Application of Independent Component Analysis for the Data Mining of Simultaneous Eeg–fMRI: Preliminary Experience on Sleep Onset

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG–fMRI acquisitions, especially when a reference paradigm is unavailable.

[1]  D. Lehmann,et al.  Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review. , 2002, Methods and findings in experimental and clinical pharmacology.

[2]  Hae-Jeong Park,et al.  Statistical parametric mapping of LORETA using high density EEG and individual MRI: Application to mismatch negativities in Schizophrenia , 2002, Human brain mapping.

[3]  Emery N. Brown,et al.  Motion and Ballistocardiogram Artifact Removal for Interleaved Recording of EEG and EPs during MRI , 2002, NeuroImage.

[4]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[5]  Louis Lemieux,et al.  Mapping of spikes, slow waves, and motor tasks in a patient with malformation of cortical development using simultaneous EEG and fMRI. , 2003, Magnetic resonance imaging.

[6]  Jong-Hwan Lee,et al.  Neurofeedback fMRI‐mediated learning and consolidation of regional brain activation during motor imagery , 2008, Int. J. Imaging Syst. Technol..

[7]  A. W. M. van den Enden,et al.  Discrete Time Signal Processing , 1989 .

[8]  J. L. Cantero,et al.  Human alpha oscillations in wakefulness, drowsiness period, and REM sleep: different electroencephalographic phenomena within the alpha band , 2002, Neurophysiologie Clinique/Clinical Neurophysiology.

[9]  Manuel Abbafati,et al.  An independent component analysis-based approach on ballistocardiogram artifact removing. , 2006, Magnetic resonance imaging.

[10]  Derong Liu,et al.  A Neural Network Method for Detection of Obstructive Sleep Apnea and Narcolepsy Based on Pupil Size and EEG , 2008, IEEE Transactions on Neural Networks.

[11]  L Bozzao,et al.  Real-time MR artifacts filtering during continuous EEG/fMRI acquisition. , 2003, Magnetic resonance imaging.

[12]  Bettina Sorger,et al.  Improved quality of auditory event-related potentials recorded simultaneously with 3-T fMRI: Removal of the ballistocardiogram artefact , 2007, NeuroImage.

[13]  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.

[14]  Andrzej Cichocki,et al.  Removal of ballistocardiogram artifacts from simultaneously recorded EEG and fMRI data using independent component analysis , 2006, IEEE Transactions on Biomedical Engineering.

[15]  G. Srivastava,et al.  ICA-based procedures for removing ballistocardiogram artifacts from EEG data acquired in the MRI scanner , 2005, NeuroImage.

[16]  Manuel Schabus,et al.  Hemodynamic cerebral correlates of sleep spindles during human non-rapid eye movement sleep , 2007, Proceedings of the National Academy of Sciences.

[17]  G. E. Meadows,et al.  Cerebral blood flow changes associated with fluctuations in alpha and theta rhythm during sleep onset in humans , 2005, The Journal of physiology.

[18]  G. Hajak,et al.  Positron emission tomography findings in obstructive sleep apnea patients with residual sleepiness treated with continuous positive airway pressure. , 2007, Journal of physiology and pharmacology : an official journal of the Polish Physiological Society.

[19]  Helmut Laufs,et al.  Where the BOLD signal goes when alpha EEG leaves , 2006, NeuroImage.

[20]  Gian Luca Romani,et al.  Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis , 2007, NeuroImage.

[21]  Helmut Laufs,et al.  Endogenous brain oscillations and related networks detected by surface EEG‐combined fMRI , 2008, Human brain mapping.

[22]  Paolo Maria Rossini,et al.  Neurophysiological correlates of sleepiness: A combined TMS and EEG study , 2007, NeuroImage.

[23]  Brent R Logan,et al.  An evaluation of spatial thresholding techniques in fMRI analysis , 2008, Human brain mapping.

[24]  Louis Lemieux,et al.  Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction , 1998, NeuroImage.

[25]  S. Daan,et al.  Subjective sleepiness correlates negatively with global alpha (8–12 Hz) and positively with central frontal theta (4–8 Hz) frequencies in the human resting awake electroencephalogram , 2003, Neuroscience Letters.

[26]  Georg Dorffner,et al.  A reliable probabilistic sleep stager based on a single EEG signal , 2005, Artif. Intell. Medicine.

[27]  Jean-Franois Cardoso High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

[28]  D. Dijk,et al.  Frontal predominance of a relative increase in sleep delta and theta EEG activity after sleep loss in humans. , 1999, Sleep research online : SRO.

[29]  Robert Turner,et al.  A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI , 2000, NeuroImage.

[30]  M. Bertini,et al.  The boundary between wakefulness and sleep: quantitative electroencephalographic changes during the sleep onset period , 2001, Neuroscience.

[31]  E. Pérez-Garci,et al.  EEG bands during wakefulness, slow-wave, and paradoxical sleep as a result of principal component analysis in the rat. , 2000, Sleep.

[32]  O. Josephs,et al.  EEG recording during fMRI experiments: Image quality , 2000, Human brain mapping.

[33]  G. Wilson,et al.  Removal of ocular artifacts from electro-encephalogram by adaptive filtering , 2004, Medical and Biological Engineering and Computing.

[34]  S Warach,et al.  Monitoring the patient's EEG during echo planar MRI. , 1993, Electroencephalography and clinical neurophysiology.

[35]  Manbir Singh,et al.  Correlation between BOLD‐fMRI and EEG signal changes in response to visual stimulus frequency in humans , 2003, Magnetic resonance in medicine.

[36]  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.

[37]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[38]  Hyun Wook Park,et al.  Improved ballistocardiac artifact removal from the electroencephalogram recorded in fMRI , 2004, Journal of Neuroscience Methods.

[39]  S. Hughes,et al.  Thalamic Mechanisms of EEG Alpha Rhythms and Their Pathological Implications , 2005, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[40]  R. Edelman,et al.  Magnetic resonance imaging (2) , 1993, The New England journal of medicine.