Nothing Special   »   [go: up one dir, main page]
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Opinion
  • Published:

Applications of real-time fMRI

Nature Reviews Neuroscience volume 9, pages 720–729 (2008)Cite this article

Abstract

For centuries people have aspired to understand and control the functions of the mind and brain. It has now become possible to image the functioning of the human brain in real time using functional MRI (fMRI), and thereby to access both sides of the mind–brain interface — subjective experience (that is, one's mind) and objective observations (that is, external, quantitative measurements of one's brain activity) — simultaneously. Developments in neuroimaging are now being translated into many new potential practical applications, including the reading of brain states, brain–computer interfaces, communicating with locked-in patients, lie detection, and learning control over brain activation to modulate cognition or even treat disease.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Imaging the human brain in real time.
Figure 2: Communication with a patient in a vegetative state using functional MRI.
Figure 3: Direct mental control over navigation through a maze by monitoring distributed brain activation with fMRI.
Figure 4: Learned explicit control over activation in a targeted brain region.
Figure 5: Training leads to spatially specific explicit control over brain activation, with corresponding changes in pain perception.

Similar content being viewed by others

References

  1. Descartes, R., Haldane, E. S. & Ross, G. R. T. The Philosophical Works of Descartes Vol. 2 (Cambridge Univ. Press, 1981).

    Google Scholar 

  2. Cabeza, R. & Kingstone, A. Handbook of Functional Neuroimaging of Cognition Vol. 3 (MIT Press, Cambridge, Massachusetts, 2001).

    Google Scholar 

  3. Gazzaniga, M. S. The Cognitive Neurosciences 3rd edn (MIT Press, Cambridge, Massachusetts, 2004).

    Google Scholar 

  4. Cox, R. W., Jesmanowicz, A. & Hyde, J. S. Real-time functional magnetic resonance imaging. Magn. Reson. Med. 33, 230–236 (1995).

    Article  CAS  PubMed  Google Scholar 

  5. Cohen, M. S. Real-time functional magnetic resonance imaging. Methods 25, 201–220 (2001).

    Article  CAS  PubMed  Google Scholar 

  6. deCharms, R. C. Reading and controlling human brain activation using real-time functional magnetic resonance imaging. Trends Cogn. Sci. 11, 473–481 (2007).

    Article  PubMed  Google Scholar 

  7. Weiskopf, N. et al. Real-time functional magnetic resonance imaging: methods and applications. Magn. Reson. Imaging 25, 989–1003 (2007).

    Article  PubMed  Google Scholar 

  8. Weiskopf, N. et al. Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI). J. Physiol. (Paris) 98, 357–373 (2004).

    Article  Google Scholar 

  9. Weiskopf, N. et al. Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans. Biomed. Eng. 51, 966–970 (2004).

    Article  PubMed  Google Scholar 

  10. Buxton, R. B. Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques (Cambridge Univ. Press, Cambridge, UK, 2002).

    Book  Google Scholar 

  11. Bandettini, P. A., Aguirre, G. K. & Moonen, C. T. W. Functional MRI. Medical Radiology (Springer, Berlin; New York, 1999).

    Google Scholar 

  12. Bernstein, M. A., King, K. F. & Zhou, Z. J. Handbook of MRI Pulse Sequences (Academic, Amsterdam; Boston, 2004).

    Google Scholar 

  13. Lopes da Silva, F. Functional localization of brain sources using EEG and/or MEG data: volume conductor and source models. Magn. Reson. Imaging 22, 1533–1538 (2004).

    Article  PubMed  Google Scholar 

  14. Irani, F. et al. Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders. Clin. Neuropsychol. 21, 9–37 (2007).

    Article  PubMed  Google Scholar 

  15. deCharms, R. C. & Zador, A. Neural representation and the cortical code. Annu. Rev. Neurosci. 23, 613–647 (2000).

    Article  CAS  PubMed  Google Scholar 

  16. Zarahn, E. Spatial localization and resolution of BOLD fMRI. Curr. Opin. Neurobiol. 11, 209–212 (2001).

    Article  CAS  PubMed  Google Scholar 

  17. deCharms, R. C. & Merzenich, M. M. Primary cortical representation of sounds by the coordination of action-potential timing. Nature 381, 610–613 (1996).

    Article  CAS  PubMed  Google Scholar 

  18. Averbeck, B. B. & Lee, D. Coding and transmission of information by neural ensembles. Trends Neurosci. 27, 225–230 (2004).

    Article  CAS  PubMed  Google Scholar 

  19. Gawne, T. J. Temporal coding as a means of information transfer in the primate visual system. Crit. Rev. Neurobiol. 13, 83–101 (1999).

    Article  CAS  PubMed  Google Scholar 

  20. Reich, D. S. et al. Interspike intervals, receptive fields, and information encoding in primary visual cortex. J. Neurosci. 20, 1964–1974 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lebedev, M. A. & Nicolelis, M. A. Brain-machine interfaces: past, present and future. Trends Neurosci. 29, 536–546 (2006).

    Article  CAS  PubMed  Google Scholar 

  22. Mason, S. G. et al. A comprehensive survey of brain interface technology designs. Ann. Biomed. Eng. 35, 137–169 (2007).

    Article  CAS  PubMed  Google Scholar 

  23. Birbaumer, N. Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. Psychophysiology 43, 517–532 (2006).

    Article  PubMed  Google Scholar 

  24. Birbaumer, N. & Cohen, L. G. Brain-computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579, 621–636 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Duda, R. O., Hart, P. E. & Stork, D. G. Pattern Classification 2nd edn (Wiley, New York, 2001).

    Google Scholar 

  26. Cox, D. D. & Savoy, R. L. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage 19, 261–270 (2003).

    Article  PubMed  Google Scholar 

  27. Haxby, J. V. et al. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001).

    Article  CAS  PubMed  Google Scholar 

  28. Norman, K. A. et al. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10, 424–430 (2006).

    Article  PubMed  Google Scholar 

  29. Carlson, T. A., Schrater, P. & He, S. Patterns of activity in the categorical representations of objects. J. Cogn. Neurosci. 15, 704–717 (2003).

    Article  PubMed  Google Scholar 

  30. Davatzikos, C. et al. Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. Neuroimage 28, 663–668 (2005).

    Article  CAS  PubMed  Google Scholar 

  31. Hanson, S. J., Matsuka, T. & Haxby, J. V. Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area? Neuroimage 23, 156–166 (2004).

    Article  PubMed  Google Scholar 

  32. Martinez-Ramon, M. et al. fMRI pattern classification using neuroanatomically constrained boosting. Neuroimage 31, 1129–1141 (2006).

    Article  PubMed  Google Scholar 

  33. O'Toole, A. J. et al. Partially distributed representations of objects and faces in ventral temporal cortex. J. Cogn. Neurosci. 17, 580–590 (2005).

    Article  PubMed  Google Scholar 

  34. Dehaene, S. et al. Inferring behavior from functional brain images. Nature Neurosci. 1, 549–550 (1998).

    Article  CAS  PubMed  Google Scholar 

  35. LaConte, S. et al. The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics. Neuroimage 18, 10–27 (2003).

    Article  PubMed  Google Scholar 

  36. Mitchell, T. M. Machine Learning. McGraw-Hill Series in Computer Science (McGraw-Hill, New York, 1997).

    Google Scholar 

  37. Kay, K. N. et al. Identifying natural images from human brain activity. Nature, 452, 352–355 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 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. 12, 25–41 (2001).

    Article  CAS  PubMed  Google Scholar 

  39. Haynes, J. D. & Rees, G. Decoding mental states from brain activity in humans. Nature Rev. Neurosci. 7, 523–534 (2006).

    Article  CAS  Google Scholar 

  40. Haynes, J. D. & Rees, G. Predicting the stream of consciousness from activity in human visual cortex. Curr. Biol. 15, 1301–1307 (2005).

    Article  CAS  PubMed  Google Scholar 

  41. Kamitani, Y. & Tong, F. Decoding the visual and subjective contents of the human brain. Nature Neurosci. 8, 679–685 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. Thirion, B. et al. Inverse retinotopy: inferring the visual content of images from brain activation patterns. Neuroimage 33, 1104–1116 (2006).

    Article  PubMed  Google Scholar 

  43. Polyn, S. M. et al. Category-specific cortical activity precedes retrieval during memory search. Science, 310, 1963–1966 (2005).

    Article  CAS  PubMed  Google Scholar 

  44. LaConte, S. et al. Support vector machines for temporal classification of block design fMRI data. Neuroimage, 26, 317–329 (2005).

    Article  PubMed  Google Scholar 

  45. Laconte, S. M., Peltier, S. J. & Hu, X. P. Real-time fMRI using brain-state classification. Hum. Brain Mapp. 28, 1033–1044 (2006).

    Article  PubMed Central  Google Scholar 

  46. Owen, A. M. et al. Detecting awareness in the vegetative state. Science 313, 1402 (2006).

    Article  CAS  PubMed  Google Scholar 

  47. Nachev, P. & Husain, M. Comment on “Detecting awareness in the vegetative state”. Science 315, 1221; author reply 1221 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Greenberg, D. L. Comment on “Detecting awareness in the vegetative state”. Science, 315, 1221; author reply 1221 (2007).

    Article  CAS  PubMed  Google Scholar 

  49. Coleman, M. R. et al. Do vegetative patients retain aspects of language comprehension? Evidence from fMRI. Brain 130, 2494–2507 (2007).

    Article  PubMed  Google Scholar 

  50. Owen, A. M. et al. Using functional magnetic resonance imaging to detect covert awareness in the vegetative state. Arch. Neurol. 64, 1098–1102 (2007).

    Article  PubMed  Google Scholar 

  51. Di, H. B. et al. Cerebral response to patient's own name in the vegetative and minimally conscious states. Neurology 68, 895–899 (2007).

    Article  CAS  PubMed  Google Scholar 

  52. Bernat, J. L. & Rottenberg, D. A. Conscious awareness in PVS and MCS: the borderlands of neurology. Neurology 68, 885–886 (2007).

    Article  PubMed  Google Scholar 

  53. Owen, A. M. & Coleman, M. R. Functional neuroimaging of the vegetative state. Nature Rev. Neurosci. 9, 235–243 (2008).

    Article  CAS  Google Scholar 

  54. Spence, S. A. et al. A cognitive neurobiological account of deception: evidence from functional neuroimaging. Philos. Trans. R. Soc. Lond. B Biol. Sci. 359, 1755–1762 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Langleben, D. D. et al. Telling truth from lie in individual subjects with fast event-related fMRI. Hum. Brain Mapp. 26, 262–272 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Spence, S. A. et al. Behavioural and functional anatomical correlates of deception in humans. Neuroreport 12, 2849–2853 (2001).

    Article  CAS  PubMed  Google Scholar 

  57. Langleben, D. D. et al. Brain activity during simulated deception: an event-related functional magnetic resonance study. Neuroimage 15, 727–732 (2002).

    Article  CAS  PubMed  Google Scholar 

  58. Ganis, G. et al. Neural correlates of different types of deception: an fMRI investigation. Cereb. Cortex 13, 830–836 (2003).

    Article  CAS  PubMed  Google Scholar 

  59. Kozel, F. A. et al. Detecting deception using functional magnetic resonance imaging. Biol. Psychiatry 58, 605–613 (2005).

    Article  PubMed  Google Scholar 

  60. Nunez, J. M. et al. Intentional false responding shares neural substrates with response conflict and cognitive control. Neuroimage 25, 267–277 (2005).

    Article  PubMed  Google Scholar 

  61. Thompson, S. K. The legality of the use of psychiatric neuroimaging in intelligence interrogation. Cornell Law Rev. 90, 1601–1637 (2005).

    PubMed  Google Scholar 

  62. Appelbaum, P. S. Law & psychiatry: the new lie detectors: neuroscience, deception, and the courts. Psychiatr. Serv. 58, 460–462 (2007).

    Article  PubMed  Google Scholar 

  63. Wild, J. Brain imaging ready to detect terrorists, say neuroscientists. Nature 437, 457 (2005).

    Article  CAS  PubMed  Google Scholar 

  64. Meegan, D. V. Neuroimaging techniques for memory detection: scientific, ethical, and legal issues. Am. J. Bioeth. 8, 9–20 (2008).

    Article  PubMed  Google Scholar 

  65. Yoo, S. S. et al. Brain-computer interface using fMRI: spatial navigation by thoughts. Neuroreport 15, 1591–1595 (2004).

    Article  PubMed  Google Scholar 

  66. Peplow, M. Mental ping-pong could aid paraplegics. Nature News 23 Aug 2004 (doi: 10.1038/news040823-18).

  67. Fetz, E. E. & Finocchio, D. V. Operant conditioning of specific patterns of neural and muscular activity. Science 174, 431–435 (1971).

    Article  CAS  PubMed  Google Scholar 

  68. Nowlis, D. P. & Kamiya, J. The control of electroencephalographic alpha rhythms through auditory feedback and the associated mental activity. Psychophysiology 6, 476–484 (1970).

    Article  CAS  PubMed  Google Scholar 

  69. Manuck, S. B. et al. Role of feedback in voluntary control of heart rate. Percept. Mot. Skills 40, 747–752 (1975).

    Article  CAS  PubMed  Google Scholar 

  70. Allen, J. J., Harmon-Jones, E. & Cavender, J. H. Manipulation of frontal EEG asymmetry through biofeedback alters self-reported emotional responses and facial EMG. Psychophysiology 38, 685–693 (2001).

    Article  CAS  PubMed  Google Scholar 

  71. Elbert, T. Self-Regulation of the Brain and Behavior (Springer, Berlin; New York, 1984).

    Book  Google Scholar 

  72. Friel, P. N. EEG biofeedback in the treatment of attention deficit hyperactivity disorder. Altern. Med. Rev. 12, 146–151 (2007).

    PubMed  Google Scholar 

  73. Lubar, J. F. & Deering, W. M. Behavioral Approaches to Neurology. Behavioral Medicine Series (Academic, New York, 1981).

    Google Scholar 

  74. deCharms, R. C. et al. Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage 21, 436–443 (2004).

    Article  PubMed  Google Scholar 

  75. Weiskopf, N. et al. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. Neuroimage 19, 577–586 (2003).

    Article  PubMed  Google Scholar 

  76. Yoo, S. S. & Jolesz, F. A. Functional MRI for neurofeedback: feasibility study on a hand motor task. Neuroreport 13, 1377–1381 (2002).

    Article  PubMed  Google Scholar 

  77. Posse, S. et al. Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness. Neuroimage 18, 760–768 (2003).

    Article  PubMed  Google Scholar 

  78. Yoo, S. S. et al. Increasing cortical activity in auditory areas through neurofeedback functional magnetic resonance imaging. Neuroreport 17, 1273–1278 (2006).

    Article  PubMed  Google Scholar 

  79. Caria, A. et al. Regulation of anterior insular cortex activity using real-time fMRI. Neuroimage 35, 1238–1246 (2007).

    Article  PubMed  Google Scholar 

  80. deCharms, R. C. et al. Control over brain activation and pain learned by using real-time functional MRI. Proc. Natl Acad. Sci. USA 102, 18626–18631 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Fetz, E. E. Volitional control of neural activity: implications for brain-computer interfaces. J. Physiol. 579, 571–579 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Rainville, P. Brain mechanisms of pain affect and pain modulation. Curr. Opin. Neurobiol. 12, 195–204 (2002).

    Article  CAS  PubMed  Google Scholar 

  83. Apkarian, A. V. Functional magnetic resonance imaging of pain consciousness: cortical networks of pain critically depend on what is implied by “pain”. Curr. Rev. Pain 3, 308–315 (1999).

    Article  CAS  PubMed  Google Scholar 

  84. Moisset, X. & Bouhassira, D. Brain imaging of neuropathic pain. Neuroimage 37 (Suppl. 1), S80–S88 (2007).

    Article  PubMed  Google Scholar 

  85. Petrovic, P. & Ingvar, M. Imaging cognitive modulation of pain processing. Pain 95, 1–5 (2002).

    Article  PubMed  Google Scholar 

  86. Peyron, R., Laurent, B. & Garcia-Larrea, L. Functional imaging of brain responses to pain. A review and meta-analysis. Neurophysiol. Clin. 30, 263–288 (2000).

    Article  CAS  PubMed  Google Scholar 

  87. Rainville, P. Brain mechanisms of pain affect and pain modulation. Curr. Opin. Neurobiol. 12, 195–204 (2002).

    Article  CAS  PubMed  Google Scholar 

  88. Tracey, I. Imaging pain. Br. J. Anaesth. 101, 32–39 (2008).

    Article  CAS  PubMed  Google Scholar 

  89. Bray, S., Shimojo, S. & O'Doherty, J. P. Direct instrumental conditioning of neural activity using functional magnetic resonance imaging-derived reward feedback. J. Neurosci. 27, 7498–7507 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Buonomano, D. V. & Merzenich, M. M. Cortical plasticity: from synapses to maps. Annu. Rev. Neurosci. 21, 149–186 (1998).

    Article  CAS  PubMed  Google Scholar 

  91. Merzenich, M. M. & deCharms, R. C. in The Mind-Brain Continuum (eds Llinas, R. & Churchland, P.) (MIT Press, Boston, 1996).

    Google Scholar 

  92. Moore, N. C. A review of EEG biofeedback treatment of anxiety disorders. Clin. Electroencephalogr. 31, 1–6 (2000).

    Article  CAS  PubMed  Google Scholar 

  93. Sterman, M. B. & Egner, T. Foundation and practice of neurofeedback for the treatment of epilepsy. Appl. Psychophysiol. Biofeedback 31, 21–35 (2006).

    Article  PubMed  Google Scholar 

  94. Gruzelier, J. & Egner, T. Critical validation studies of neurofeedback. Child Adolesc. Psychiatr. Clin. N. Am. 14, 83–104, vi (2005).

    Article  PubMed  Google Scholar 

  95. Phan, K. L. et al. Real-time fMRI of cortico-limbic brain activity during emotional processing. Neuroreport 15, 527–532 (2004).

    Article  PubMed  Google Scholar 

  96. Adcock, R. A. et al. Real time fMRI during the psychotherapy session: toward a methodology to augment therapeutic benefit. 11th Ann. Meeting Organ. Hum. Brain Mapp. (Toronto, Ontario, Canada, 2005).

    Google Scholar 

Download references

Acknowledgements

This work was funded by US National Institutes of Health Grants and Contracts MH067290, NS050642, NS049673, N43DA-4-7748, DA021877, and N43DA-7-4408.

Author information

Authors and Affiliations

  1. R. Christopher deCharms is at the Omneuron 3T MRI Research Center, 99 El Camino Real, Menlo Park, California 94025, USA. decharms@gmail.com,

    R. Christopher deCharms

Authors
  1. R. Christopher deCharms
    View author publications

    You can also search for this author in PubMed Google Scholar

Ethics declarations

Competing interests

This work was funded by US National Institutes of Health Grants and Contracts MH067290, NS050642, NS049673, N43DA-4-7748, DA021877 and N43DA-7-4408 to Omneuron Inc., a venture that is developing clinical applications of real-time functional MRI and that is conducting ongoing clinical trials of this approach. R. Christopher deCharms is CEO of this venture and has an ownership interest.

Related links

Related links

FURTHER INFORMATION

Christopher deCharms' homepage

Glossary

Biofeedback

A technique in which a continuous measure of some aspect of a person's biology is presented to that person for the purpose of training them to control the measure and, thereby, the corresponding biological function.

Contrast agent

A chemical agent that, when injected into a person, increases the measured contrast (the difference in image intensity) between different types of tissue. For example, a Gadolinium-based dye is sometimes used in MRI.

EEG

(Electroencephalography). A method for measuring the fast electrical activity in the brain that is associated with neuronal activation.

Locked-in syndrome

A medical state in which a patient has very limited or no ability to communicate with the world, often owing to extensive paralysis.

MEG

(Magnetoencephalography). A method for measuring the fast magnetic activity in the brain that is associated with neuronal activation.

Near-infrared spectroscopy

A method used for measuring brain blood flow and oxygenation near the cranial surface by shining near-infrared light through the skull and measuring the resulting emitted light spectrum, which is indicative of blood properties.

Pattern-classification algorithm

A computer-modelling method for classifying statistical patterns in complex multi-parameter data. For example, pattern-classification algorithms have been built that will classify spatial patterns of fMRI data (2D images or 3D volumes) by estimating what task a subject was undertaking when each particular fMRI pattern was measured.

Persistent vegetative state

A medical condition in which a patient shows sustained unresponsiveness and does not show evidence of awareness.

Region of interest (ROI) analysis

A method for measuring the time course of activation from a selected volume of the brain. This method can be used to infer the average activation in a region of a person's brain that has been caused by a stimulus or task, or conversely a level of ROI activation can be used to attempt to infer what task is being undertaken by a person.

Spatial-point spread function

The amount of spread, through space, of the measured signal that arises from an idealized single point in space. Spatial spread is caused by noise and imperfections in the measurement technique, for instance MRI.

Voxel

A 3D volume element of measurement (for example, a cube). Voxels are the 3D volume equivalent of a pixel in a 2D image.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Christopher deCharms, R. Applications of real-time fMRI. Nat Rev Neurosci 9, 720–729 (2008). https://doi.org/10.1038/nrn2414

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrn2414

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing