methodology to augment therapeutic benefit". NeuroImage. 11th Annual Meeting of the Organization For Human Brain Mapping. June 12-16, 2005. Toronto, Ontario, Canada.
  2. Mark Peplow (2004-08-27). "Mental ping-pong could aid paraplegics". Nature news. doi:10.1038/news040823-18. Unknown parameter |month= ignored (help); Check date values in: |year= (help)


  1. Bagarinao, E., et al., Enabling on-demand real-time functional MRI analysis using grid technology. Methods Inf Med, 2005. 44(5): p. 665-73.
  2. Birbaumer, N., Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. Psychophysiology, 2006. 43(6): p. 517-32.
  3. Busch, M., et al., Fast "real time" imaging with different k-space update strategies for interventional procedures. J Magn Reson Imaging, 1998. 8(4): p. 944-54.
  4. Caria, A., et al., Regulation of anterior insular cortex activity using real-time fMRI. Neuroimage, 2007. 35(3): p. 1238-46.
  5. Cohen, M.S., Real-time functional magnetic resonance imaging. Methods, 2001. 25(2): p. 201-20.
  6. Cox, D.D. and R.L. Savoy, Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 2003. 19(2 Pt 1): p. 261-70.
  7. Cox, R.W. and A. Jesmanowicz, Real-time 3D image registration for functional MRI. Magn Reson Med, 1999. 42(6): p. 1014-8.
  8. Cox, R.W., A. Jesmanowicz, and J.S. Hyde, Real-time functional magnetic resonance imaging. Magn Reson Med, 1995. 33(2): p. 230-6.
  9. deCharms, R.C., et al., Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage, 2004. 21(1): p. 436-43.
  10. deCharms, R.C., et al., Control over brain activation and pain learned by using real-time functional MRI. Proc Natl Acad Sci U S A, 2005. 102(51): p. 18626-31.
  11. Esposito, F., et al., Real-time independent component analysis of fMRI time-series. NeuroImage, 2003. 20(4): p. 2209-24.
  12. Fernandez, G., et al., Language mapping in less than 15 minutes: real-time functional MRI during routine clinical investigation. Neuroimage, 2001. 14(3): p. 585-94.
  13. Gasser, T., et al., Intraoperative functional MRI: implementation and preliminary experience. Neuroimage, 2005. 26(3): p. 685-93.
  14. Gasser, T., et al., Functional magnetic resonance imaging in anesthetized patients: a relevant step toward real-time intraoperative functional neuroimaging. Neurosurgery, 2005. 57(1 Suppl): p. 94-9; discussion 94-9.
  15. Gembris, D., et al., Functional magnetic resonance imaging in real time (FIRE): sliding-window correlation analysis and reference-vector optimization. Magn Reson Med, 2000. 43(2): p. 259-68.
  16. Gering, D.T. and D.M. Weber, Intraoperative, real-time, functional MRI. J Magn Reson Imaging, 1998. 8(1): p. 254-7.
  17. Goddard, N., et al., Online Analysis of Functional MRI Datasets on Parallel Platforms. J. Supercomputing, 1997. 11(3): p. 295-318
  18. Greenberg, D.L., Comment on "Detecting awareness in the vegetative state". Science, 2007. 315(5816): p. 1221; author reply 1221.
  19. Hesser, J., et al., Real-time direct volume rendering in functional magnetic resonance imaging. Magma, 1997. 5(2): p. 87-91.
  20. Hinterberger, T., et al., Neuronal mechanisms underlying control of a brain-computer interface. Eur J Neurosci, 2005. 21(11): p. 3169-81.
  21. Laconte, S.M., S.J. Peltier, and X.P. Hu, Real-time fMRI using brain-state classification. Hum Brain Mapp, 2006.
  22. Lee, C.C., et al., Real-time adaptive motion correction in functional MRI. Magn Reson Med, 1996. 36(3): p. 436-44.
  23. Lee, C.C., et al., Real-time reconstruction and high-speed processing in functional MR imaging. AJNR Am J Neuroradiol, 1998. 19(7): p. 1297-300.
  24. Lee, J.H., et al., Atlas-based multichannel monitoring of functional MRI signals in real-time: Automated approach. Hum Brain Mapp, 2007.
  25. Martinez-Ramon, M., et al., fMRI pattern classification using neuroanatomically constrained boosting. Neuroimage, 2006. 31(3): p. 1129-41.
  26. Mathiak, K. and S. Posse, Evaluation of motion and realignment for functional magnetic resonance imaging in real time. Magn Reson Med, 2001. 45(1): p. 167-71.
  27. Miller, G., Neuroscience. A better view of brain disorders. Science, 2006. 313(5792): p. 1376-9.
  28. Moller, M., et al., Real time fMRI: a tool for the routine presurgical localisation of the motor cortex. Eur Radiol, 2005. 15(2): p. 292-5.
  29. Nachev, P. and M. Husain, Comment on "Detecting awareness in the vegetative state". Science, 2007. 315(5816): p. 1221; author reply 1221.
  30. Nagel, E., et al., Magnetic resonance real-time imaging for the evaluation of left ventricular function. J Cardiovasc Magn Reson, 2000. 2(1): p. 7-14.
  31. Nakai, T., et al., Dynamic monitoring of brain activation under visual stimulation using fMRI--the advantage of real-time fMRI with sliding window GLM analysis. J Neurosci Methods, 2006. 157(1): p. 158-67.
  32. Nakai, T., et al., Dynamic monitoring of brain activation under visual stimulation using fMRI--the advantage of real-time fMRI with sliding window GLM analysis. J Neurosci Methods, 2006. 157(1): p. 158-67.
  33. Nayak, K.S., et al., Real-time cardiac MRI at 3 tesla. Magn Reson Med, 2004. 51(4): p. 655-60.
  34. Owen, A.M., et al., Detecting awareness in the vegetative state. Science, 2006. 313(5792): p. 1402.
  35. Owen, A.M., et al., Detecting residual cognitive function in persistent vegetative state. Neurocase, 2002. 8(5): p. 394-403.
  36. Phan, K.L., et al., Real-time fMRI of cortico-limbic brain activity during emotional processing. Neuroreport, 2004. 15(3): p. 527-32.
  37. Posse, S., et al., A new approach to measure single-event related brain activity using real-time fMRI: feasibility of sensory, motor, and higher cognitive tasks. Hum Brain Mapp, 2001. 12(1): p. 25-41.
  38. Posse, S., et al., Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness. Neuroimage, 2003. 18(3): p. 760-8.
  39. Riva, G. and B.K. Wiederhold, Introduction to the special issue on virtual reality environments in behavioral sciences. IEEE Trans Inf Technol Biomed, 2002. 6(3): p. 193-7.
  40. Santos, J.M., et al., Single breath-hold whole-heart MRA using variable-density spirals at 3T. Magn Reson Med, 2006. 55(2): p. 371-9.
  41. Schwindack, C., et al., Real-time functional magnetic resonance imaging (rt-fMRI) in patients with brain tumours: preliminary findings using motor and language paradigms. Br J Neurosurg, 2005. 19(1): p. 25-32.
  42. Smyser, C., et al., Real-time multiple linear regression for fMRI supported by time-aware acquisition and processing. Magn Reson Med, 2001. 45(2): p. 289-98.
  43. Sorger, B., et al. Voluntary modulation of regional brain activity to different target levels based on real-time fMRI neurofeedback. in Society for Neuroscience. 2004. Washington DC.
  44. Speck, O., J. Hennig, and M. Zaitsev, Prospective Real-Time Slice-by-Slice Motion Correction for fMRI in Freely Moving Subjects. Magma, 2006.
  45. Stainsby, J.A., et al., Real-time magnetic resonance with physiologic monitoring for improved scan localization. Magn Reson Med, 2005. 53(4): p. 954-9.
  46. Thesen, S., et al., Prospective acquisition correction for head motion with image-based tracking for real-time fMRI. Magn Reson Med, 2000. 44(3): p. 457-65.
  47. Toma, K. and T. Nakai, Functional MRI in human motor control studies and clinical applications. Magn Reson Med Sci, 2002. 1(2): p. 109-20.
  48. Voyvodic, J.T., Real-time fMRI paradigm control, physiology, and behavior combined with near real-time statistical analysis. Neuroimage, 1999. 10(2): p. 91-106.
  49. Ward, H.A., et al., Prospective multiaxial motion correction for fMRI. Magn Reson Med, 2000. 43(3): p. 459-69.
  50. Weiskopf, N., et al., Single-shot compensation of image distortions and BOLD contrast optimization using multi-echo EPI for real-time fMRI. Neuroimage, 2005. 24(4): p. 1068-79.
  51. Weiskopf, N., et al., Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans Biomed Eng, 2004. 51(6): p. 966-70.
  52. Weiskopf, N., et al., Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI). J Physiol Paris, 2004. 98(4-6): p. 357-73.
  53. Weiskopf, N., et al., Real-time functional magnetic resonance imaging: methods and applications. Magn Reson Imaging, 2007.
  54. Weiskopf, N., et al., Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. Neuroimage, 2003. 19(3): p. 577-86.
  55. Yang, S., et al., Head motion suppression using real-time feedback of motion information and its effects on task performance in fMRI. Neuroimage, 2005. 27(1): p. 153-62.
  56. Yoo, S.S., et al., Brain-computer interface using fMRI: spatial navigation by thoughts. Neuroreport, 2004. 15(10): p. 1591-5.
  57. Yoo, S.S., et al., Real-time adaptive functional MRI. Neuroimage, 1999. 10(5): p. 596-606.
  58. Yoo, S.S. and F.A. Jolesz, Functional MRI for neurofeedback: feasibility study on a hand motor task. Neuroreport, 2002. 13(11): p. 1377-81.
  59. Yoo, S.S., et al., Increasing cortical activity in auditory areas through neurofeedback functional magnetic resonance imaging. Neuroreport, 2006. 17(12): p. 1273-8.
  60. Yoo, S.S., et al., Reproducibility of trial-based functional MRI on motor imagery. Int J Neurosci, 2007. 117(2): p. 215-27.