[Available on CD-ROM in NeuroImage 19 (2), Supplement, 2003]


Luca A. Finelli1,2, Tzyy-Ping Jung1,2, Jeng-Ren Duann1,2, Frank Haist4, Scott Makeig1,2, and Terrence J. Sejnowski1,2,3


1. Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, CA, USA

2. Computational Neurobiology Laboratory, The Salk Institute, San Diego, CA, USA

3. Howard Hughes Medical Institute

4. Department of Neurosciences, University of California, San Diego, CA, USA



Simultaneous recording of EEG and fMRI combines the exquisite time resolution of the former with fMRI, the current gold standard for studying the functional neuroanatomy of brain function. Yet the acquisition of weak electric signals in an environment distinguished by strong magnetic fields is problematical and entails the technical challenge to remove EEG artifacts that may completely obscure signals of physiological relevance. As new scanners with higher static fields (3T, 4T, non-human primates 7T) are becoming available, the ability to identify and remove such artifacts is becoming increasingly important. Based on principles of information theory, a method is demonstrated for modeling and removing artifacts arising from the main static magnetic field, including pulse artifacts, the gradient system and possibly other interacting sources.



High-density EEG and electrooculogram (70+2 leads) data of six healthy subjects were recorded continuously prior to and during EPI scanning by a MR compatible polygraphic amplifier with timeout circuits synchronized to shut down signal acquisition briefly during scanner pulses (SA Instruments, San Diego). In 3 subjects the electrodes were referenced to the right mastoid, whereas in the other 3 data were acquired from bipolar derivations, using either twisted or untwisted pairs. Ten axial slices were acquired using an EPI protocol (matrix = 64 x 64 x 10; FOV = 256 x 256 mm; slice thickness = 7 mm) in a 1.5-T Siemens Vision MRI scanner. The experiment design consisted of 4-5 successive 6-min bouts with different cognitive tasks. 120-130 volumes per bout were acquired for each volunteer. Data from each bout were analyzed separately. The EEG was decomposed into spatially fixed, temporally independent components with distinct but not orthogonal topographies. The EEG data and the resulting independent components were analyzed with statistical and signal processing methods including spectral, time-frequency analyses, and event-related 'ERP images'.



Compared to other references, twisted bipolar pairs attenuated the effects of MR-induced currents. Several independent components from each individual data decomposition identified signal sources of distinct artifactual nature. These differed between subjects and could be classified according to their origin. The identified pulse artifact components were analyzed in more detail. Data were divided into 1-s epochs centered around the pulsatile peak of the component with highest amplitude and were then averaged as heartbeat-related responses. The spatiotemporal dynamics of their averaged, back-projected sum, when visualized using short scalp-map movies, revealed a pattern moving rapidly across the scalp sensors.



Independent component analysis was able to separate the EEG acquired in the magnet into components with characteristic time courses accounting for distinct sources of signal and noise. In particular, identification and back-projection of the pulse-related components allowed detailed modeling of their summed spatio-temporal dynamics. The method is not affected by complex beat-to-beat variations, and can model those as well. In addition, it does not require acquisition of electrocardiographic reference signals, and uses principles of information theory to obtain detailed spatio-temporal characteristics of the different artifacts, an approach that may help to understand their complex nature.