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To characterize Parkinson's disease, abnormal phase-amplitude coupling is assessed in the cortico-basal circuit using invasive recordings. It is unknown whether the same phenomenon might be found in regions other than the cortico-basal ganglia circuit. We hypothesized that using magnetoencephalography to assess phase-amplitude coupling in the whole brain can characterize Parkinson's disease. We recorded resting-state magnetoencephalographic signals in patients with Parkinson's disease and in healthy age- and sex-matched participants. We compared whole-brain signals from the two groups, evaluating the power spectra of 3 frequency bands (alpha, 8-12 Hz; beta, 13-25 Hz; gamma, 50-100 Hz) and the coupling between gamma amplitude and alpha or beta phases. Patients with Parkinson's disease showed significant beta-gamma phase-amplitude coupling that was widely distributed in the sensorimotor, occipital, and temporal cortices; healthy participants showed such coupling only in parts of the somatosensory and temporal cortices. Moreover, beta- and gamma-band power differed significantly between participants in the two groups (P < 0.05). Finally, beta-gamma phase-amplitude coupling in the sensorimotor cortices correlated significantly with motor symptoms of Parkinson's disease (P < 0.05); beta- and gamma-band power did not. We thus demonstrated that beta-gamma phase-amplitude coupling in the resting state characterizes Parkinson's disease., (© 2022. The Author(s).)

The application of deep learning to neuroimaging big data will help develop computer-aided diagnosis of neurological diseases. Pattern recognition using deep learning can extract features of neuroimaging signals unique to various neurological diseases, leading to better diagnoses. In this study, we developed MNet, a novel deep neural network to classify multiple neurological diseases using resting-state magnetoencephalography (MEG) signals. We used the MEG signals of 67 healthy subjects, 26 patients with spinal cord injury, and 140 patients with epilepsy to train and test the network using 10-fold cross-validation. The trained MNet succeeded in classifying the healthy subjects and those with the two neurological diseases with an accuracy of 70.7 ± 10.6%, which significantly exceeded the accuracy of 63.4 ± 12.7% calculated from relative powers of six frequency bands (δ: 1-4 Hz; θ: 4-8 Hz; low-α: 8-10 Hz; high-α: 10-13 Hz; β: 13-30 Hz; low-γ: 30-50 Hz) for each channel using a support vector machine as a classifier (p = 4.2 × 10 -2 ). The specificity of classification for each disease ranged from 86-94%. Our results suggest that this technique would be useful for developing a classifier that will improve neurological diagnoses and allow high specificity in identifying diseases.

Background: Intra-axial brain tumors located at anatomically eloquent areas are challenging conditions. On one hand, it is often difficult to pursue maximum extent of resection of tumor in these locations. On the other hand, neuroplasticity occurs in some patients with low-grade glioma, and the primary neural functions are known to sometimes shift from conventional "eloquent cortices.", Case Description: In a patient with a lower-grade glioma located at the precentral gyrus, shift of primary motor function from the precentral gyrus to the postcentral gyrus was detected on magnetoencephalography and functional magnetic resonance imaging. Aggressive removal of the pathologic precentral gyrus was accomplished via awake craniotomy without causing obvious motor function deficit., Conclusions: This case highlights the importance of preoperative multimodal neurophysiologic imaging in patients with low-grade gliomas in eloquent areas., (Copyright © 2017 Elsevier Inc. All rights reserved.)

High gamma band (>50 Hz) activity is a key oscillatory phenomenon of brain activation. However, there has not been a non-invasive method established to detect language-related high gamma band activity. We used a 160-channel whole-head magnetoencephalography (MEG) system equipped with superconducting quantum interference device (SQUID) gradiometers to non-invasively investigate neuromagnetic activities during silent reading and verb generation tasks in 15 healthy participants. Individual data were divided into alpha (8-13 Hz), beta (13-25 Hz), low gamma (25-50 Hz), and high gamma (50-100 Hz) bands and analysed with the beamformer method. The time window was consecutively moved. Group analysis was performed to delineate common areas of brain activation. In the verb generation task, transient power increases in the high gamma band appeared in the left middle frontal gyrus (MFG) at the 550-750 ms post-stimulus window. We set a virtual sensor on the left MFG for time-frequency analysis, and high gamma event-related synchronization (ERS) induced by a verb generation task was demonstrated at 650 ms. In contrast, ERS in the high gamma band was not detected in the silent reading task. Thus, our study successfully non-invasively measured language-related prefrontal high gamma band activity.

Twin studies have suggested that there are genetic influences on inter-individual variation in terms of verbal abilities, and candidate genes have been identified by genome-wide association studies. However, the brain activities under genetic influence during linguistic processing remain unclear. In this study, we investigated neuromagnetic activities during a language task in a group of 28 monozygotic (MZ) and 12 dizygotic (DZ) adult twin pairs. We examined the spatio-temporal distribution of the event-related desynchronizations (ERDs) in the low gamma band (25-50Hz) using beamformer analyses and time-frequency analyses. Heritability was evaluated by comparing the respective MZ and DZ correlations. The genetic and environmental contributions were then estimated by structural equation modeling (SEM). We found that the peaks of the low gamma ERDs were localized to the left frontal area. The power of low gamma ERDs in this area exhibited higher similarity between MZ twins than that between DZ twins. SEM estimated the genetic contribution as approximately 50%. In addition, these powers were negatively correlated with the behavioral verbal scores. These results improve our understanding of how genetic and environmental factors influence cerebral activities during linguistic processes., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Objective: A neuroprosthesis using a brain-machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects' ability to control a neuroprosthesis., Method: Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition., Results: The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 ± 12.9% (mean ± SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 ± 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 ± 13.7%; p = 0.0072, paired two-tailed Student's t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition., Conclusions: Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI.

Introduction: Identification of language-related cortical functions can be carried out noninvasively by transcranial magnetic stimulation (TMS) and magnetoencephalography (MEG), which allow for lesion-based interrogation and global temporospatial investigation of cortices, respectively. Combining these two modalities can improve the accuracy of the identification, but the relationships between them remain unclear. We compared TMS and MEG responses during the same language task to elucidate their temporospatial relationships and used the results to develop a novel method to identify language-related cortical functions., Methods: Twelve healthy right-handed volunteers performed a picture-naming task during TMS and MEG. TMS was applied on the right or left inferior frontal gyrus (IFG) at five time points, and the reaction times (RTs) for naming the pictures were measured. The temporospatial oscillatory changes measured by MEG during the same task were then compared with the TMS results., Results: Transcranial magnetic stimulation of the left IFG significantly lengthened RTs at 300 and 375 msec after picture presentation, whereas TMS of the right IFG did not change RTs significantly. Interestingly, the stimulus time point at which RTs increased significantly for each individual was correlated with when the low gamma event-related desynchronizations (ERDs) peaked in the left IFG. Moreover, combining the results of TMS and MEG improved the detection rate for identifying the laterality of language function., Conclusions: These results suggest that the low gamma ERDs measured by MEG strongly relate to the language function of picture naming in the left IFG. Finally, we propose a novel method to identify language-related cortical functions by combining TMS and MEG.

A brain machine interface (BMI) provides the possibility of controlling such external devices as prosthetic arms for patients with severe motor dysfunction using their own brain signals. However, there have been few studies investigating the decoding accuracy for multiclasses of useful unilateral upper limb movements using non-invasive measurements. We investigated the decoding accuracy for classifying three types of unilateral upper limb movements using single-trial magnetoencephalography (MEG) signals. Neuromagnetic activities were recorded in 9 healthy subjects performing 3 types of right upper limb movements: hand grasping, pinching, and elbow flexion. A support vector machine was used to classify the single-trial MEG signals. The movement types were predicted with an average accuracy of 66 ± 10% (chance level: 33.3%) using neuromagnetic activity during a 400-ms interval (-200 ms to 200 ms from movement onsets). To explore the time-dependency of the decoding accuracy, we also examined the time course of decoding accuracy in 50-ms sliding windows from -500 ms to 500 ms. Decoding accuracies significantly increased and peaked once before (50.1 ± 4.9%) and twice after (58.5 ± 7.5% and 64.4 ± 7.6%) movement onsets in all subjects. Significant variability in the decoding features in the first peak was evident in the channels over the parietal area and in the second and third peaks in the channels over the sensorimotor area. Our results indicate that the three types of unilateral upper limb movement can be inferred with high accuracy by detecting differences in movement-related brain activity in the parietal and sensorimotor areas., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The frequency profiles and time courses of oscillatory changes when reading words are not fully understood, although there have been many reports that oscillatory dynamics reflect local brain function. In order to clarify oscillatory dynamics, we investigated the frequency and spatiotemporal distributions of neuromagnetic activities during silent reading of words in 23 healthy subjects. Individual data were divided into the following frequency bands: theta (5-8 Hz), alpha (8-13 Hz), beta (13-25 Hz), low gamma (25-50 Hz), and high gamma (50-100 Hz), and were analyzed by synthetic aperture magnetometry (SAM). The time window was consecutively moved in steps of 50 ms. Group analysis was performed to delineate common areas of brain activation. A transient power increase in the theta band occurred first in the bilateral occipital cortices, and then rapidly propagated to the left temporo-occipital areas, left inferior and middle frontal gyri, bilateral medial prefrontal cortices, and finally to the left anterior temporal cortices, which possibly reflects a serial cognitive process. This serial propagation of the transient power increase in the theta band was followed by sustained power decreases in the alpha, beta and low gamma bands. These results suggest that the transient power increase in the theta bands may be associated with priming and propagation of local activities, while sustained power decreases in the alpha, beta and low gamma bands reflect parallel neural processes related to silent reading words. Our results showed a relationship between frequency bands of oscillatory changes and locations. This may have implications in the relationship between frequency bands and functions., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Object: Event-related cerebral oscillatory changes reflect regional brain activation. In a previous study, the authors proposed a new method to determine language dominance: examine frontal oscillatory changes during silent reading by using synthetic aperture magnetometry (SAM). The authors' aims in the present study were to establish a normal template for this method, to confirm the results of their previous study with a larger patient population, and to evaluate their method with respect to language localization., Methods: A statistical group analysis of 14 healthy volunteers was conducted to establish a normal control. Language dominance and localization were then evaluated in a larger population of 123 consecutive patients. Study participants were instructed to silently read 100 visually presented words. Using SAM, the spatial distribution of the oscillatory changes was obtained as the Student t statistic by comparing the current density for each voxel between 1 second before and 1 second after each word presentation. Group analyses of the healthy volunteers were performed using statistical nonparametric mapping. Language dominance in the patients was determined according to the laterality index (LI) calculated using peak t values of the left and right frontal desynchronizations. Language dominance was prospectively assessed, and the results were compared with those of the Wada test (63 patients). Language localization results were quantitatively compared with those of stimulation mapping (17 patients)., Results: Group analysis of the healthy volunteers indicated beta to low gamma band desynchronization in the left frontal area and alpha to beta desynchronization in the left parietotemporal areas. In patients, the frontal language areas were detected in 118 persons (95.9%). Lateralization of beta or low gamma desynchronization in the inferior or middle frontal gyri corresponded well with language dominance. The introduction of the LI resulted in a quantitative evaluation of language dominance, whose results were concordant with those of the Wada test in 51 (85.0%) of 60 cases. The distance between the estimated frontal language areas and stimulation-positive sites was 6.0 +/- 7.1 mm (mean +/- SD)., Conclusions: This study is the first in which magnetoencephalography (MEG) was used to determine language dominance in a large population, and the results were compared with those of the Wada test. Moreover, language localization results obtained using MEG were compared with those obtained by invasive mapping. The authors' method, which is based on neuromagnetic oscillatory changes, is a new approach for noninvasively evaluating the frontal language areas, a procedure that has been problematic using MEG dipole methods. Synthetic aperture magnetometry is a noninvasive alternative to Wada testing for language dominance and helps to determine stimulation sites for invasive mapping.

Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training. Method: We trained nine young healthy subjects with normal motor function. In open-loop conditions, they were instructed to grasp or open their right hands during MEG recording. Time-averaged MEG signals were then used to train a real decoder to control the robotic arm in real time. Then, subjects were instructed to control the BMI-controlled robotic hand by moving their right hands for 10 min while watching the robot's movement. During this closed-loop session, subjects tried to improve their ability to control the robot. Finally, subjects performed the same offline task to compare cortical activities related to the hand movements. As a control, we used a random decoder trained by the MEG signals with shuffled movement labels. We performed the same experiments with the random decoder as a crossover trial. To evaluate the cortical representation, cortical currents were estimated using a source localization technique. Hand movements were also decoded by a support vector machine using the MEG signals during the offline task. The classification accuracy of the movements was compared among offline tasks. Results: During the BMI training with the real decoder, the subjects succeeded in improving their accuracy in controlling the BMI robotic hand with correct rates of 0.28 ± 0.13 to 0.50 ± 0.11 (p = 0.017, n = 8, paired Student's t-test). Moreover, the classification accuracy of hand movements during the offline task was significantly increased after BMI training with the real decoder from 62.7 ± 6.5 to 70.0 ± 11.1% (p = 0.022, n = 8, t(7) = 2.93, paired Student's t-test), whereas accuracy did not significantly change after BMI training with the random decoder from 63.0 ± 8.8 to 66.4 ± 9.0% (p = 0.225, n = 8, t(7) = 1.33). Conclusion: BMI training is a useful tool to train the cortical activity necessary for BMI control and to induce some plastic changes in the activity. [ABSTRACT FROM AUTHOR]

Objectives Phantom limb pain is neuropathic pain after amputation of a limb and partial or complete deafferentation such as brachial plexus root avulsion. The underlying cause of this pain has been attributed to maladaptive plasticity of the sensorimotor cortex. It has been suggested that experimental reorganization would affect pain, especially if it results in functional restoration. We tested the hypothesis that restoration of hand motor function using a brain–machine interface (BMI) based on magnetoencephalographic (MEG) signals will normalize maladapted cortical representation and relieve pain. Methods This study included 10 phantom limb patients (9 brachial plexus root avulsion and 1 amputee). MEG signals during movements of the phantom hand or intact hand were used to train the decoder inferring movements of each hand. The robotic hand was controlled by the decoder. Patients controlled the robotic hand by moving the phantom hand. The training effects were compared among trainings with the phantom decoder, real hand decoder, and random decoder in a randomized cross-over trial. Results BMI training with the phantom decoder increased the decoding accuracy of phantom hand movements and pain. In contrast, BMI training with the intact hand decoder reduced accuracy and pain. Discussion It was suggested that BMI training to modulate the motor representation of phantom hand controlled pain. The sensorimotor cortical plasticity might induce pain. Conclusions and Significance Phantom limb pain was controlled by BMI training to induce sensorimotor cortical plasticity. [ABSTRACT FROM AUTHOR]

We recognize body parts in categories. Previous studies have shown that event-related magnetic fields (ERF) called M190 are evoked by perceptions of the human body, either as a whole or in part. This response occurs approximately 190 ms after visualization of body images. However, it is not revealed how M190 involves categorical perceptions of body parts. We evaluated the M190 responses using a decoding method to quantify body information categories. Nine subjects underwent measurements of brain activities with magnetoencephalography (MEG) while viewing 14 images of feet, hands, mouths, and objects. We decoded categories of the presented images from the MEG signal using a support vector machine (SVM) and calculated accuracy by 10-fold cross-validation. For each subject, the response that seemed to be M190 was observed. By using the signals estimated in the occipitotemporal cortex ( p < 0.01, Student’s t -test), MEG signals corresponding to three types of body categories were classified with significantly higher accuracy (with a peak at 48%) compared with those corresponding to randomized categories. According to the time course and location, these responses involving categorical classifications were suggested to be M190. Therefore, M190 is suggested to include information regarding body part categories. [ABSTRACT FROM AUTHOR]

To estimate genetic influences on cerebral language function, we conducted a twin analysis to investigate the similarities of cerebral oscillatory changes using magnetoencephalography (MEG). Twenty-eight monozygotic (MZ) and nine dizygotic (DZ) twins participated in this study. Brain activities were measured during a verb generation task using MEG, and the spatiotemporal distributions of task-related cerebral oscillatory changes (ERS/ERD) were investigated using an adaptive beamformer and a group statistical analysis. After the detection of ERD peak coordinates at 25–50 Hz, we estimated the power of the ERD in the peak coordinates using a time–frequency analysis. To compare the similarities in the power of the ERD, the correlation coefficients of the powers between the members of each twin were determined. The ERD peak was estimated in the left frontal area based on the group statistical analysis. In the peak coordinate, the ERD power showed a significantly higher correlation among MZ twins compared with that among DZ twins. The ERDs in the left frontal area of MZ twins were highly similar. These findings suggested that cerebral language activities in the left frontal area during a language task were genetically influenced. [ABSTRACT FROM AUTHOR]

Mirror neuron system activates, when we imitate another’s action, the inferior parietal lobule (IPL) and the inferior frontal gyrus (IFG). Imitation is a fundamental function of the human brain from the infancy; therefore, we hypothesized that the brain activity in the mirror neuron system is influenced by genetic rather than environment factors. To investigate this issue, we measured oscillatory changes during the execution of an imitation task using a magnetoencephalography (MEG). This study evaluated 17 monozygotic (MZ) twins and nine dizygotic (DZ) twins to analyze the effects of genetic and environmental factors on the trait. Participants were instructed to observe the finger movement and then imitate the same action. In all participants, the MEG data were analyzed by adaptive spatial filtering and group statistical analyses to detect activated areas. As a result, event-related desynchronization (ERD) in the range of 13–25 Hz was estimated in the left IPL after the onset of the observation. Then, we calculated the correlation coefficient in MZ and DZ twins regarding the power of ERD in this area. The correlation of MZ twins was significantly higher than that of DZ twins. These results suggest that mirror neuron activity in the left IPL is influenced by genetic factors. [ABSTRACT FROM AUTHOR]