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Handbook of Clinical Neurology 2019Since the discovery of electroencephalography (EEG), when it was hoped that EEG would offer "a window into the brain," researchers and clinicians have attempted to... (Review)
Review
Since the discovery of electroencephalography (EEG), when it was hoped that EEG would offer "a window into the brain," researchers and clinicians have attempted to localize the neuronal activity in the brain that generates the scalp potentials measured noninvasively with EEG. Early explorations in the 1950s using electric field theory to infer the location and orientation of the current dipole in the brain from the scalp potential distribution triggered considerable efforts to quantitatively deduce these sources. Initially, dipole fitting, or dipole localization, was the method of choice and many studies used this approach in experimental and clinical studies with remarkable success. Later on, new methods were proposed that attempted to overcome the problem of having to fix the number of sources a priori; these methods are known as distributed source imaging techniques. The introduction and increasing availability of magnetic resonance imaging, allowing detailed realistic anatomy of the brain and head to be incorporated in source localization methods, has drastically increased the precision of such approaches. Today, source localization of EEG (and magnetoencephalography, or MEG) has reached a level of consistency and precision that allows these methods to be placed in the family of brain imaging techniques. The particular advantage that they have over other imaging methods is their high temporal resolution, which allows the origin of activity to be distinguished from its propagation and information flow in large-scale brain networks to be examined. This chapter gives an overview of these methods and illustrates them with several examples, thereby focusing on EEG source imaging in epilepsy and presurgical planning, as clinical applications with remarkable maturation.
Topics: Brain; Brain Mapping; Electroencephalography; Humans; Magnetoencephalography
PubMed: 31277878
DOI: 10.1016/B978-0-444-64032-1.00006-0 -
Journal of Clinical Neurophysiology :... Nov 2020Normal variants, although not occurring frequently, may appear similar to epileptic activity. Misinterpretation may lead to false diagnoses. In the context of... (Review)
Review
Normal variants, although not occurring frequently, may appear similar to epileptic activity. Misinterpretation may lead to false diagnoses. In the context of presurgical evaluation, normal variants may lead to mislocalizations with severe impact on the viability and success of surgical therapy. While the different variants are well known in EEG, little has been published in regard to their appearance in magnetoencephalography. Furthermore, there are some magnetoencephalography normal variants that have no counterparts in EEG. This article reviews benign epileptiform variants and provides examples in EEG and magnetoencephalography. In addition, the potential of oscillatory configurations in different frequency bands to appear as epileptic activity is discussed.
Topics: Action Potentials; Brain; Electroencephalography; Epilepsy; Humans; Magnetoencephalography
PubMed: 33165225
DOI: 10.1097/WNP.0000000000000484 -
Nature Neuroscience Jan 2022
Topics: Electroencephalography; Magnetoencephalography
PubMed: 34992289
DOI: 10.1038/s41593-021-00993-4 -
Neurotherapeutics : the Journal of the... Apr 2021Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes.... (Review)
Review
Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes. This review highlights notable recent advancements in hardware, sequences, methods, analyses, and applications of human neuroimaging techniques utilized to assess epilepsy. These structural, functional, and metabolic assessments include magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG). Advancements that highlight non-invasive neuroimaging techniques used to study the whole brain are emphasized due to the advantages these provide in clinical and research applications. Thus, topics range across presurgical evaluations, understanding of epilepsy as a network disorder, and the interactions between epilepsy and comorbidities. New techniques and approaches are discussed which are expected to emerge into the mainstream within the next decade and impact our understanding of epilepsies. Further, an increasing breadth of investigations includes the interplay between epilepsy, mental health comorbidities, and aberrant brain networks. In the final section of this review, we focus on neuroimaging studies that assess bidirectional relationships between mental health comorbidities and epilepsy as a model for better understanding of the commonalities between both conditions.
Topics: Brain; Electroencephalography; Epilepsy; Humans; Magnetic Resonance Imaging; Magnetoencephalography; Neuroimaging; Positron-Emission Tomography
PubMed: 33942270
DOI: 10.1007/s13311-021-01049-y -
Journal of Neuroscience Methods Dec 2022Neuronal electroencephalography (EEG) signals arise from the cortical postsynaptic currents. Due to the conductive properties of the head, these neuronal sources produce... (Review)
Review
Neuronal electroencephalography (EEG) signals arise from the cortical postsynaptic currents. Due to the conductive properties of the head, these neuronal sources produce relatively smeared spatial patterns in EEG. We can model these topographies to deduce which signals reflect genuine TMS-evoked cortical activity and which data components are merely noise and artifacts. This review will concentrate on two source-based artifact-rejection techniques developed for TMS-EEG data analysis, signal-space-projection-source-informed reconstruction (SSP-SIR), and the source-estimate-utilizing noise-discarding algorithm (SOUND). The former method was designed for rejecting TMS-evoked muscle artifacts, while the latter was developed to suppress noise signals from EEG and magnetoencephalography (MEG) in general. We shall cover the theoretical background for both methods, but most importantly, we will describe some essential practical perspectives for using these techniques effectively. We demonstrate and explain what approaches produce the most reliable inverse estimates after cleaning the data or how to perform non-biased comparisons between cleaned datasets. All noise-cleaning algorithms compromise the signals of interest to a degree. We elaborate on how the source-based methods allow objective quantification of the overcorrection. Finally, we consider possible future directions. While this article concentrates on TMS-EEG data analysis, many theoretical and practical aspects, presented here, can be readily applied in other EEG/MEG applications. Overall, the source-based cleaning methods provide a valuable set of TMS-EEG preprocessing tools. We can objectively evaluate their performance regarding possible overcorrection. Furthermore, the overcorrection can always be taken into account to compare cleaned datasets reliably. The described methods are based on current electrophysiological and anatomical understanding of the head and the EEG generators; strong assumptions of the statistical properties of the noise and artifact signals, such as independence, are not needed.
Topics: Artifacts; Transcranial Magnetic Stimulation; Electroencephalography; Magnetoencephalography; Algorithms
PubMed: 36057330
DOI: 10.1016/j.jneumeth.2022.109693 -
Journal of Clinical Neurophysiology :... Nov 2020The report generated by the magnetoencephalographer's interpretation of the patient's magnetoencephalography examination is the magnetoencephalography laboratory's most... (Review)
Review
The report generated by the magnetoencephalographer's interpretation of the patient's magnetoencephalography examination is the magnetoencephalography laboratory's most important product and is a representation of the quality of the laboratory and the clinical acumen of the personnel. A magnetoencephalography report is not meant to enumerate all the technical details that went into the test nor to fulfill some imagined requirements of the electronic health record. It is meant to clearly and concisely answer the clinical question posed by the referring doctor and to convey the key findings that may inform the next step in the patient's care. The graphical component of a magnetoencephalography report is ordinarily the most welcomed by the referring doctor. Much of the text of the report may be glossed over, so the illustrations must be sufficiently annotated to provide clear and unambiguous findings. The particular images chosen for the report will be a function of the analysis software but should be selected and edited for maximum clarity. There should be a composite pictorial summary slide at the beginning or at the end of the report, which accurately conveys the gist of the report. Along with representative source localizations, reports should contain examples of the simultaneously recorded EEG that enable the referring physician to determine whether epileptic discharges occurred and whether they are consistent with the patient's previously recorded spikes. Information and images (e.g., statistics, magnetic field patterns) that provide convincing evidence of the validity of the source location should also be included.
Topics: Brain; Electroencephalography; Epilepsy; Humans; Magnetoencephalography; Research Design; Software
PubMed: 33165227
DOI: 10.1097/WNP.0000000000000700 -
Brain Research Bulletin Oct 2015
Topics: Animals; Electrophysiological Phenomena; Humans; Machine Learning; Magnetoencephalography; Microelectrodes; Neurons; Neurophysiology
PubMed: 26481043
DOI: 10.1016/j.brainresbull.2015.10.006 -
Biological Psychiatry Apr 2023Aberrant patterns of cognition, perception, and behavior seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at... (Review)
Review
Aberrant patterns of cognition, perception, and behavior seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between spatial and temporal resolutions inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography, often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illnesses such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications that seek to drive a mechanistic understanding of psychopathology and the realization of preclinical translation.
Topics: Humans; Magnetoencephalography; Neuroimaging; Mental Disorders; Cognition; Psychiatry; Brain
PubMed: 36376110
DOI: 10.1016/j.biopsych.2022.08.016 -
Practical Neurology Oct 2014
Review
Topics: Brain; Brain Mapping; Humans; Magnetoencephalography; Neurodegenerative Diseases; Neuroimaging
PubMed: 24647614
DOI: 10.1136/practneurol-2013-000768 -
Nature Neuroscience Feb 2017We review the aspects that uniquely characterize magnetoencephalography (MEG) among the techniques available to explore and resolve brain function and dysfunction. While... (Review)
Review
We review the aspects that uniquely characterize magnetoencephalography (MEG) among the techniques available to explore and resolve brain function and dysfunction. While emphasizing its specific strengths in terms of millisecond source imaging, we also identify and discuss current practical challenges, in particular in signal extraction and interpretation. We also take issue with some perceived disadvantages of MEG, including the misconception that the technique is redundant with electroencephalography. Overall, MEG contributes uniquely to our deeper comprehension of both regional and large-scale brain dynamics: from the functions of neural oscillations and the nature of event-related brain activation, to the mechanisms of functional connectivity between regions and the emergence of modes of network communication in brain systems. We expect MEG to play an increasing and pivotal role in the elucidation of these grand mechanistic principles of cognitive, systems and clinical neuroscience.
Topics: Brain; Brain Mapping; Electroencephalography; Electrophysiological Phenomena; Humans; Image Processing, Computer-Assisted; Magnetoencephalography
PubMed: 28230841
DOI: 10.1038/nn.4504