Type Of Brain Activity Research Articles

Functional states of prelimbic and related circuits during the acquisition of a GO/noGO task in rats.

GO/noGO tasks enable assessing decision-making processes and the ability to suppress a specific action according to the context. Here, rats had to discriminate between 2 visual stimuli (GO or noGO) shown on an iPad screen. The execution (for GO) or nonexecution (for noGO) of the selected action (to touch or not the visual display) were reinforced with food. The main goal was to record and to analyze local field potentials collected from cortical and subcortical structures when the visual stimuli were shown on the touch screen and during the subsequent activities. Rats were implanted with recording electrodes in the prelimbic cortex, primary motor cortex, nucleus accumbens septi, basolateral amygdala, dorsolateral and dorsomedial striatum, hippocampal CA1, and mediodorsal thalamic nucleus. Spectral analyses of the collected data demonstrate that the prelimbic cortex was selectively involved in the cognitive and motivational processing of the learning task but not in the execution of reward-directed behaviors. In addition, the other recorded structures presented specific tendencies to be involved in these 2 types of brain activity in response to the presentation of GO or noGO stimuli. Spectral analyses, spectrograms, and coherence between the recorded brain areas indicate their specific involvement in GO vs. noGO tasks.

Intracerebral electrographic activity following a single dose of diazepam nasal spray: A pilot study.

Rescue benzodiazepine medication can be used to treat seizure clusters, which are intermittent, stereotypic episodes of frequent seizure activity that are distinct from a patient's usual seizure pattern. The NeuroPace RNS® System is a device that detects abnormal electrographic activity through intracranial electrodes and administers electrical stimulation to control seizures. Reductions in electrographic activity over days to weeks have been associated with the longer-term efficacy of daily antiseizure medications (ASMs). In this pilot study, electrographic activity over hours to days was examined to assess the impact of a single dose of a proven rescue therapy (diazepam nasal spray) with a rapid onset of action. Adult volunteers (>18 years old) with clinically indicated RNS (stable settings and ASM usage) received a weight-based dose of diazepam nasal spray in the absence of a clinical seizure. Descriptive statistics for a number of detections and a sum of durations of detections at 10-min, hourly, and 24-h intervals during the 7-day (predose) baseline period were calculated. Post-dose detections at each time interval were compared with the respective baseline-detection intervals using a 1 SD threshold. The number of long episodes that occurred after dosing also were compared with the baseline. Five participants were enrolled, and four completed the study; the excluded participant had recurrent seizures during the study. There were no consistent changes (difference >1 SD) in detections between post-dose and mean baseline values. Although variability was high (1 SD was often near or exceeded the mean), three participants showed possible trends for reductions in one or more electrographic variables following treatment. RNS-assessed electrographic detections and durations were not shown to be sensitive measures of short-term effects associated with a single dose of rescue medication in this small group of participants. The variability of detections may have masked a measurable drug effect. Rescue drugs are used to treat seizure clusters. Responsive neurostimulation (RNS) devices detect and record epilepsy brain waves and then send a pulse to help stop seizures. This pilot study looked at whether one dose of a rescue treatment changes brain activity detected by RNS. There was a very wide range of detections, which made it difficult to see if or how the drug changed brain activity. New studies should look at other types of brain activity, multiple doses, and larger patient groups.

Sleep stage detection using chaotic feature analysis of Electroencephalography (EEG) signals

The present study was conducted to detect the sleep stages by electroencephalography (EEG) using chaotic features. The method used in this study was the content analysis method. First, the sleep stages and EEG have been analyzed, and the EEG with chaotic features was used to detect the sleep stages. Detection of artifacts in sleep electroencephalography (EEG) is one of the vital tasks in the pre-processing stage. Despite many artifact exploration algorithms over the years, lots of them lose their advantages to use sleep EEG. Types of brain activities can be measured, and the involved brain areas can be detected using EEG. Electroencephalography (EEG) signal includes different rhythms, which are dependent on various sensory and movement conditions. Detection of each rhythm of this signal needs experience and skills. As a result, analysis of the signal recorded by EEG can be used widely for detection and academic purposes.

Nonlinear analysis of periodic waves in a neural field model.

Various types of brain activity, including motor, visual, and language, are accompanied by the propagation of periodic waves of electric potential in the cortex, possibly providing the synchronization of the epicenters involved in these activities. One example is cortical electrical activity propagating during sleep and described as traveling waves [Massimini et al., J. Neurosci. 24, 6862-6870 (2004)]. These waves modulate cortical excitability as they progress. Clinically related examples include cortical spreading depression in which a wave of depolarization propagates not only in migraine but also in stroke, hemorrhage, or traumatic brain injury [Whalen et al., Sci. Rep. 8, 1-9 (2018)]. Here, we consider the possible role of epicenters and explore a neural field model with two nonlinear integrodifferential equations for the distributions of activating and inhibiting signals. It is studied with symmetric connectivity functions characterizing signal exchange between two populations of neurons, excitatory and inhibitory. Bifurcation analysis is used to investigate the emergence of periodic traveling waves and of standing oscillations from the stationary, spatially homogeneous solutions, and the stability of these solutions. Both types of solutions can be started by local oscillations indicating a possible role of epicenters in the initiation of wave propagation.

Modeling of stochastic brain function in artificial intelligence

Objectives -research of stochastic brain function in respect to creation of artificial intelligence. Material and methods. Mathematical modeling principles were used for simulation of brain functioning in a stochastic mode. Results. Two types of brain activity were considered: determinated type, usually modeled using the perceptron, and stochastic type. It is shown, that stochastic brain function modeling is the necessary condition for AI to become capable of creativity, generation of new knowledge. Mathematical modeling of a neural network of the cerebral cortex, consisting of the set of the cyclic neuronal circuits (memory units), was performed for the stochastic mode of brain functioning. Models of "two-dimensional" and "one-dimensional" brain were analyzed. The pattern of excitation in memory units was calculated in the "one-dimensional" brain model. Conclusion. Relying on the knowledge of the stochastic mode of brain function, a way of creation of AI can be offered. а-rhythm of a patient is a recommended focus of the therapist's attention in diagnostics and treatment of brain disorders. It was noted, that the alpha wave amplitude and frequency could indicate the cognitive, creative and intuitive abilities of a person.

Detection of Neural Activity of Brain Functional Site Based on Microwave Scattering Principle

A method of detecting brain neural activity with the microwave transmission technology is presented in this paper. This method is based on the fact that the dielectric properties of brain functional site vary with the electrophysiological neural activity. The analysis of the uniform plane wave propagation of dynamic layered medium shows that the phase change of transmission coefficient (S-parameters) is consistent with the variation frequency of permittivity in the dynamic dielectric layer. A 3D head model was introduced to assign dynamic dielectric properties to the cerebral cortex by embedding a tissue piece in this head model. A six-element horn antenna array around the head model was designed, and two types of brain activity at various locations (visual cortex and auditory cortex) inside the head model has been discussed. The simulation results of microwave scattering in different directions show that forward scattering has better applicability. The frequency of the permittivity variation associated with the brain neuronal activity (10 Hz, 20 Hz) can be extracted from the phase variation on S-parameters. In addition, the specific absorption rate analysis ensures the safety of using the microwave in the design of human head detection systems.

The use of optimal spatial filtering by the method of common spatial pattern for the classification of EEG signals according to the type of brain activity

The use of optimal spatial filtering by the method of common spatial pattern for the classification of EEG signals according to the type of brain activity

Architecture and neuroscience; what can the EEG recording of brain activity reveal about a walk through everyday spaces?

New digital media and quantitative data have been increasingly used in an attempt to map, understand and analyse spaces. Each different medium with which we analyse and map spaces offers a different insight, and can potentially increase our tools and methods for mapping spaces and understanding human experience. The emergence of such technologies has the potential to influence the way in which we map, analyse and perceive spaces. Given this context, the project presented in this paper examines how neurophysiological data, recorded with the use of portable electroencephalography (EEG) devices, can help us understand how the brain responds to physical environments in different individuals. In this study we look into how a number of participants navigate in an urban environment; between specific identified buildings in the city. The brain activity of the participants is recorded with a portable EEG device, while simultaneously video recording the route. Through this experiment we aim to observe and analyse the relationship between the physical environment and the participant’s type of brain activity. We attempt to correlate how key moments of their journey, such as moments of decision-making, relate to recordings of specific brain waves. We map and analyse certain common patterns observed. We look into how the variation of the physical attributes of the built environment around them is related to the fluctuation of specific brain waves. This paper presents a specific project of an ongoing cross-disciplinary study between architecture and neuroscience, and the key findings of a specific experiment in an urban environment.

Correlation between the Functional Connectivity of Brain Areas Active in the Resting State with Behavioral and Psychological Indicators

Recent years have seen extensive data on continuous recording of brain activity in the state of restful waking. This activity is particularly reflected in spatial maps of brain activity obtained using functional MRI scanning (fMRI) and termed resting state networks (RSN). RSN have stable spatial structure which is reproducible over time in healthy subjects, but can be significantly different from normal in the presence of clinical pathology. One important parameter of this type of brain activity is the functional connectivity (FC) of individual elements within the network or between RSN. This review describes results from studies of the interaction of behavioral parameters and FC in brain areas active in the state of restful waking. Data on the reproducibility of RSN parameters over time in healthy subjects are presented, along with data on changes in the FC of networks depending on individual experience.

Macroscopic and microscopic spectral properties of brain networks during local and global synchronization.

We introduce a practical and computationally not demanding technique for inferring interactions at various microscopic levels between the units of a network from the measurements and the processing of macroscopic signals. Starting from a network model of Kuramoto phase oscillators, which evolve adaptively according to homophilic and homeostatic adaptive principles, we give evidence that the increase of synchronization within groups of nodes (and the corresponding formation of synchronous clusters) causes also the defragmentation of the wavelet energy spectrum of the macroscopic signal. Our methodology is then applied to getting a glance into the microscopic interactions occurring in a neurophysiological system, namely, in the thalamocortical neural network of an epileptic brain of a rat, where the group electrical activity is registered by means of multichannel EEG. We demonstrate that it is possible to infer the degree of interaction between the interconnected regions of the brain during different types of brain activities and to estimate the regions' participation in the generation of the different levels of consciousness.

Altered Coupling between Motion-Related Activation and Resting-State Brain Activity in the Ipsilesional Sensorimotor Cortex after Cerebral Stroke.

Functional connectivity maps using resting-state functional magnetic resonance imaging (rs-fMRI) can closely resemble task fMRI activation patterns, suggesting that resting-state brain activity may predict task-evoked activation or behavioral performance. However, this conclusion was mostly drawn upon a healthy population. It remains unclear whether the predictive ability of resting-state brain activity for task-evoked activation would change under different pathological conditions. This study investigated dynamic changes of coupling between patterns of resting-state functional connectivity (RSFC) and motion-related activation in different stages of cerebral stroke. Twenty stroke patients with hand motor function impairment were involved. rs-fMRI and hand motion-related fMRI data were acquired in the acute, subacute, and early chronic stages of cerebral stroke on a 3-T magnetic resonance (MR) scanner. Sixteen healthy participants were enrolled as controls. For each subject, an activation map of the affected hand was first created using general linear model analysis on task fMRI data, and then an RSFC map was determined by seeding at the peak region of hand motion activation during the intact hand task. We then measured the extent of coupling between the RSFC maps and motion-related activation maps. Dynamic changes of the coupling between the two fMRI maps were estimated using one-way repeated measures analysis of variance across the three stages. Moreover, imaging parameters were correlated with motor performances. Data analysis showed that there were different coupling patterns between motion-related activation and RSFC maps associating with the affected motor regions during the acute, subacute, and early chronic stages of stroke. Coupling strengths increased as the recovery from stroke progressed. Coupling strengths were correlated with hand motion performance in the acute stage, while coupling recovery was negatively correlated with the recovery outcome of hand motion performance in the early chronic stages. Couplings between RSFC and motion-related activation were dynamically changed with stroke progression, which suggested changes in the prediction of resting-state brain activity for task-evoked brain activity in different pathological states. The changes in coupling strength between these two types of brain activity implicate a reparative mechanism of brain injury and may represent a biomarker for predicting motor recovery in cerebral stroke.

Update on the mechanisms and roles of high-frequency oscillations in seizures and epileptic disorders.

High-frequency oscillations (HFOs) are a type of brain activity that is recorded from brain regions capable of generating seizures. Because of the close association of HFOs with epileptogenic tissue and ictogenesis, understanding their cellular and network mechanisms could provide valuable information about the organization of epileptogenic networks and how seizures emerge from the abnormal activity of these networks. In this review, we summarize the most recent advances in the field of HFOs and provide a critical evaluation of new observations within the context of already established knowledge. Recent improvements in recording technology and the introduction of optogenetics into epilepsy research have intensified experimental work on HFOs. Using advanced computer models, new cellular substrates of epileptic HFOs were identified and the role of specific neuronal subtypes in HFO genesis was determined. Traditionally, the pathogenesis of HFOs was explored mainly in patients with temporal lobe epilepsy and in animal models mimicking this condition. HFOs have also been reported to occur in other epileptic disorders and models such as neocortical epilepsy, genetically determined epilepsies, and infantile spasms, which further support the significance of HFOs in the pathophysiology of epilepsy. It is increasingly recognized that HFOs are generated by multiple mechanisms at both the cellular and network levels. Future studies on HFOs combining novel high-resolution invivo imaging techniques and precise control of neuronal behavior using optogenetics or chemogenetics will provide evidence about the causal role of HFOs in seizures and epileptogenesis. Detailed understanding of the pathophysiology of HFOs will propel better HFO classification and increase their information yield for clinical and diagnostic purposes.

Neuroscience, Sincerity, and the Law

This article focuses on the United States to consider how recent developments in neuroscience, specifically in the ability of researchers to measure directly certain types of brain activity through technologies such as functional Magnetic Resonance Imaging (fMRI), might be used so as to render states of mind, such as sincerity, measurable as “objective facts” on a par with other observable phenomena for the purposes of resolving specific legal cases. The primary focus will be on sincerity itself, particularly in relation to religious beliefs that are claimed as a basis for exemption for compliance with an existing statutory regime of general applicability. Part One examines how the issue of sincerity has manifested in disputes concerning conscientious objections to participating in the military draft. Part Two reviews the science of fMRI and consider issues raised by the use of fMRI scans as evidence in legal cases. Part Three addresses these issues specifically as they might bear on considerations raised in the conscientious objector cases discussed in Part One. It considers the challenges of applying fMRI technology to these sorts of cases. Part Four considers the recent case of Burwell v. Hobby Lobby Stores, Inc ., where the U.S. Supreme Court upheld a closed corporation’s claim under the Religious Freedom Restoration Act to be exempt from complying with a provision of the Affordable Care Act concerning the provision of medical insurance to employees that covered certain forms of contraception because it substantially burdened the religious beliefs of its owners – effectively attributing their beliefs to the corporation. The Court explicitly did not consider the sincerity of claims in this case, but this paper will explore how the adoption of fMRI technology could potentially change this approach.

Near-infrared spectroscopy (NIRS) in functional research of prefrontal cortex.

Near-infrared spectroscopy (NIRS) in functional research of prefrontal cortex.

Multi-objective genetic algorithm as channel selection method for P300 and motor imagery data set

As different region of the brain is associated with different mental activity, channel selection is commonly used to enhance the performance of multi-electrode electroencephalography (EEG) system by removing task-irrelevant and redundant channels. Various channel selection methods are successfully implemented in Brain–Computer Interface (BCI) system that uses one type of brain activity by earlier researchers. Upon realizing the limitation of conventional BCI systems, there has been increasing number of hybrid BCI systems. These hybrid systems use combinations of two brain activity patterns to enhance the functionality of a system. In this paper, three multi-objective genetic algorithms (GAs) are proposed to optimize the number of channels selected and system accuracy. The objective of this research is to investigate the optimal tradeoff between the classification accuracy of a BCI system and the number of selected channels. This tradeoff is important because different BCI applications have different priorities; some implementations prefer minimum number of channels while others favor the classification accuracy. The second objective of this research is to investigate the effectiveness of the GAs adopted as a channel selection method for BCI systems based on different brain activity. Three BCI Competition data sets are used to evaluate the performance of the proposed GAs. Non-parametric Friedman test (p-value=0.635) is also conducted and the result reveals that the significant reduction in number of channels does not have significant impact in the classification accuracy on the evaluation data. This confirms the validity of genetic algorithms as a channel selection method for both P300 and motor imagery data.

Electrophysiological correlates of the BOLD signal for EEG-informed fMRI.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are important tools in cognitive and clinical neuroscience. Combined EEG–fMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level‐dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological–haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals and proceed by reviewing EEG‐informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (EEG–fMRI mapping), or exploring a range of EEG‐derived quantities to determine which best explain colocalised BOLD fluctuations (local EEG–fMRI coupling). While reviewing studies of different forms of brain activity (epileptic and nonepileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG‐informed fMRI. Our review is focused on EEG‐informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG‐informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG–fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations. Hum Brain Mapp, 36:391–414, 2015. © 2014 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Simultaneous recording of MEG, EEG and intracerebral EEG during visual stimulation: From feasibility to single-trial analysis

Electroencephalography (EEG), magnetoencephalography (MEG), and intracerebral stereotaxic EEG (SEEG) are the three neurophysiological recording techniques, which are thought to capture the same type of brain activity. Still, the relationships between non-invasive (EEG, MEG) and invasive (SEEG) signals remain to be further investigated. In early attempts at comparing SEEG with either EEG or MEG, the recordings were performed separately for each modality. However such an approach presents substantial limitations in terms of signal analysis. The goal of this technical note is to investigate the feasibility of simultaneously recording these three signal modalities (EEG, MEG and SEEG), and to provide strategies for analyzing this new kind of data. Intracerebral electrodes were implanted in a patient with intractable epilepsy for presurgical evaluation purposes. This patient was presented with a visual stimulation paradigm while the three types of signals were simultaneously recorded. The analysis started with a characterization of the MEG artifact caused by the SEEG equipment. Next, the average evoked activities were computed at the sensor level, and cortical source activations were estimated for both the EEG and MEG recordings; these were shown to be compatible with the spatiotemporal dynamics of the SEEG signals. In the average time–frequency domain, concordant patterns between the MEG/EEG and SEEG recordings were found below the 40Hz level. Finally, a fine-grained coupling between the amplitudes of the three recording modalities was detected in the time domain, at the level of single evoked responses. Importantly, these correlations have shown a high level of spatial and temporal specificity. These findings provide a case for the ability of trimodal recordings (EEG, MEG, and SEEG) to reach a greater level of specificity in the investigation of brain signals and functions.

An SSVEP BCI to Control a Hand Orthosis for Persons With Tetraplegia

Brain-computer interface (BCI) systems allow people to send messages or commands without moving, and hence can provide an alternative communication and control channel for people with limited motor function. In this study, we demonstrate a BCI system for orthosis control. Our BCI was asynchronous, meaning that subjects could move the orthosis whenever they wanted, instead of pacing themselves to external cues. Seven subjects each performed two tasks with a BCI that relied on steady state visual evoked potentials (SSVEPs). Although none of the subjects had any training, six subjects showed good control with a positive predictive value (PPV) higher than 60%. The overall PPV for all subjects reached 78% ±10%. However, the false positive rate was high, and some subjects dislike the flickering lights required in SSVEP BCIs. In follow-up work, we hope to reduce both the false positive rate and the annoyance produced by flickering lights by hybridizing this BCI with a "brain switch," which could allow people to turn the SSVEP system on or off using a second type of brain activity when they do not wish to control the orthosis. We also hope to validate this approach with people with tetraplegia.

What's New in Neuroimaging Methods?

The rapid advancement of neuroimaging methodology and its growing availability has transformed neuroscience research. The answers to many questions that we ask about how the brain is organized depend on the quality of data that we are able to obtain about the locations, dynamics, fluctuations, magnitudes, and types of brain activity and structural changes. In this review an attempt is made to take a snapshot of the cutting edge of a small component of the very rapidly evolving field of neuroimaging. For each area covered, a brief context is provided along with a summary of a few of the current developments and issues. Then, several outstanding papers, published in the past year or so, are described, providing an example of the directions in which each area is progressing. The areas covered include functional magnetic resonance imaging (fMRI), voxel-based morphometry (VBM), diffusion tensor imaging (DTI), electroencephalography (EEG), magnetoencephalography (MEG), optical imaging, and positron emission tomography (PET). More detail is included on fMRI; its subsections include fMRI interpretation, new fMRI contrasts, MRI technology, MRI paradigms and processing, and endogenous oscillations in fMRI.

Dissociations between motor-related EEG measures in a cued movement sequence task

Different aspects of preparation, especially processes related to knowing what to prepare versus applying that foreknowledge effectively, may be reflected in different types of brain activity, e.g., the lateralized readiness potential (LRP), beta-band event-related desynchronization and phase locking. In a previous study in which subjects had to switch between response hands, dissociations were found between types of preparatory hand-related lateralization in evoked potentials, amplitude and phase locking (Gladwin et al., 2006) knowing what task set to switch to and effectively preparing that task set affected the different measures of lateralization. Similarly to how, in task switching, stimuli and responses must be correctly related to each other, in the preparation of movement sequences relations must be specified concerning motor processes. Similar dissociations as found in the task switching data might then be found in a cued movement sequence task. This possibility was explored by precueing elements of a two-movement sequence involving the left and right index fingers, and comparing various measures of electroencephalogram activity. Cues could specify the full sequence, either the first or the second element, or neither element. Knowing the first element was sufficient to lateralize the pattern of phase locking, but effects were found in the LRP and lateralized amplitude only when the full sequence was known. It seems likely that subjects only fully prepared the first response when they had full knowledge of the sequence so that the dissociation may be closely related to that found for task switching. Thus, the present data would appear to agree with previous results that couple response-foreknowledge with phase locking and the transformation of that foreknowledge into effective changes in component processes with evoked potential shifts. The results further underscore the general importance of considering different types of brain activity: depolarization, desynchronization and phase locking all appear to be involved in different aspects of cognitive control.