Magnetoencephalography: Detection of the Brain's Electrical Activity with a Superconducting Magnetometer

Cohen, David B.

doi:10.1126/science.175.4022.664

Cited by 644 publications

(259 citation statements)

References 9 publications

(4 reference statements)

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“…Hence, MEG measurements are usually carried out in a magnetically shielded room using sensitive detectors of magnetic flux called superconducting quantum interference devices (SQUIDs) (Zimmerman, Thiene, & Harding, 1970). The first MEG measurement of brain activity using a SQUID sensor was conducted at MIT (Cohen, 1972).…”

Section: Meg Data Acquisition and Analysismentioning

confidence: 99%

Head movements of children in MEG: Quantification, effects on source estimation, and compensation

et al. 2008

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Head movements during MEG recordings in children may lead to inaccurate localization of brain activity. In this thesis, we examined the effects of head movements on source estimation in twenty children performing a simple auditory cognitive task. In addition, we tested the ability of a recently introduced spherical harmonic expansion method, signal space separation (SSS), to compensate for the effects of head movements on two source models: equivalent current dipoles (ECDs) and minimum norm estimates (MNE). In the majority of subjects, the goodness-of-fit of ECDs fit to the peak of the auditory N100m response was increased following the SSS correction compared with the averaged forward solution method proposed earlier. The spatial spread of ECDs as determined with a bootstrapping approach was also reduced after SSS correction. In addition, the MNE source estimates were spatially sharpened following SSS application, indicative of an increase in signal to noise ratio. Together these results suggest that SSS is an effective method to compensate for head movements in MEG recordings in children.

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Section: Meg Data Acquisition and Analysismentioning

confidence: 99%

Head movements of children in MEG: Quantification, effects on source estimation, and compensation

et al. 2008

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“…Magnetoencephalography (MEG) characterizes electrical activity in the brain via measurement of extracranial magnetic fields (26). The MEG signal from any one brain region is dominated by neural oscillations (rhythmic changes in electrical activity) that are observable in the 1-to 200-Hz frequency range.…”

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confidence: 99%

Relationships between cortical myeloarchitecture and electrophysiological networks

Hunt

Tewarie

Mougin

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

101

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The human brain relies upon the dynamic formation and dissolution of a hierarchy of functional networks to support ongoing cognition. However, how functional connectivities underlying such networks are supported by cortical microstructure remains poorly understood. Recent animal work has demonstrated that electrical activity promotes myelination. Inspired by this, we test a hypothesis that gray-matter myelin is related to electrophysiological connectivity. Using ultra-high field MRI and the principle of structural covariance, we derive a structural network showing how myelin density differs across cortical regions and how separate regions can exhibit similar myeloarchitecture. Building upon recent evidence that neural oscillations mediate connectivity, we use magnetoencephalography to elucidate networks that represent the major electrophysiological pathways of communication in the brain. Finally, we show that a significant relationship exists between our functional and structural networks; this relationship differs as a function of neural oscillatory frequency and becomes stronger when integrating oscillations over frequency bands. Our study sheds light on the way in which cortical microstructure supports functional networks. Further, it paves the way for future investigations of the gray-matter structure/function relationship and its breakdown in pathology.network | functional connectivity | myelination | magnetoencephalography | MRI T he way in which integration of functionally specific brain regions supports ongoing cognition is one of the most important questions in neuroscience, and noninvasive in vivo imaging provides a tool to investigate this interregional connectivity in terms of both brain function and structure. Functional connectivity refers to statistical interdependencies between patterns of brain "activity" measured at separate cortical locations (1) and, even in the "resting state," measured spontaneous brain activity defines nonrandom networks that are related to cognitive processes (2). The way in which these functional networks are supported by structural white-matter pathways is reasonably well understood (3). However, it is likely that the structure-function association extends to graymatter morphology, for which fundamental understanding is lacking. Structural morphology of the cortex is known to vary significantly between individuals, and does so in an organized fashion. For example, individuals with a high cortical volume in Broca's area typically exhibit high cortical volume in Wernicke's area, reflecting a language network (4). Similar observations can be made between other associated cortical regions (5, 6). This is known as structural covariance (7-9) and it allows the formation of matrices showing how structural properties of individual brain regions covary over subjects. In this paper, we assess structural covariance based upon cortical myeloarchitecture and probe its relationship to functional networks that are assessed based upon measured spontaneous brain activity.Noninvasive map...

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“…MEG involves measurement of magnetic fields that are induced by synchronized current flow in neuronal assemblies (14). Unlike their electrical equivalent (EEG), magnetic fields are not distorted by inhomogeneous conductivity in the head.…”

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confidence: 99%

Investigating the electrophysiological basis of resting state networks using magnetoencephalography

Brookes

Woolrich

Luckhoo

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

911

958

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In recent years the study of resting state brain networks (RSNs) has become an important area of neuroimaging. The majority of studies have used functional magnetic resonance imaging (fMRI) to measure temporal correlation between blood-oxygenationlevel-dependent (BOLD) signals from different brain areas. However, BOLD is an indirect measure related to hemodynamics, and the electrophysiological basis of connectivity between spatially separate network nodes cannot be comprehensively assessed using this technique. In this paper we describe a means to characterize resting state brain networks independently using magnetoencephalography (MEG), a neuroimaging modality that bypasses the hemodynamic response and measures the magnetic fields associated with electrophysiological brain activity. The MEG data are analyzed using a unique combination of beamformer spatial filtering and independent component analysis (ICA) and require no prior assumptions about the spatial locations or patterns of the networks. This method results in RSNs with significant similarity in their spatial structure compared with RSNs derived independently using fMRI. This outcome confirms the neural basis of hemodynamic networks and demonstrates the potential of MEG as a tool for understanding the mechanisms that underlie RSNs and the nature of connectivity that binds network nodes.functional connectivity | neural oscillations I n recent years interest has grown in the study of connectivity between spatially separate functionally specific brain regions. The way in which separate areas synchronize to form networks is integral to information processing (1, 2). Abnormal communication between regions is thought to be the basis for a number of neurological pathologies (e.g., schizophrenia) (3). It follows that if we are to generate a complete understanding of brain function (and dysfunction), then elucidation of the role of brain networks will be critical. The majority of research in this area has been conducted using functional magnetic resonance imaging (fMRI). During the "resting state", blood-oxygenation-level-dependent (BOLD) fMRI signals originating in spatially separate brain regions are correlated in time (4-6). This correlation implies connectivity between those areas, even in the absence of a task. Temporally correlated BOLD signals have led to the discovery of a number of resting state networks (RSNs) that are consistent across time and subjects. These networks are known to have functional relevance and clinical significance (7,8). Whereas RSNs have also been investigated using noninvasive measures of electrophysiology [electroencephalography (EEG) (9) and magnetoencephalography (MEG) (10-12)], this investigation has been limited to analysis in sensor space or has relied on prior assumptions about spatial locations or patterns of the networks. To date, it has not been shown that MEG (or EEG) can independently measure the spatial pattern of RSNs in the manner that has been demonstrated in fMRI (13). This result would confirm a neural basis for t...

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Magnetoencephalography: Detection of the Brain's Electrical Activity with a Superconducting Magnetometer

Cited by 644 publications

References 9 publications

Head movements of children in MEG: Quantification, effects on source estimation, and compensation

Head movements of children in MEG: Quantification, effects on source estimation, and compensation

Relationships between cortical myeloarchitecture and electrophysiological networks

Investigating the electrophysiological basis of resting state networks using magnetoencephalography

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