Magnetoencephalography: Fundamentals and Established and Emerging Clinical Applications in Radiology

Braeutigam, Sven

doi:10.5402/2013/529463

Cited by 33 publications

(18 citation statements)

References 127 publications

(134 reference statements)

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“…Furthermore, unlike the electrical currents measured by EEG, magnetic fields measured by MEG are not affected by the skull, fontanels, sutures, intervening tissue layers between the scalp and the brain (Lew et al, 2013; Okada et al, 2006, 2016), or the several layers of maternal abdominal muscle and tissue between the sensors and the fetal brain (Lowery et al, 2009). MEG signals, unlike EEG, are reference-free which makes them unaffected by the conductivity differences of the magnetic flux, providing an absolute measurement of brain activity (Braeutigam, 2013). For a comprehensive review of MEG as a technology and its comparison to EEG, see (Baillet, 2017; Braeutigam, 2013; Hamalainen et al, 1993).…”

Section: Functional Developmentmentioning

confidence: 99%

Exploring early human brain development with structural and physiological neuroimaging

et al. 2019

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Early brain development, from the embryonic period to infancy, is characterized by rapid structural and functional changes. These changes can be studied using structural and physiological neuroimaging methods. In order to optimally acquire and accurately interpret this data, concepts from adult neuroimaging cannot be directly transferred. Instead, one must have a basic understanding of fetal and neonatal structural and physiological brain development, and the important modulators of this process. Here, we first review the major developmental milestones of transient cerebral structures and structural connectivity (axonal connectivity) followed by a summary of the contributions from ex vivo and in vivo MRI. Next, we discuss the basic biology of neuronal circuitry development (synaptic connectivity, i.e. ensemble of direct chemical and electrical connections between neurons), physiology of neurovascular coupling, baseline metabolic needs of the fetus and the infant, and functional connectivity (defined as statistical dependence of low-frequency spontaneous fluctuations seen with functional magnetic resonance imaging (fMRI)). The complementary roles of magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS) are discussed. We include a section on modulators of brain development where we focus on the placenta and emerging placental MRI approaches. In each section we discuss key technical limitations of the imaging modalities and some of the limitations arising due to the biology of the system. Although neuroimaging approaches have contributed significantly to our understanding of early brain development, there is much yet to be done and a dire need for technical innovations and scientific discoveries to realize the future potential of early fetal and infant interventions to avert long term disease.

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Section: Functional Developmentmentioning

confidence: 99%

Exploring early human brain development with structural and physiological neuroimaging

et al. 2019

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“…During the last decade, MEG has become increasingly available as a noninvasive, reliable, fast, and patient-friendly technique for recording brain activity [11][12][13][14]. MEG has been widely applied for studying epilepsy disorders, particularly the localization of pathological brain activity or lesions in candidates for epilepsy surgery [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

Chen

et al. 2018

Complexity

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Correct lateralization of temporal lobe epilepsy (TLE) is critical for improving surgical outcomes. As a relatively new noninvasive clinical recording system, magnetoencephalography (MEG) has rarely been applied for determining lateralization of unilateral TLE. Here we propose a framework for using resting-state brain-network features and support vector machine (SVM) for TLE lateralization based on MEG. We recruited 15 patients with left TLE, 15 patients with right TLE, and 15 age-and sex-matched healthy controls. The lateralization problem was then transferred into a series of binary classification problems, including left TLE versus healthy control, right TLE versus healthy control, and left TLE versus right TLE. Brain-network features were extracted for each participant using three network metrics (nodal degree, betweenness centrality, and nodal efficiency). A radial basis function kernel SVM (RBF-SVM) was employed as the classifier. The leave-one-subject-out cross-validation strategy was used to test the ability of this approach to overcome individual differences. The results revealed that the nodal degree performed best for left TLE versus healthy control and right TLE versus healthy control, with accuracy of 80.76% and 75.00%, respectively. Betweenness centrality performed best for left TLE versus right TLE with an accuracy of 88.10%. The proposed approach demonstrated that MEG is a good candidate for solving the lateralization problem in unilateral TLE using various brain-network features.

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“…При проведении у 15 пациентов с РАС и СЭА комплексного исследования функции головного мозга, включающего ЭЭГ, МЭГ и однофотонную эмиссионную томографию (ОФЭКТ), выявленные при ЭЭГ изменения оказались полностью сопоставимы с таковыми при МЭГ и ОФЭКТ, при этом авторы не обнаружили связи между выраженностью изменений при ОФЭКТ и тяжестью симптомов аутизма. Авторы предполагают, что регистрируемая СЭА не являлась причиной РАС, несмотря на то что ЭА часто регистрируется в областях мозга, которые функционально вовлечены в РАС [19]. В отдельных исследованиях указывается, что у большой части (46 %) пациентов с аутизмом и ЭА индекс СЭА во сне достаточно низкий -менее 1 разряда за 2 мин [15].…”

Section: субклиническая эпилептиформная активностьunclassified

Controversial issues in comorbidity between epilepsy and autism: subclinical epileptiform activity and autistic epileptiform regression

Кузьмич¹,

Синельникова²,

Мухин³

2019

Rus. ž. det. nevrol.

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Early childhood autism, or autism spectrum disorders, is an extremely heterogeneous group of conditions that share similar symptoms of dysontogenesis. The most significant comorbidity in patients with autism is epilepsy, which is still associated with a variety of controversies. The present article covers the most controversial aspects of comorbidity between autism and epilepsy, including the impact of psychopharmacotherapy on the risk of epilepsy, clinical significance of epileptiform activity on the electroencephalogram in patients without epilepsy, and criteria for and prevalence of autistic epileptiform regression syndrome. We found that there is still a lack of reliable evidence for the majority of issues related to the combination of autism and epilepsy. We emphasize the need for further studies. We also provide a detailed description of the history, criteria, prevalence, and clinical examples of autistic epileptiform regression syndrome.

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Magnetoencephalography: Fundamentals and Established and Emerging Clinical Applications in Radiology

Cited by 33 publications

References 127 publications

Exploring early human brain development with structural and physiological neuroimaging

Exploring early human brain development with structural and physiological neuroimaging

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

Controversial issues in comorbidity between epilepsy and autism: subclinical epileptiform activity and autistic epileptiform regression

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