Three-Dimensional Imaging of Complex Neural Activation in Humans From EEG

Electro- or magnetoencephalography (EEG/MEG) are of utmost advantage in studying transient neuronal activity and its timing with respect to behavior in the working human brain. Direct localization of the neural substrates underlying EEG/MEG is commonly achieved by modeling neuronal activity as dipoles. However, the success of neural source localization with the dipole model has only been demonstrated in relatively simple localization tasks owing to the simplified model and its insufficiency in differentiating cortical sources with different extents. It would be of great interest to image complex neural activation with multiple sources of different cortical extensions directly from EEG/MEG. We have investigated this crucial issue by adding additional parameters to the dipole model, leading to the multipole model to better represent the extended sources confined to the convoluted cortical surface. The localization of multiple cortical sources is achieved by using the subspace source localization method with the multipole model. Its performance is evaluated with simulated data as compared with the dipole model, and further illustrated with the real data obtained during visual stimulations in human subjects. The interpretation of the localization results is fully supported by our knowledge about their anatomic locations and functional magnetic resonance imaging data in the same experimental setting. Methods for estimating multiple neuronal sources at cortical areas will facilitate our ability to characterize the cortical electrical activity from simple, early sensory components to more complex networks, such as in visual, motor, and cognitive tasks.     

A computer simulation study of cortical imaging from scalppotentials

Computer simulation studies were conducted to test the feasibility of imaging brain electrical activity from the scalp electroencephalograms. The inhomogeneous three-concentric-sphere head model was used to represent the head volume conductor. Closed spherical dipole layers, consisting of several thousand uniformly distributed dipoles, were used to reconstruct the cortical potential maps corresponding to neuronal sources located inside the brain. Simulation results indicate that the present procedure can image both cortical and deep sources, and for the cortical sources, a spatial resolution as high as 1.2 cm can be achieved     

Estimation of neural dynamics from MEG/EEG cortical current density maps: application to the reconstruction of large-scale cortical synchrony

There is a growing interest in elucidating the role of specific patterns of neural dynamics--such as transient synchronization between distant cell assemblies--in brain functions. Magnetoencephalography (MEG)/electroencephalography (EEG) recordings consist in the spatial integration of the activity from large and multiple remotely located populations of neurons. Massive diffusive effects and poor signal-to-noise ratio (SNR) preclude the proper estimation of indices related to cortical dynamics from nonaveraged MEG/EEG surface recordings. Source localization from MEG/EEG surface recordings with its excellent time resolution could contribute to a better understanding of the working brain. We propose a robust and original approach to the MEG/EEG distributed inverse problem to better estimate neural dynamics of cortical sources. For this, the surrogate data method is introduced in the MEG/EEG inverse problem framework. We apply this approach on nonaveraged data with poor SNR using the minimum norm estimator and find source localization results weakly sensitive to noise. Surrogates allow the reduction of the source space in order to reconstruct MEG/EEG data with reduced biases in both source localization and time-series dynamics. Monte Carlo simulations and results obtained from real MEG data indicate it is possible to estimate non invasively an important part of cortical source locations and dynamic and, therefore, to reveal brain functional networks.     

Ultralow Impedance Graphene Microelectrodes with High Optical Transparency for Simultaneous Deep Two‐Photon Imaging in Transgenic Mice

The last decades have witnessed substantial progress in optical technologies revolutionizing our ability to record and manipulate neural activity in genetically modified animal models. Meanwhile, human studies mostly rely on electrophysiological recordings of cortical potentials, which cannot be inferred from optical recordings, leading to a gap between our understanding of dynamics of microscale populations and brain‐scale neural activity. By enabling concurrent integration of electrical and optical modalities, transparent graphene microelectrodes can close this gap. However, the high impedance of graphene constitutes a big challenge toward the widespread use of this technology. Here, it is experimentally demonstrated that this high impedance of graphene microelectrodes is fundamentally limited by quantum capacitance. This quantum capacitance limit is overcome by creating a parallel conduction path using platinum nanoparticles. A 100 times reduction in graphene electrode impedance is achieved, while maintaining the high optical transparency crucial for deep two‐photon microscopy. Using a transgenic mouse model, simultaneous electrical recording of cortical activity with high fidelity is demonstrated while imaging calcium signals at various cortical depths right beneath the transparent microelectrodes. Multimodal analysis of Ca²⁺ spikes and cortical surface potentials offers unique opportunities to bridge our understanding of cellular dynamics and brain‐scale neural activity.     

共形阵列非圆信号波达方向估计算法

受共形载体变曲率结构的影响,各天线单元指向不尽相同,使得共形天线阵列呈现极化多样性。因此,共形天线阵列的建模过程中需考虑不同阵元的极化响应特性。基于柱面共形天线阵列的快拍数据模型,利用非圆信号的特性对阵列输出进行扩展,基于秩亏理论和子空间原理实现信号波达方向（DOA）估计,所提方法估计精度高,不需要参数配对。存在相干信源时,提出对扩展后的虚拟阵列进行划分,对划分出的子阵进行虚拟的空间平滑,实现解相干的预处理操作。仿真结果表明该方法能有效应用于柱面共形阵列非圆信号DOA估计,并提高了空间分辨率。      

基于fMRI功能网络和贝叶斯矩阵分解的脑电源成像方法

脑电(EEG)是一种重要的脑功能成像技术,根据头皮记录的EEG信号重构皮层脑活动称为EEG源成像。然而脑源活动位置和尺寸的准确重构依然是一个挑战。为充分利用EEG和功能磁共振(fMRI)信号在时空分辨率上的互补信息,该文提出一个新的源成像方法——基于fMRI脑网络和时空约束的EEG源重构算法(FN-STCSI)。该方法在参数贝叶斯框架下,基于矩阵分解思想将源信号分解为若干时间基函数的线性组合。此外,为融合fMRI的高空间分辨率信息,FN-STCSI利用独立成分分析提取fMRI信号的功能网络,构建EEG源成像的空间协方差基,通过变分贝叶斯推断技术确定每个空间协方差基的相对贡献,实现EEG-fMRI融合。通过蒙特卡罗数值仿真和实验数据分析比较了FN-STCSI与现有算法在不同信噪比和不同先验条件下的性能,结果表明FN-STCSI能有效融合EEG-fMRI在时空上的互补信息,提高EEG弥散源成像的性能。     

Coherence multiplexing of fiber-optic interferometric sensors

This paper describes a method of multiplexing several optical signals onto a single spatial channel (e.g., a single-mode fiber) using a short coherence length continuous wave light source. Several system configurations which utilize this technique are proposed, and some design considerations are discussed. Experimental results for a single sensor and receiver are presented and compared with theoretical predictions.     

An Approximately Efficient TDOA Localization Algorithm in Closed-Form for Locating Multiple Disjoint Sources With Erroneous Sensor Positions

This paper considers the problem of time difference-of-arrival (TDOA) source localization when the TDOA measurements from multiple disjoint sources are subject to the same sensor position displacements from the available sensor positions. This is a challenging problem and closed-form solution with good localization accuracy has yet to be found. This paper proposes an estimator that can achieve this purpose. The proposed algorithm jointly estimates the unknown source and sensor positions to take the advantage that the TDOAs from different sources have the same sensor position displacements. The joint estimation is a highly nonlinear problem due to the coupling of source and sensor positions in the measurement equations. We introduce the novel idea of hypothesized source locations in the algorithm development to enable the formulation of psuedolinear equations, thereby leading to the establishment of closed-form solution for source location estimates. Besides the advantage of closed-form, the newly developed algorithm is shown analytically, under the condition that the TDOA measurement noise and the sensor position errors are sufficiently small, to reach the CRLB accuracy. For clarity, the localization of two disjoint sources is used in the algorithm development. The developed algorithm is then examined under the special case of a single source and extended to the more general case of more than two unknown sources. The theoretical developments are supported by simulations.      

Simultaneous Estimation and Testing of Sources in Multiple MEG Data Sets

The proposed Extended Couple Dipole Model (ECDM) is a trilinear component model that can be used to analyze multiple, related MEG data sets simultaneously. Related MEG data sets are data sets that contain activity of the same sources or activity of sources that have proportional source amplitudes. The simultaneous model uses a set of common sources and a set of common source time functions (wave shapes) to model the measured data in each data set. The set of common sources contains all sources that are active in at least one of the data sets to be analyzed. The number of common spatial and temporal components is specified by the user. The model for each data set is a linear combination of these common spatial and temporal components. This linear combination is estimated in a coupling matrix. Unlike the Coupled Dipole Model, where the user selects certain entries of the coupling matrix to be zero, the entire coupling matrix is estimated in the ECDM. This yields a more objective and statistically transparent estimation method, of which the identifiability constraints do not depend on the user's chosen design as in the CDM. CramÈr–Rao Bounds are derived for the ECDM, and the significance of the estimated source activity is computed and illustrated by confidence regions around estimated source time functions.     

基于有限新息率的非瞬时扩散点源参数估计方法

扩散现象在农业真菌传播、大气污染等现实场景广泛存在,扩散源参数估计也因此在农业、工业等实际应用中具有重要意义。目前针对扩散源参数估计提出的方法大多针对理想的瞬时点源信号,对于非瞬时的实际扩散过程存在模型失配问题,极大地限制了算法的实际应用场景。为了解决模型不匹配的问题,同时有效估计扩散源持续时间参数,该文将扩散源信号模型拓展为脉宽可变信号,并提出相应的非瞬时点源模型的参数估计算法。该算法中,利用无线传感网络采样得到实际测量值,找到一个组合系数将实际测量值线性组合为指数函数,再根据有限新息率(FRI)采样理论对组合后的数据用零化滤波器方法求解扩散源参数。仿真结果表明,在信噪比20 dB,位置参数重构MAE能够达到0.008左右,脉宽参数能够达到0.1左右,持续时间参数能够达到0.05左右,这验证了非瞬时点源参数估计的准确性。同时我们分析了传感器个数等因素对参数恢复性能的影响。     

A broadband maximum likelihood imager for a class of extendedspace-time separable sources

An algorithm is presented that reconstructs the shape of an extended incoherent source using only the broadband signals radiated from the source to a sparse array. The source is modeled with a small set of parameters. For the specific class of space-time separable sources, which have similar spatial structure at multiple frequencies, the information radiated at each frequency is recombined into a broadband likelihood function in order to improve the estimation of the source parameters. Expressions for the Cramer-Rao bounds (CRBs) are provided, and the performance of the algorithm is demonstrated for a “Gaussian-lump” shaped source     

A multiresolution framework to MEG/EEG source imaging

A new method based on a multiresolution approach for solving the ill-posed problem of brain electrical activity reconstruction from electroencephaloram (EEG)/magnetoencephalogram (MEG) signals is proposed in a distributed source model. At each step of the algorithm, a regularized solution to the inverse problem is used to constrain the source space on the cortical surface to be scanned at higher spatial resolution. We present the iterative procedure together with an extension of the ST-maximum a posteriori method [1] that integrates spatial and temporal a priori information in an estimator of the brain electrical activity. Results from EEG in a phantom head experiment with a real human skull and from real MEG data on a healthy human subject are presented. The performances of the multiresolution method combined with a nonquadratic estimator are compared with commonly used dipolar methods, and to minimum-norm method with and without multiresolution. In all cases, the proposed approach proved to be more efficient both in terms of computational load and result quality, for the identification of sparse focal patterns of cortical current density, than the fixed scale imaging approach.     

Spatio-temporal EEG source localization using a three-dimensional subspace FINE approach in a realistic geometry inhomogeneous head model

The subspace source localization approach, i.e., first principle vectors (FINE), is able to enhance the spatial resolvability and localization accuracy for closely-spaced neural sources from EEG and MEG measurements. Computer simulations were conducted to evaluate the performance of the FINE algorithm in an inhomogeneous realistic geometry head model under a variety of conditions. The source localization abilities of FINE were examined at different cortical regions and at different depths. The present computer simulation results indicate that FINE has enhanced source localization capability, as compared with MUSIC and RAP-MUSIC, when sources are closely spaced, highly noise-contaminated, or inter-correlated. The source localization accuracy of FINE is better, for closely-spaced sources, than MUSIC at various noise levels, i.e., signal-to-noise ratio (SNR) from 6 dB to 16 dB, and RAP-MUSIC at relatively low noise levels, i.e., 6 dB to 12 dB. The FINE approach has been further applied to localize brain sources of motor potentials, obtained during the finger tapping tasks in a human subject. The experimental results suggest that the detailed neural activity distribution could be revealed by FINE. The present study suggests that FINE provides enhanced performance in localizing multiple closely spaced, and inter-correlated sources under low SNR, and may become an important alternative to brain source localization from EEG or MEG.     

Standardized shrinking LORETA-FOCUSS (SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction

This paper presents a new algorithm called Standardized Shrinking LORETA-FOCUSS (SSLOFO) for solving the electroencephalogram (EEG) inverse problem. Multiple techniques are combined in a single procedure to robustly reconstruct the underlying source distribution with high spatial resolution. This algorithm uses a recursive process which takes the smooth estimate of sLORETA as initialization and then employs the re-weighted minimum norm introduced by FOCUSS. An important technique called standardization is involved in the recursive process to enhance the localization ability. The algorithm is further improved by automatically adjusting the source space according to the estimate of the previous step, and by the inclusion of temporal information. Simulation studies are carried out on both spherical and realistic head models. The algorithm achieves very good localization ability on noise-free data. It is capable of recovering complex source configurations with arbitrary shapes and can produce high quality images of extended source distributions. We also characterized the performance with noisy data in a realistic head model. An important feature of this algorithm is that the temporal waveforms are clearly reconstructed, even for closely spaced sources. This provides a convenient way to estimate neural dynamics directly from the cortical sources.     

Multiscale causal connectivity analysis by canonical correlation: theory and application to epileptic brain

Multivariate Granger causality is a well-established approach for inferring information flow in complex systems, and it is being increasingly applied to map brain connectivity. Traditional Granger causality is based on vector autoregressive (AR) or mixed autoregressive moving average (ARMA) model, which are potentially affected by errors in parameter estimation and may be contaminated by zero-lag correlation, notably when modeling neuroimaging data. To overcome this issue, we present here an extended canonical correlation approach to measure multivariate Granger causal interactions among time series. The procedure includes a reduced rank step for calculating canonical correlation analysis (CCA), and extends the definition of causality including instantaneous effects, thus avoiding the potential estimation problems of AR (or ARMA) models. We tested this approach on simulated data and confirmed its practical utility by exploring local network connectivity at different scales in the epileptic brain analyzing scalp and depth-EEG data during an interictal period.     

Effects of Measurement Errors and Noise on MEG Moving Dipole Inverse Solutions

Magnetoencephalograms (MEG's) are increasingly being used with the moving dipole method to localize electrical sources in the brain. In this method, also known as the dipole location method, a dipolar source is moved about in a model of the head while its amplitude and orientation are also adjusted to obtain a solution dipole which gives the least squares error fit between the measured MEG's and those produced by the dipolar source. The accuracy of this solution is affected by various measurement errors such as errors in the size of the measurement grid, size of the head model, etc., and by noise in the measured MEG's. This study uses computer modeling methods to investigate the effects of these factors on the localization accuracy of sources in the cortical region of the brain for several different ways of making MEG measurements using single channel and/or multichannel detectors.     

Spatial averaging of time-frequency distributions for signalrecovery in uniform linear arrays

This paper presents a new approach based on spatial time-frequency averaging for separating signals received by a uniform linear antenna array. In this approach, spatial averaging of the time-frequency distributions (TFDs) of the sensor data is performed at multiple time-frequency points. This averaging restores the diagonal structure of the source TFD matrix necessary for source separation. With spatial averaging, cross-terms move from their off-diagonal positions in the source TFD matrix to become part of the matrix diagonal entries. It is shown that the proposed approach yields improved performance over the case when no spatial averaging is performed. Further, we demonstrate that in the context of source separation, the spatially averaged Wigner-Ville distribution outperforms the combined spatial-time-frequency averaged distributions, such as the one obtained by using the Choi-Williams (1989) distribution. Simulation examples involving the separation of two sources with close AM and FM modulations are presented     

Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function

Frequency-derived identification of the propagation of information between brain regions has quickly become a popular area in the neurosciences. Of the various techniques used to study the propagation of activation within the central nervous system, the directed transfer function (DTF) has been well used to explore the functional connectivity during a variety of brain states and pathological conditions. However, the DTF method assumes the stationarity of the neural electrical signals and the time invariance of the connectivity among different channels over the investigated time window. Such assumptions may not be valid in the abnormal brain signals such as seizures and interictal spikes in epilepsy patients. In the present study, we have developed an adaptive DTF (ADTF) method through the use of a multivariate adaptive autoregressive model to study the time-variant propagation of seizures and interictal spikes in simulated electrocorticogram (ECoG) networks. The time-variant connectivity reconstruction is achieved by the Kalman filter algorithm, which can incorporate time-varying state equations. We study the performance of the proposed method through simulations with various propagation models using either sample seizures or interictal spikes as the source waveform. The present results suggest that the new ADTF method correctly captures the temporal dynamics of the propagation models, while the DTF method cannot, and even returns erroneous results in some cases. The present ADTF method was tested in real epileptiform ECoG data from an epilepsy patient, and the ADTF results are consistent with the clinical assessments performed by neurologists.