ObjectiveTo evaluate the role of several examinations in the presurgical localization of insular/peri-insular cortex epilepsy (IPICE). MethodsThe data of patients with IPICE who were identified by resective surgery from 2011.1 to 2015.4 were retrospectively analyzed. The role of semiology, scalp EEG, MRI and magnetoencephalography (MEG)in the localization of epileptogenic zones for patients with IPICE were evaluated. Results18 patients were selected according to the criteria. The localization of IPICE was supported by semiology in 16 patients, supported by MRI in 6 patients, supported by MEG in 17 patients. In 12 patients with negative MRI, the dipoles were showed in insular/peri-insular area in 11 patients. The localization role of MEG for patients with IPICE is more obvious than that of MRI (P < 0.05). The MEG result played conclusive role in 9 patients. According to result of MEG, the plans of intracranial recording were canceled in 3 patients, and the plans of intracranial electrodes implanting were modified in 5 patients. The resective surgery involving the insular/peri-insular cortex was performed in all the 18 patients. During the follow-up of 12~48 months, seizure-free was reported in 11 patients, although 2 patients were missed. ConclusionThe combination of the results of semiology, scalp EEG, MRI and MEG was helpful in the localization of epileptogenic zones for patients with IPICE, and MEG played a valuable role in this localization.
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.
Intracranial electrographic recording, especially stereoencephalography (SEEG), remains the gold standard for preoperative localization in epilepsy patients. However, this method is invasive and has low spatial resolution. In 1982, magnetoencephalography (MEG) began to be used in epilepsy clinics. MEG is not affected by the skull and scalp, can provide signals with high temporal and spatial resolution, and can be used to determine the epiletogensis zone (EZ) and the seizure onset zone (SOZ). Magnetic source imaging (MSI) is a method that superimposes the MEG data on a magnetic resonance image (MRI) and has become a major tool for presurgical localization. The applicability of MEG data has been largely improved by the development of many post-MRI processing methods in the last 20 years. In terms of the sensitivity of localization, MEG is superior to VEEG, MRI, PET and SPECT, despite inferiority to SEEG. MEG can also assist in the intracranial placement of electrodes and improve preoperative planning. Limitations of MEG include high cost, insensitivity to radiation source, and difficulty in locating deep EZ in the medial regions of the brain. These limitations could be overcome by new generations of equipment and improvement of algorithmics.
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.
Objective To investigate biological markers that differentiate states during various seizure periods of childhood absence epilepsy (CAE) by examining the spatiotemporal dynamics of magnetoencephalographic (MEG) signals from Default Mode Network (DMN) nodes, revealing the neurophysiological mechanisms underlying changes in consciousness during CAE seizures. MethodsThirty-six drug-native children diagnosed with CAE were recruited. The interictal data, ictal data of CAE children were collected using a CTF-225 channel MEG system. Conduct temporal homogeneity partitioning for all seizure period data, co-registering 14 distinct seizure states. Identify 12 brain regions associated with the default mode network (DMN) as regions of interest (ROI); employ minimum norm estimation in conjunction with the Welch method to compute the power spectral density (PSD) of the ROI; conduct differential analysis on the relative PSD values; and use a random forest model to identify significant PSD features that differentiate between states of epilepsy. ResultsPower changes in DMN-related brain regions across various frequency bands show significant synchrony. During a seizure, the power in the δ band rapidly increases at the onset and quickly decreases at the end. Meanwhile, the power in the θ-γ2 bands decreases at the beginning and gradually recovers after the seizure. During the O+2 phase following seizure onset, the power in the β band peaks briefly before rapidly declining. The medial frontal cortex has lower power in the δ frequency band during seizures compared to other DMN brain regions, but higher power in the α frequency band. The random forest model's feature importance analysis reveals that the precuneus, lateral temporal lobe and medial temporal lobe play important roles in identifying seizure states. Power changes in the precuneus in the α and δ frequency bands improve the model's classification accuracy. ConclusionsThis study investigated the dynamic spatiotemporal characteristics of the DMN during CAE seizures, revealing the typical manifestations of power changes in specific brain regions and frequency bands at the onset, maintenance, and termination of seizures. It was discovered that power of the precuneus can act as an important feature to distinguish between different stages of CAE seizures. These findings shed new light on the pathophysiological mechanisms underlying changes in consciousness states in CAE.