Local control of non-local information flow in oscillatory neuronal networks

The virtual machine in the fine-grained information flow tracking is the basis for realization of transparent cloud platform program level control. The information flow control access to sensitive information in the process, because the authority transfer security level and cannot read or write the non sensitive data, the coarse granularity information flow control is difficult to meet the actual demand of diversification, this paper proposes extended DIFC (Distributed Information Flow Control) model, this model avoids component of cloud platform virtual machine because of the higher level of security sensitive data through reading, it sends or modifies the defects of non sensitive data by transfering the authority, and effectively overcomes the defect that the existing information flow control method for the coarse granularity, and the shortcomings which unable to meet the actual demand, this model guarantees the tracking and control of fine-grained information flow within the virtual machine application, and it does not affect the original cloud service operation.

Making Data Flow

The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and lefthand MI tasks. TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely relief-F, Fisher, Laplacian, and local learningbased clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) methods are used for classification. Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via the Relief-F algorithm as feature selection and support vector machine (SVM) classification with 91.02% accuracy. The TE index and a hierarchical feature selection and classification can be useful for the discrimination of right- and left-hand MI tasks from multichannel EEG signals. Effective connectivity features were extracted from electroencephalogram (EEG) to analyze relationships between regions.Four feature selection methods used to select most significant effective features.Support vector machine (SVM) used for discrimination of right and left hand motor imagery (MI) task. In this study, we investigated brain activity using effective connectivity during MI task based on EEG signals. The motor imagery task can accomplish the same goal as motor execution, since they are both activated by the same brain area. Transfer entropy, coherence, and Granger casualty were employed to extract the features. Differential patterns of activity between the left vs. right MI task showed activity around the motor area rather than other areas. In order to reduce redundant information and select the most significant effective connectivity features, four feature subset selection algorithms are used: Relief-F, Fisher, Laplacian, and learning-based clustering feature selection (LLCFS). Then, support vector machine (SVM) and linear discriminant analysis (LDA) are used to classify left and right hand MI task. Comparison of three different connectivity methods showed that TE index had the highest classification accuracy, and could be useful for the discrimination of right and left hand MI tasks from multichannel EEG signals.

The security of software-intensive systems is frequently attacked. High fines or loss in reputation are potential consequences of not maintaining confidentiality, which is an important security objective. Detecting confidentiality issues in early software designs enables cost-efficient fixes. A Data Flow Diagram (DFD) is a modeling notation, which focuses on essential, functional aspects of such early software designs. Existing confidentiality analyses on DFDs support either information flow control or access control, which are the most common confidentiality mechanisms. Combining both mechanisms can be beneficial but existing DFD analyses do not support this. This lack of expressiveness requires designers to switch modeling languages to consider both mechanisms, which can lead to inconsistencies. In this article, we present an extended DFD syntax that supports modeling both, information flow and access control, in the same language. This improves expressiveness compared to related work and avoids inconsistencies. We define the semantics of extended DFDs by clauses in first-order logic. A logic program made of these clauses enables the automated detection of confidentiality violations by querying it. We evaluate the expressiveness of the syntax in a case study. We attempt to model nine information flow cases and six access control cases. We successfully modeled fourteen out of these fifteen cases, which indicates good expressiveness. We evaluate the reusability of models when switching confidentiality mechanisms by comparing the cases that share the same system design, which are three pairs of cases. We successfully show improved reusability compared to the state of the art. We evaluated the accuracy of confidentiality analyses by executing them for the fourteen cases that we could model. We experienced good accuracy.

This research is supported by the China National R&D Key Research Program (2019YFB1705703) and the In-terdisciplinary Program of SJTU, Shanghai, China (No. YG2019ZDA07).

Cloud now provides a wide range of services hosted by different providers from different domains. These services can be composed together dynamically to realize important tasks. In a composite service, information may flow from one service to subsequent services from different domains. Such information flow, if not properly controlled, may cause undesired leakage of critical data. Existing works on access control for web service do not consider the information flow problem in composite services. Existing information flow control (IFC) techniques is not flexible and cannot work with domain-specific information flow control policies. Existing works on access control for web service do not consider the information flow problem in composite services. Existing information flow control (IFC) techniques are not flexible and cannot work with domain-specific information flow control policies. In this paper, we define the WS-AIFC infrastructure for enforcing access and information flow control. The major goal of WS-AIFC is to provide a new IFC mechanism that can allow each domain to define their own IFC policies while WS-AIFC is capable of preventing undesired information leakage (IFC policy violation) among benign, semi-honest service domains. The main idea in WS-AIFC is to derive and record the dependency list for each data object. The system, upon receiving an access request to a critical data object, not only validates the conventional access control policy for the access, but also extracts the data and the corresponding domains in the dependency list and consults these domains to validate their IFC policies for the indirect access. In summary, WS-AIFC empowers individual domains to control how their information flows and achieves enhanced security for service based systems.

Objective. Recently, effective connectivity (EC) calculation methods for functional near-infrared spectroscopy (fNIRS) data mainly face two problems: the first problem is that noise can seriously affect the EC calculation and even lead to false connectivity; the second problem is that it ignores the various real neurotransmission delays between the brain region, and instead uses a fixed delay coefficient for calculation. Approach. To overcome these two issues, a delay symbolic phase transfer entropy (dSPTE) is proposed by developing traditional transfer entropy (TE) to estimate EC for fNIRS. Firstly, the phase time sequence was obtained from the original sequence by the Hilbert transform and state-space reconstruction was realized using a uniform embedding scheme. Then, a symbolization technique was applied based on a neural-gas algorithm to improve its noise robustness. Finally, the EC was calculated on multiple time delay scales to match different inter-region neurotransmission delays. Main results. A linear AR model, a nonlinear model and a multivariate hybrid model were introduced to simulate the performance of dSPTE, and the results showed that the accuracy of dSPTE was the highest, up to 74.27%, and specificity was 100% which means no false connectivity. The results confirmed that the dSPTE method realized better noise robustness, higher accuracy, and correct identification even if there was a long delay between series. Finally, we applied dSPTE to fNIRS dataset to analyse the EC during the finger-tapping task, the results showed that EC strength of task state significantly increased compared with the resting state. Significance. The proposed dSPTE method is a promising way to measure the EC for fNIRS. It incorporates the phase information TE with a symbolic process for fNIRS analysis for the first time. It has been confirmed to be noise robust and suitable for the complex network with different coupling delays.

Phase synchronization of neuronal oscillations has been suggested to underlie the coordination and integration of anatomically distributed processing [1,2]. To quantify causal or directional inter-areal phase-phase interactions, a phase-based measure of effective connectivity is needed. Methods for detecting effective connectivity can be divided into model-based (e.g., Dynamic Causal Modeling [3]) and model-free techniques (e.g., Granger Causality [4]). Transfer Entropy (TE) [5] is a model-free measure of effective connectivity based on information theory. Prior implementations of TE, however, focus on real-valued time series where the signal amplitude is a major bias and phase is only an implicit variable. Furthermore, the robustness of existing TE methods to narrow-band filtering, noise, and linear mixing between signals is limited. Finally, current approaches are dependent on accurate a priori estimation of several parameters, which is not feasible for connectomics approaches with all-to-all mappings of inter-areal interactions. Here we advance a novel measure, Phase Transfer Entropy (Phase TE), to estimate directional connectivity between complex phase time series of filtered signals. In this study, we first assess the reliability of Phase TE in quantifying directional connectivity. Second, we compare its performance to other TE implementations using real-valued narrowband and broadband signals. To quantify a unidirectional effect of signal 1 to signal 2, we define differential TE (dTE) as: dTE(1→2) = TE(1→2) - TE(2→1) and determine the sensitivity and specificity of our methods from the dTE distributions of uncoupled and coupled signals. We simulated ecologically valid neuronal-like oscillations with coupled Neural Mass Models [6] and estimated the phase time series with Morlet filtering. Phase TE increased monotonically with coupling strength and discriminated between coupled and non-coupled time series. Phase TE was robust to realistic amounts of noise and/or linear mixing. Effective connectivity even with small coupling values was reliably detected for moderate signal-to-noise ratios using an amount of data commonly acquired in neuroimaging experiments. We found that Phase dTE did not yield false positives in the presence of mixing and/or noise. Across a range of noise and mixing values, the sensitivity of Phase TE was comparable to or better than the sensitivities of prior TE implementations. Finally, Phase TE was computationally much faster than prior TE implementations. In conclusion, Phase TE is a computationally efficient method for detecting directed interactions between band-limited activities in neurophysiological time series. Phase TE works well with filtering and is robust against noise and linear mixing, which are the elementary confounders in electrophysiological data. Given that Phase TE is also essentially parameter free, we propose it to be an efficient and reliable method for assessing effective connectivity in connectomics analyses.

Background:It is a commonly held view that information flow between widely separated regions of the cerebral cortex is a necessary component in the generation of wakefulness (also termed “connected” consciousness). This study therefore hypothesized that loss of wakefulness caused by propofol anesthesia should be associated with loss of information flow, as estimated by the effective connectivity in the scalp electroencephalogram (EEG) signal.Methods:Effective connectivity during anesthesia was quantified by applying bivariate Granger to multichannel EEG data recorded from 16 adult subjects undergoing a slow induction of, and emergence from, anesthesia with intravenous propofol. During wakefulness they were conducting various auditory and motor tasks. Functional connectivity using EEG coherence was also estimated.Results:There was an abrupt, substantial, and global decrease in effective connectivity around the point of loss of responsiveness. Recovery of behavioral responsiveness was associated with a comparable recovery in information flow pattern (expressed as normalized values). The median (interquartile range) change was greatest in the delta frequency band: decreasing from 0.15 (0.21) 2 min before loss of behavioral response, to 0.06 (0.04) 2 min after loss of behavioral response (P < 0.001). Regional decreases in information flow were maximal in a posteromedial direction from lateral frontal and prefrontal regions (0.82 [0.24] 2 min before loss of responsiveness, decreasing to 0.17 [0.05] 2 min after), and least for information flow from posterior channels. The widespread decrease in bivariate Granger causality reflects loss of cortical coordination. The relationship between functional connectivity (coherence) and effective connectivity (Granger causality) was inconsistent.Conclusions:Propofol-induced unresponsiveness is marked by a global decrease in information flow, greatest from the lateral frontal and prefrontal brain regions in a posterior and medial direction. Loss of information flow may be a useful measure of connected consciousness.

目的使用七种不同的脑网络有效连接分析方法，探索静息态功能磁共振（resting state functional MRI, rs-fMRI）不同频段下的人脑信息流模式。方法基于人脑连接组项目（Human Connectome Project, HCP）数据库，选取60例健康青年人（22～35岁，男女各半）的rs-fMRI影像数据。使用基于线性、核函数和非参数回归的格兰杰因果关系分析（Granger causality analysis, GCA）模型、基于分箱、k-邻近和置换的转移熵算法以及收敛交叉映射分别计算低频（0.01～0.08 Hz）、高频（0.08～0.69 Hz）和全频（0.01～0.69 Hz）下的优势信息流方向。结果低频段（0.01~0.08 Hz）信息流主要表现为皮层下核团、边缘叶和额颞叶区域定向流入枕叶、顶叶及部分额颞叶区域。所有计算分析方法均显示出相似的有向连接，并表现为相似信息流模式。而高频段（0.08~0.69 Hz）和全频段（0.01~0.69 Hz）的信息流方向与低频段相反。进一步分析发现，优势信息流方向与低频/高频段的相对功率呈显著负相关（P < 0.05）。结论本研究通过多模态有效连接分析揭示了rs-fMRI频率依赖的人脑信息流模式，验证了不同计算方法在刻画脑网络定向信息传递中的一致性，为理解静息态脑功能调控机制提供了新证据。

The striatum’s role in obsessive-compulsive disorder (OCD) pathology is recognized. However, the specific contributions of individual striatal subregions (SSs) to OCD pathology are underexplored. We recruited 49 drug-naive OCD patients and 53 healthy controls, conducting clinical assessments and resting-state functional magnetic resonance imaging (rs-fMRI) scans pre- and post-4-week paroxetine treatment. Inter-group comparisons were conducted to investigate baseline and treatment-related changes in the patients’ striatum using several fMRI metrics, including amplitude of low-frequency fluctuation, regional homogeneity, and degree centrality (DC). Furthermore, these metrics, along with functional connectivity (FC), and effective connectivity (EC) of SSs, were analyzed. Associations between gene expression patterns and altered information flow patterns in SSs were examined, where information flow was measured using EC, followed by enrichment analysis of relevant genes. While no significant alterations were observed in the patients’ striata in whole-brain statistical analyses, significant changes in DC, FC, and EC were identified in SSs pre- and post-treatment. In particular, the EC analysis unveiled an enhanced top-down control and diminished bottom-up regulation in drug-naive OCD patients. Following treatment, bottom-up EC improved, along with an improvement in clinical symptoms. Additionally, information flow alteration-related genes were enriched in various biological processes and pathways. They substantially overlapped between bidirectional information flows among SSs and the rest of brain and between information flows among homotopical SSs and the rest of brain. This study highlights the diverse contributions of each striatal subregion to OCD pathology. Paroxetine may alleviate OCD symptoms by enhancing bottom-up regulation, specifically the normalization of aberrant connectivity. Furthermore, integrating transcriptomic and rs-fMRI findings offer novel insights into the biological substrates underlying the altered EC of SSs in OCD patients.

Information Flow Control at Operating System (OS) level features interesting properties and have been an active topic of research for years. However, no implementation can work reliably if there does not exist a way to correctly and precisely track all information flows occurring in the system. The existing implementations for Linux are based on the Linux Security Modules (LSM) framework which implements hooks at specific points in code where any security mechanism may interpose a security decision in the execution. However, previous works on the verification of LSM only addressed access control and no work has raised the question of the reliability of information flow control systems built on LSM@. In this work, we present a compiler-assisted and reproducible static analysis on the Linux kernel to verify that the LSM hooks are correctly placed with respect to operations generating information flows so that LSM-based information flow monitors can properly track all information flows. Our results highlight flaws in LSM that we propose to solve, thus improving the suitability of this framework for the implementation of information flow monitors.

Introduction:Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different areas can be a key factor in the anesthetic process.Methods:In this paper, by combining the Wiener causality concept and the conditional mutual information, a nonlinear effective connectivity measure called Transfer Entropy (TE) is presented to describe the relationship between EEG signals at frontal and temporal regions from eight volunteers in three anesthetic states (awake, unconscious and recovery). This index is also compared with Granger causality and partial directional coherence methods as common effective connectivity indexes.Results:Based on a statistical analysis of the probability predictive value and Kruskal-Wallis statistical method, TE can effectively fallow the effect-site concentration of propofol and distinguish the anesthetic states well, and perform better than the other effective connectivity indexes. This index is also better than Bispectral Index (BIS) as commercial DOA monitor because of the faster response and higher correlation with the drug concentration effect-site, less irregularity in the unconscious state and better ability to distinguish three states of anesthestesia.Conclusion:TE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.

We present Relational Hoare Type Theory (RHTT), a novel language and verification system capable of expressing and verifying rich information flow and access control policies via dependent types. We show that a number of security policies which have been formalized separately in the literature can all be expressed in RHTT using only standard type-theoretic constructions such as monads, higher-order functions, abstract types, abstract predicates, and modules. Example security policies include conditional declassification, information erasure, and state-dependent information flow and access control. RHTT can reason about such policies in the presence of dynamic memory allocation, deallocation, pointer aliasing and arithmetic. The system, theorems and examples have all been formalized in Coq.

Dedicated to the memory of John C. Reynolds (1935--2013). We present Relational Hoare Type Theory (RHTT), a novel language and verification system capable of expressing and verifying rich information flow and access control policies via dependent types. We show that a number of security policies which have been formalized separately in the literature can all be expressed in RHTT using only standard type-theoretic constructions such as monads, higher-order functions, abstract types, abstract predicates, and modules. Example security policies include conditional declassification, information erasure, and state-dependent information flow and access control. RHTT can reason about such policies in the presence of dynamic memory allocation, deallocation, pointer aliasing and arithmetic.