Utilizing Excitatory and Inhibitory Activity Derived from Interictal Intracranial Electroencephalography as Potential Biomarkers for Epileptogenicity.

Epileptogenic zones (EZs), where epileptic seizures cease after resection, are localized by assessing the seizure-onset zone using ictal electroencephalography (EEG). Owing to the difficulty in capturing unpredictable seizures, biomarkers capable of identifying EZs from interictal EEG are anticipated. Recent studies using intracranial EEG have identified several potential candidate biomarkers for epileptogenicity.

Development and validation of a scoring system to predict vasovagal reaction upon whole-blood donation.

Background And Objectives: Risk factors for vasovagal reaction (VVR) have been extensively studied. With knowledge of the relative importance of these risk factors for VVR, collection staff could take care of blood donors from the same standpoint, leading to improved donor safety. We therefore developed a scoring system to predict VVR, which incorporates registration information.

Efficient, continual, and generalized learning in the brain - neural mechanism of Mental Schema 2.0.

There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing.

Spatiotemporal target selection for intracranial neural decoding of abstract and concrete semantics.

Decoding the inner representation of a word meaning from human cortical activity is a substantial challenge in the development of speech brain-machine interfaces (BMIs). The semantic aspect of speech is a novel target of speech decoding that may enable versatile communication platforms for individuals with impaired speech ability; however, there is a paucity of electrocorticography studies in this field. We decoded the semantic representation of a word from single-trial cortical activity during an imageability-based property identification task that required participants to discriminate between the abstract and concrete words.

Vagus Nerve Stimulation Amplifies Task-Induced Cerebral Blood Flow Increase.

Background: Vagus nerve stimulation (VNS) is an established palliative surgical treatment for refractory epilepsy. Recently, pairing VNS with rehabilitation received growing attention for their joint effect on neural plasticity. However, objective biological measurements proving the interaction between VNS effects and cortical recruitment are lacking.

Musical Auditory Alpha Wave Neurofeedback: Validation and Cognitive Perspectives.

Neurofeedback through visual, auditory, or tactile sensations improves cognitive functions and alters the activities of daily living. However, some people, such as children and the elderly, have difficulty concentrating on neurofeedback for a long time. Constant stressless neurofeedback for a long time may be achieved with auditory neurofeedback using music.

Convolutional neural network with autoencoder-assisted multiclass labelling for seizure detection based on scalp electroencephalography.

Objective: In long-term video-monitoring, automatic seizure detection holds great promise as a means to reduce the workload of the epileptologist. A convolutional neural network (CNN) designed to process images of EEG plots demonstrated high performance for seizure detection, but still has room for reducing the false-positive alarm rate.

Methods: We combined a CNN that processed images of EEG plots with patient-specific autoencoders (AE) of EEG signals to reduce the false alarms during seizure detection.

Syncopal-type reactions tend to be delayed and result in falls among elderly blood donors.

Background: Delayed syncopal-type complications are infrequent among blood donors, but sometimes have critical consequences, such as severe injury. We retrospectively investigated the characteristics of donors with delayed syncopal-type complications or falls.

Study Design And Methods: We defined a delayed reaction (DR) as syncopal-type complications occurring >20 min after needle removal.

Visualization of AMPA receptors in living human brain with positron emission tomography.

Although aberrations in the number and function of glutamate AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) receptors are thought to underlie neuropsychiatric disorders, no methods are currently available for visualizing AMPA receptors in the living human brain. Here we developed a positron emission tomography (PET) tracer for AMPA receptors. A derivative of 4-[2-(phenylsulfonylamino)ethylthio]-2,6-difluoro-phenoxyacetamide radiolabeled with C ([C]K-2) showed specific binding to AMPA receptors.

Regulatory T cells induce CD4 NKT cell anergy and suppress NKT cell cytotoxic function.

Background: Due to the strong tumoricidal activities of activated natural killer T (NKT) cells, invariant NKT cell-based immunotherapy has shown promising clinical efficacy. However, suppressive factors, such as regulatory T cells (Tregs), may be obstacles in the use of NKT cell-based cancer immunotherapy for advanced cancer patients. Here, we investigated the suppressive effects of Tregs on NKT cells and the underlying mechanisms with the aim to improve the antitumor activities of NKT cells.

Autoencoding of long-term scalp electroencephalogram to detect epileptic seizure for diagnosis support system.

Introduction: Epileptologists could benefit from a diagnosis support system that automatically detects seizures because visual inspection of long-term electroencephalograms (EEGs) is extremely time-consuming. However, the diversity of seizures among patients makes it difficult to develop universal features that are applicable for automatic seizure detection in all cases, and the rarity of seizures results in a lack of sufficient training data for classifiers.

Methods: To overcome these problems, we utilized an autoencoder (AE), which is often used for anomaly detection in the field of machine learning, to perform seizure detection.