Magnetoencephalography and its Achilles' heel

Magnetoencephalography (MEG) has practically unlimited temporal resolution. Fundamental physical reasons, however, restrict the capability of MEG to separate simultaneously active sources. After a brief tutorial introduction into MEG, various aspects of spatial resolution are reviewed with the help of examples. First the estimation of a single current dipole is examined. A consideration of the resolution field shows that the spatial selectivity of the estimated dipole moment is highly dependent on methodological issues. A subsequent consideration of various two-dipole configurations illustrates how the topography of the magnetic field depends on the distance between the two dipoles and their relative orientations. The resolution fields associated with the estimation of the dipole moments reveal a strong interference for closely spaced dipoles. A simple model suggests that the standard deviations of the estimated moments are inversely proportional to the distance of the dipoles. Spatial information provided by techniques like functional magnetic resonance imaging (fMRI) could help to overcome problems resulting from the limited spatial resolution of MEG (multimodal integration). But a straightforward synthesis, according to the principle that fMRI provides the spatial structure of the sources and MEG adds the temporal information, is probably doomed to failure in many situations. A serious dilemma, among other problems, is that the fMRI signal generally represents a temporal integral over several seconds: The knowledge that a certain brain region was active sometime or other is not necessarily helpful for disentangling the MEG activity within a specified short time window. An intriguing fact is that the spatio-temporal pattern of the MEG signals can be considered as a signature of the brain which is suitable for hypothesis testing with high temporal and spatial resolution.     

Studies on magnetism and bioelectromagnetics for 45 years: from magnetic analog memory to human brain stimulation and imaging

Forty-five years of studies on magnetism and bioelectromagnetics, in our laboratory, are presented. This article is prepared for the d'Arsonval Award Lecture. After a short introduction of our early work on magnetic analog memory, we review and discuss the following topics: (1) Magnetic nerve stimulation and localized transcranial magnetic stimulation (TMS) of the human brain by figure-eight coils; (2) Measurements of weak magnetic fields generated from the brain by superconducting quantum interference device (SQUID) systems, called magnetoencephalography (MEG), and its application in functional brain studies; (3) New methods of magnetic resonance imaging (MRI) for the imaging of impedance of the brain, called impedance MRI, and the imaging of neuronal current activities in the brain, called current MRI; (4) Cancer therapy and other medical treatments by pulsed magnetic fields; (5) Effects of static magnetic fields and magnetic control of cell orientation and cell growth; and (6) Effects of radio frequency magnetic fields and control of iron ion release and uptake from and into ferritins, iron cage proteins. These bioelectromagnetic studies have opened new horizons in magnetism and medicine, in particular for brain research and treatment of ailments such as depression, Parkinson's, and Alzheimer's diseases.     

Comparative power spectral analysis of simultaneous elecroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive extracellular media

The resistive or non-resistive nature of the extracellular space in the brain is still debated, and is an important issue for correctly modeling extracellular potentials. Here, we first show theoretically that if the medium is resistive, the frequency scaling should be the same for electroencephalogram (EEG) and magnetoencephalogram (MEG) signals at low frequencies (<10 Hz). To test this prediction, we analyzed the spectrum of simultaneous EEG and MEG measurements in four human subjects. The frequency scaling of EEG displays coherent variations across the brain, in general between 1/f and 1/f ², and tends to be smaller in parietal/temporal regions. In a given region, although the variability of the frequency scaling exponent was higher for MEG compared to EEG, both signals consistently scale with a different exponent. In some cases, the scaling was similar, but only when the signal-to-noise ratio of the MEG was low. Several methods of noise correction for environmental and instrumental noise were tested, and they all increased the difference between EEG and MEG scaling. In conclusion, there is a significant difference in frequency scaling between EEG and MEG, which can be explained if the extracellular medium (including other layers such as dura matter and skull) is globally non-resistive.     

脑磁源的定位研究

在脑电脑磁研究中,常将大脑视为一电磁系统,利用准静态近似下的麦克斯韦方程,可以发现代表脑神经元活动的原在电流密度与脑外磁场之间呈线性关系。在高斯哭喊杨存在的情况下,采用极大似然估计理论,讨论了一种基于脑磁场时域－罕注解磁源定位问题的一般方法。球对称导体模型下的模拟计算表明,这一方法是有效的。对于考虑真实头模型下的磁源定位问题求解磁源定位,提出了一种联合使用脑磁脑电数据的近似方法。     

Accounting for Linear Transformations of EEG and MEG Data in Source Analysis

Analyses of electro- and magnetoencephalography (EEG, MEG) data often involve a linear modification of signals at the sensor level. Examples include re-referencing of the EEG, computation of synthetic gradiometer in MEG, or the removal of artifactual independent components to clean EEG and MEG data. A question of practical relevance is, if such modifications must be accounted for by adapting the physical forward model (leadfield) before subsequent source analysis. Here, we show that two scenarios need to be differentiated. In the first scenario, which corresponds to re-referencing the EEG and synthetic gradiometer computation in MEG, the leadfield must be adapted before source analysis. In the second scenario, which corresponds to removing artifactual components to ‘clean’ the data, the leadfield must not be changed. We demonstrate and discuss the consequences of wrongly modifying the leadfield in the latter case for an example. Future EEG and MEG studies employing source analyses should carefully consider whether and, if so, how the leadfield must be modified as explicated here.     

Combined mapping of human auditory EEG and MEG responses

Auditory electric and magnetic P50(m), N1(m) and MMN(m) responses to standard, deviant and novel sounds were studied by recording brain electrical activity with 25 EEG electrodes simultaneously with the corresponding magnetic signals measured with 122 MEG gradiometer coils. The sources of these responses were located on the basis of the MEG responses; all were found to be in the supratemporal plane. The goal of the present paper was to investigate to what degree the source locations and orientations determined from the magnetic data account for the measured EEG signals. It was found that the electric P50, N1 and MMN responses can to a considerable degree be explained by the sources of the corresponding magnetic responses. In addition, source-current components not detectable by MEG were shown to contribute to the measured EEG signals.     

Effects of Spatial Pattern Scale of Brain Activity on the Sensitivity of DOT,fMRI, EEG and MEG

The objective of this work is to quantify how patterns of cortical activity at different spatial scales are measured by noninvasive functional neuroimaging sensors. We simulated cortical activation patterns at nine different spatial scales in a realistic head model and propagated this activity to magnetoencephalography (MEG), electroencephalography (EEG), diffuse optical tomography (DOT), and functional magnetic resonance imaging (fMRI) sensors in arrangements that are typically used in functional neuroimaging studies. We estimated contrast transfer functions (CTF), correlation distances in sensor space, and the minimum resolvable spatial scale of cortical activity for each modality. We found that CTF decreases as the spatial extent of cortical activity decreases, and that correlations between nearby sensors depend on the spatial extent of cortical activity. For cortical activity on the intermediate spatial scale of 6.7 cm², the correlation distances (r>0.5) were 1.0 cm for fMRI, 2.0 cm for DOT, 12.8 for EEG, 9.5 cm for MEG magnetometers and 9.7 cm for MEG gradiometers. The resolvable spatial pattern scale was found to be 1.43 cm² for MEG magnetometers, 0.88 cm² for MEG gradiometers, 376 cm² for EEG, 0.75 cm² for DOT, and 0.072 cm² for fMRI. These findings show that sensitivity to cortical activity varies substantially as a function of spatial scale within and between the different imaging modalities. This information should be taken into account when interpreting neuroimaging data and when choosing the number of nodes for network analyses in sensor space.     

Coupled oscillators for modeling and analysis of EEG/MEG oscillations.

This study presents three EEG/MEG applications in which the modeling of oscillatory signal components offers complementary analysis and an improved explanation of the underlying generator structures. Coupled oscillator networks were used for modeling. Parameters of the corresponding ordinary coupled differential equation (ODE) system are identified using EEG/MEG data and the resulting solution yields the modeled signals. This model-related analysis strategy provides information about the coupling quantity and quality between signal components (example 1, neonatal EEG during quiet sleep), allows identification of the possible contribution of hidden generator structures (example 2, 600-Hz MEG oscillations in somatosensory evoked magnetic fields), and can explain complex signal characteristics such as amplitude-frequency coupling and frequency entrainment (example 3, EEG burst patterns in sedated patients).     

高时空分辨的脑功能光学成像研究进展

脑功能成像技术对深入分析脑的信息加工过程,揭示脑的高级功能至关重要,是目前国际研究热点,已经在神经科学研究和神经系统疾病的临床诊断方面取得了很大的进展.已有脑功能成像技术如:功能磁共振成像(fMRI)、正电子断层成像(PET)、脑电图(EEG)、脑磁图(MEG)等等,虽然已被成功用于脑功能研究,但是目前这些方法也存在着时间或空间分辨率不够的局限.比较而言,光学成像方法表现出其独特魅力.激光散斑衬比成像和内源信号光学成像由于能提供空间取样、时间分辨率及空间分辨率三者的最佳组合和不需加入外源性标记物等特点,与其他脑功能成像技术相比其优势可能更为突出.具有较高的时间和空间分辨率的这两种脑功能光学成像技术及其应用都取得了重大发展,成为研究脑皮层功能构筑和脑病理生理的有力工具.但是目前这两种成像方法也面临着一些挑战.     

Fluctuations in Neuronal Synchronization in Brain Activity Correlate with the Subjective Experience of Visual Recognition

The scientific study of subjective experience is a current major research area in the neurosciences. Coordination patterns of brain activity are being studied to address the question of how brain function relates to behaviour, and particularly methods to estimate neuronal synchronization can unravel the spatio-temporal dynamics of the transient formation of neuronal assemblies. We report here a biophysical correlate of subjective experience. Subjects visualised figures with different levels of noise, while their brain activity was recorded using magnetoencephalography (MEG), and reported the moment in time (corresponding to a noise level) of figure recognition, which varied between individuals, as well as the moment when they saw the figure more clearly, which was mostly common among the participants (thus less subjective). This latter moment is considered to represent psychophysical stochastic resonance (PSR). Fluctuations in neuronal synchronization, quantified using a diffusion coefficient, were lower in occipital cortex when subjects recognised the figure, for a certain noise level, but did not correlate with the moment of PSR. A different pattern was observed in frontal cortex, where lower values of the diffusion coefficient in neuronal synchronization was maintained from the moment of recognition to the moment of PSR. No specific pattern was found analysing signals from temporal or parietal cortical areas. These observations provide support for distinct synchronization patterns in different cortical areas, and represent another demonstration that the subjective, first-person perspective is accessible to scientific methods.      

A fast and reliable method for simultaneous waveform, amplitude and latency estimation of single-trial EEG/MEG data

The amplitude and latency of single-trial EEG/MEG signals may provide valuable information concerning human brain functioning. In this article we propose a new method to reliably estimate single-trial amplitude and latency of EEG/MEG signals. The advantages of the method are fourfold. First, no a-priori specified template function is required. Second, the method allows for multiple signals that may vary independently in amplitude and/or latency. Third, the method is less sensitive to noise as it models data with a parsimonious set of basis functions. Finally, the method is very fast since it is based on an iterative linear least squares algorithm. A simulation study shows that the method yields reliable estimates under different levels of latency variation and signal-to-noise ratio?s. Furthermore, it shows that the existence of multiple signals can be correctly determined. An application to empirical data from a choice reaction time study indicates that the method describes these data accurately.     

Brain connectivity at different time-scales measured with EEG

We present an overview of different methods for decomposing a multichannel spontaneous electroencephalogram (EEG) into sets of temporal patterns and topographic distributions. All of the methods presented here consider the scalp electric field as the basic analysis entity in space. In time, the resolution of the methods is between milliseconds (time-domain analysis), subseconds (time- and frequency-domain analysis) and seconds (frequency-domain analysis). For any of these methods, we show that large parts of the data can be explained by a small number of topographic distributions. Physically, this implies that the brain regions that generated one of those topographies must have been active with a common phase. If several brain regions are producing EEG signals at the same time and frequency, they have a strong tendency to do this in a synchronized mode. This view is illustrated by several examples (including combined EEG and functional magnetic resonance imaging (fMRI)) and a selective review of the literature. The findings are discussed in terms of short-lasting binding between different brain regions through synchronized oscillations, which could constitute a mechanism to form transient, functional neurocognitive networks.     

Practical genetic control strategies for industrial bioprocesses

Optimization of metabolism to maximize production of bio-based chemicals must consistently balance cellular resources for biocatalyst growth and desired compound synthesis. This mini-review discusses synthetic biology strategies for dynamically controlling expression of genes to enable dual-phase fermentations in which growth and production are separated into dedicated phases. Emphasis is placed on practical examples which can be reliably scaled to commercial production with the current state of technology. Recent case studies are presented, and recommendations are provided for environmental signals and genetic control circuits.     

Effects of very weak magnetic fields on radical pair reformation

We can expect that biological responses to very weak ELF electromagnetic fields will be masked by thermal noise. However, the spin of electrons bound to biologically important molecules is not strongly coupled to the thermal bath, and the effects of the precession of those spins by external magnetic fields is not bounded by thermal noise. Hence, the known role of spin orientation in the recombination of radical pairs (RP) may constitute a mechanism for the biological effects of very weak fields. That recombination will generally take place only if the valence electrons in the two radicals are in a singlet state and the effect of the magnetic field is manifest through differential spin precessions that affect the occupation of that state. Because the spin relaxation times are of the order of microseconds, any effects must be largely independent of frequency up to values of a few megahertz. Using exact calculations on an appropriately general model system, we show that one can expect small, but significant, modifications of the recombination rate by a 50 microT field only under a narrow range of circumstances: the cage time during which the two elements are together must be exceptionally long--of the order of 50 ns or longer; the hyperfine field of either radical must not be so great as to generate a precession period greater than the cage containment time; and the characteristic recombination time of the radical pair in the singlet state must be about equal to the containment time. Thus, even under such singularly favorable conditions, fields as small as 5 microT (50 milligauss) cannot change the recombination rate by as much as 1%. Hence, we conclude that environmental magnetic fields much weaker than the earth's field cannot be expected to affect biology significantly by modifying radical pair recombination probabilities.     

Thermal noise limit on the sensitivity of cellular membranes to power frequency electric and magnetic fields

Several authors have concluded that thermal electromagnetic noise will be of sufficient magnitude to overwhelm electric and/or magnetic fields induced by environmentally generated, power frequency electric and magnetic fields in the membranes of living cells located in the bodies of humans. Yet, there are research reports that indicate that living cells may respond to power frequency electric and/or magnetic field levels well below the limits set by these thermal noise arguments. The purpose of this study is to suggest that published thermal arguments may not make a full accounting of all membrane force fields of thermal origin, and that when such an accounting is made, the net thermal noise fields may be smaller in the power frequency range than previously thought. If this analysis is correct, there may be no thermal noise barrier that precludes the possibility of cellular membranes of human cells responding to environmental levels of power frequency electric or magnetic fields.     

Modelling and detecting deep brain activity with MEG and EEG

We introduce an anatomical and electrophysiological model of deep brain structures dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) source imaging. So far, most imaging inverse models considered that MEG/EEG surface signals were predominantly produced by cortical, hence superficial, neural currents. Here we question whether crucial deep brain structures such as the basal ganglia and the hippocampus may also contribute to distant, scalp MEG and EEG measurements. We first design a realistic anatomical and electrophysiological model of these structures and subsequently run Monte-Carlo experiments to evaluate the respective sensitivity of the MEG and EEG to signals from deeper origins. Results indicate that MEG/EEG may indeed localize these deeper generators, which is confirmed here from experimental MEG data reporting on the modulation of alpha (10–12 Hz) brain waves.     

基于独立元分析的MEG(脑磁图)数据分析和处理

独立元分析(independent component analysis,ICA)可用于分离混迭的MEG(Magnetoencephalography)多通道信号中的信号源。从ICA分解的结果中消除干扰源和噪声,并将剩余分量投影回MEG多通道数据空间,可得到净化的MEG信号,表示各个信号源的各独立元分别投影回多通道,可对各活动源进行空间定位。特别是,响应于外界刺激的诱发活动源亦可从重叠的MEG多通道信号中得到分离,这对脑功能研究及脑医学临床应用极有吸引力。提出了一个简单有效的基于ICA的MEG数据分析和处理方法,研究和分析了一些实际应用问题,特别是给出了听觉诱发响应的一些有意义的分析结果。     

Improved Detection of Magnetic Signals by a MEMS Sensor Using Stochastic Resonance

We introduce the behavior of the electrical output response of a magnetic field sensor based on microelectromechanical systems (MEMS) technology under different levels of controlled magnetic noise. We explored whether a particular level of magnetic noise applied on the vicinity of the MEMS sensor can improve the detection of subthreshold magnetic fields. We examined the increase in the signal-to-noise ratio (SNR) of such detected magnetic fields as a function of the magnetic noise intensity. The data disclosed an inverted U-like graph between the SNR and the applied magnetic noise. This finding shows that the application of an intermediate level of noise in the environment of a MEMS magnetic field sensor improves its detection capability of subthreshold signals via the stochastic resonance phenomenon.     

A review of EEG and MEG for brainnetome research

The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain’s multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new –ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG’s applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.     

Neuromagnetic Abnormality of Motor Cortical Activation and Phases of Headache Attacks in Childhood Migraine

The cerebral cortex serves a primary role in the pathogenesis of migraine. This aberrant brain activation in migraine can be noninvasively detected with magnetoencephalography (MEG). The objective of this study was to investigate the differences in motor cortical activation between attacks (ictal) and pain free intervals (interictal) in children and adolescents with migraine using both low- and high-frequency neuromagnetic signals. Thirty subjects with an acute migraine and 30 subjects with a history of migraine, while pain free, were compared to age- and gender-matched controls using MEG. Motor cortical activation was elicited by a standardized, validated finger-tapping task. Low-frequency brain activation (1∼50 Hz) was analyzed with waveform measurements and high-frequency oscillations (65–150 Hz) were analyzed with wavelet-based beamforming. MEG waveforms showed that the ictal latency of low-frequency brain activation was significantly delayed as compared with controls, while the interictal latency of brain activation was similar to that of controls. The ictal amplitude of low-frequency brain activation was significantly increased as compared with controls, while the interictal amplitude of brain activation was similar to that of controls. The ictal source power of high-frequency oscillations was significantly stronger than that of the controls, while the interictal source power of high-frequency oscillations was significantly weaker than that of controls. The results suggest that aberrant low-frequency brain activation in migraine during a headache attack returned to normal interictally. However, high-frequency oscillations changed from ictal hyper-activation to interictal hypo-activation. Noninvasive assessment of cortical abnormality in migraine with MEG opens a new window for developing novel therapeutic strategies for childhood migraine by maintaining a balanced cortical excitability.