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Journal of Clinical Neurophysiology :... Nov 2020Noise sources in magnetoencephalography (MEG) include: (1) interference from outside the shielded room, (2) other people and devices inside the shielded room, (3)... (Review)
Review
Noise sources in magnetoencephalography (MEG) include: (1) interference from outside the shielded room, (2) other people and devices inside the shielded room, (3) physiologic or nonphysiologic sources inside the patient, (4) activity from inside the head that is unrelated to the signal of interest, (5) intrinsic sensor and recording electronics noise, and (6) artifacts from other apparatus used during recording such as evoked response stimulators. There are other factors which corrupt MEG recording and interpretation and should also be considered "artifacts": (7) inadequate positioning of the patient, (8) changes in the head position during the recording, (9) incorrect co-registration, (10) spurious signals introduced during postprocessing, and (11) errors in fitting. The major means whereby magnetic interference can be reduced or eliminated are by recording inside a magnetically shielded room, using gradiometers that measure differential magnetic fields, real-time active compensation using reference sensors, and postprocessing with advanced spatio-temporal filters. Many of the artifacts that plague MEG are also seen in EEG, so an experienced electroencephalographer will have the advantage of being able to transfer his knowledge about artifacts to MEG. However, many of the procedures and software used during acquisition and analysis may themselves contribute artifact or distortion that must be recognized or prevented. In summary, MEG artifacts are not worse than EEG artifacts, but many are different, and-as with EEG-must be attended to.
Topics: Artifacts; Brain; Data Analysis; Electroencephalography; Humans; Magnetoencephalography; Metals; Patient Positioning; Prostheses and Implants; Wearable Electronic Devices
PubMed: 33165224
DOI: 10.1097/WNP.0000000000000699 -
The Journal of Neuroscience : the... May 2022Pupil size has been established as a versatile marker of noradrenergic and cholinergic neuromodulation, which has profound effects on neuronal processing, cognition, and...
Pupil size has been established as a versatile marker of noradrenergic and cholinergic neuromodulation, which has profound effects on neuronal processing, cognition, and behavior. However, little is known about the cortical control and effects of pupil-linked neuromodulation. Here, we show that pupil dynamics are tightly coupled to temporally, spectrally, and spatially specific modulations of local and large-scale cortical population activity in the human brain. We quantified the dynamics of band-limited cortical population activity in resting human subjects using magnetoencephalography and investigated how neural dynamics were linked to simultaneously recorded pupil dynamics. Our results show that pupil-linked neuromodulation does not merely affect cortical population activity in a stereotypical fashion. Instead, we identified three frontal, precentral, and occipitoparietal networks, in which local population activity with distinct spectral profiles in the theta, beta, and alpha bands temporally preceded and followed changes in pupil size. Furthermore, we found that amplitude coupling at ∼16 Hz in a large-scale frontoparietal network predicted pupil dynamics. Our results unravel network-specific spectral fingerprints of cortical neuromodulation in the human brain that likely reflect both the causes and effects of neuromodulation. Brain function is constantly affected by modulatory neurotransmitters. Pupil size has been established as a versatile marker of noradrenergic and cholinergic neuromodulation. However, because the cortical correlates of pupil dynamics are largely unknown, fundamental questions remain unresolved. Which cortical networks control pupil-linked neuromodulation? Does neuromodulation affect cortical activity in a stereotypical or region-specific fashion? To address this, we quantified the dynamics of cortical population activity in human subjects using magnetoencephalography. We found that pupil dynamics are coupled to highly specific modulations of local and large-scale cortical activity in the human brain. We identified four cortical networks with distinct spectral profiles that temporally predicted and followed pupil size dynamics. These effects likely reflect both the cortical control and effect of neuromodulation.
Topics: Brain; Cholinergic Agents; Cognition; Humans; Magnetoencephalography; Pupil
PubMed: 35361704
DOI: 10.1523/JNEUROSCI.1801-21.2022 -
Arquivos de Neuro-psiquiatria May 2022Magnetoencephalography (MEG) is a neurophysiological technique that measures the magnetic fields associated with neuronal activity in the brain. It is closely related...
Magnetoencephalography (MEG) is a neurophysiological technique that measures the magnetic fields associated with neuronal activity in the brain. It is closely related but distinct from its counterpart electroencephalography (EEG). The first MEG was recorded more than 50 years ago and has technologically evolved over this time. It is now well established in clinical practice particularly in the field of epilepsy surgery and functional brain mapping. However, underutilization and misunderstanding of the clinical applications of MEG is a challenge to more widespread use of this technology. A fundamental understanding of the neurophysiology and physics of MEG is discussed in this article as well as practical issues related to implementation, analysis, and clinical applications. The future of MEG and some potential clinical applications are briefly reviewed.
Topics: Brain; Brain Mapping; Electroencephalography; Epilepsy; Humans; Magnetoencephalography
PubMed: 35486819
DOI: 10.1590/0004-282X-ANP-2021-0083 -
Journal of Neurophysiology Mar 2021Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the... (Review)
Review
Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.
Topics: Action Potentials; Animals; Biophysical Phenomena; Brain; Humans; Magnetoencephalography; Neurosciences; Physics
PubMed: 33567968
DOI: 10.1152/jn.00530.2020 -
NeuroImage Feb 2021This paper proposes Shared Component Analysis (SCA) as an alternative to Principal Component Analysis (PCA) for the purpose of dimensionality reduction of neuroimaging...
This paper proposes Shared Component Analysis (SCA) as an alternative to Principal Component Analysis (PCA) for the purpose of dimensionality reduction of neuroimaging data. The trend towards larger numbers of recording sensors, pixels or voxels leads to richer data, with finer spatial resolution, but it also inflates the cost of storage and computation and the risk of overfitting. PCA can be used to select a subset of orthogonal components that explain a large fraction of variance in the data. This implicitly equates variance with relevance, and for neuroimaging data such as electroencephalography (EEG) or magnetoencephalography (MEG) that assumption may be inappropriate if (latent) sources of interest are weak relative to competing sources. SCA instead assumes that components that contribute to observable signals on multiple sensors are of likely interest, as may be the case for deep sources within the brain as a result of current spread. In SCA, steps of normalization and PCA are applied iteratively, linearly transforming the data such that components more widely shared across channels appear first in the component series. The paper explains the motivation, defines the algorithm, evaluates the outcome, and sketches a wider strategy for dimensionality reduction of which this algorithm is an example. SCA is intended as a plug-in replacement for PCA for the purpose of dimensionality reduction.
Topics: Algorithms; Brain Mapping; Electroencephalography; Humans; Magnetoencephalography; Signal Processing, Computer-Assisted
PubMed: 33301941
DOI: 10.1016/j.neuroimage.2020.117614 -
NeuroImage Nov 2023Multivariate pattern analysis (MVPA) of Magnetoencephalography (MEG) and Electroencephalography (EEG) data is a valuable tool for understanding how the brain represents...
Multivariate pattern analysis (MVPA) of Magnetoencephalography (MEG) and Electroencephalography (EEG) data is a valuable tool for understanding how the brain represents and discriminates between different stimuli. Identifying the spatial and temporal signatures of stimuli is typically a crucial output of these analyses. Such analyses are mainly performed using linear, pairwise, sliding window decoding models. These allow for relative ease of interpretation, e.g. by estimating a time-course of decoding accuracy, but have limited decoding performance. On the other hand, full epoch multiclass decoding models, commonly used for brain-computer interface (BCI) applications, can provide better decoding performance. However interpretation methods for such models have been designed with a low number of classes in mind. In this paper, we propose an approach that combines a multiclass, full epoch decoding model with supervised dimensionality reduction, while still being able to reveal the contributions of spatiotemporal and spectral features using permutation feature importance. Crucially, we introduce a way of doing supervised dimensionality reduction of input features within a neural network optimised for the classification task, improving performance substantially. We demonstrate the approach on 3 different many-class task-MEG datasets using image presentations. Our results demonstrate that this approach consistently achieves higher accuracy than the peak accuracy of a sliding window decoder while estimating the relevant spatiotemporal features in the MEG signal.
Topics: Humans; Magnetoencephalography; Brain; Electroencephalography; Brain Mapping; Neural Networks, Computer; Brain-Computer Interfaces; Algorithms
PubMed: 37805019
DOI: 10.1016/j.neuroimage.2023.120396 -
Clinical Neurophysiology : Official... Aug 2018Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting... (Review)
Review
Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG.
Topics: Brain Mapping; Electroencephalography; Humans; Magnetoencephalography; Models, Neurological; Nervous System Diseases; Practice Guidelines as Topic
PubMed: 29724661
DOI: 10.1016/j.clinph.2018.03.042 -
Current Neurology and Neuroscience... Feb 2024Magnetoencephalography (MEG) is a functional neuroimaging technique that records neurophysiology data with millisecond temporal resolution and localizes it with... (Review)
Review
PURPOSE OF THE REVIEW
Magnetoencephalography (MEG) is a functional neuroimaging technique that records neurophysiology data with millisecond temporal resolution and localizes it with subcentimeter accuracy. Its capability to provide high resolution in both of these domains makes it a powerful tool both in basic neuroscience as well as clinical applications. In neurology, it has proven useful in its ability to record and localize epileptiform activity. Epilepsy workup typically begins with scalp electroencephalography (EEG), but in many situations, EEG-based localization of the epileptogenic zone is inadequate. The complementary sensitivity of MEG can be crucial in such cases, and MEG has been adopted at many centers as an important resource in building a surgical hypothesis. In this paper, we review recent work evaluating the extent of MEG influence of presurgical evaluations, novel analyses of MEG data employed in surgical workup, and new MEG instrumentation that will likely affect the field of clinical MEG.
RECENT FINDINGS
MEG consistently contributes to presurgical evaluation and these contributions often change the plan for epilepsy surgery. Extensive work has been done to develop new analytic methods for localizing the source of epileptiform activity with MEG. Systems using optically pumped magnetometry (OPM) have been successfully deployed to record and localize epileptiform activity. MEG remains an important noninvasive tool for epilepsy presurgical evaluation. Continued improvements in analytic methodology will likely increase the diagnostic yield of the test. Novel instrumentation with OPM may contribute to this as well, and may increase accessibility of MEG by decreasing cost.
Topics: Humans; Magnetoencephalography; Epilepsy; Electroencephalography; Neuroimaging; Functional Neuroimaging
PubMed: 38148387
DOI: 10.1007/s11910-023-01328-5 -
Journal of Neural Engineering Dec 2015Oscillations are an important aspect of brain activity, but they often have a low signal-to-noise ratio (SNR) due to source-to-electrode mixing with competing brain...
OBJECTIVE
Oscillations are an important aspect of brain activity, but they often have a low signal-to-noise ratio (SNR) due to source-to-electrode mixing with competing brain activity and noise. Filtering can improve the SNR of narrowband signals, but it introduces ringing effects that may masquerade as genuine oscillations, leading to uncertainty as to the true oscillatory nature of the phenomena. Likewise, time-frequency analysis kernels have a temporal extent that blurs the time course of narrowband activity, introducing uncertainty as to timing and causal relations between events and/or frequency bands.
APPROACH
Here, we propose a methodology that reveals narrowband activity within multichannel data such as electroencephalography, magnetoencephalography, electrocorticography or local field potential. The method exploits the between-channel correlation structure of the data to suppress competing sources by joint diagonalization of the covariance matrices of narrowband filtered and unfiltered data.
MAIN RESULTS
Applied to synthetic and real data, the method effectively extracts narrowband components at unfavorable SNR.
SIGNIFICANCE
Oscillatory components of brain activity, including weak sources that are hard or impossible to observe using standard methods, can be detected and their time course plotted accurately. The method avoids the temporal artifacts of standard filtering and time-frequency analysis methods with which it remains complementary.
Topics: Acoustic Stimulation; Brain; Brain Waves; Electroencephalography; Humans; Magnetoencephalography
PubMed: 26501393
DOI: 10.1088/1741-2560/12/6/066020 -
Advances in Neurobiology 2023Magnetoencephalography (MEG) detects synchronized activity within a neuronal network by measuring the magnetic field changes generated by intracellular current flow.... (Review)
Review
Magnetoencephalography (MEG) detects synchronized activity within a neuronal network by measuring the magnetic field changes generated by intracellular current flow. Using MEG data, we can quantify brain region networks with similar frequency, phase, or amplitude of activity and thereby identify patterns of functional connectivity seen with specific disorders or disease states. In this review, we examine and summarize MEG-based literature on functional networks in dystonias. Specifically, we inspect literature evaluating the pathogenesis of focal hand dystonia, cervical dystonia, embouchure dystonia, the effects of sensory tricks, treatment with botulinum toxin and deep brain stimulation, and rehabilitation approaches. This review additionally highlights how MEG has potential for application to clinical care of patients with dystonia.
Topics: Humans; Magnetoencephalography; Dystonia; Brain; Dystonic Disorders
PubMed: 37338700
DOI: 10.1007/978-3-031-26220-3_8