Neuroimaging in Sports-Related Brain Injury
Mark R. Lovell, Ruben J. Echemendia, Jeffrey T. Barth, Michael W. Collins in Traumatic Brain Injury in Sports, 2020
The relatively recent development of large array of biomagnetometer systems has enabled the use of magnetoencephalography (MEG) in the clinical evaluation of a variety of neurological conditions including epilepsy, neoplasms, cerebrovascular disease, psychiatric disorders, learning disorders, developmental disabilities and head trauma. MEG is a method for the detection, localization, and characterization of magnetic fields that emanate from the brain. Current flowing within a wire produces surrounding magnetic fields, and in a similar manner the current flow within neurons produces such a field. Using a sensitive set of magnetic detectors arranged in a helmet design around the head allows for detailed evaluation of the magnetic fields associated with electrical events that occur within the neurons. The recorded neuromagnetic signals appear similar to those identified using electroencephalography (EEG), but can also be mapped as magnetic fields for more precise localization. MEG signals can be recorded spontaneously from the brain, or as a response to a peripheral stimulus.
Magnetic Particle Imaging
Shoogo Ueno in Bioimaging, 2020
The above equation means that the position information of the MNP is given by the relative distance, rk − ri, between the pickup coil and the MNP position. The signal vector, Vk, obtained with a pickup coil array is used to reconstruct the concentration vector, c, from the solution of Eq. (7.55). This procedure is the same as that used in magnetoencephalography (MEG) and magnetocardiography (MCG). We note that inversion problem becomes simpler compared with MEG and MCG because magnetization has only the z component. The system is useful when MNPs are located in a shallow position from the body surface, e.g., for application in breast cancer and sentinel lymph node detection [43–46].
Magnetoencephalography
Andrei I. Holodny in Functional Neuroimaging, 2019
Although magnetic resonance imaging (MRI)-based functional imaging has advanced considerably over recent years, an inherent limitation of functional MRI (fMRI) is the poor temporal resolution intrinsic to hemodynamic phenomena (blood oxygenation and blood flow). As such, fMRI is unable to track rapidly changing neural activity within and across brain regions. Alternative imaging methods are needed that complement fMRI in evaluating brain activity in the healthy brain as well as in patients with neurologic and psychiatric disorders. Magnetoencephalography (MEG) measures electromagnetic neural activity. The temporal resolution of MEG is limited only by the data acquisition rate, thus allowing real-time assessment of brain electrophysiology. As such, MEG has the temporal resolution needed to detect ongoing oscillatory activity as well as isolated bursts of electrical discharge (e.g., interictal epileptiform activity). Propagation of brain activity can also be assessed.
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).
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