Magnetoencephalography and Magnetic Source Imaging

Trends in Neurosciences, 1994

Magnetoencephalography provides a new dimension to the functional imaging of the brain. The cerebral magnetic fields recorded noninvasively enable the accurate determination of locations of cerebral activ@ with an uncompromized time resolution. The first whole-scalp sensor arrays have just recently come into operation, and significant advances are to be expected in both neurophysiological and cognitive studies, as well as in clinical practice. However, although the accuracy of locating isolated sources of brain activity has improved, identification of multiple simultaneous sources can still be a problem. Therefore, attempts are being made to combine magnetoencephalography with other brainimaging methods to improve spatial localization of multiple sources and, simultaneously, to achieve a more complete characterization of different aspects qf brain activi& during cognitive processing. Owing to its good time resolution and considerably better spatial accuracy than that provided by E E G, magnetoencephalography holds great promise as a tool for revealing informationprocessing sequences of the human brain.

Blind source separation (BSS) decomposes a multidimensional time series into a set of sources, each with a one-dimensional time course and a xed spatial distribution. For EEG and MEG, the former corresponds to the simultaneously separated and temporally overlapping signals for continuous non-averaged data; the latter corresponds to the set of attenuations from the sources to the sensors. These sensor projection vectors give information on the spatial locations of the sources. Here we use standard Neuromag dipole-tting software to localize BSS-separated components of MEG data collected in several tasks in which visual, auditory, and somatosensory stimuli all play a role. We found that BSS-separated components with stimulusor motor-locked responses can be localized to physiological and anatomically meaningful locations within the brain. 1. INTRODUCTION Blind source separation (BSS) algorithms, such as Infomax (Bell and Sejnowski, 1995), second-order blind identication (SOBI) (Belouc...

IEEE Transactions on Magnetics, 1996

In neuromagnetism research, it is important to accurately estimate internal electrical sources in the human brain from the spatial and temporal distributions of magnetoencephalogram (MEC) activities over the head. In this study, we compared the performance of distributed internal source estimation in the human brain under two different MEG measurement conditions: (a) measurements of only the normal components of the external magnetic fields and (b) three-dimensional vector measurements of the external magnetic fields. We applied the sub-optimal leastsquares subspace scanning source estimation technique to both measurement conditions. The results showed that with the measurements ts of only the normal components, distributed source tends to be estimated deeper in the brain than they should be, while the three-dimensional vector measurements had the possibility to provide a better estimation for the depth of the internal source distribution.

Annals of Neurology, 1990

It is believed that the magnetoencephalogram (MEG) localizes an electrical source in the brain to within several millimeters and is therefore more accurate than electroencephalogram (EEG) localization, reported as 20 mm. To test this belief, the localization accuracy of the MEG and EEG were directly compared. The signal source was a dipole at a known location in the brain; this was made by passing a weak current pulse simulating a neural signal through depth electrodes already implanted in patients for seizure monitoring. First, MEGs and EEGs from this dipole were measured at 16 places on the head. Then, computations were performed on the MEG and EEG data separately to determine the apparent MEG and EEG source locations. Finally, these were compared with the actual source location to determine the MEG and EEG localization errors. Measurements were made of four dipoles in each of three patients. After MEGs with weak signals were discounted, the MEG average error of localization was found to be 8 mm, which was worse than expected. The average EEG error was 10 mm, which was better than expected. These results suggest that the MEG offers no significant advantage over the EEG in localizing a focal source. However, this does not diminish other uses of the MEG. Schomer DL. MEG versus EEG localization test using implanted sources in the human brain. Ann Neurol 1990;28:811-817 There is increasing interest in the magnetoencephalogram (MEG) both as a clinical and as a research tool El-41, and elaborate magnetic systems are beginning to appear in hospitals and research laboratories [ S , 61. The interest is due in part to the belief that the MEG can localize a source in the human brain to about 2 or 3 mm {7-141, hence, is more accurate than the electroencephalogram (EEG), reported to have an accuracy of 20 mm [ 151. However, this belief is based only on indirect evidence, not on a direct MEG-EEG comparison; therefore, it could be in error. For example, it is based on MEG measurements only [9, 10, 131 or an MEG-EEG comparison due to a source of unconfirmed location in the brain . Because of the importance of this belief, we performed a pilot study to compare MEG and EEG localization in a direct way. We compared them for the first time in the same human subject, due to a source of precisely known location in the brain. Our aim was to either validate the belief or see whether it is significantly in error. The results of this comparison are presented here. The subjects in these measurements were epileptic patients being evaluated for surgical resection; they had previously received implantations of intracerebral electrodes to record their seizure activity {161. On completion of those recordings, the same electrodes instead of recording signals were now used to produce a signal. A brief pulse of current, too weak to stimulate the brain but simulating a neural signal, was passed between two electrodes a short distance apart. This constituted a simple focal source (a dipole) where the location was accurately known from roentgenographs. First, the MEG and EEG signals due to this source were measured at the conventional number of 16 places over the head. Then, a computation (an inverse solution) was performed on the MEG and EEG data separately to determine the location of the MEG and EEG apparent sources; a significant part of this compu-From the *Francis Bitter

Human Brain Mapping, 2005

We discuss the application of beamforming techniques to the field of magnetoencephalography (MEG). We argue that beamformers have given us an insight into the dynamics of oscillatory changes across the cortex not explored previously with traditional analysis techniques that rely on averaged evoked responses. We review several experiments that have used beamformers, with special emphasis on those in which the results have been compared to those observed in functional magnetic resonance imaging (fMRI) and on those studying induced phenomena. We suggest that the success of the beamformer technique, despite the assumption that there are no linear interactions between the mesoscopic local field potentials across distinct cortical areas, may tell us something of the balance between functional integration and segregation in the human brain. What is more, MEG beamformer analysis facilitates the study of these complex interactions within cortical networks that are involved in both sensory-motor and cognitive processes. Hum. Brain Mapp 25:199–211, 2005. © 2005 Wiley-Liss, Inc.

A method for classifying types of brain activity in magnetoencephalographic (MEG) signals is proposed. Sources of abnormal cortical activity are localized by performing a generalized spectral analysis in the space of Fourier coefficients of the expansions of recorded signals in adaptive orthogonal bases. The basic principles of the method are discussed, and the results of its application to actual MEG records are presented for functional brain mapping in normal and pathological states.

2009

Magnetoencephalography (MEG) provides dynamic spatial-temporal insight of neural activities in the cortex. Because the number of possible sources is far greater than the number of MEG detectors, the proposition to localize sources directly from MEG data is notoriously ill-posed. Here we develop a method based on data processing procedures including clustering, forward and backward filtering, and the method of maximum entropy. We show that taking as a starting point the assumption that the sources lie in the general area of the auditory cortex (an area of about 40 mm by 15 mm), our approach is capable of achieving reasonable success in pinpointing active sources with a resolution of a few mm and reducing the spatial distribution and number of false positives to a very low level.

2000

Independent component analysis (ICA) is a class of decomposition methods that separate sources from mixtures of signals. In this chapter, we used second order blind identification (SOBI), one of the ICA method, to demonstrate its advantages in identifying magnetic signals associated with neural information processing. Using 122-channel MEG data collected during both simple sensory activation and complex cognitive tasks, we explored SOBI's ability to help isolate and localize underlying neuronal sources, particularly under relatively poor signal-to-noise conditions. For these identified and localized neuronal sources, we developed a simple threshold-crossing method, with which single-trial response onset times could be measured with a detection rate as high as 96%. These results demonstrated that, with the aid of ICA, it is possible to non-invasively measure human single trial response onset times with millisecond resolution for specific neuronal populations from multiple sensory modalities. This capability makes it possible to study a wide range of perceptual and memory functions that critically depend on the timing of discrete neuronal events.

Methods in Molecular Biology, 2009

Magnetoencephalography (MEG) encompasses a family of non-contact, non-invasive techniques for detecting the magnetic field generated by the electrical activity of the brain, for analyzing this MEG signal and for using the results to study brain function. The overall purpose of MEG is to extract estimates of the spatiotemporal patterns of electrical activity in the brain from the measured magnetic field outside the head. The electrical activity in the brain is a manifestation of collective neuronal activity and, to a large extent, the currency of brain function. The estimates of brain activity derived from MEG can therefore be used to study mechanisms and processes that support normal brain function in humans and help us understand why, when and how they fail.

Clinical Neurophysiology, 2003

A. Tarkiainen, M. Liljeström, M. Seppä, and R. Salmelin. 2003. The 3D topography of MEG source localization accuracy: effects of conductor model and noise.