Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data

A brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) is presented which allows human subjects to observe and control changes of their own blood oxygen level-dependent (BOLD) response. This BCI performs data preprocessing (including linear trend removal, 3D motion correction) and statistical analysis on-line. Local BOLD signals are continuously fed back to the subject in the magnetic resonance scanner with a delay of less than 2 s from image acquisition. The mean signal of a region of interest is plotted as a time-series superimposed on color-coded stripes which indicate the task, i.e., to increase or decrease the BOLD signal. We exemplify the presented BCI with one volunteer intending to control the signal of the rostral-ventral and dorsal part of the anterior cingulate cortex (ACC). The subject achieved significant changes of local BOLD responses as revealed by region of interest analysis and statistical parametric maps. The percent signal change increased across fMRI-feedback sessions suggesting a learning effect with training. This methodology of fMRI-feedback can assess voluntary control of circumscribed brain areas. As a further extension, behavioral effects of local self-regulation become accessible as a new field of research.

[1]  R W Cox,et al.  Real‐time 3D image registration for functional MRI , 1999, Magnetic resonance in medicine.

[2]  Douglas C. Noll,et al.  Online Analysis of Functional MRI Datasets on Parallel Platforms , 1997, The Journal of Supercomputing.

[3]  James T. Voyvodic,et al.  Real-Time fMRI Paradigm Control, Physiology, and Behavior Combined with Near Real-Time Statistical Analysis , 1999, NeuroImage.

[4]  J. Lancaster,et al.  Using the talairach atlas with the MNI template , 2001, NeuroImage.

[5]  G. Fricchione,et al.  Functional brain mapping of the relaxation response and meditation , 2000, Neuroreport.

[6]  J. Ashburner,et al.  Nonlinear spatial normalization using basis functions , 1999, Human brain mapping.

[7]  Wolfgang Grodd,et al.  Mismatch responses to randomized gradient switching noise as reflected by fMRI and whole‐head magnetoencephalography , 2002, Human brain mapping.

[8]  Karl J. Friston,et al.  A unified statistical approach for determining significant signals in images of cerebral activation , 1996, Human brain mapping.

[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]  Ravi S. Menon,et al.  Field Strength Dependence of Functional MRI Signals , 2000 .

[11]  U Klose,et al.  Improvement of the acquisition of a large amount of MR images on a conventional whole body system. , 1999, Magnetic resonance imaging.

[12]  D T Gering,et al.  Intraoperative, real‐time, functional MRI , 1998, Journal of magnetic resonance imaging : JMRI.

[13]  Karl J. Friston,et al.  Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.

[14]  M. Cohen,et al.  Real-Time functional MRI , 1998, NeuroImage.

[15]  David Collier-Brown,et al.  Using Samba , 2003 .

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

[17]  N Birbaumer,et al.  Biofeedback-produced hemispheric asymmetry of slow cortical potentials and its behavioural effects. , 1990, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[18]  O. Arthurs,et al.  How well do we understand the neural origins of the fMRI BOLD signal? , 2002, Trends in Neurosciences.

[19]  A. Deutman,et al.  Retinoic acid delays transcription of human retinal pigment neuroepithelium marker genes in ARPE‐19 cells , 2000, Neuroreport.

[20]  T. Neumann-Haefelin,et al.  Assessment of cerebrovascular reactivity with functional magnetic resonance imaging: comparison of CO(2) and breath holding. , 2001, Magnetic resonance imaging.

[21]  U. Strehl,et al.  Modification of Slow Cortical Potentials in Patients with Refractory Epilepsy: A Controlled Outcome Study , 2001, Epilepsia.

[22]  B. Feige,et al.  The Role of Higher-Order Motor Areas in Voluntary Movement as Revealed by High-Resolution EEG and fMRI , 1999, NeuroImage.

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

[24]  T. Mulholland,et al.  Feedback delay and amplitude threshold and control of the occipital EEG , 1979, Biofeedback and self-regulation.

[25]  Karl J. Friston,et al.  Spatial registration and normalization of images , 1995 .

[26]  H. Critchley,et al.  Neuroanatomical basis for first- and second-order representations of bodily states , 2001, Nature Neuroscience.

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

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

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

[30]  Rainer Goebel,et al.  Cortex-based real-time fMRI , 2001, NeuroImage.

[31]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[32]  R S Menon,et al.  The functional scout image: Immediate mapping of cortical function at 4 tesla using receiver phase cycling , 1997, Magnetic resonance in medicine.

[33]  JM Guérit Nowinski RN Bryan R Raghavan , 1998, Neurophysiologie Clinique/Clinical Neurophysiology.

[34]  M. Posner,et al.  Cognitive and emotional influences in anterior cingulate cortex , 2000, Trends in Cognitive Sciences.

[35]  Thomas Eickermann,et al.  A new approach to measure single‐event related brain activity using real‐time fMRI: Feasibility of sensory, motor, and higher cognitive tasks , 2001, Human brain mapping.

[36]  T. Carpenter,et al.  Linear coupling between functional magnetic resonance imaging and evoked potential amplitude in human somatosensory cortex , 2000, Neuroscience.

[37]  B. Vogt,et al.  Contributions of anterior cingulate cortex to behaviour. , 1995, Brain : a journal of neurology.

[38]  M. Bradley,et al.  Measuring emotion: the Self-Assessment Manikin and the Semantic Differential. , 1994, Journal of behavior therapy and experimental psychiatry.

[39]  T. Paus Primate anterior cingulate cortex: Where motor control, drive and cognition interface , 2001, Nature Reviews Neuroscience.

[40]  R. Deichmann,et al.  Compensation of Susceptibility-Induced BOLD Sensitivity Losses in Echo-Planar fMRI Imaging , 2001, NeuroImage.

[41]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[42]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[43]  T J Grabowski,et al.  Real‐time multiple linear regression for fMRI supported by time‐aware acquisition and processing , 2001, Magnetic resonance in medicine.

[44]  M S Cohen,et al.  Real-time functional magnetic resonance imaging. , 2001, Methods.

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

[46]  B. Vogt,et al.  Human cingulate cortex: Surface features, flat maps, and cytoarchitecture , 1995, The Journal of comparative neurology.

[47]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[48]  B. Rockstroh,et al.  Slow potentials of the cerebral cortex and behavior. , 1990, Physiological reviews.

[49]  C. Rorden,et al.  Stereotaxic display of brain lesions. , 2000, Behavioural neurology.