fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

Sitaram, Ranganatha; Caria, Andrea; Veit, Ralf; Gaber, Tilman; Rota, Giuseppina; Kuebler, Andrea; Birbaumer, Niels

doi:https://doi.org/10.1155/2007/25487

Computational Intelligence and Neuroscience

On this page

Abstract References Copyright Related Articles

Special Issue

Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

View this Special Issue

Review Article | Open Access

Volume 2007 | Article ID 025487 | https://doi.org/10.1155/2007/25487

fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

Ranganatha Sitaram,¹Andrea Caria,¹Ralf Veit,^1,2Tilman Gaber,^1,2Giuseppina Rota,^1,3Andrea Kuebler,¹and Niels Birbaumer^1,4

Academic Editor: Shangkai Gao

Received28 Feb 2007

Revised02 Aug 2007

Accepted18 Sept 2007

Published22 Nov 2007

Abstract

Brain-computer interfaces based on functional magnetic resonance imaging (fMRI-BCI) allow volitional control of anatomically specific regions of the brain. Technological advancement in higher field MRI scanners, fast data acquisition sequences, preprocessing algorithms, and robust statistical analysis are anticipated to make fMRI-BCI more widely available and applicable. This noninvasive technique could potentially complement the traditional neuroscientific experimental methods by varying the activity of the neural substrates of a region of interest as an independent variable to study its effects on behavior. If the neurobiological basis of a disorder (e.g., chronic pain, motor diseases, psychopathy, social phobia, depression) is known in terms of abnormal activity in certain regions of the brain, fMRI-BCI can be targeted to modify activity in those regions with high specificity for treatment. In this paper, we review recent results of the application of fMRI-BCI to neuroscientific research and psychophysiological treatment.

References

N. Birbaumer, N. Ghanayim, T. Hinterberger et al., “A spelling device for the paralysed,” Nature, vol. 398, no. 6725, pp. 297–298, 1999.
View at: Publisher Site | Google Scholar
J. P. Donoghue, “Connecting cortex to machines: recent advances in brain interfaces,” Nature Neuroscience, vol. 5, pp. 1085–1088, 2002.
View at: Publisher Site | Google Scholar
J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clinical Neurophysiology, vol. 113, no. 6, pp. 767–791, 2002.
View at: Publisher Site | Google Scholar
M. A. L. Nicolelis, “Brain-machine interfaces to restore motor function and probe neural circuits,” Nature Reviews Neuroscience, vol. 4, no. 5, pp. 417–422, 2003.
View at: Publisher Site | Google Scholar
J. R. Wolpaw and D. J. McFarland, “Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 51, pp. 17849–17854, 2004.
View at: Publisher Site | Google Scholar
L. R. Hochberg and J. P. Donoghue, “Sensors for brain-computer interfaces: options for turning thought into action,” IEEE Engineering in Medicine and Biology Magazine, vol. 25, no. 5, pp. 32–38, 2006.
View at: Publisher Site | Google Scholar
N. Weiskopf, R. Sitaram, O. Josephs et al., “Real-time functional magnetic resonance imaging: methods and applications,” Magnetic Resonance Imaging, vol. 25, no. 6, pp. 989–1003, 2007.
View at: Publisher Site | Google Scholar
A. Caria, R. Veit, R. Sitaram et al., “Regulation of anterior insular cortex activity using real-time fMRI,” NeuroImage, vol. 35, no. 3, pp. 1238–1246, 2007.
View at: Publisher Site | Google Scholar
N. K. Logothetis, J. Pauls, M. Augath, T. Trinath, and A. Oeltermann, “Neurophysiological investigation of the basis of the fMRI signal,” Nature, vol. 412, no. 6843, pp. 150–157, 2001.
View at: Publisher Site | Google Scholar
A. Shmuel, M. Augath, A. Oeltermann, and N. K. Logothetis, “Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1,” Nature Neuroscience, vol. 9, no. 4, pp. 569–577, 2006.
View at: Publisher Site | Google Scholar
S.-S. Yoo and F. A. Jolesz, “Functional MRI for neurofeedback: feasibility study on a hand motor task,” NeuroReport, vol. 13, no. 11, pp. 1377–1381, 2002.
View at: Publisher Site | Google Scholar
S. Posse, D. Fitzgerald, K. Gao et al., “Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness,” NeuroImage, vol. 18, no. 3, pp. 760–768, 2003.
View at: Publisher Site | Google Scholar
N. Weiskopf, R. Veit, M. Erb et al., “Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data,” NeuroImage, vol. 19, no. 3, pp. 577–586, 2003.
View at: Publisher Site | Google Scholar
R. C. DeCharms, K. Christoff, G. H. Glover, J. M. Pauly, S. Whitfield, and J. D. E. Gabrieli, “Learned regulation of spatially localized brain activation using real-time fMRI,” NeuroImage, vol. 21, no. 1, pp. 436–443, 2004.
View at: Publisher Site | Google Scholar
N. Weiskopf, F. Scharnowski, R. Veit, R. Goebel, N. Birbaumer, and K. Mathiak, “Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI),” Journal of Physiology, vol. 98, no. 4–6, pp. 357–373, 2004.
View at: Publisher Site | Google Scholar
S.-S. Yoo, T. Fairneny, N.-K. Chen et al., “Brain-computer interface using fMRI: spatial navigation by thoughts,” NeuroReport, vol. 15, no. 10, pp. 1591–1595, 2004.
View at: Publisher Site | Google Scholar
R. C. DeCharms, F. Maeda, G. H. Glover et al., “Control over brain activation and pain learned by using real-time functional MRI,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 51, pp. 18626–18631, 2005.
View at: Publisher Site | Google Scholar
R. Sitaram, A. Caria, R. Veit, T. Gaber, A. Kuebler, and N. Birbaumer, “Real-time fMRI based brain-computer interface enhanced by interactive virtual worlds,” in Proceedings of the 45th Annual Meeting Society for Psychophysiological Research, Lisbon, Portugal, 2005.
View at: Google Scholar
A. Caria, R. Veit, T. Gaber, R. Sitaram, A. Kuebler, and N. Birbaumer, “Can we learn to increase our emotional involvement? real-time fMRI of anterior cingulate cortex during emotional processing,” in Human Brain Mapping, Florence, Italy, June 2006.
View at: Google Scholar
G. Rota, R. Sitaram, R. Veit, N. Weiskopf, N. Birbaumer, and G. Dogil, “ fMRI-neurofeedback for operant conditioning and neural plasticity investigation: a study on the physiological self-induced regulation of the BA 45,” in Proceedings of the Cognitive Neuroscience Conference, San Francisco, Calif, USA, 2006.
View at: Google Scholar
R. Veit, H. Flor, M. Erb et al., “Brain circuits involved in emotional learning in antisocial behavior and social phobia in humans,” Neuroscience Letters, vol. 328, no. 3, pp. 233–236, 2002.
View at: Publisher Site | Google Scholar
C. H. Wagner and M. L. Barrett, 2004, Published online by PsycExtra.
J.-D. Haynes and G. Rees, “Decoding mental states from brain activity in humans,” Nature Reviews Neuroscience, vol. 7, no. 7, pp. 523–534, 2006.
View at: Publisher Site | Google Scholar
S. M. Laconte, S. J. Peltier, and X. P. Hu, “Real-time fMRI using brain-state classification,” in Human Brain Mapping, 2006.
View at: Google Scholar
R. Goebel, “Cortex-based real-time fMRI,” NeuroImage, vol. 13, no. 6, p. 129, 2001.
View at: Publisher Site | Google Scholar
C. T. W. Moonen and P. A. Bandettini, Eds., Functional MRI, Springer, Berlin, Germany, 2000.
T. E. Feinberg and M. J. Farah, Eds., Behavioral Neurology and Neuropsychology, McGraw-Hill, New York, NY, USA, 2nd edition, 2003.
B. Rockstroh, T. Elbert, N. Birbaumer, and W. Lutzenberger, “Biofeedback-produced hemispheric asymmetry of slow cortical potentials and its behavioural effects,” International Journal of Psychophysiology, vol. 9, no. 2, pp. 151–165, 1990.
View at: Publisher Site | Google Scholar
F. Pulvermüller, B. Mohr, H. Schleichert, and R. Veit, “Operant conditioning of left-hemispheric slow cortical potentials and its effect on word processing,” Biological Psychology, vol. 53, no. 2-3, pp. 177–215, 2000.
View at: Publisher Site | Google Scholar
T. Egner and J. H. Gruzelier, “Ecological validity of neurofeedback: modulation of slow wave EEG enhances musical performance,” NeuroReport, vol. 14, no. 9, pp. 1221–1224, 2003.
View at: Publisher Site | Google Scholar
M. M. Bradley and P. J. Lang, “Measuring emotion: the self-assessment manikin and the semantic differential,” Journal of Behavior Therapy and Experimental Psychiatry, vol. 25, no. 1, pp. 49–59, 1994.
View at: Publisher Site | Google Scholar
A. Caria, R. Veit, R. Sitaram et al., “Regulation of anterior insular cortex activity using real-time fMRI,” Neuroimage, vol. 35, no. 3, pp. 1238–1246, 2007.
View at: Publisher Site | Google Scholar
G. Dogil, I. Frese, H. Haider, D. RÖhm, and W. Wokurek, “Where and how does grammatically geared processing take place-and why is broca’s area often involved. A coordinated fMRI/ERBP study of language processing,” Brain and Language, vol. 89, no. 2, pp. 337–345, 2004.
View at: Publisher Site | Google Scholar
F. Tong, K. Nakayama, J. T. Vaughan, and N. Kanwisher, “Binocular rivalry and visual awareness in human extrastriate cortex,” Neuron, vol. 21, no. 4, pp. 753–759, 1998.
View at: Publisher Site | Google Scholar
T. Fuchs, N. Birbaumer, W. Lutzenberger, J. H. Gruzelier, and J. Kaiser, “Neurofeedback treatment for attention-deficit/hyperactivity disorder in children: a comparison with methylphenidate,” Applied Psychophysiology Biofeedback, vol. 28, no. 1, pp. 1–12, 2003.
View at: Publisher Site | Google Scholar
B. Kotchoubey, U. Strehl, C. Uhlmann et al., “Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study,” Epilepsia, vol. 42, no. 3, pp. 406–416, 2001.
View at: Publisher Site | Google Scholar
H. Kato, M. Izumiyama, H. Koizumi, A. Takahashi, and Y. Itoyama, “Near-infrared spectroscopic topography as a tool to monitor motor reorganization after hemiparetic stroke: a comparison with functional MRI,” Stroke, vol. 33, no. 8, pp. 2032–2036, 2002.
View at: Publisher Site | Google Scholar
B. H. Dobkin, “Functional MRI: a potential physiologic indicator for stroke rehabilitation interventions,” Stroke, vol. 34, no. 5, pp. e23–e28, 2003.
View at: Google Scholar
F. Scharnowski, N. Weiskopf, K. Mathiak et al., “Self-regulation of the BOLD signal of supplementary motor area (SMA) and parahippocampal place area (PPA): fMRI-neurofeedbackand its behavioural consequences,” in Proceedings of 10th International Conference on Functional Mapping of the Human Brain, Budapest, Hungary, 2004.
View at: Google Scholar
S. de Vries and T. Mulder, “Motor imagery and stroke rehabilitation: a critical discussion,” Journal of Rehabilitation Medicine, vol. 39, no. 1, pp. 5–13, 2007.
View at: Google Scholar
F. Maeda, “Learning to explicitly control activation in a localized brain region through real-time fMRI feedback based training, with result impact on pain perception,” in Proceedings of the Society for Neuroscience, pp. 601–604, Washington, DC, USA, 2004.
View at: Google Scholar
K. L. Phan, D. A. Fitzgerald, K. Gao, G. J. Moore, M. E. Tancer, and S. Posse, “Real-time fMRI of cortico-limbic brain activity during emotional processing,” NeuroReport, vol. 15, no. 3, pp. 527–532, 2004.
View at: Publisher Site | Google Scholar
S. Anders, M. Lotze, M. Erb, W. Grodd, and N. Birbaumer, “Brain activity underlying emotional valence and arousal: a response-related fMRI study,” Human Brain Mapping, vol. 23, no. 4, pp. 200–209, 2004.
View at: Publisher Site | Google Scholar
E. Viding, “On the nature and nurture of antisocial behavior and violence,” Annals of the New York Academy of Sciences, vol. 1036, pp. 267–277, 2004.
View at: Publisher Site | Google Scholar
E. Viding, “Annotation: understanding the development of psychopathy,” Journal of Child Psychology and Psychiatry and Allied Disciplines, vol. 45, no. 8, pp. 1329–1337, 2004.
View at: Publisher Site | Google Scholar
P. A. Brennan and A. Raine, “Biosocial bases of antisocial behavior: psychophysiological, neurological, and cognitive factors,” Clinical Psychology Review, vol. 17, no. 6, pp. 589–604, 1997.
View at: Publisher Site | Google Scholar
R. J. R. Blair, “Neurobiological basis of psychopathy,” British Journal of Psychiatry, vol. 182, pp. 5–7, 2003.
View at: Publisher Site | Google Scholar
J. LeDoux, “The emotional brain, fear, and the amygdala,” Cellular and Molecular Neurobiology, vol. 23, no. 4-5, pp. 727–738, 2003.
View at: Publisher Site | Google Scholar
N. Birbaumer, R. Veit, M. Lotze et al., “Deficient fear conditioning in psychopathy: a functional magnetic resonance imaging study,” Archives of General Psychiatry, vol. 62, no. 7, pp. 799–805, 2005.
View at: Publisher Site | Google Scholar
K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” NeuroImage, vol. 19, no. 4, pp. 1273–1302, 2003.
View at: Publisher Site | Google Scholar
S. LaConte, S. Strother, V. Cherkassky, J. Anderson, and X. Hu, “Support vector machines for temporal classification of block design fMRI data,” NeuroImage, vol. 26, no. 2, pp. 317–329, 2005.
View at: Publisher Site | Google Scholar
J. Mouräo-Miranda, A. L. W. Bokde, C. Born, H. Hampel, and M. Stetter, “Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data,” NeuroImage, vol. 28, no. 4, pp. 980–995, 2005.
View at: Publisher Site | Google Scholar
F. Esposito, E. Seifritz, E. Formisano et al., “Real-time independent component analysis of fMRI time-series,” NeuroImage, vol. 20, no. 4, pp. 2209–2224, 2003.
View at: Publisher Site | Google Scholar
S. Thesen, O. Heid, E. Mueller, and L. R. Schad, “Prospective acquisition correction for head motion with image-based tracking for real-time fMRI,” Magnetic Resonance in Medicine, vol. 44, no. 3, pp. 457–465, 2000.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2007 Ranganatha Sitaram et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation Order printed copies

Views

1973

Downloads

3512

Citations