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Brain stimulation: new directions
Published in Alan Weiss, The Electroconvulsive Therapy Workbook, 2018
Magnetoencephalography (MEG) is fast, non- invasive, patient-friendly functional neuroimaging technique for mapping brain activity. It is reputed to be the most modern imaging tool available to radiologists (Braeutigam, 2013). Magnetic fields that are produced naturally by electrical currents within the brain are detected by very sensitive magnetometers. There are two types: supercon-ducting quantum interference devices (SQUIDs), which are the most common, and spin exchange relation-free (SERF) magnetometers (Braeutigam, 2013).
Developmental Stuttering
Published in Ivanka V. Asenova, Brain Lateralization and Developmental Disorders, 2018
In a magnetoencephalography (MEG) study, Beal et al. [14] examined vocalization-induced suppression, a phenomenon that is considered to reflect the interaction between speech motor and auditory regions in children who stutter and in fluently speaking children, all aged 6–12 years. The researchers measured the brain’s evoked responses to listening to a tone, listening to a vowel and producing a vowel and found that there were no differences between stutterers and non-stutterers in their evoked response when simply listening to the tone, but there were clear differences in their response to vowel perception and production. Children who stutter had a delayed latency of the evoked responses in both hemispheres, indicating that timely and synchronized interactions between auditory and motor areas are affected in stutterers.
Concussion
Published in Rolland S. Parker, Concussive Brain Trauma, 2016
A particular finding may not be specific to TBI. While routine EEG is unlikely to reveal abnormalities, quantitative EEG is considered more sensitive. One study utilized mild head trauma patients (blunt object) with symptoms persisting >1 year, that is, psychiatric, somatic, and/or cognitive complaints that had developed within the first week following ED-documented head trauma. Any exclusive whiplash injury was excluded. Procedures vary in their sensitivity to TBI. Some microscopic lesions are not detected by presumably sensitive procedures such as MRI. Magnetoencephalography (MEG) was the most sensitive (19/30 patients), followed by SPECT (12/30), and least sensitive was MRI (4/30). MEG-measured slow waves are associated with cell loss and disrupted local interconnections, but areas that appear structurally intact but with decreased blood flow measured by SPECT. MRI, as routinely used clinically, is significantly less likely to be abnormal in these patients than functional methods. MEG and SPECT did not provide only redundant information (Lewine, 2007). SPECT was able to detect some basal ganglia lesions after concussions that were not detectable by MRI and CT (Abu-Judeh et al., 1999).
Seizure detection and epileptogenic zone localisation on heavily skewed MEG data using RUSBoost machine learning technique
Published in International Journal of Neuroscience, 2022
Nipun Bhanot, N. Mariyappa, H. Anitha, G. K. Bhargava, J. Velmurugan, Sanjib Sinha
All the above studies have been done in EEG, and magnetoencephalography (MEG) is very much unexplored in this domain. There have been several studies in which features were computed from EEG signals and then classifications were performed using variegated machine learning algorithms. The novelty that this study proposes is that it uses short time energy (STE), short time mean (STM), short time permutation entropy (STPE), and its gradient as features for classification. Also, it has been observed that there has not been any EEG study which has performed simultaneous seizure detection along with the lateralisation/localisation of brain regions from the classification. This is another novelty in this current study approach as this algorithm very accurately classifies the epileptogenic region of the brain by region-specific classification using a boosting machine learning algorithm. This algorithm uses RUSBoost as the machine learning model which works very well with heavily skewed data such as the MEG data acquired for this study.
A review of magnetoencephalography use in pediatric epilepsy: an update on best practice
Published in Expert Review of Neurotherapeutics, 2021
Hiroshi Otsubo, Hiroshi Ogawa, Elizabeth Pang, Simeon M Wong, George M Ibrahim, Elysa Widjaja
Magnetoencephalography (MEG) detects magnetic fields and sources that are produced by the same electrical current movements that produce EEG potentials. MEG is selectively sensitive for sources that are tangentially oriented corresponding to the neurons on the banks of the sulci, whereas EEG is mainly sensitive for radially oriented sources corresponding to the neurons on the top of the gyri [1]. MEG is considered superior to scalp EEGs, as magnetic signals can pass through skull, skin, and other tissues without significant distortion [2,3]. MEG can detect interictal epileptiform discharges that extend 3–4 cm2 of activated lateral frontal neocortex and up to 6 cm2 for more basal frontal and temporal neocortex, whereas scalp EEG only detects interictal epileptiform discharges when >10 cm2 of the neocortex is activated [4,5]. MEG has higher spatial and temporal resolution than EEG, with temporal resolution of less than a millisecond and spatial resolution of several millimeters [6].
Frequency-specific equivalence of brain activity on motor imagery during action observation and action execution
Published in International Journal of Neuroscience, 2021
Jiu Chen, Wenwu Kan, Yong Liu, Xinhua Hu, Ting Wu, Yuanjie Zou, Hongyi Liu, Kun Yang
The human magnetoencephalography (MEG) is identified as a relatively new clinical neuroimaging technology. MEG with magnetic signals is considered to have an advantage over passing through the skull, skin, and other tissues without significant distortion compared with scalp EEG [15]. Some studies have indicated that the frequency and spectral signatures of MEG data can be well used to reconstruct functional brain activity in both low and high frequency ranges [16]. Furthermore, the neuromagnetic signals is also thought to be noninvasively detected and localized by using the quantitative MEG technology [17]. Particularly, based on its rich time-varying signal, MEG can well reveal and reflect the neural mechanism of brain processing [18,19]. Indeed, the MEG studies have shown a robust event-related desynchronization of the β band in the proximity of primary motor and premotor cortices during both motor execution (ME) and MI [20]. Recent MEG studies have also reported significant event-related desynchronization of the β band (13–25 Hz) at the left inferior parietal lobule (IPL) during both action observation and execution [21] and a activation in posterior occipitotemporal region during motor action [22]. Furthermore, a recent MEG study has also reported the use of observing videos of normal movements during action observation therapy in stroke rehabilitation [23]. Therefore, this study utilized the quantitative MEG technology to investigate brain processing on MI during finger action observation (i.e. combining MI with AO).