The ability of magnetoencephalography (MEG) to accurately localize neuronal currents and obtain tangential components of the source is largely due to MEG's insensitivity to the conductivity profile of the head tissues. However, MEG cannot reliably detect the radial component of the neuronal current. In contrast, the localization accuracy of electroencephalography (EEG) is not as good as MEG, but EEG can detect both the tangential and radial components of the source. In the present study, we investigated the conductivity dependence in a new approach that combines MEG and EEG to accurately obtain, not only the location and tangential components, but also the radial component of the source. In this approach, the source location and tangential components are obtained from MEG alone, and optimal conductivity values of the EEG model are estimated by best-fitting EEG signal, while precisely matching the tangential components of the source in EEG and MEG. Then, the radial components are obtained from EEG using the previously estimated optimal conductivity values. Computer simulations testing this integrated approach demonstrated two main findings. First, there are well-organized optimal combinations of the conductivity values that provide an accurate fit to the combined MEG and EEG data. Second, the radial component, in addition to the location and tangential components, can be obtained with high accuracy without needing to know the precise conductivity profile of the head. We then demonstrated that this new approach performed reliably in an analysis of the 20-ms component from human somatosensory responses elicited by electric median-nerve stimulation.
Although impairments related to somatosensory perception are common in schizophrenia, they have rarely been examined in functional imaging studies. In the present study, magnetoencephalography (MEG) was used to identify neural networks that support attention to somatosensory stimuli in healthy adults and abnormalities in these networks in patient with schizophrenia. A median-nerve oddball task was used to probe attention to somatosensory stimuli, and an advanced, high-resolution MEG source-imaging method was applied to assess activity throughout the brain. In nineteen healthy subjects, attention-related activation was seen in a sensorimotor network involving primary somatosensory (S1), secondary somatosensory (S2), primary motor (M1), pre-motor (PMA), and paracentral lobule (PCL) areas. A frontal–parietal–temporal “attention network”, containing dorsal- and ventral–lateral prefrontal cortex (DLPFC and VLPFC), orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), superior parietal lobule (SPL), inferior parietal lobule (IPL)/supramarginal gyrus (SMG), and temporal lobe areas, was also activated. Seventeen individuals with schizophrenia showed early attention-related hyperactivations in S1 and M1 but hypo-activation in S1, S2, M1, and PMA at later latency in the sensorimotor network. Within this attention network, hypoactivation was found in SPL, DLPFC, orbitofrontal cortex, and the dorsal aspect of ACC. Hyperactivation was seen in SMG/IPL, frontal pole, and the ventral aspect of ACC in patients. These findings link attention-related somatosensory deficits to dysfunction in both sensorimotor and frontal–parietal–temporal networks in schizophrenia.
Signal space separation (SSS) method is an advanced signal-processing approach that can be used to recover bio-magnetic signal and remove external disturbance in empirical magnetoencephalography (MEG) measurements. SSS is based on the solution of the quasi-static approximation of Maxwell equations (i.e., Laplace's equation) which can be expressed as linear combinations of spherical harmonic functions. In applying SSS, MEG measurements can be split into two parts: brain signals and external interferences. In this paper, after a brief review of the basics of SSS, we evaluate SSS systematically via computer simulation and real MEG data. In the simulations of this paper, two types of interference sources with magnetic and electric current dipoles are used. The interference suppression effects and the quality of the reconstruction of the interested signal are investigated. Also, the degree of spherical harmonic functions and its relationship with signal reconstruction and interference suppression are studied thoroughly. Finally, we provide objective assessments of the advantages and limitations of the SSS approach, and its practical value in MEG measurements.
Magnetoencephalography (MEG) has been successfully applied to presurgical epilepsy foci localization and brain functional mapping. Because the neuronal magnetic signals from the brain are extremely weak, MEG measurement requires both low environment noise and the subject/patient being free of artifact-generating metal objects. This strict requirement makes it hard for patients with vagus nerve stimulator, or other similar medical devices, to benefit from the presurgical MEG examinations. Therefore, an approach that can effectively reduce the environmental noise and faithfully recover the brain signals is highly desirable. We applied spatiotemporal signal space separation method, an advanced signal processing approach that can recover bio-magnetic signal from inside the MEG sensor helmet and suppress external disturbance from outside the helmet in empirical MEG measurements, on MEG recordings from normal control subjects and patients who has vagus nerve stimulator. The original MEG recordings were heavily contaminated, and the data could not be assessed. After applying temporal signal space separation, the strong external artifacts from outside the brain were successfully removed, and the neuronal signal from the human brain was faithfully recovered. Both of the goodness-of-fit and 95% confident limit volume confirmed the significant improvement after temporal signal space separation. Hence, temporal signal space separation makes presurgical MEG examinations possible for patients with implanted vagus nerve stimulator or similar medical devices.
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