Characteristics of fMRI BOLD signal and its neurophysiological mechanism

The functional magnetic resonance imaging (fMRI) based on blood oxygen level dependent (BOLD) contrast has emerged as one of the most potent noninvasive tools for mapping brain function and has been widely used to explore physiological, pathological changes and mental activity in the brain. Exploring the nature and property of BOLD signal has recently attracted more attentions. Despite that great progress has been made in investigation of the characteristics and neurophysiological basis, the exact nature of BOLD signal remains unclear. In this paper we discuss the characteristics of BOLD signals, the nonlinear BOLD response to external stimuli and the relation between BOLD signals and neural electrophysiological recordings. Furthermore, we develop our new opinions regarding nonlinear BOLD response and make some perspectives on future study.     

Neurophysiological investigation of the basis of the fMRI signal.

Functional magnetic resonance imaging (fMRI) is widely used to study the operational organization of the human brain, but the exact relationship between the measured fMRI signal and the underlying neural activity is unclear. Here we present simultaneous intracortical recordings of neural signals and fMRI responses. We compared local field potentials (LFPs), single- and multi-unit spiking activity with highly spatio-temporally resolved blood-oxygen-level-dependent (BOLD) fMRI responses from the visual cortex of monkeys. The largest magnitude changes were observed in LFPs, which at recording sites characterized by transient responses were the only signal that significantly correlated with the haemodynamic response. Linear systems analysis on a trial-by-trial basis showed that the impulse response of the neurovascular system is both animal- and site-specific, and that LFPs yield a better estimate of BOLD responses than the multi-unit responses. These findings suggest that the BOLD contrast mechanism reflects the input and intracortical processing of a given area rather than its spiking output.     

Bold claims for optogenetics

In a recent Letter to Nature, Lee and colleagues combined optogenetic stimulation with functional magnetic resonance imaging (ofMRI) to examine the relationship between pyramidal-cell spiking and the blood oxygenation level dependent (BOLD) signal. To do so, they injected an adeno-associated viral vector into the primary motor cortex (M1) of adult rats to drive the expression of channelrhodopsin (ChR2) in cortical projection neurons, thus making them sensitive to light. The authors then used combined light stimulation and functional magnetic resonance imaging (fMRI) to examine the effects of selective activation of the light-sensitive pyramidal cells on the BOLD signal, as well as to probe the value of this methodology for mapping brain connectivity. They found that excitation of these neurons induced positive BOLD signals both in the injected M1 region and in remote target thalamic nuclei receiving direct projections from that region, and concluded that ofMRI reliably links positive BOLD signals with increased local neuronal excitation. However, their analysis neglects the almost immediate activation of other circuits that could lead to the generation of BOLD signals through local perisynaptic rather than spiking activity. Their experiments therefore do not pin down the identity of the specific neuronal signals that give rise to the BOLD signal.     

音乐增强大脑网络小世界特性

近年来实验发现音乐相比其他机械噪声对人脑感知系统更能增强大脑内部复杂网络特性.优美音乐在频谱上普遍具有1/f统计特征.在音乐增强脑电信号记录分析中,我们发现对比具有1/f特征的音乐信号,在仍保留1/f特征的随机乱乐刺激下,大部分脑皮层区域的脑功能网络连接密度普遍下降,并且增强的脑功能网络小世界特性在一定阈值范围内也会有显著下降.随机打乱的音乐虽然仍保留长程相关特征,但打乱后每分钟节拍数和节拍清晰度出现了明显降低.这两种音乐特性的降低与音乐打乱前后的大脑小世界网络统计指标的CMean/LMean降低有显著关联.说明音乐信号除了1/f长程相关统计特征之外的其他有效音乐信息在增强脑功能网络方面也起到重要作用.     

基于信号盲分离的通信信道干扰抑制算法

针对移动通信双向传输过程中一直存在的抗干扰性能差的问题。提出基于窄带信号盲分离的移动通信信道干扰抑制算法。构建移动通信基站信道的安全承载模型和移动网络基站路由节点数据分配模型,进行信道干扰分析,通过阈值对优先级进行判断,保证OBS网络中偏射路由节点数据的服务质量,构建单通道窄带信号检测模型,设计单通道窄带处理器,实现对窄带信号的盲分离,降低了网络突发冲突阻塞概率与丢包率,基于时延-多普勒域的稀疏性特征计算动态反馈线性非线性抗干扰信道的时域脉冲响应,得到移动通信信道均衡和特征配准结果,实现非线性抗干扰滤波。实验结果表明,改进算法能有效实现干扰抑制和滤波,提高通信信号的输出信噪比。     

基于去趋势波动分析的不同特征音乐刺激下脑电特性

旨在研究受试者对不同特征音乐的心理生理反应, 探索脑电长程关联特性. 招募 10 名在校学生作为受试者, 参与 4 种具有不同物理特征的音乐聆听任务, 并完成自我情绪评价, 同步采集受试者任务期间的头皮脑电信号. 针对音乐刺激脑电的非平稳非线性特性, 使用一种检测非平稳时间序列的长程相关性非线性方法——去趋势波动分析, 通过计算脑电信号分频段序列的标度指数分析脑电信号长程相关性, 并结合行为学数据, 探究不同音乐特征对情绪加工的影响. 实验结果显示, 升调版欢快乐曲诱发的积极情绪感受会显著降低, 而无论升调还是降调都会显著降低悲伤音乐诱发的悲伤情绪效应; 在不同音调特征的音乐刺激诱发下, 受试者在 alpha, beta 频段上还表现出明显大脑偏侧化特点, 左半球脑动力表现更活跃. 所应用的标度指数可以反映不同音乐刺激下脑电的特异性.     

车用柴油机振动信号的去噪声处理

针对柴油机振动信号的瞬时非线性特点,论述了车用柴油机振动信号处理理论与方法.采用柴油机振动信号的IMF分量进行特征频带识别,将柴油机振动信号经验模态分解,去掉主要干扰因素所对应的IMF分量,再将剩余IMF分量进行重构得到柴油机振动信号.重构后的信号反映了车用柴油机机身振动的真实信息.     

基于Hilbert-Huang变换的室内地磁信号处理分析

提出一种利用Hilbert-Huang变换(HHT)处理分析室内地磁信号的方法。介绍HHT原理与方法,设计出一种室内地磁信号采集平台,对实际测量的室内地磁信号进行经验模态分解(EMD),并对分解后的固有模态函数(IMF)进行频谱和时频剖析,最后将经过HHT处理的原始信号进行重组。结果表明,HHT能用于描述复杂的室内地磁信号的非线性时变特征,为室内地磁信号的处理分析提供一种可行的方法。     

Multifractal mass exponent spectrum of complex physiological time series

Physiological signal belongs to the kind of nonstationary and time-variant ones. Thus, the nonlinear analysis methods may be better to disclose its characteristics and mechanisms. There have been plenty of evidences that physiological signal generated by complex self-regulated system may have a fractal structure. In this work, we introduce a new measure to characterize multifractality, the mass exponent spectrum curvature, which can disclose the complexity of fractal structure from total bending degree of the spectrum. This parameter represents the nonlinear superpositions of the discrepancies of fractal dimension from all adjacent points in the curve and therefore solves the problem of original parameters for not fully reflecting the information of entire subsets in the fractal structure. The evaluations of deterministic fractal system Cantor measure validate that it is completely effective in exploring the complexity of chaotic series, and is also not affected by nonstability of the signal as well as disturbances of the noises. We then apply it to the analysis of human heart rate variability (HRV) signals and sleep electroencephalogram (EEG) signals. The experimental results show that this method can be better to discriminate cohorts under different physiological and pathological conditions. Compared with the indicator of singularity spectrum width, there are some improvements both on the computing efficiency and accuracy. Such conclusion may provide some valuable information for clinical diagnoses.     

基于近似熵快速算法的静息态脑磁信号分析

为了探究静息态精神分裂症患者脑磁信号的非线性动力学特性,提出了一种将小波变换和近似熵相结合的特征提取方法.该方法首先通过小波变换,将10个正常人和10个精神分裂症患者的脑磁信号进行6层小波分解,提取对应于脑磁信号θ波段和α波段的小波系数,继而计算和比较两类人近似熵的分布情况.实验结果表明,相同情况下精神分裂症患者MEG信号的各脑区和各通道间的近似熵都普遍高于正常人,α波段的额叶和中央区域尤为突出.该结果为进一步研究患者MEG信号特征进而建立相应的分类诊断模型提供了思路.     

Hilbert-Huang变换与大地电磁信号的时频分析

将Hilbert-Huang变换引入大地电磁信号的时频分析中,介绍HHT(Hilbert-Huang transform)时频分析原理及方法,给出仿真信号的经验模态分解及其时频分布,并对实测大地电磁信号进行HHT时频处理与剖析.研究结果表明:Hilbert能量谱随时频的具体分布具有很强的非稳态动态变换时频刻画能力;时频谱的时间、频率分辨率不受Heisenberg测不准原理的限制,且其时间、频率分辨率都很高,有很好的时频聚集性;HHT方法能用于描述大地电磁信号的非线性时变特征,是大地电磁信号时频分析的有效工具.     

基于切比雪夫最佳逼近的LVDT位移传感器信号处理

针对LVDT位移传感器两端输出信号的非线性问题,提出了一种基于切比雪夫最佳逼近原理的信号处理方法. 该方法将传感器有效量程自适应地分为线性和非线性区域. 线性工作范围和对应直线逼近函数利用切比雪夫一次最佳逼近自适应确定,非线性区域信号采用有理B样条函数进行线性化处理. 设计了基于MSP430单片机的信号处理器,搭建了基于步进电机直线台和标准激光传感器的试验平台,对该算法进行实验验证. 实验选用量程为85 mm的LVDT位移传感器,实验结果表明,该方法将传感器的非线性误差从2.47%降至0.30%,测量平均误差绝对值从0.64 mm降至0.12 mm,有效改善了传感器的线性度和精度,延展了其工作范围.      

基于局部均值分解和迭代随机森林的脑电分类

为实现癫痫患者的脑电信号有效识别,进而提高患者的生活质量,针对脑电信号的非平稳、非线性特点, 提出一种基于局部均值分解和迭代随机森林相结合的脑电信号分类方法。首先利用局部均值分解将脑电信号 分解成若干个乘积函数分量和一个残余分量,然后对所有分量进行特征提取,并使用支持向量机、随机森林和 迭代随机森林方法进行分类。实验结果表明,迭代随机森林的分类准确率高于支持向量机和随机森林方法。 此方法为准确识别癫痫脑电信号提供了一个可行有效的途径,具有较好的推广和应用价值。     

离散小波框架消除瞬态散射回波噪声

利用离散小波框架(DWF)结合非线性软阈值方法对瞬态雷达反射回波信号进行去噪处理。通过对模拟雷达散射回波信号去噪,并与传统的傅立叶方法,样条拟合,标准正交Daubechies小波基,sym4小波基,双正交bior2.2小波基法进行了数据对比,表明该方法对瞬态非平稳信号去噪效果更为明显。     

Bidirectional control of CNS capillary diameter by pericytes

Neural activity increases local blood flow in the central nervous system (CNS), which is the basis of BOLD (blood oxygen level dependent) and PET (positron emission tomography) functional imaging techniques. Blood flow is assumed to be regulated by precapillary arterioles, because capillaries lack smooth muscle. However, most (65%) noradrenergic innervation of CNS blood vessels terminates near capillaries rather than arterioles, and in muscle and brain a dilatory signal propagates from vessels near metabolically active cells to precapillary arterioles, suggesting that blood flow control is initiated in capillaries. Pericytes, which are apposed to CNS capillaries and contain contractile proteins, could initiate such signalling. Here we show that pericytes can control capillary diameter in whole retina and cerebellar slices. Electrical stimulation of retinal pericytes evoked a localized capillary constriction, which propagated at approximately 2 microm s(-1) to constrict distant pericytes. Superfused ATP in retina or noradrenaline in cerebellum resulted in constriction of capillaries by pericytes, and glutamate reversed the constriction produced by noradrenaline. Electrical stimulation or puffing GABA (gamma-amino butyric acid) receptor blockers in the inner retina also evoked pericyte constriction. In simulated ischaemia, some pericytes constricted capillaries. Pericytes are probably modulators of blood flow in response to changes in neural activity, which may contribute to functional imaging signals and to CNS vascular disease.     

一种基于sEMG信号多重分形的肌肉疲劳特征分析方法

针对由表面肌电信号(sEMG)非平稳、非线性、自相似性等复杂特性导致的肌肉疲劳估计不准的问题,提出一种基于sEMG信号多重分形降趋移动平均法(MFDMA)的肌肉疲劳特征分析方法。首先,利用MFDMA方法对采集的sEMG信号、洗牌信号和高斯白噪声信号进行非线性动力学分析;其次,利用MFDMA方法计算sEMG信号的多重分形谱宽度、Hurst指数变化差值、概率测度值和峰值奇异指数4种多重分形特征;最后,利用t-检验法分析肌肉疲劳与非疲劳状态下的多重分形特征的显著差异性。结果表明,MFDMA方法能够描述sEMG信号的多重分形行为,谱宽等多重分形特征在肌肉疲劳与非疲劳状态下具有显著性差异。所提方法能够可靠表征运动性肌肉疲劳,可为肌肉疲劳识别模型建构、康复医学研究提供特征参考。     

Extracting periodic driving signal from chaotic noise

After periodic signals pass through some nonlinear systems, they are usually transformed into noise-like and wide-band chaotic signals. The discrete spectrums of the original periodic signals are often covered by the chaotic spectrums. Recovering the periodic driving signals from the chaotic signals is important not only in theory but also in practical applications. Based on the modeling theory of nonlinear dynamic system, a "polynomial-simple harmonic drive" non-autonomous equation (P-S equation) to approximate the original system is proposed and the approximation error between P-S equation and the original system is obtained. By changing the drive frequency, we obtain the curve of the approximation error vs. drive frequency. Based on the relation between this curve and the spectrums of the original periodic signals, the spectrum of the original driving signal is extracted and the original signal is recovered.     

ARIMA模型结合小波去噪的贵阳城市交通流预测

由于交通流量具有非线性和强干扰性的特征,在不同的时频域空间具有不同的特性;本文首先应用小波分析的方法,将含有综合信息的一组原始交通流信号分解为多组特征不同的时间序列信号,再利用ARIMA模型良好的线性拟合能力,将经过小波分析的时间信号通过ARIMA模型进行处理.利用Matlab和SPSS,对实测交通流数据进行了验证分析...     

级联自适应局部投影降噪方法

针对具有非线性、非平稳性特征的信号，提出一种邻域半径自适应局部投影和小波阈值去噪级联的降噪方法.首先，利用经验模态分解得到信号中的高频分量并以此估计噪声水平;再根据噪声水平确定邻域半径;最后，利用该半径进行局部投影处理并结合小波阈值方法进行细节平滑.Lorenz系统时间序列的降噪结果表明，本方法能够提高信噪比并降低均方误差，并在信号结构失真时恢复其原始吸引子形态，去噪和还原信号特征的能力皆优于小波阈值去噪方法.对桡、颈、肱动脉脉搏信号、心电信号的降噪结果展示了本方法在生理信号噪声抑制和特征保留方面的优越性能.     

基于麻雀搜索算法优化支持向量机的刀具磨损识别

针对微小深孔钻削刀具磨损状态检测的工程需求,提出了基于钻削声信号的麻花钻头磨损状态识别方法。根据不同磨损程度的麻花钻在钻削过程中的声信号,使用经验模态分解(empirical mode decomposition, EMD)将声信号分解成若干个固有模态函数(intrinsic mode functions, IMFs),通过时频联合分析探索刀具磨损与声信号特征之间的关联规律;再使用麻雀搜索算法(sparrow search algorithm, SSA)优化支持向量机(support vector machine, SVM)的参数,并利用SVM实现基于声信号特征的刀具磨损状态识别。实验结果表明,微小深孔钻头磨损程度与钻削声信号特征之间存在非线性耦合关系,声信号高频特征对钻头磨损程度的变化非常敏感;采用经过SSA优化后的SVM算法,基于优选的IMF特征能够准确识别钻削刀具磨损状态,识别准确率可达98.246%。