Human Brain Processes Research Articles

Can Implicit or Explicit Time Processing Impact Numerical Representation? Evidence From a Dual Task Paradigm

Whether the human brain processes various types of magnitude, such as numbers and time, through a shared representation or whether there are different representations for each type of magnitude is still debated. Here, we investigated two aspects of number-time interaction: the effects of implicit and explicit processing of time on numbers and the bi-directional interaction between time and number processing. Thirty-two participants were randomly assigned into two experimental groups that performed, respectively, a Single task (number comparison, with implicit time processing) and a Dual task (number comparison as a primary task, with explicit time processing as a secondary task). Results showed that participants, only in the Dual task, were faster and more accurate when processing large numbers paired with long rather than short durations, whereas the opposite pattern was not evident for small numbers. Moreover, participants were more accurate when judging long durations after having processed large rather than small numbers, whereas the opposite pattern emerged for short durations. We propose that number processing influences time processing more than vice versa, suggesting that numbers and time might be at least partially independently represented. This finding can pave the way for investigating the hierarchical representation of space, numbers, and time.

Fish Shoals Behavior Detection Based on Convolutional Neural Network and Spatiotemporal Information

Behavior is the first visible change in an animal species after exposure to its own or environmental stressors and is a sensitive indicator. Fish are social animals, and the abnormality of group behavior is more indicative about a particular event than individual behavior, providing more effective informeqation about environmental or group social changes. The group behavior is not only reflected in the spatial distribution, but also reflected in the temporal behavior of the group and individual movement changes under the influence of pressure factors. This paper proposes a group behavior discrimination method based on convolutional neural network and spatiotemporal information fusion, which intends to make use of the prominent performance of convolutional neural network in image recognition and state classification, and imitating the attentional mechanism of ventral channel and dorsal channel when the human brain processes visual signals. Some pressure environments are made in laboratory, the behavior states of fish shoals are recorded, and the sample database of shoals’ behavior state is established by combining the spatial information of shoals’ spatial distribution with the time information reflected in the movement behavior. A simple convolutional neural network is constructed to quickly identify the behavior state of fish shoals. The effects of bath size and training epoch on network training speed and recognition accuracy are discussed, and the visualization of the intermediate data of the convolutional neural network is studied. Shown from the results of experiments of this paper, different behavior states of fish shoals can be recognized and classified effectively by using the simple convolutional neural network and spatiotemporal fusion images. What’s more, from the visualization of network intermediate data, it is found that the convolutional neural network has a higher discrimination power to the image edge feature than the image gray-value feature.

Functional Neuroimaging Biomarkers in Migraine: Diagnostic, Prognostic and Therapeutic Implications.

In current migraine clinical practice, conventional neuroimaging examinations are often sought to exclude possible causes of secondary headaches or migraineassociated disorders. Contrariwise, although advanced Magnetic Resonance Imaging (MRI) has improved tremendously our understanding of human brain processes in migraine patients, to the state of the art they have not superseded the conventional neuroimaging techniques in the migraine clinical setting. A comprehensive review was conducted of PubMed citations by entering the keyword "marker" and/or "biomarker" combined with "migraine" and/or "headache". Other keywords included "imaging" or "neuroimaging", "structural" or "functional". The only restriction was English-language publication. The abstracts of all articles meeting these criteria were reviewed, and the full text was retrieved and examined for relevant references. Several authors tried to identify imaging biomarkers able to identify different migraine phenotypes or, even better, to follow-up the same migraine patients during the course of the disease, to predict the evolution into more severe phenotypes and, finally, the response to specific treatment. The identification of diagnostic, prognostic and therapeutic advanced neuroimaging biomarkers in the migraine clinical setting, in order to approach to patients in a more and more rational and "tailored" way, is extremely intriguing and futuristic. Unfortunately, reliable and robust neuroimaging biomarkers are still lacking for migraine, probably due to both not completely understood pathogenesis and clinical and neuroimaging heterogeneity. Although further longitudinal advanced neuroimaging studies, aimed to identify effective neuroimaging biomarkers, are needed, this review aims to collect the main and most recent works on this topic.

Prediction of Emoji from News Headlines using Machine Learning Techniques

Emojis are generated from the news headlines of Malayalam language using Naive Bayes (NB) and Support Vector Machine(SVM) classifiers. The human brain processes visual data faster than text data. Assigning emoji to the text helps the people easily categorize the news based on the emotion without reading the entire sentence. Six different emojis are assigned to the news headlines based on the emotional contents of the text. These emojis are used for representing emotions like sad, angry, fear, happy, love, and neutral. The dataset contains 3111 sentences which are retrieved from the tweets of Manorama Online. Both Bag-of-Words (BOW) and Term Frequency versus Inverse Document Frequency (TFIDF) features are used for feature vector formation of the dataset. The SVM shows better accuracy compared with the NB classifier.

Room-temperature fabricated artificial synapse modulated by ultrathin SiOx buffer layer

In this work, a LiCoO2 (LCO)-based artificial synapse was fabricated at room temperature. Under continuous pulses stimuli, the device exhibited remarkable biological behaviors such as potentiating, depressing, forgetting, relearning, short-term plasticity (STP) and long-term plasticity, which were similar to the switching process in human brain. Switching process in LCO-based artificial synapse was controlled by the transport of Li ions between LCO film and ultrathin SiOx buffer layer, the latter was formed in LCO/Si interface during the fabrication process. This work provides a feasible way to realize low-temperature processed nerve synapse for neural network and artificial intelligence machines.

Neurotransmitters and neuromodulators involved in learning and memory

Learning and memory being highly specialized process of human brain involves complex interaction between neurotransmitters and cellular events. Over the years, the understandings of these processes have been evolving from psychological, neurophysiological, and pharmacological perspectives. The most widely appraised model of learning and memory involves attention, acquisition, storage and retrieval. Each of these events involve interplay of neurotransmitters such as dopamine, acetylcholine, norepinephrine, N-methyl-d-aspartic acid, gamma-aminobutyric acid, though preponderance of specific neurotransmitter have been documented. The formation of long-term memory involves cellular events with neuroplasticity. Further, dopamine is documented to play crucial role in the process of forgetting. Understanding of the processes of learning and memory not only facilitates drug discovery, but also helps to understand actions of several existing drugs. In addition, it would also help to enhance psychological interventions in children with learning disabilities. Thus, the review intends to summarize role of neurotransmitters and neuromodulators during different phases of learning and memory.

Unraveling nonlinear electrophysiologic processes in the human visual system with full dimension spectral analysis

Natural sensory signals have nonlinear structures dynamically composed of the carrier frequencies and the variation of the amplitude (i.e., envelope). How the human brain processes the envelope information is still poorly understood, largely due to the conventional analysis failing to quantify it directly. Here, we used a recently developed method, Holo-Hilbert spectral analysis, and steady-state visually evoked potential collected using electroencephalography (EEG) recordings to investigate how the human visual system processes the envelope of amplitude-modulated signals, in this case with a 14 Hz carrier and a 2 Hz envelope. The EEG results demonstrated that in addition to the fundamental stimulus frequencies, 4 Hz amplitude modulation residing in 14 Hz carrier and a broad range of carrier frequencies covering from 8 to 32 Hz modulated by 2 Hz amplitude modulation are also found in the two-dimensional frequency spectrum, which have not yet been recognized before. The envelope of the stimulus is also found to dominantly modulate the response to the incoming signal. The findings thus reveal that the electrophysiological response to amplitude-modulated stimuli is more complex than could be revealed by, for example, Fourier analysis. This highlights the dynamics of neural processes in the visual system.

Line Spread Function of Specular Reflection and Gloss Unevenness Analysis

In this paper, we examined the physical meaning of the widely used gloss evaluation. Gloss is one of the important qualities for materials. A visual test of the sharpness of a specular reflection image, i.e. gloss, on the surface of materials is often performed to estimate the gloss of materials. If the specular reflection image is sharp, we estimate that it has high gloss. This means that gloss can be estimated by measuring the sharpness of the specular reflection image. In this case, a line light source such as a fluorescent lamp is often used. Here we call it the line light source observation method. We developed a measurement apparatus based on the line light source observation method. As a result of experimental verification, it is confirmed that the Line Spread Function of Specular Reflection (SR-LSF) can be derived from the line light source observation images. However, the reflected image of line light source observation method is considered to be strictly different from SR-LSF. On the other hand, it is clarified that the line source observation method is an approximate SR-LSF measurement method. We also introduce the reconstruct method of gloss unevenness image by following visual observation conditions and image processing in human brain.

Sampling-Tree Model: Efficient Implementation of Distributed Bayesian Inference in Neural Networks

Experimental observations from neuroscience have suggested that the cognitive process of human brain is realized as probabilistic reasoning and further modeled as Bayesian inference. However, it remains unclear how Bayesian inference could be implemented by network of neurons in the brain. Here a novel implementation of neural circuit, named the sampling-tree model, is proposed to fulfill this aim. By using a deep tree structure to implement sampling with simple and stackable basic neural network motifs for any given Bayesian networks, one can perform local inference while guaranteeing the accuracy of global inference. We show that these task-independent motifs can be used in parallel for fast inference without intensive iteration and scale-limitation. As a result, this model utilizes the structure benefit of neuronal system, i.e., neuronal abundance and multihierarchy, to perform fast inference in an extendable way.

Amygdala responds to direct gaze in real but not in computer-generated faces

Computer-generated (CG) faces are an important visual interface for human-computer interaction in social contexts. Here we investigated whether the human brain processes emotion and gaze similarly in real and carefully matched CG faces. Real faces evoked greater responses in the fusiform face area than CG faces, particularly for fearful expressions. Emotional (angry and fearful) facial expressions evoked similar activations in the amygdala in real and CG faces. Direct as compared with averted gaze elicited greater fMRI responses in the amygdala regardless of facial expression but only for real and not for CG faces. We observed an interaction effect between gaze and emotion (i.e., the shared signal effect) in the right posterior temporal sulcus and other regions, but not in the amygdala, and we found no evidence for different shared signal effects in real and CG faces. Taken together, the present findings highlight similarities (emotional processing in the amygdala) and differences (overall processing in the fusiform face area, gaze processing in the amygdala) in the neural processing of real and CG faces.

Reward Boosts Neural Coding of Task Rules to Optimize Cognitive Flexibility.

Cognitive flexibility is critical for intelligent behavior. However, its execution is effortful and often suboptimal. Recent work indicates that flexible behavior can be improved by the prospect of reward, which suggests that rewards optimize flexible control processes. Here we investigated how different reward prospects influence neural encoding of task rule information to optimize cognitive flexibility. We applied representational similarity analysis to human electroencephalograms, recorded while female and male participants performed a rule-guided decision-making task. During the task, the prospect of reward varied from trial to trial. Participants made faster, more accurate judgements on high-reward trials. Critically, high reward boosted neural coding of the active task rule, and the extent of this increase was associated with improvements in task performance. Additionally, the effect of high reward on task rule coding was most pronounced on switch trials, where rules were updated relative to the previous trial. These results suggest that reward prospect can promote cognitive performance by strengthening neural coding of task rule information, helping to improve cognitive flexibility during complex behavior.SIGNIFICANCE STATEMENT The importance of motivation is evident in the ubiquity with which reward prospect guides adaptive behavior and the striking number of neurological conditions associated with motivational impairments. In this study, we investigated how dynamic changes in motivation, as manipulated through reward, shape neural coding for task rules during a flexible decision-making task. The results of this work suggest that motivation to obtain reward modulates the encoding of task rules needed for flexible behavior. The extent to which reward increased task rule coding also tracked improvements in behavioral performance under high-reward conditions. These findings help to inform how motivation shapes neural processing in the healthy human brain.

Television as an instrument of consciousness transformation of modern man

This article is dedicated to the transforming influence of television on consciousness of modern man. Forall this we paid a particular attention to those aspects of its moderative effect which had not been enough studiedand reflected in a wide spectrum of materials.First of all, the reasons of TV popularity as the most perfect and reflective means to date of socio-cultural lifeof the society such as an inclusion of all the sections of the population, address to the emotional human sphereand use of universal cinema language have been considered.The telemoderation of human consciousness in the sphere of formation of his/her basic cultural and socialknowledge is of special significance for us. With this purpose we studied in the article a positive moderating roleas well as a negative one of such forms of the contact with the audience as serials, talk shows or aestheticizingTV programmes.At the same time, the main accent was given to the hidden forms of TV influence. The major conclusion basedon the research of competent authors such as mediaphilosopher H. McLuhan and psychiatrist J. O’Keeffe is in thatTV images organize a multiscaled reformation of thinking processes in human brain. The activization of his/hercreative right cerebral hemisphere and percepting perfection become the result of this productive moderation.The telemoderation of emotional human sphere is not of lesser importance. The analysis of researchoutcomes made by the German mediapsychologist Winterhoff-Spurk confirms the positive significance of TVcontent, especially one of horror genre, for increasing a general level of interior motivation. However, the resultsof experiments of quite a number of other researchers proclaim a negative moderation aspect by the telecastsbeing permeated with sense of alarm and fear because the negative feelings accumulate in the subconsciousnessthat raises a level of social and personal anxiety. The research outcomes on latent TV influence upon human physiology and at the same time upon his/herpersonal activity proved to be the most important. The conclusions of multiple experiments collected by theresearcher R. Patzlaff state that a TV review disconnects the proper activity of eyes, provokes its immobilitythat also decreases the interior activity. This aspect has a particular importance in the development of a child’sintellect and his/her verbal organs because the TV role in his/her life grew immeasurably. So the unlimitedcontact of the child with the screen results in the problem of development of his/her speech because any dialogueis not supposed to be here.The conclusion motivates the necessity of thorough study of negative aspects of telemoderation with thepurpose of minimizing its nonproductive effect.

Integrating the Washington Media Scholars Case Study Competition into a Capstone Course

Integrating the WMSF Case Study competition into a capstone course will create a whole new dynamic for students. The case studies provided by WMSF are valuable for students because they demonstrate that strategically selecting a target and creating a media mix that will communicate to the chosen target sometimes requires a flexible, human brain processing the information into the best possible choices. The case studies are more than media choices. They are actual problems or opportunities based in the domain of nonpartisan political communication that require students to delve into the organizations or individuals involved and utilize media to communicate messages. Many students want to just look at the given budget and divide it up equally, but I encourage the students to read the case study over several times to catch the nuances in the case.

Deposits of iron oxides in the human globus pallidus

Abstract Samples taken from the human brain (Globus Pallidus) have been investigated by physical techniques such as light microscopy, scanning electron microscopy, transmission electron microscopy, Mössbauer spectroscopy and SQUID magnetometry. SEM-EDX/TEM investigation reveals multielemental composition of hematite and magnetite nanocrystals with sizes ranging from 40 nm to 100 nm and hematite microcrystals from 3 μm to 7 μm. Room temperature Mössbauer spectra show quadrupole doublets assigning to hematite and ferrihydrite. SQUID measurements of temperature dependence of the mass magnetic susceptibility between T = 2 – 300 K at DC field B 0 = 0.1 T, the field dependence of the mass magnetization taken at the fixed temperature T 0 = 2.0 and 4.6 K and the zero-field cooled and field cooled magnetization experiments (ZFCM/FCM) confirm a presence of ferrimagnetic phases such as maghemite and/or magnetite with hysteresis loops surviving until the room temperature. Differences between these measurements from the point of view of iron oxides detected can indicate important processes in human brain and interactions between ferritin as a physiological source of iron and surrounding environment.

2D electrical resistivity imaging based on backpropagation artificial neural network

Dipole-dipole is one configuration design survey in electrical resistivity method which is common to interpreted shallow subsurface base on resistivity parameter, and the model can be developed using inversion formula. Otherwise, we developed some model without empirical mathematic formula; it is called Artificial Neural Network (ANN). ANN is a system which has a pattern like the human brain process to solve the complex problems. The research aims to develop a neural network algorithm using Matlab and compare the result 2D model resistivity between ANN and inversion by Res2Dinv (existing procedure) software. The research was done in Taman Rumah Kita (TRK) where located in Faculty of Science and Mathematics, Diponegoro University. A cylinder was buried in the center of TRK and getting the best architecture of the network and the value of Mean Square Error (MSE) of the output network. The backpropagation artificial neural network was built from many layers such as an input layer, hidden layer, and output layer and developed by Matlab programs. The Network train was tested using synthetic data and field data. The synthetic data was made with forwarding modeling method by Res2Mod software, and the field data was obtained by doing the measurement at the measurement site using dipole-dipole resistivity method. The comparing result models are present the best architecture obtained one input layer with three input units, three hidden layers with each layer has 100 neurons and one output layer that obtained by trial and error process. MSE obtained respectively in observation lines are 0.0210 at Line 1, and 0.0345 at Line 2.

Neurophysiological Correlates of Gait Retraining With Real-Time Visual and Auditory Feedback.

Most people acquire motor skills through feedback-based training. How the human brain processes sensory feedbacks during training, especially in a gait training, remain largely unclear. The purpose of this paper is to explore how humans adopt a new gait pattern to reduce impacts during walking-with the aid of visual and audio feedbacks. This paper demonstrates the features of underlying brain activity in incorporating the visual or auditory cues to acquire a new gait pattern. Electroencephalography (EEG) and peak positive acceleration (PPA) of the heel were collected from 23 participants during walking on a treadmill with no feedback, with visual feedback, or with audio feedback. The feedbacks were presented after each foot strike, where a sub-threshold PPA triggered a positive feedback (green/low-pitched), and a suprathreshold PPA triggered a negative feedback (red/high-pitched). The participants were instructed to voluntarily control their gait, so that low PPA could be achieved. This control was perturbed in some sessions by an additional cognitive task, and the influence of such distraction was also explored. The PPA was significantly lower in the sessions with visual or audio feedback than in sessions without feedback, showing an immediate improvement in gait pattern, when the feedback was provided. Different feedbacks modulated neural activities at different locations and/or levels during training. Alpha event-related synchronization (ERS) was particularly increased during the encoding of auditory feedback or the introduction of a distracting task. In the meantime, prominent frontal and posterior theta ERS were coupled with negative feedback, and strong beta event-related desynchronization (ERD) was observed only in sessions with feedbacks. Our results indicate that feedback effectively enhances motor planning when acquiring a new gait.

Investigation of functional brain network reconfiguration during vocal emotional processing using graph-theoretical analysis

Vocal expression is essential for conveying the emotion during social interaction. Although vocal emotion has been explored in previous studies, little is known about how perception of different vocal emotional expressions modulates the functional brain network topology. In this study, we aimed to investigate the functional brain networks under different attributes of vocal emotion by graph-theoretical network analysis. Functional magnetic resonance imaging (fMRI) experiments were performed on 36 healthy participants. We utilized the Power-264 functional brain atlas to calculate the interregional functional connectivity (FC) from fMRI data under resting state and vocal stimuli at different arousal and valence levels. The orthogonal minimal spanning trees method was used for topological filtering. The paired-sample t-test with Bonferroni correction across all regions and arousal–valence levels were used for statistical comparisons. Our results show that brain network exhibits significantly altered network attributes at FC, nodal and global levels, especially under high-arousal or negative-valence vocal emotional stimuli. The alterations within/between well-known large-scale functional networks were also investigated. Through the present study, we have gained more insights into how comprehending emotional speech modulates brain networks. These findings may shed light on how the human brain processes emotional speech and how it distinguishes different emotional conditions.

Physics of decision processes

We provide evidence that information processing in linguistic elaboration entails an uncertainty relation and entangled states, without having to strictly rely on quantum processes. Thus far, reported evidences of quantum effects in human brain processes lack a plausible framework, since either no assignment of an appropriate quantum constant had been associated, or speculating on microscopic processes dependent on Planck’s constant resulted in unrealistic de-coherence times. In the human brain formulation of linguistic processes, a word coded as a neuron spike sequence is compared with a previous word retrieved via the short term memory; comparison yields a judgment. Synchronization of the finite neuron spike sequences (SFSS), coding the two words, is the way two brain regions compare their content and extract the most suitable sequence. It consists of an inverse Bayes procedure. In SFSS, an uncertainty relation emerges between the bit size of a word and its duration. This uncertainty affects the task of synchronizing spike trains of different duration representing different words, entailing the occurrence of entangled sequences (ES). ES justifies the inverse Bayes inference that connects different words in a linguistic task. ES decays after a finite de-coherence time. The behavior here exhibited provides an explanation for the previously reported evidences of quantum effects in decision processes.

Deep Learning Applications in the Medical Image Recognition

In this essay, the researcher is focusing on the deep learning systems and its major applications in various fields. Song Yukun uses the relu incentive algorithm and the convolution functions to make the program automatically recognize different things or same type of things with different features. Before actually processing the image recognition part, the researcher adds a transforming program which change all kinds of image into one small form. Then, using this modelled image, the program could delicately determine the type of the contents in the image. This technological program is automatic and performs as an essential part of artificial intelligences. The main work it does is imitating the learning process of human brain, which accumulate experiences from thousands of events. It realizes this function by adding different algorithms in the program including the relu incentive algorithm which “teaches” the program particular types of images. After massive input, this technological program could quickly solve current problems with the lack of human labor force doing repetitive but intelligent works like checking particular tumor in the X-ray films. Besides, learning by themselves, the programs could generate results more specific than humans do. This deep-learning principle could be widely utilized since everything in human lives are learning and accumulating experiences. It could change any previous mechanical program into “intelligent” programs which would have an acceleration in their delicacy of determination.

Spatiotemporal dynamics of the processing of lexical information in word recognition revealed by intracerebral potentials

Event Abstract Back to Event Spatiotemporal dynamics of the processing of lexical information in word recognition revealed by intracerebral potentials Yi Liu1, Shi Gaofeng2, Gaofeng Shi1, Yuguang Guan3* and Zaizhu Han1* 1 Beijing Normal University, China 2 Beijing Language and Culture University, China 3 Sanbo Brain Hospital, Capital Medical University, China One of the fundamental questions in language neuroscience is how the human brain processes lexical information. A neural network has been found which supports lexical information processing during word recognition (Paul et al., 2015; Lee et al., 2010; Wei et al., 2016). However, the exact temporal and spatial distribution of processing in this brain network is poorly understood. To address this issue, stereoelectroencephalography (SEEG) data in 17 Chinese native speakers with epilepsy were collected from 1393 electrodes implanted in the left hemisphere as the patients performed a lexical decision task (including 150 real Chinese characters and 150 Chinese non-characters). We examined when and where the lexical effect (i.e., the difference between non-characters and real characters) appeared in the high gamma-band activity (GBA) (60 HZ and 140 Hz) of SEEG signals. The earliest lexical effect (GBA signal was stronger for non-characters than for real characters; i.e., non-characters > real characters GBA) was observed in the occipital lobe (around 167 ms). Then, the reversed effect (real characters > non-characters GBA) appeared in the superior occipital lobe (around 136 ms), parietal lobe (around 332 ms) and inferior frontal gyrus (around 409 ms). Finally, the lexical effect was again detected in the middle frontal gyrus (around 515 ms). These findings highlight the ventral and dorsal pathways that collaboratively underlie the recognition of lexical information. Acknowledgements This work was supported by the National Natural Science Foundation of China [81171019] and the Beijing Natural Science Foundation [7182088]. References Hoffman, P. , Lambon Ralph, M. A. , & Woollams, A. M. . (2015). Triangulation of the neurocomputational architecture underpinning reading aloud. Proceedings of the National Academy of Sciences, 112(28), E3719-E3728. Lee, C. Y. , Huang, H. W. , Kuo, W. J. , Tsai, J. L. , & Tzeng, J. L. O. . (2010). Cognitive and neural basis of the consistency and lexicality effects in reading chinese. Journal of Neurolinguistics,23(1), 1-27. Wei, Z. , Xiaojuan, W. , Zhichao, X. , Yanchao, B. , Ping, L. , & Hua, S. . (2016). Neural mechanisms of dorsal and ventral visual regions during text reading. Frontiers in Psychology, 7. Keywords: lexical information, spatiotemporal dynamics, leixcal decision, SEEG, high gamma Conference: Academy of Aphasia 57th Annual Meeting, Macau, Macao, SAR China, 27 Oct - 29 Oct, 2019. Presentation Type: Poster presentation Topic: Eligible for student award Citation: Liu Y, Gaofeng S, Shi G, Guan Y and Han Z (2019). Spatiotemporal dynamics of the processing of lexical information in word recognition revealed by intracerebral potentials. Front. Hum. Neurosci. Conference Abstract: Academy of Aphasia 57th Annual Meeting. doi: 10.3389/conf.fnhum.2019.01.00059 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 03 May 2019; Published Online: 09 Oct 2019. * Correspondence: Dr. Yuguang Guan, Sanbo Brain Hospital, Capital Medical University, Beijing, 100093, China, ygguan2000@163.com Dr. Zaizhu Han, Beijing Normal University, Beijing, China, zzhhan@bnu.edu.cn Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Yi Liu Shi Gaofeng Gaofeng Shi Yuguang Guan Zaizhu Han Google Yi Liu Shi Gaofeng Gaofeng Shi Yuguang Guan Zaizhu Han Google Scholar Yi Liu Shi Gaofeng Gaofeng Shi Yuguang Guan Zaizhu Han PubMed Yi Liu Shi Gaofeng Gaofeng Shi Yuguang Guan Zaizhu Han Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.