Biomarkers.

Background: Investigating age-related changes in MEG brain networks offers significant potential for comprehending aging trajectories and unveiling anomalous patterns associated with neurodegenerative disorders, such as Alzheimer's disease. In this study, we extended a deep learning model called Fully Hyperbolic Neural Network (FHNN) to embed MEG brain connectivity graphs into a Lorentz Hyperboloid model for hyperbolic space. Through these embeddings, we then explored the impact of aging on brain functional connectivity across multiple decades.

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline.

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls.

Comparison of 2 Robotic Systems for Pediatric Stereoelectroencephalography Implantation.

Background: Stereoelectroencephalography (SEEG) remains critical in guiding epilepsy surgery. Robot-assisted techniques have shown promise in improving SEEG implantation outcomes but have not been directly compared. In this single-institution series, we compared ROSA and Stealth AutoGuide robots in pediatric SEEG implantation.

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline.

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls.

Role of optically pumped magnetometers in presurgical epilepsy evaluation: Commentary of the American Clinical Magnetoencephalography Society.

One of the major challenges of modern epileptology is the underutilization of epilepsy surgery for treatment of patients with focal, medication resistant epilepsy (MRE). Aggravating this distressing failure to deliver optimum care to these patients is the underuse of proven localizing tools, such as magnetoencephalography (MEG), a clinically validated, non-invasive, neurophysiological method used to directly measure and localize brain activity. A sizable mass of published evidence indicates that MEG can improve identification of surgical candidates and guide pre-surgical planning, increasing the yield of SEEG and improving operative outcomes.

Effects of spaceflight on the EEG alpha power and functional connectivity.

Electroencephalography (EEG) can detect changes in cerebral activity during spaceflight. This study evaluates the effect of spaceflight on brain networks through analysis of the Default Mode Network (DMN)'s alpha frequency band power and functional connectivity (FC), and the persistence of these changes. Five astronauts' resting state EEGs under three conditions were analyzed (pre-flight, in-flight, and post-flight).

An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application.

Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30-70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient's magnetoencephalography recording and MRI.

Noise characteristics in spaceflight multichannel EEG.

The cognitive performance of the crew has a major impact on mission safety and success in space flight. Monitoring of cognitive performance during long-duration space flight therefore is of paramount importance and can be performed using compact state-of-the-art mobile EEG. However, signal quality of EEG may be compromised due to the vicinity to various electronic devices and constant movements.

Complete corpus callosotomy using a frameless navigation probe through a minicraniotomy in children with medically refractory epilepsy: A case series and technical note.

Background: Medically refractory epilepsy constitutes up to one-third of the epilepsy pediatric patients. Corpus callosotomy (CC) has been used for the treatment of medically refractory epilepsy in children with atonic seizures and generalized tonic-clonic (GTC) seizures. In this case series study, we are describing a novel technique for CC using the frameless navigation probe through a minicraniotomy.

Containment measures during the COVID pandemic: The role of non-pharmaceutical health policies.

Many countries have imposed a set of non-pharmaceutical health policy interventions in an effort to slow the spread of the COVID-19 pandemic. The objective of this paper is to examine the effects of the interventions, drawing on evidence from the OECD countries. A special feature here is the mechanism that underlies the impact of the containment policies.

Has Germany's temporary VAT rates cut as part of the COVID-19 fiscal stimulus boosted growth?

On 3 June 2020, the German government announced a EUR 130 billion fiscal stimulus package to stimulate market demand and jumpstart the economy in the wake of the COVID-19 pandemic lockdown in the spring of 2020. The most prominent measure of this package is an unconventional fiscal policy in the form of a temporary VAT rates cut for six months, from 1 July to 31 December 2020. Employing a dynamic stochastic general equilibrium (DSGE) framework, we study the efficiency of the VAT tax rates cut for ameliorating the consequences of the pandemic recession.

Gamma band functional connectivity reduction in patients with amnestic mild cognitive impairment and epileptiform activity.

There is growing evidence for neuronal hyperexcitability in Alzheimer's disease. Hyperexcitability is associated with an increase in epileptiform activity and the disruption of inhibitory activity of interneurons. Interneurons fire at a high rate and are frequently associated with high-frequency oscillations in the gamma frequency band (30-150 Hz).

A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data.

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls.

Indications for Inpatient Magnetoencephalography in Children - An Institution's Experience.

Magnetoencephalography (MEG) is recognized as a valuable non-invasive clinical method for localization of the epileptogenic zone and critical functional areas, as part of a pre-surgical evaluation for patients with pharmaco-resistant epilepsy. MEG is also useful in localizing functional areas as part of pre-surgical planning for tumor resection. MEG is usually performed in an outpatient setting, as one part of an evaluation that can include a variety of other testing modalities including 3-Tesla MRI and inpatient video-electroencephalography monitoring.

Convolutional Neural Networks for Pediatric Refractory Epilepsy Classification Using Resting-State Functional Magnetic Resonance Imaging.

Objective: This study aims to evaluate the performance of convolutional neural networks (CNNs) trained with resting-state functional magnetic resonance imaging (rfMRI) latency data in the classification of patients with pediatric epilepsy from healthy controls.

Methods: Preoperative rfMRI and anatomic magnetic resonance imaging scans were obtained from 63 pediatric patients with refractory epilepsy and 259 pediatric healthy controls. Latency maps of the temporal difference between rfMRI and the global mean signal were calculated using voxel-wise cross-covariance.

Towards Best Practices in Clinical Magnetoencephalography: Patient Preparation and Data Acquisition.

A magnetoencephalography (MEG) recording for clinical purposes requires a different level of attention and detail than that for research. As contrasted with a research subject, the MEG technologist must work with a patient who may not fully cooperate with instructions. The patient is on a clinical schedule, with generally no opportunity to return due to an erroneous or poor acquisition.

The 10 Common Evidence-Supported Indications for MEG in Epilepsy Surgery: An Illustrated Compendium.

Unfamiliarity with the indications for and benefits of magnetoencephalography (MEG) persists, even in the epilepsy community, and hinders its acceptance to clinical practice, despite the evidence. The wide treatment gap for patients with drug-resistant epilepsy and immense underutilization of epilepsy surgery had similar effects. Thus, educating referring physicians (epileptologists, neurologists, and neurosurgeons) both about the value of epilepsy surgery and about the potential benefits of MEG can achieve synergy and greatly improve the process of selecting surgical candidates.

The Wisdom and Vision From the ACMEGS Inaugural Decade.

Concise history of fascinating magnetoencephalography (MEG) technology and catalog of very selected milestone preclinical and clinical MEG studies are provided as the background. The focus is the societal context defining a journey of MEG to and through clinical practice and formation of the American Clinical MEG Society (ACMEGS). We aspired to provide an objective historic perspective and document contributions of many professionals while focusing on the role of ACMEGS in the growth and maturation of clinical MEG field.

The People's bank of China's response to the coronavirus pandemic: A quantitative assessment.

The People's Bank of China (PBoC) has implemented numerous measures to cushion the impacts of the COVID-19 health crisis on the Chinese economy. Since the current monetary policy framework features a multi-instrument mix of liquidity tools and pricing signals, we employ a dynamic-factor modelling approach to derive a composite indicator of China's monetary policy stance. Our quantitative assessment shows that the PBoC's policy response to the outbreak of the COVID-19 pandemic has been swift and decisive.

Corrigendum: The Importance of the Validation of M/EEG With Current Biomarkers in Alzheimer's Disease.

[This corrects the article DOI: 10.3389/fnhum.2019.

The Importance of the Validation of M/EEG With Current Biomarkers in Alzheimer's Disease.

Current biomarkers used in research and in clinical practice in Alzheimer's Disease (AD) are the analysis of cerebral spinal fluid (CSF) to detect levels of Aβ42 and phosphorylated-tau, amyloid and FDG-PET, and MRI volumetry. Some of these procedures are still invasive for patients or expensive. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive techniques able to detect the early synaptic dysfunction and track the course of the disease.