Objective Idiopathic rolandic epilepsy syndrome （IRES） is the most common epilepsy syndrome in childhood， and its lesion site remains undetermined. This article aims to investigate the source of epileptiform discharges in IRES using magnetoencephalography （MEG）.Methods A total of 70 patients with IRES were enrolled in this prospective MEG-based study， among whom there were 53 children with benign epilepsy of childhood with centrotemporal spikes （BECTS）， 12 children with atypical benign partial epilepsy （ABPE）， 3 children with Landau-Kleffner syndrome （LKS）， and 2 children with epileptic encephalopathy with continuous spike-and-waves during slow-wave sleep （CSWS）. Epileptiform discharges were collected independently from each patient 10 times， and an MEG source analysis was performed. Standardized low-resolution brain electromagnetic tomography was used to perform source localization of the distributed source model. The spike source density was quantified into amplitude， and source location was determined according to the Desikan-Killiany atlas. The association between the distribution of spike source in brain and clinical manifestations was analyzed.Results In IRES， there were significant differences in the source locations of epilepsy discharge between BECTS， ABPE， LKS， and CSWS. The current source density of CSWS was stronger in the frontal lobe， the temporal lobe， and the anterior cingulate gyrus， while that of ABPE was stronger in the frontal lobe， and that of BECTS and LKS were stronger in the temporal lobe. The more severe phenotype of epilepsy， such as generalized tonic-clonic seizure， was associated with a stronger current source density in the brain， which was consistent with electroencephalography manifestations.Conclusion This study identifies different sources of epileptiform discharges in IRES. The density distribution of these spike sources may help to explain the discharge， cognitive， and neuropsychological characteristics in different subtypes of IRES.
Magnetoencephalography

Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.
Humans ; Magnetoencephalography/methods* ; Brain/physiopathology* ; Depression/diagnosis* ; Electroencephalography ; Magnetic Resonance Imaging

Neural activities differentiating bodies versus non-body stimuli have been identified in the occipitotemporal cortex of both humans and nonhuman primates. However, the neural mechanisms of coding the similarity of different individuals' bodies of the same species to support their categorical representations remain unclear. Using electroencephalography (EEG) and magnetoencephalography (MEG), we investigated the temporal and spatial characteristics of neural processes shared by different individual body silhouettes of the same species by quantifying the repetition suppression of neural responses to human and animal (chimpanzee, dog, and bird) body silhouettes showing different postures. Our EEG results revealed significant repetition suppression of the amplitudes of early frontal/central activity at 180-220 ms (P2) and late occipitoparietal activity at 220-320 ms (P270) in response to animal (but not human) body silhouettes of the same species. Our MEG results further localized the repetition suppression effect related to animal body silhouettes in the left supramarginal gyrus and left frontal cortex at 200-440 ms after stimulus onset. Our findings suggest two neural processes that are involved in spontaneous categorical representations of animal body silhouettes as a cognitive basis of human-animal interactions.
Humans ; Animals ; Male ; Electroencephalography ; Magnetoencephalography ; Female ; Young Adult ; Adult ; Pattern Recognition, Visual/physiology* ; Brain Mapping ; Photic Stimulation ; Brain/physiology* ; Dogs

Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.
Brain/physiology* ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Magnetoencephalography ; Technology

Speech comprehension is a central cognitive function of the human brain. In cognitive neuroscience, a fundamental question is to understand how neural activity encodes the acoustic properties of a continuous speech stream and resolves multiple levels of linguistic structures at the same time. This paper reviews the recently developed research paradigms that employ electroencephalography (EEG) or magnetoencephalography (MEG) to capture neural tracking of acoustic features or linguistic structures of continuous speech. This review focuses on two questions in speech processing: (1) The encoding of continuously changing acoustic properties of speech; (2) The representation of hierarchical linguistic units, including syllables, words, phrases and sentences. Studies have found that the low-frequency cortical activity tracks the speech envelope. In addition, the cortical activities on different time scales track multiple levels of linguistic units and constitute a representation of hierarchically organized linguistic units. The article reviewed these studies, which provided new insights into the processes of continuous speech in the human brain.
Acoustic Stimulation ; Electroencephalography ; Humans ; Magnetoencephalography ; Speech ; physiology ; Speech Perception

The neuroimaging has been applied in the study of pathophysiology in major depressive disorder (MDD). In this review article, several kinds of methodologies of neuroimaging would be discussed to summarize the promising biomarkers in MDD. For the magnetic resonance imaging (MRI) and magnetoencephalography field, the literature review showed the potentially promising roles of frontal lobes, such as anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). In addition, the limbic regions, such as hippocampus and amygdala, might be the potentially promising biomarkers for MDD. The structures and functions of ACC, DLPFC, OFC, amygdala and hippocampus might be confirmed as the biomarkers for the prediction of antidepressant treatment responses and for the pathophysiology of MDD. The functions of cognitive control and emotion regulation of these regions might be crucial for the establishment of biomarkers. The near-infrared spectroscopy studies demonstrated that blood flow in the frontal lobe, such as the DLPFC and OFC, might be the biomarkers for the field of near-infrared spectroscopy. The electroencephalography also supported the promising role of frontal regions, such as the ACC, DLPFC and OFC in the biomarker exploration, especially for the sleep electroencephalogram to detect biomarkers in MDD. The positron emission tomography (PET) and single-photon emission computed tomography (SPECT) in MDD demonstrated the promising biomarkers for the frontal and limbic regions, such as ACC, DLPFC and amygdala. However, additional findings in brainstem and midbrain were also found in PET and SPECT. The promising neuroimaging biomarkers of MDD seemed focused in the fronto-limbic regions.
Amygdala ; Biomarkers ; Brain Stem ; Depression ; Depressive Disorder, Major ; Electroencephalography ; Frontal Lobe ; Gyrus Cinguli ; Hippocampus ; Magnetic Resonance Imaging ; Magnetoencephalography ; Mesencephalon ; Neuroimaging ; Positron-Emission Tomography ; Prefrontal Cortex ; Spectroscopy, Near-Infrared ; Tomography, Emission-Computed ; Tomography, Emission-Computed, Single-Photon

Integrating different visual features into a coherent object is a central challenge for the visual system, which is referred as the binding problem. Firstly, this review introduces the conception of the binding problem and the theoretical and empirical controversies regarding whether and how the binding processes are implemented in visual system. Although many neurons throughout the visual hierarchy are known to code multiple features, feature binding is recruited by visual system. Feature misbinding (or illusory conjunction) is probably the most striking evidence for the existence of the binding mechanism. Next, this review summarizes some critical issues in feature binding literature, including early binding theories, late binding theories, neural synchrony theory, the feature integration theory and re-entry processing theory. Feature binding is not a fully automatic or bottom-up processing. Reentrant connection from higher visual areas to early visual cortex (top-down processes) plays a critical role in feature binding, especially in active feature binding (i.e. feature misbinding). In addition, with electrophysiology, electroencephalography (EEG), magnetoencephalography (MEG) and transcranial electric stimulation (tEs) approaches, recent studies explored both correlational and causal relations between brain oscillations and feature binding, suggesting that brain oscillations are of great importance for feature binding. Finally, this review discusses some potential problems and open questions associated with visual feature binding mechanisms which need to be addressed in future studies.
Brain ; physiology ; Electroencephalography ; Humans ; Magnetoencephalography ; Neurons ; physiology ; Transcranial Direct Current Stimulation ; Visual Cortex ; physiology ; Visual Perception

Advances in network science and computer engineering have enabled brain connectivity analysis using clinical big data such as brain magnetic resonance imaging (MRI), electroencephalography (EEG), or magnetoencephalography (MEG). Resting-state functional connectivity analysis aims to reveal the characteristics of functional brain network in various diseases and normal brain maturation using resting-state EEG. Simplified sequence of resting-state functional connectivity analysis methods will be reviewed in this article. The outcomes from EEG resting-state connectivity analysis are comprised of connectivity itself of the specific condition and the network topology measure which describe the characteristics of specific connectivity. An increasing number of studies report the differences in the functional connection itself, global network measures including segregation (connectedness), integration (efficiency), and importance of specific nodes (centrality or node degree). Several issues that are relevant in the resting-state connectivity analysis are obtaining good quality EEG for analysis, consideration of particular features of EEG signal, understanding different types of association measures, and statistics for comparison of connectivities. Well-designed and carefully analyzed EEG resting-state connectivity analysis can provide useful information for patient care in pediatric neurology.
Brain ; Electroencephalography* ; Magnetic Resonance Imaging ; Magnetoencephalography ; Neurology ; Patient Care

Data from magnetoencephalography (MEG) and electroencephalography (EEG) suffer from a rather limited signal-to-noise-ratio (SNR) due to cortical background activities and other artifacts. In order to study the effect of the SNR on the size and distribution of dipole clusters reconstructed from interictal epileptic spikes, we performed simulations using realistically shaped volume conductor models and extended cortical sources with different sensor configurations. Head models and cortical surfaces were derived from an averaged magnetic resonance image dataset (Montreal Neurological Institute). Extended sources were simulated by spherical patches with Gaussian current distributions on the folded cortical surface. Different patch sizes were used to investigate cancellation effects from opposing walls of sulcal foldings and to estimate corresponding changes in MEG and EEG sensitivity distributions. Finally, white noise was added to the simulated fields and equivalent current dipole reconstructions were performed to determine size and shape of the resulting dipole clusters. Neuronal currents are oriented perpendicular to the local cortical surface and show cancellation effects of source components on opposing sulcal walls. Since these mostly tangential aspects from large cortical patches cancel out, large extended sources exhibit more radial components in the head geometry. This effect has a larger impact on MEG data as compared to EEG, because in a spherical head model radial currents do not yield any magnetic field. Confidence volumes of single reconstructed dipoles from simulated data at different SNRs show a good correlation with the extension of clusters from repeated dipole reconstructions. Size and shape of dipole clusters reconstructed from extended cortical sources do not only depend on spike and timepoint selection, but also strongly on the SNR of the measured interictal MEG or EEG data. In a linear approximation the size of the clusters is proportional to the inverse SNR.
Artifacts ; Dataset ; Electroencephalography* ; Head ; Magnetic Fields ; Magnetoencephalography ; Neurons ; Noise

Establishing the significance of observed effects is a preliminary requirement for any meaningful interpretation of clinical and experimental Electroencephalography or Magnetoencephalography (MEG) data. We propose a method to evaluate significance on the level of sensors whilst retaining full temporal or spectral resolution. Input data are multiple realizations of sensor data. In this context, multiple realizations may be the individual epochs obtained in an evoked-response experiment, or group study data, possibly averaged within subject and event type, or spontaneous events such as spikes of different types. In this contribution, we apply Statistical non-Parametric Mapping (SnPM) to MEG sensor data. SnPM is a non-parametric permutation or randomization test that is assumption-free regarding distributional properties of the underlying data. The method, referred to as Maps SnPM, is demonstrated using MEG data from an auditory mismatch negativity paradigm with one frequent and two rare stimuli and validated by comparison with Topographic Analysis of Variance (TANOVA). The result is a time- or frequency-resolved breakdown of sensors that show consistent activity within and/or differ significantly between event or spike types. TANOVA and Maps SnPM were applied to the individual epochs obtained in an evoked-response experiment. The TANOVA analysis established data plausibility and identified latencies-of-interest for further analysis. Maps SnPM, in addition to the above, identified sensors of significantly different activity between stimulus types.
Electroencephalography ; Magnetoencephalography ; Methods ; Random Allocation