A novel method for detection of voxels for decision making: An fMRI study

This study explores the patterns of activation in brain regions toward classifying decision making voxels from among four major Brodmann areas (BAs) upon stimulus of visual tasks. Toward this goal, a well‐known clustering analysis has been performed on real‐time data of the human brain obtained using functional magnetic resonance imaging (fMRI). The functional connectivity among various brain regions was detected by leveraging a distance correlation graph. Graphical methods have been employed to visualize the clusters elicited in the process. The analysis of the results sheds new light on how four significantly activated BAs of the brain exhibit effective connectively to perform a visual task in the context of decision making.     

New fMRI analysis method for multiple stimuli using reference estimation

The stimulation paradigms of a functional MRI (fMRI) usually consist of one or more stimulations and a resting state in the block‐based and event‐related designs. To localize the activation areas in the human brain, each voxel is statistically analyzed using the fMRI data measured with the stimulation. The conventional method can be inefficient for experiments with multiple stimuli because of measuring the resting‐state signals repeatedly, causing redundancy in the scanning process. Although the phase mapping method can be applied to reduce the redundant resting‐state measurements, there are still limitations in the detection of regions activated by multiple stimuli and the periodic sequence of the multiple stimuli. In this article, a new fMRI data analysis method is presented that enables the detection of functional activations without the resting‐state images. This method estimates the reference signal from the signals acquired during multiple stimuli, and a random sequence and various durations of the multiple stimuli can be applied. Therefore, it can be used in the event‐related design as well as the block‐based design. The results of simulation and fMRI experiments show that the proposed method can correctly detect the activation regions of multiple stimuli, even for overlap regions, and can reduce the imaging time by skipping the resting‐state imaging. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 315–322, 2011     

Differences in brain networks of children with ADHD: Whole-brain analysis of resting-state fMRI

The aim of this study was to investigate changes of functional connectivity (FC) in resting state networks (RSNs) in medication-naive children with attention-deficit/hyperactivity disorder (ADHD). Twenty children with a diagnosis of ADHD (11.8 ± 2.29 years; 16 boys) and 20 age-matched typically developing (TD) children (13 ± 1.78 years; 10 boys) were included. It was found that brain FC differences in children with ADHD are not only in the well known RSNs such as default mode, visual, sensory motor, attentional, frontal, central executive, and frontal networks but also involve interaction among whole brain functional networks. In addition, basal ganglia and cerebellum networks which later gained importance were examined in this study. All RSNs has been shown significant differences in special regions which belongs related RSN. The highest positive (HPC) and negative (HNC) correlation were calculated between 14 independent components including 11 different RSNs. We observed different FC changes (decreased/increased) according to the RSNs between ADHD and control children. The HPC was defined between the visual and cerebellum network in ADHD children and between the dorsal attentional network and sensory motor network in TD children. Also, the HNC was detected between the visual and basal ganglia network in both groups. Investigating intra and inter network FC could provide a framework to better understand the neural basis that underlies core symptom dimensions in ADHD.     

The study of brain networks driven by steady‐state visual evoked potentials

In this article, we investigated the brain networks during the steady‐state visual evoked potential (SSVEP) task. Two questions: (1) SSVEP‐driven network structures; and (2) the relationship between SSVEP‐driven networks and stimulus frequencies were studied from a network point of view. Method of directed transfer function was applied to brain active signals recorded from electroencephalography (EEG). The resulting connectivity matrices then were converted to graphs by applying a threshold, so that graph theoretical could be used to analyze the characteristics of SSVEP‐driven networks. The results showed that network connections exist in many scalp locations beyond occipital regions. Different from the outflow areas located mainly around the parietal areas, the inflow areas had a widely distribution pattern including the frontal, temporal, and occipital areas. Furthermore, for a wide range of thresholds, with increasing frequency (7–30 Hz), the distribution of clustering coefficient and characteristic path length presented positive and negative correlation with the three parallel flicker SSVEP subsystems, respectively. The results suggested that a specific frequency may evoke certain SSVEP components more than others, and, therefore, one may generate different evoked potentials which results in different network pattern.     

从人脑的结构机理看互联网的进化

对比互联网应用模式的发展和人类大脑的结构机理,发现二者有惊人的重合。从电子邮件、电子公告牌到博客、社会化网络,互联网的每一个应用创新都能映射到人脑的功能结构中,作者预测,互联网的应用模式将形成一个与人类大脑功能结构高度相似的网络虚拟结构,互联网正朝着使人脑充分互联的方向发展和进化。     

Classification of Alzheimer disease among susceptible brain regions

Statistical and machine learning techniques are frequently employed in the study of neuroimaging data for finding Alzheimer disease (AD) in clinical studies and in additional inquiries about research settings. AD affects the whole brain and as a result the quality of life, where most affected regions are the hippocampus (HP), middle temporal gyrus (MTG), entorhinal cortex, and posterior cingulate cortex (PCC). We used well-known classification methods to diagnose the affected regions of the brain at different stages of age using biomarker modalities and functional magnetic resonance imaging (fMRI) at the resting state, and later marked the affected brain region on MRI. We have used well-known support vector machine (SVM), Fisher's linear discriminant analysis, artificial neural network, and logistic regression for the classification of AD. In the context of receiver operating characteristic (ROC) curves, an SVM provided the best classification among AD stages. Moreover, analysis showed development of AD.     

基于DS-VSMM的声网络低空机动目标跟踪

针对低空机动目标的声网络无源跟踪问题,提出一种基于有向图切换的变结构多模型算法.首先,将多个声传感器组网并对其时延的方位角数据进行配准,改善无源声探测网络的融合性能.其次,通过变结构多模型对目标状态进行估计,并利用有向图切换方法对新激活的模型分配权重,跟踪结果为多个模型估计结果的融合输出.通过与交互式多模型算法的仿真比...     

Evaluating the performance of BSBL methodology for EEG source localization on a realistic head model

In this paper, we evaluate the performance of block sparse Bayesian learning (BSBL) method for EEG source localization. By exploiting the internal block structure, the BSBL method solves the ill‐posed inverse problem more efficiently than other methods that do not consider block structure. Simulation experiments were conducted on a realistic head model obtained by segmentation of MRI images of the head. Two definitions of blocks were considered: Brodmann areas and automated anatomical labeling (AAL). The experiments were performed both with and without the presence of noise. Six different noise levels were considered having SNR values from 5 dB to 30 dB with 5dB increment. The evaluation reveals several potential findings—first, BSBL is more likely to produce better source localization than sparse Bayesian learning (SBL), however, this is true up until a limited number of simultaneously active areas only. Experimental results show that for 71‐channel electrodes setup BSBL outperforms SBL for up to three simultaneously active blocks. From four simultaneously active blocks SBL turns out to be marginally better and the difference between them is statistically insignificant. Second, different anatomical block structures such as Brodmann areas or AAL does not seem to produce any significant difference in EEG source localization relying on BSBL. Third, even when the block partitions are not known exactly BSBL ensures better localization than SBL as soon as block structure persists in the signal. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 46–56, 2017     

Shaping the interdisciplinary knowledge network of China: a network analysis based on citation data from 1981 to 2010

This study builds the interdisciplinary knowledge network of China, which is used to catch the knowledge exchange structure of disciplines, and investigates the evolution process from 1981 to 2010. A network analysis was performed to examine the special structure and we compare state of the networks in different periods to determine how the network has got such properties. The dataset are get from the reference relationship in literature on important Chinese academic journals from 1980 to 2010. The analytical results reveal the hidden network structure of interdisciplinary knowledge flows in China and demonstrate that the network is highly connected and has a homogeneous link structure and heterogeneous weight distribution. Through comparing of the network in three periods, that is 1981–1990, 1991–2000 and 2001–2010, we find that the special evolution process, which is limited by the number of nodes, play an important influence on interdisciplinary knowledge flows.     

The effect of temperature on the viscoelastic response of rubbery polymers at finite strains

Summary Constitutive equations are derived for the viscoelastic response of rubbery polymers at finite strains. A polymer is thought of as a network of long chains connected to temporary junctions. At random times, chains detach from the junctions, which is treated as transition from their active state to the dangling state. A dangling chain captures a new junction in the vicinity of its free end at a random instant and returns to its active state. Breakage and reformation of long chains are modeled as thermo-mechanically activated processes. Stress-strain relations for a rubbery polymer are developed using the laws of thermodynamics. Adjustable parameters in the model are found by fitting observations in uniaxial tensile tests for a carbon black filled rubber at various temperatures. Fair agreement is demonstrated between experimental data and results of numerical simulation.     

参与中文配对词语联想学习记忆的脑区和神经机制——脑功能磁共振的研究

通过脑功能磁共振技术,研究健康人参与语言的词语配对联想学习记忆任务的脑区和神经机制。对16名右利手健康志愿者进行一项词语配对联想学习记忆任务作业的同时,进行脑功能磁共振扫描。实验采用组块设计,实验任务（包括记忆编码相和记忆提取相）与对照任务（共两个相）交替进行;数据采用SPM99软件进行数据分析和脑功能区定位。结果表明：左侧额叶,特别是左侧额叶的额中下回和枕叶的18,19区在词语联想学习记忆的编码阶段中起重要作用;而左侧顶上小叶、缘上回和角回则在进行记忆提取阶段起重要作用;左侧纹状体边缘区参与了人脑词语联想学习记忆作业的编码阶段。揭示了人大脑完成语言联想学习记忆任务时,除额、顶、枕和颞叶的皮层结构参与外,还新发现有皮层下结构如纹状体参与了词语联想学习记忆。在配对词语的编码和提取阶段,激活的脑区有所变化,显示了这两个语言阶段的神经活动变化机制。     

MS网点的合理布局

奥运会临时超市网点设计问题是一个离散的最优化设计问题,首先对所给的数据用统计学中的方法进行统计,得出观众在出行、用餐和购物等方面的规律。在各类人流量的计算时先将各商区场地编号进行转换、再引入商区人流理特征矩阵,使无序的人流量分布在矩阵中得以有规律的表现,算出20个商区的人流量和消费流量的分布。由于各商区消费流量和消费额的相关系数ρ为0.997,认为两者线性相关,所以先对商区消费金额进行单变量聚类,初步把商区分成四类。再用人流量的指标对分类结果适当调整,运用整数规划模型得到20个商区内MS网点分布的具体方案,同类商区中MS网点分布基本上是相同的。     

Brain tumor segmentation using a deep Shuffled-YOLO network

Brain tumor is an anomalous proliferation of cells in the brain that can evolve to malignant and benign tumors. Currently, segmentation of brain tumor is the most important surgical and pharmaceutical procedures. However, manually segmenting brain tumors is hard because it is hard to find erratically shaped tumors with only one modality; the MRI modalities are integrated to provide multi-modal images with data that can be utilized to segment tumors. The recent developments in machine learning and the accessibility of medical diagnostic imaging have made it possible to tackle the challenges of segmenting brain tumors with deep neural networks. In this work, a novel Shuffled-YOLO network has been proposed for segmenting brain tumors from multimodal MRI images. Initially, the scalable range-based adaptive bilateral filer (SCRAB) pre-processing technique was used to eliminate the noise artifacts from MRI while preserving the edges. In the segmentation phase, we propose a novel deep Shuffled-YOLO architecture for segmenting the internal tumor structures that include non-enhancing, edema, necrosis, and enhancing tumors from the multi-modality MRI sequences. The experimental fallouts reveal that the proposed Shuffled-YOLO network achieves a better accuracy range of 98.07% for BraTS 2020 and 97.04% for BraTS 2019 with very minimal computational complexity compared to the state-of-the-art models.     

The effect of short cardio on inhibitory control ability of obese people

To study the effect of short cardio on obese male college students’ inhibitory control ability, we investigate difference on brain activation patterns before and after short cardio by analyzing the resting state fMRI data. The experiment results of this study show that short cardio can improve the inhibitory control ability of obese people.     

Developmental tendencies in the academic field of intellectual property through the identification of invisible colleges

Intellectual property became a relevant academic cross-disciplinary field in an international context with the demand for the global governance of knowledge. However, the degree of consolidation of cross-disciplinary academic communities is not clear. To determine how closely related are these communities, this paper proposes a mixed methodology to find invisible colleges in the production of intellectual property. Scientific articles from 1994 to 2016 were extracted from web of science, taking into account the signature of the agreement on trade-related aspects of intellectual property rights in the early 1990s. A total of 1580 papers were processed through bibliographic coupling network analysis. A special technique was applied, which combines algorithms of community detection and defines a population of articles through thresholds of shared references. To contrast the invisible colleges that emerged with the existence of formal institutional relations, a qualitative tracking of the authors was made with respect to their institutional affiliation, lines of research, and meeting places. Both methods show that the subjects of interest can be grouped into 13 thematic modules related to the intellectual property field. Even though most are related to law and economics, there are weak linkages between disciplines which could indicate the construction of a cross-disciplinary field.

     

Cell‐to‐Cell Communication of Neurons Is Impaired by Metals

Several metals are known to block voltage‐activated calcium channels at relatively high concentrations, and some also block agonist‐activated channels. However some of the actions of metals which occur at the lowest concentrations are on plastic responses of neurons, that is, responses which show prolonged changes after specific kinds of manipulation. We have investigated the effects of lead on the process of long‐term potentiation (LTP), an electrical response which is believed to be a component of learning and memory. LTP is an increased response to a stimulus after a patterned input, and LTP is reduced in aged animals and in animals with genetic backgrounds that result in a poor ability to learn. In humans there is strong evidence that children exposed to lead early in life have a reduced IQ. Therefore our hypothesis was that lead might be acting on LTP, and that this could be the basis of the IQ decrement seen in exposed children. We exposed rats to lead using two different protocols: chronic in vivo exposure and perfusion of lead‐containing solutions over brain slices of control rats. We find similar effects in both studies. We recorded LTP in two different regions of hippocampus, in CA1 where LTP is dependent upon activation of N‐methyl‐D‐aspartate (NMDA) receptors, and in CA3 where LTP is primarily presynaptic in origin and independent of NMDA receptors. We recorded from isolated and perfused brain slices of either control or exposed animals at two ages, 30 and 60 days. In CA1 lead did not significantly alter the synaptic responses recorded at about 30% of maximal amplitude, but reduced LTP at either age. In contrast, in CA3 lead reduced LTP in slices from 30‐day old animals after exposure to lead either with chronic in vivo exposure or with acute in vitro exposure, but increased the response in CA3 in 60‐day old animals exposed by either route. We have evidence that this action is mediated neither by blockade of voltage‐activated calcium nor NMDA channels, but is likely an action of protein kinase C, an enzyme necessary for induction of LTP at both sites.     

Rehabilitation of the Parkinson's tremor by using robust adaptive sliding mode controller: a simulation study

One of the efficient methods in controlling the Parkinson''s tremor is Deep Brain Stimulation (DBS) therapy. The stimulation of Basal Ganglia (BG) by DBS brings no feedback though the existence of feedback reduces the additional stimulatory signal delivered to the brain. So this study offers a new adaptive architecture of a closed‐loop control system in which two areas of BG are stimulated simultaneously to decrease the following three indicators: hand tremor, the level of a delivered stimulation signal in the disease condition, and the level of a delivered stimulation signal in health condition to the disease condition. One area (STN: subthalamic nucleus) is stimulated with an adaptive sliding mode controller and the other area (GPi: Globus Pallidus internal) with partial state feedback controller. The simulation results of stimulating two areas of BG showed satisfactory performance.Inspec keywords: bioelectric phenomena, diseases, variable structure systems, brain models, biomedical electrodes, adaptive control, closed loop systems, state feedback, feedback, neurophysiology, brain, robust controlOther keywords: DBS, additional stimulatory signal, adaptive architecture, closed‐loop control system, hand tremor, delivered stimulation signal, disease condition, health condition, partial state feedback controller, Parkinson''s tremor, robust adaptive sliding mode controller, simulation study, efficient methods, Deep Brain Stimulation therapy, Basal Ganglia     

Brain image segmentation using a combination of expectation‐maximization algorithm and watershed transform

Watershed transformation is an effective segmentation algorithm that originates from the mathematical morphology field. This algorithm is widely used in medical image segmentation because it produces complete division even under poor contrast. However, over‐segmentation is its most significant limitation. Therefore, this article proposes a combination of watershed transformation and the expectation‐maximization (EM) algorithm to segment MR brain images efficiently. The EM algorithm is used to form clusters. Then, the brightest cluster is considered and converted into a binary image. A Sobel operator applied on the binary image generates the initial gradient image. Morphological reconstruction is applied to find the foreground and background markers. The final gradient image is obtained using the minima imposition technique on the initial gradient magnitude along with markers. In addition, watershed segmentation applied on the final gradient magnitude generates effective gray matter and cerebrospinal fluid segmentation. The results are compared with simple marker controlled watershed segmentation, watershed segmentation combined with Otsu multilevel thresholding, and local binary fitting energy model for validation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 225–232, 2016     

Attractive regions formed by the environment in configuration space: The possibility of achieving high precision sensorless manipulation in production

One of the main purposes in robotic manipulation is to eliminate the uncertainty in the location of objects. So far, the controller system with sensory feedback has been used in most dynamic systems to eliminate the uncertainty of the system state. However, sensorless manipulation can also eliminate the uncertainty of the system state, which avoids problems caused by sensors. Recently, the approach on sensorless manipulation has drawn increasing attention and many important results have been presented. However, in general, it is believed that sensorless manipulation can only achieve low-precision or simple tasks that are greatly limited in their application areas. Stimulated by work in related areas, an interesting concept of 'attractive region in configuration space' was proposed. Through a unique method of formulation and utilization of the attractive region in the configuration space, (1) the strategy to achieve high-precision assembly in 3D without a force sensor and a flexible wrist was designed directly and (2) the available initial region from which an object can be grasped into a stable state with a certain gripper was obtained directly. These show that the strategy to achieve high-precision sensorless manipulation can be formed systematically through the formulation and utilization of attractive regions in configuration space, if the strategy exists. The work may also include the approaches on 'how to find the maximum attractive region' and 'when the attractive region exists'.     

基于BSA-RELM的纯电动汽车锂离子电池SOC估计

提出一种基于鸟群算法优化鲁棒极限学习机的锂离子电池荷电状态估计算法。鲁棒极限学习机克服了极限学习机不能处理异常值的缺点,提高了网络的预测准确率。利用鸟群算法优化鲁棒极限学习机的隐层节点数和调节因子等参数,解决隐层节点数和调节因子等参数难以确定的问题,可进一步提高网络的收敛速度,且利于寻找全局最优值。利用ADVISOR软件采集影响电池荷电状态的主要参数:电流、电压、温度和内阻等进行建模和测试。仿真结果表明,采用鸟群算法优化鲁棒极限学习机比BPNN、RBFNN和FNN的估计误差更小,具有更高的预测精度。