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.     

格兰杰因果关系在神经科学领域的发展及缺陷

针对格兰杰因果关系在神经科学领域的应用,查阅国内外格兰杰应用的相关文献资料,选取有效信息进行归纳整理。介绍了格兰杰因果关系在神经科学领域的发展,包括时频域格兰杰因果关系、非线性格兰杰因果关系的应用。根据格兰杰因果关系的定义,指出该关系存在仅使用线性回归模型的部分信息等缺陷,并对其在神经科学领域的发展进行了展望,且提出对格兰杰因果关系不断改进的必要性。     

人民币实际有效汇率与外商直接投资关系的实证分析

通过对1995—2011年人民币实际有效汇率和外商直接投资的数据进行分析,运用Johansen协整检验、Granger因果检验、脉冲响应分析等实证方法对外商直接投资与人民币实际有效汇率及其波动之间的关系进行了研究。结果表明,人民币实际有效汇率与FDI之间不仅存在显著相关性,而且也存在单向格兰杰因果关系,即外国直接投资会影响我国实际有效汇率。     

A model selection method for nonlinear system identification based FMRI effective connectivity analysis

In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study.     

Probabilistic framework for brain connectivity from functional MR images

This paper unifies our earlier work on detection of brain activation (Rajapakse and Piyaratna, 2001) and connectivity (Rajapakse and Zhou, 2007) in a probabilistic framework for analyzing effective connectivity among activated brain regions from functional magnetic resonance imaging (fMRI) data. Interactions among brain regions are expressed by a dynamic Bayesian network (DBN) while contextual dependencies within functional images are formulated by a Markov random field. The approach simultaneously considers both the detection of brain activation and the estimation of effective connectivity and does not require a priori model of connectivity. Experimental results show that the present approach outperforms earlier fMRI analysis techniques on synthetic functional images and robustly derives brain connectivity from real fMRI data.     

Tents,Tweets, and Events: The Interplay Between Ongoing Protests and Social Media

Recent protests have fuelled deliberations about the extent to which social media ignites popular uprisings. In this article, we use time‐series data of Twitter, Facebook, and onsite protests to assess the Granger causality between social media streams and onsite developments at the Indignados, Occupy, and Brazilian Vinegar protests. After applying Gaussianization to the data, we found contentious communication on Twitter and Facebook forecasted onsite protest during the Indignados and Occupy protests, with bidirectional Granger causality between online and onsite protest in the Occupy series. Conversely, the Vinegar demonstrations presented Granger causality between Facebook and Twitter communication, and separately between protestors and injuries/arrests onsite. We conclude that the effective forecasting of protest activity likely varies across different instances of political unrest.     

A New Secure Authenticated Group Key Transfer Protocol

Group key transfer protocols depend on a mutually trusted key generation center (KGC) to generate group keys and transport group keys to all group members secretly. Generally, KGC encrypts group keys under another secret key shared with each user during registration. In this paper, we propose a novel secure authenticated group key transfer protocol using a linear secret sharing scheme (LSSS) and ElGamal cryptosystem, where KGC broadcasts group key information to all group members. The confidentiality of this transformation is guaranteed by this LSSS and ElGamal cryptosystem. We also provide authentication for transporting this group key. Goals and security threats of our protocol are analyzed in detail.     

Unified Framework for Robust Estimation of Brain Networks From fMRI Using Temporal and Spatial Correlation Analyses

There is a rapidly growing interest in the neuroimaging field to use functional magnetic resonance imaging (fMRI) to explore brain networks, i.e., how regions of the brain communicate with one another. This paper presents a general and novel statistical framework for robust and more complete estimation of brain functional connectivity from fMRI based on correlation analyses and hypothesis testing. In addition to the ability of examining the correlations with each individual seed as in the standard and existing methods, the proposed framework can detect functional interactions by simultaneously examining multiseed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through noncentral $F$ hypothesis tests. The associated issues for the multiple testing and the effective degrees-of-freedom are considered as well. Furthermore, partial multiple correlations are introduced and formulated to measure any additional task-induced but not stimulus-locked relation over brain regions so that we can take the analysis of functional connectivity closer to the characterization of direct functional interactions of the brain. Evaluation for accuracy and advantages, and comparisons of the new approaches in the presented general framework are performed using both realistic synthetic data and in vivo fMRI data.      

An information-theoretic criterion for intrasubject alignment of FMRI time series: motion corrected independent component analysis

A three-dimensional image registration method for motion correction of functional magnetic resonance imaging (fMRI) time-series, based on independent component analysis (ICA), is described. We argue that movement during fMRI data acquisition results in a simultaneous increase in the joint entropy of the observed time-series and a decrease in the joint entropy of a nonlinear function of the derived spatially independent components calculated by ICA. We propose this entropy difference as a reliable criterion for motion correction and refer to a method that maximizes this as motion-corrected ICA (MCICA). Specifically, a given motion-corrupted volume may be corrected by determining the linear combination of spatial transformations of the motion-corrupted volume that maximizes the proposed criterion. In essence, MCICA consists of designing an adaptive spatial resampling filter which maintains maximum temporal independence among the recovered components. In contrast with conventional registration methods, MCICA does not require registration of motion-corrupted volumes to a single reference volume which can introduce artifacts because corrections are applied without accounting for variability due to the task-related activation. Simulations demonstrate that MCICA is robust to activation level, additive noise, random motion in the reference volumes and the exact number of independent components extracted. When the method was applied to real data with minimal estimated motion, the method had little effect and, hence, did not introduce spurious changes in the data. However, in a data series from a motor fMRI experiment with larger motion, preprocessing the data with the proposed method resulted in the emergence of activation in primary motor and supplementary motor cortices. Although mutual information (MI) was not explicitly optimized, the MI between all subsequent volumes and the first one was consistently increased for all volumes after preprocessing the data with MCICA. We suggest MCICA represents a robust and reliable method for preprocessing of fMRI time-series corrupted with motion.     

Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators

The directed transfer function (DTF) and the partial directed coherence (PDC) are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods is based on the multivariate autoregressive modelling (MVAR) of time series, which requires the stationarity of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR) and to apply it to a set of real high resolution EEG data. This approach will allow the observation of rapidly changing influences between the cortical areas during the execution of a task. The simulation results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of signal-to-noise ratio (SNR) ad number of trials. An SNR of five and a number of trials of at least 20 provide a good accuracy in the estimation. After testing the method by the simulation study, we provide an application to the cortical estimations obtained from high resolution EEG data recorded from a group of healthy subject during a combined foot-lips movement and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected with the proposed methods, one constant across the task and the other evolving during the preparation of the joint movement.     

无证书聚合签名方案的安全性分析和改进

该文分析了He等人(2014)提出的无证书签名方案和Ming等人(2014)提出的无证书聚合签名方案的安全性,指出Ming方案存在密钥生成中心(KGC)被动攻击,He方案存在KGC被动攻击和KGC主动攻击。该文描述了KGC对两个方案的攻击过程,分析了两个方案存在KGC攻击的原因,最后对Ming方案提出了两类改进。改进方案不仅克服了原方案的安全性问题,同时也保持了原方案聚合签名长度固定的优势。     

基于格兰杰因果关系贝叶斯网络的大规模无线局域网流量预测方法

研究了大规模无线局域网内的流量特性,发现不同接入点间的流量存在格兰杰因果关系。流量的格兰杰因果关系说明,可以通过多个存在因果关系的接入点的历史流量,提高对目标接入点的当前流量预测的准确性。通过贝叶斯网络对存在因果关系的接入点流量进行建模,并利用多个接入点的历史流量对目标接入点的流量进行预测,提高了预测的准确性。最后,通过接入点数量大于 100 个的无线局域网的实际流量数据,验证了该方法的有效性及准确性,建立了一套完整的数据特征分析、建模及预测的流量数据处理流程。     

Weaknesses and improvements of an efficient certificateless signature scheme without using bilinear pairings

The certificateless signature (CLS) scheme is a special signature scheme that solves the key escrow problem in identity‐based signature schemes. In CLS schemes, the private key is generated cooperatively by the key generator center (KGC) and signer, such that a malicious KGC cannot masquerade as the signer and sign a message. He et al. in 2011 proposed an efficient CLS scheme without using bilinear pairings. However, we discovered that the CLS scheme by He et al. cannot resist a strong type 2 adversary if this adversary replaces the master public key of the KGC. This work proposes an improved scheme that overcomes this weakness. Copyright © 2012 John Wiley & Sons, Ltd.     

Conditional Correlation as a Measure of Mediated Interactivity in fMRI and MEG/EEG

Many measures have been proposed so far to extract brain functional interactivity from functional magnetic resonance imaging (fMRI) and magnetoencephalography/electroencephalography (MEG/EEG) data sets. Unfortunately, none has been able to provide a relevant, self-contained, and common definition of brain interaction. In this paper, we propose a first step in this direction. We first introduce a common terminology together with a cross-modal definition of interaction. In this setting, we investigate the commonalities shared by some measures of interaction proposed in the literature. We show that temporal correlation, nonlinear correlation, mutual information, generalized synchronization, phase synchronization, coherence, and phase locking value (PLV) actually measure the same quantity (namely correlation) when one is investigating linear interactions between independently and identically distributed Gaussian variables. We also demonstrate that these data-driven measures can only partly account for the interaction patterns that can be expressed by the effective connectivity of structural equation modeling (SEM) . To bridge this gap, we suggest the use of conditional correlation, which is shown to be related to mediated interaction.     

强安全无证书签名方案的安全性分析和改进

对王亚飞等人提出的强安全性无证书签名方案进行安全性分析,指出其方案难以抵抗不诚实KGC下的公钥替换攻击。针对此类问题,采用改变传统无证书算法顺序,以KGC公告板形式公开用户公钥,用户可以通过本地保存的私钥和公钥验证公钥的真实性,从而对KGC的行为进行约束。安全性分析表明,改进后的方案能够抵抗基于不诚实 KGC安全级别下的公钥替换攻击,而且方案避开了无双线性对和逆运算,效率优于已有方案。     

A Connectivity-Based Method for Defining Regions-of-Interest in fMRI Data

In this paper, we describe a new methodology for defining brain regions-of-interset (ROIs) in functional magnetic resonance imaging (fMRI) data. The ROIs are defined based on their functional connectivity to other ROIs, i.e., ROIs are defined as sets of voxels with similar connectivity patterns to other ROIs. The method relies on 1) a spatially regularized canonical correlation analysis for identifying maximally correlated signals, which are not due to correlated noise; 2) a test for merging ROIs which have similar connectivity patterns to the other ROIs; and 3) a graph-cuts optimization for assigning voxels to ROIs. Since our method is fully connectivity-based, the extracted ROIs and their corresponding time signals are ideally suited for a subsequent brain connectivity analysis.      

Centralized Group Key Establishment Protocol without a Mutually Trusted Third Party

The type of centralized group key establishment protocols is the most commonly used one due to its efficiency in computation and communication. A key generation center (KGC) in this type of protocols acts as a server to register users initially. Since the KGC selects a group key for group communication, all users must trust the KGC. Needing a mutually trusted KGC can cause problem in some applications. For example, users in a social network cannot trust the network server to select a group key for a secure group communication. In this paper, we remove the need of a mutually trusted KGC by assuming that each user only trusts himself. During registration, each user acts as a KGC to register other users and issue sub-shares to other users. From the secret sharing homomorphism, all sub-shares of each user can be combined into a master share. The master share enables a pairwise shared key between any pair of users. A verification of master shares enables all users to verify their master shares are generated consistently without revealing the master shares. In a group communication, the initiator can become the server to select a group key and distribute it to each other user over a pairwise shared channel. Our design is unique since the storage of each user is minimal, the verification of master shares is efficient and the group key distribution is centralized. There are public-key based group key establishment protocols without a trusted third party. However, these protocols can only establish a single group key. Our protocol is a non-public-key solution and can establish multiple group keys which is computationally efficient.     

Media,Instability, and Democracy: Examining the Granger‐Causal Relationships of 122 Countries From 1946 to 2003

Using cross‐national time‐series data in sequences of Granger causality tests, this study analyzed the democratic effects of media technologies with a sample of 122 countries. This process revealed that communication technologies are vital, but not exclusive or universal prerequisites of democratic growth. As expected by media system dependency (MSD) theory, media diffusion was shown to have Granger‐caused democracy only in countries where media served more information functions or where sociopolitical instability levels were higher. Results further indicated that media diffusion is central to the development of sociopolitical instability, which suggests certain direct and indirect macrolevel democratic effects of mass media diffusion. The conditions of MSD theory observed here also demonstrated an integrative relationship with the economic development thesis.     

Accounting for respiration is necessary to reliably infer Granger causality from cardiovascular variability series

This study was designed to demonstrate the need of accounting for respiration (R) when causality between heart period (HP) and systolic arterial pressure (SAP) is under scrutiny. Simulations generated according to a bivariate autoregressive closed-loop model were utilized to assess how causality changes as a function of the model parameters. An exogenous (X) signal was added to the bivariate autoregressive closed-loop model to evaluate the bias on causality induced when the X source was disregarded. Causality was assessed in the time domain according to a predictability improvement approach (i.e., Granger causality). HP and SAP variability series were recorded with R in 19 healthy subjects during spontaneous and controlled breathing at 10, 15, and 20 breaths/min. Simulations proved the importance of accounting for X signals. During spontaneous breathing, assessing causality without taking into consideration R leads to a significantly larger percentage of closed-loop interactions and a smaller fraction of unidirectional causality from HP to SAP. This finding was confirmed during paced breathing and it was independent of the breathing rate. These results suggest that the role of baroreflex cannot be correctly assessed without accounting for R.     

Knowledge Graph Completion Based on GCN of Multi-Information Fusion and High-Dimensional Structure Analysis Weight

Knowledge graph completion(KGC)can solve the problem of data sparsity in the knowledge graph.A large number of models for the KGC task have been proposed in rec...