fMRI Brain-Computer Interfaces

Brain-computer interfaces based on fMRI enable real-time feedback of circumscribed brain regions to learn volitional regulation of those regions. This is an emerging field of intense research, with potential for multiple applications in neuroscientific research in brain plasticity and reorganization, movement restoration due to stroke, clinical rehabilitation of emotional disorders, quality assurance of fMRI experiments, and teaching functional imaging. This article presents a general architecture of an fMRI-BCI, with descriptions of each of its subsystems, and factors influencing their performance. The study has attempted to describe and compare a variety of approaches toward signal acquisition, preprocessing, analysis, and feedback. Technological advancement in higher-field MRI scanners, data acquisition sequences and image reconstruction techniques, preprocessing algorithms to correct for artifacts, more intelligent and robust analysis and interpretation methods, and faster feedback and visualization technology are anticipated to make fMRI-BCI widely applicable. FMRI-BCI could potentially be used for training patients to learn self-regulation of specific brain areas for transferring them later on to a more portable EEG-BCI system. FMRI-BCI has the potential of establishing itself as a tool for neuroscientific research and experimentation and also as an aid for psychophysiological treatment.

[1]  R. S. Hinks,et al.  Real‐time shimming to compensate for respiration‐induced B0 fluctuations , 2007, Magnetic resonance in medicine.

[2]  R. Deichmann,et al.  Optimized EPI for fMRI studies of the orbitofrontal cortex: compensation of susceptibility-induced gradients in the readout direction , 2007, Magnetic Resonance Materials in Physics, Biology and Medicine.

[3]  Seung-Schik Yoo,et al.  Functional MRI for neurofeedback: feasibility studyon a hand motor task , 2002, Neuroreport.

[4]  Tom M. Mitchell,et al.  Classifying Instantaneous Cognitive States from fMRI Data , 2003, AMIA.

[5]  Nikolaus Weiskopf,et al.  An EEG-driven brain-computer interface combined with functional magnetic resonance imaging (fMRI) , 2004, IEEE Transactions on Biomedical Engineering.

[6]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited , 1995, NeuroImage.

[7]  Fred Tam,et al.  Retrospective coregistration of functional magnetic resonance imaging data using external monitoring , 2005, Magnetic resonance in medicine.

[8]  Michael Erb,et al.  Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data , 2003, NeuroImage.

[9]  S Posse,et al.  Functional magnetic resonance imaging in real time (FIRE): Sliding‐window correlation analysis and reference‐vector optimization , 2000, Magnetic resonance in medicine.

[10]  Epifanio Bagarinao,et al.  Estimation of general linear model coefficients for real-time application , 2003, NeuroImage.

[11]  Michael Erb,et al.  Brain areas activated in fMRI during self-regulation of slow cortical potentials (SCPs) , 2003, Experimental Brain Research.

[12]  N. Logothetis The Underpinnings of the BOLD Functional Magnetic Resonance Imaging Signal , 2003, The Journal of Neuroscience.

[13]  Steven Laureys,et al.  When thoughts become action: An fMRI paradigm to study volitional brain activity in non-communicative brain injured patients , 2007, NeuroImage.

[14]  Wolfgang Grodd,et al.  Regulation of anterior insular cortex activity using real-time fMRI , 2007, NeuroImage.

[15]  Kâmil Uludağ,et al.  Functional Magnetic Resonance Imaging based BCI for Neurorehabilitation , 2006 .

[16]  Dinggang Shen,et al.  Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection , 2005, NeuroImage.

[17]  Lawrence L. Wald,et al.  Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters , 2005, NeuroImage.

[18]  C. Jack,et al.  Prospective multiaxial motion correction for fMRI , 2000, Magnetic resonance in medicine.

[19]  Frank Schneider,et al.  Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness , 2003, NeuroImage.

[20]  Janaina Mourão Miranda,et al.  Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data , 2005, NeuroImage.

[21]  Xiaoping P. Hu,et al.  Real‐time fMRI using brain‐state classification , 2007, Human brain mapping.

[22]  Soo-Young Lee,et al.  Brain–computer interface using fMRI: spatial navigation by thoughts , 2004, Neuroreport.

[23]  R. Veit,et al.  Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI) , 2004, Journal of Physiology-Paris.

[24]  I. Wickram Biofeedback: A Practitioner's Guide , 1987 .

[25]  Cuntai Guan,et al.  Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface , 2007, NeuroImage.

[26]  G H Glover,et al.  Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR , 2000, Magnetic resonance in medicine.

[27]  G. Rees,et al.  Neuroimaging: Decoding mental states from brain activity in humans , 2006, Nature Reviews Neuroscience.

[28]  Gary H. Glover,et al.  Learned regulation of spatially localized brain activation using real-time fMRI , 2004, NeuroImage.

[29]  K Mathiak,et al.  Evaluation of motion and realignment for functional magnetic resonance imaging in real time , 2001, Magnetic resonance in medicine.

[30]  D. S. G. Pollock,et al.  A handbook of time-series analysis, signal processing and dynamics , 1999 .

[31]  Peter A. Bandettini,et al.  Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.

[32]  John D E Gabrieli,et al.  Control over brain activation and pain learned by using real-time functional MRI. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[33]  R W Cox,et al.  Real‐Time Functional Magnetic Resonance Imaging , 1995, Magnetic resonance in medicine.

[34]  Wolfgang Grodd,et al.  Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) , 2004, IEEE Transactions on Biomedical Engineering.

[35]  R. S. Hinks,et al.  Time course EPI of human brain function during task activation , 1992, Magnetic resonance in medicine.

[36]  R. Deichmann,et al.  Real-time functional magnetic resonance imaging: methods and applications. , 2007, Magnetic resonance imaging.

[37]  L. Cohen,et al.  Brain–computer interfaces: communication and restoration of movement in paralysis , 2007, The Journal of physiology.

[38]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[39]  Daniel Gembris,et al.  Functional Magnetic Resonance Imaging in Real-Time (FIRE) , 2000 .

[40]  Vaidehi S. Natu,et al.  Category-Specific Cortical Activity Precedes Retrieval During Memory Search , 2005, Science.