Human Brain Signals Research Articles

Rhythmic fluctuations in evidence accumulation during decision making in the human brain

Categorical choices are preceded by the accumulation of sensory evidence in favor of one action or another. Current models describe evidence accumulation as a continuous process occurring at a constant rate, but this view is inconsistent with accounts of a psychological refractory period during sequential information processing. During multisample perceptual categorization, we found that the neural encoding of momentary evidence in human electrical brain signals and its subsequent impact on choice fluctuated rhythmically according to the phase of ongoing parietal delta oscillations (1-3 Hz). By contrast, lateralized beta-band power (10-30 Hz) overlying human motor cortex encoded the integrated evidence as a response preparation signal. These findings draw a clear distinction between central and motor stages of perceptual decision making, with successive samples of sensory evidence competing to pass through a serial processing bottleneck before being mapped onto action.

Autonomous emotion development using incremental modified adaptive neuro-fuzzy inference system

In this paper, we propose an autonomous emotion development system with incremental learning for interacting with human subjects, and autonomously understanding the emotional status of humans. For the understanding of human emotion the proposed system needs human-like visual senses perceiving natural scenes as stimuli. According to the relationship between emotional factors and characteristics of an image, we incorporate the fuzzy concept to extract emotional features using L⁎C⁎H⁎ color and orientation information. Additionally, it can sense inputs that have no analog in human senses-reading brain signals in human subjects. We consider the electroencephalography (EEG) signals which are stimulated by natural stimuli to form the semantic emotional features as well. We develop a novel adaptive neuro-fuzzy inference system (ANFIS) based on an incremental learning algorithm to autonomously develop the capability of understanding complex emotions. The proposed incremental modified ANFIS needs only the newly arrived data to adjust the shape of Gaussian membership functions with full covariance matrix, to generate new membership functions or new rules for labeling emotion according to the characteristics of the new data. Utilizing the developmental process, the proposed system can autonomously develop the mental ability to understand more complex human emotions by mining the characteristics of emotional features and interacting with human subjects.

Predicting vocal emotion expressions from the human brain

Speech is an important carrier of emotional information. However, little is known about how different vocal emotion expressions are recognized in a receiver's brain. We used multivariate pattern analysis of functional magnetic resonance imaging data to investigate to which degree distinct vocal emotion expressions are represented in the receiver's local brain activity patterns. Specific vocal emotion expressions are encoded in a right fronto-operculo-temporal network involving temporal regions known to subserve suprasegmental acoustic processes and a fronto-opercular region known to support emotional evaluation, and, moreover, in left temporo-cerebellar regions covering sequential processes. The right inferior frontal region, in particular, was found to differentiate distinct emotional expressions. The present analysis reveals vocal emotion to be encoded in a shared cortical network reflected by distinct brain activity patterns. These results shed new light on theoretical and empirical controversies about the perception of distinct vocal emotion expressions at the level of large-scale human brain signals.

Detection of brain auditory evoked magnetic field based on low-Tc superconducting quantum interface device

Superconducting quantum interface devices (SQUID) is widely used in human brain signal detection. As one of the applications of magnetoencephalography (MEG) system, the detection of the auditory evoked response is useful for the development of MEG system and the research into auditory mechanism of human brain. Generally, the auditory evoked response includes three peaks which are P50m, N100m and P200m. We develop a single-channel MEG system in a magnetically shielded room based on the superconducting quantum interface device (SQUID) and second-order axial gradiometer. The responses of the main peak N100m under different tone frequencies are preliminarily studied by using our system. The typical evoked response of N100m to 1 kHz pure tone and 100 ms duration is measured to be 0.4 pT. Under the tone stimulus at low frequency, the delay of the peak N100m to the tone onset is 125 ms at 100 Hz, which is longer than the typical value of 100 ms. In comparison with the response to 1 kHz pure tone stimulus, the amplitude of the evoked response in a random frequency range from 1 kHz to 4 kHz is stronger and the delay is several milliseconds. This work lays the foundation of the studies of the auditory mechanism and multichannel MEG system by using software gradiometers.

Magnetic resonance spectroscopy of 1.28ppm signal in live human brain meets neurogenesis criteria

Magnetic resonance spectroscopy of 1.28ppm signal in live human brain meets neurogenesis criteria

Decoding Action Intentions from Preparatory Brain Activity in Human Parieto-Frontal Networks

How and where in the human brain high-level sensorimotor processes such as intentions and decisions are coded remain important yet essentially unanswered questions. This is in part because, to date, decoding intended actions from brain signals has been primarily constrained to invasive neural recordings in nonhuman primates. Here we demonstrate using functional MRI (fMRI) pattern recognition techniques that we can also decode movement intentions from human brain signals, specifically object-directed grasp and reach movements, moments before their initiation. Subjects performed an event-related delayed movement task toward a single centrally located object (consisting of a small cube attached atop a larger cube). For each trial, after visual presentation of the object, one of three hand movements was instructed: grasp the top cube, grasp the bottom cube, or reach to touch the side of the object (without preshaping the hand). We found that, despite an absence of fMRI signal amplitude differences between the planned movements, the spatial activity patterns in multiple parietal and premotor brain areas accurately predicted upcoming grasp and reach movements. Furthermore, the patterns of activity in a subset of these areas additionally predicted which of the two cubes were to be grasped. These findings offer new insights into the detailed movement information contained in human preparatory brain activity and advance our present understanding of sensorimotor planning processes through a unique description of parieto-frontal regions according to the specific types of hand movements they can predict.

Detection of neuronal current MRI in human without BOLD contamination

Controversial results regarding the detectability of neuronal current magnetic resonance imaging (ncMRI) have been reported in different studies on human subjects. In all the previous studies, the ncMRI signal was detected under a continuous and paradigm task-induced blood oxygen level dependent (BOLD) signal background. The aim of this study is to investigate the possibility of detecting ncMRI signal in human brain in the situation that task-induced BOLD background is absent or minimum. In this study, by adopting an event-related visuomotor paradigm with long interstimulus interval (=20 s), the ncMRI signal was detected when the BOLD signal fully returned to its baseline, and the potential BOLD background contamination was avoided effectively. The results showed that the visuomotor stimulation elicited BOLD activation in visual and motor cortices, but no significant ncMRI signal change (in magnitude) was detected in human brain. These experimental findings are consistent with theoretical predications.

Synthesis, characterization, and monkey positron emission tomography (PET) studies of [18F]Y1-973, a PET tracer for the neuropeptide Y Y1 receptor

Neuropeptide Y receptor subtype 1 (NPY Y1) has been implicated in appetite regulation, and antagonists of NPY Y1 are being explored as potential therapeutics for obesity. An NPY Y1 PET tracer is useful for determining the level of target engagement by NPY Y1 antagonists in preclinical and clinical studies. Here we report the synthesis and evaluation of [18F]Y1-973, a novel PET tracer for NPY Y1. [18F]Y1-973 was radiolabeled by reaction of a primary chloride with [18F]KF/K2.2.2 followed by deprotection with HCl. [18F]Y1-973 was produced with high radiochemical purity (>98%) and high specific activity (>1000Ci/mmol). PET studies in rhesus monkey brain showed that the distribution of [18F]Y1-973 was consistent with the known NPY Y1 distribution; uptake was highest in the striatum and cortical regions and lowest in the pons, cerebellum nuclei, and brain stem. Blockade of [18F]Y1-973 uptake with NPY Y1 antagonist Y1-718 revealed a specific signal that was dose-dependently reduced in all regions of grey matter to a similarly low level of tracer uptake, indicative of an NPY Y1 specific signal. In vitro autoradiographic studies with [18F]Y1-973 in rhesus monkey and human brain tissue slices revealed an uptake distribution consistent with the in vivo PET studies. Highest binding density was observed in the dentate gyrus, caudate-putamen, and cortical regions; moderate binding density in the hypothalamus and thalamus; and lowest binding density in the globus pallidus and cerebellum. In vitro saturation binding studies in rhesus monkey and human caudate-putamen homogenates confirmed a similarly high Bmax/Kd ratio for [18F]Y1-973, suggesting the tracer may provide a specific signal in human brain of similar magnitude to that observed in rhesus monkey. [18F]Y1-973 is a suitable PET tracer for imaging NPY Y1 in rhesus monkey with potential for translation to human PET studies.

Signals from living biomaterials: analysis of human brain signals through wavelets

Electroencephalography signals were analysed through Daubechies wavelets, and the coefficients were computed based on their energy content, for both normal and learning disabled children. Statistically, normal children showed higher energy content in coefficient no. 6.

The agmatine-degrading enzyme agmatinase: a key to agmatine signaling in rat and human brain?

Agmatinase, an ureohydrolase belonging to the arginase family, is widely expressed in mammalian tissues including the brain. Here, it may serve two different functions, the inactivation of the arginine derivative agmatine, a putative neurotransmitter, and the formation of the diamine putrescine. In order to identify the cellular sources of agmatinase expression in the brain, we generated a polyclonal monospecific antibody against recombinant rat agmatinase. With immunocytochemistry, selected areas of rat and human brain were screened. Clearly, in both species agmatinase-like immunoreactivity was predominantly detected in distinct populations of neurons, especially cortical interneurons. Also, principal neurons in limbic regions like the habenula and in the cerebellum robustly expressed agmatinase protein. When comparing the overall agmatinase expression with immunocytochemical data available for agmatine and polyamine biosynthetic enzymes, the observed pattern may argue in favor of an agmatine inactivating function rather than fueling the alternative pathway of polyamine synthesis. The putative neurotransmitter agmatine is seemingly involved with mental disorders. Therefore, agmatinase may be similarly important for pathogenesis. The normal expression profile of the protein as described here may therefore be altered under pathological conditions.

Emotion recognition using EEG signals with relative power values and Bayesian network

Many researchers use electroencephalograms (EEGs) to study brain activity in the context of seizures, epilepsy, and lie detection. It is desirable to eliminate EEG artifacts to improve signal collection. In this paper, we propose an emotion recognition system for human brain signals using EEG signals. We measure EEG signals relating to emotion, divide them into five frequency ranges on the basis of power spectrum density, and eliminate low frequencies from 0 to 4 Hz to eliminate EEG artifacts. The resulting calculations of the frequency ranges are based on the percentage of the selected range relative to the total range. The calculated values are then compared to standard values from a Bayesian network, calculated from databases. Finally, we show the emotion results as a human face avatar.

Neural Activity in the Human Brain Signals Logical Rule Identification

To select an appropriate action, we conform to a behavioral rule determined uniquely in each behavioral context. If the rule is not predetermined and must be discovered, we often test hypotheses concerning rules by applying one candidate rule after another. The neural mechanisms underlying such rule identification are still unknown. To explore which brain areas are involved in the process of logical rule identification and to determine whether such areas differ from those taking part in implementing the rule to find a suitable action, we measured brain activation using functional magnetic resonance imaging while subjects performed a rule-identification task. The subjects were required to select a red or blue square on a screen based on either a "sequence rule" or a "probability rule." Positive or negative feedback to the subject's choice led the subject to identify the correct rule. We found that the posterior medial frontal cortex (pMFC), caudate nucleus, fusiform gyrus, and middle temporal cortex exhibited significant activation during the period when subjects underwent the hypothesis testing. Among these brain areas, the pMFC and caudate nucleus were also activated in response to the critical feedback signals selectively during the trials when the subjects identified a rule. Furthermore, we found a significant enhancement in effective connectivity between the active regions in the pMFC and caudate regions.

Decoding visual consciousness from human brain signals

Despite many years of research on the neural correlates of consciousness (NCCs), it is still unclear how the detailed contents of consciousness are represented in the human brain. It is often assumed that specific contents of consciousness are encoded in dedicated core NCCs - one for each different aspect of conscious experience. Now, the approach of multivariate decoding provides a novel framework for studying the relationship between consciousness and content-selective processing in more detail. This approach makes it possible to assess how conscious experience is encoded in the brain and how the encoding of sensory information is affected when it enters awareness.

Distribution of Relaxin‐3 mRNA and Immunoreactivity and RXFP3‐Binding Sites in the Brain of the Macaque, Macaca fascicularis

This study examined the presence and distribution of relaxin-3 (RLN3) and its receptor, RXFP3, throughout the brain of Macaca fascicularis. In perfusion-fixed sections, RLN3 immunoreactivity (IR) was observed throughout the fore-, mid-, and hindbrain and was enriched in the septum, hippocampus, and interpeduncular and supramammillary nuclei. Neurons in the pontine central gray area contained RLN3-IR and RLN3 mRNA. The distribution of RXFP3 protein, mapped by autoradiography of [(125)I]R3/I5-binding sites, correlated with the distribution of RLN3-IR. The broad distributions of RLN3-positive neuronal axons and terminals and of RXFP3 in the brain of Macaca fascicularis are consistent with findings in the rat and with an important role for RLN3 signaling in nonhuman primate and human brain.

Prospects of brain–machine interfaces for space system control

The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical and electronic systems. This paper describes the state of the art of non-invasive brain–machine interfaces (BMIs) and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation.

P1-023: Is Alzheimer's disease a consequence of dysregulated WNT signaling in the aged human brain?

P1-023: Is Alzheimer's disease a consequence of dysregulated WNT signaling in the aged human brain?

Classification images reveal the information sensitivity of brain voxels in fMRI

Reverse correlation methods have been widely used in neuroscience for many years and have recently been applied to study the sensitivity of human brain signals (EEG, MEG) to complex visual stimuli. Here we employ one such method, Bubbles (Gosselin, F., Schyns, P.G., 2001. Bubbles: A technique to reveal the use of information in recognition tasks. Vis. Res. 41, 2261–2271), in conjunction with fMRI in the context of a 3AFC facial expression categorization task. We highlight the regions of the brain showing significant sensitivity with respect to the critical visual information required to perform the categorization judgments. Moreover, we reveal the actual subset of visual information which modulates BOLD sensitivity within each such brain region. Finally, we show the potential which lies within analyzing brain function in terms of the information states of different brain regions. Thus, we can now analyse human brain function in terms of the specific visual information different brain regions process.

An effect of electrical double layer on MEG phantom with saline water

We examined the non-linear impedance of a saline water phantom using a prototype with 150-mm diameter sphere and with 5-mm dipole electrodes located at 20 mm from the center of the sphere. We found that the electrical impedance is a series of 0.84-μF capacitor and 250-Ω resistor, which requires unpractical source impedance and voltage, 1.9 MΩ and 760 V respectively, to drive the phantom without distortion. Also observed was a non-linearity due to the electrolysis, which can be avoided by constraining the frequency to be more than 1 kHz, in which frequency human brain signal is not properly emulated. Due to these difficulties, the phantom with saline water is not a good candidate for “the evaluation standard”.

Spatial-temporal analysis of fetal bio-magnetic signals

Non-invasive technique such as magneto-encephalography (MEG), initially pioneered to study human brain signals, has found many other applications in medicine. SQUID11Superconducting Quantum Interference Device. Array for Reproductive Assessment (SARA) is a unique non-invasive scanning-device developed at the University of Arkansas for Medical Sciences (UAMS) that can detect fetal brain and other signals. The fetal magneto-encephalography (fMEG) signals often have many bio-magnetic signals mixed in. Examples include the movement of the fetus or muscle contraction of the mother. As a result, the recorded signals may show unexpected patterns, other than the target signal of interest. These “interventions” make it difficult for a physician to assess the exact fetal condition, including its response to various stimuli. We propose using intervention analysis and spatial-temporal auto-regressive moving-average (STARMA) modeling to address the problem. STARMA is a statistical method that examines the relationship between the current observations as a linear combination of past observations as well as observations at neighboring sensors. Through intervention analysis, the change in a pattern due to “interfering” signals can be accounted for. When these interferences are “removed,” the end product is a “template” time series, or a typical signal from the target of interest. In this research, a “universal” template is obtained. The template is then used to detect intervention in other datasets by the method of template matching. By this method, it is possible to detect if there is an intervention in any dataset. It will assist physicians in monitoring the actual signal generated by fetal brain and other organs of interest.

Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.