Exploratory analysis of brain connectivity with ICA.

Covariance-based methods of exploration of functional connectivity of the brain from functional magnetic resonance imaging (fMRI) experiments, such as principal component analysis (PCA) and structural equation modeling (SEM), require a priori knowledge such as an anatomical model to infer functional connectivity. In this research, a hybrid method, combining independent component analysis (ICA) and SEM, which is capable of deriving functional connectivity in an exploratory manner without the need of a prior model is introduced. The spatial ICA (SICA) derives independent neural systems or sources involved in task-related brain activation, while an automated method based on the SEM finds the structure of the connectivity among the elements in independent neural systems. Unlike second-order approaches used in earlier studies, the task-related neural systems derived from the ICA provide brain connectivity in the complete statistical sense. The use and efficacy of this approach is illustrated on two fMRI datasets obtained from a visual task and a language reading task.     

A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia

Objective

In this paper, we develop a dynamic functional network connectivity (FNC) analysis approach using correlations between windowed time-courses of different brain networks (components) estimated via spatial independent component analysis (sICA). We apply the developed method to fMRI data to evaluate it and to study task-modulation of functional connections.

Materials and methods

We study the theoretical basis of the approach, perform a simulation analysis and apply it to fMRI data from schizophrenia patients (SP) and healthy controls (HC). Analyses on the fMRI data include: (a) group sICA to determine regions of significant task-related activity, (b) static and dynamic FNC analysis among these networks by using maximal lagged-correlation and time–frequency analysis, and (c) HC–SP group differences in functional network connections and in task-modulation of these connections.

Results

This new approach enables an assessment of task-modulation of connectivity and identifies meaningful inter-component linkages and differences between the two study groups during performance of an auditory oddball task (AOT). The static FNC results revealed that connectivities involving medial visual–frontal, medial temporal–medial visual, parietal–medial temporal, parietal–medial visual and medial temporal–anterior temporal were significantly greater in HC, whereas only the right lateral fronto-parietal (RLFP)–orbitofrontal connection was significantly greater in SP. The dynamic FNC revealed that task-modulation of motor–frontal, RLFP–medial temporal and posterior default mode (pDM)–parietal connections were significantly greater in SP, and task modulation of orbitofrontal–pDM and medial temporal–frontal connections were significantly greater in HC (all P < 0.05).

Conclusion

The task-modulation of dynamic FNC provided findings and differences between the two groups that are consistent with the existing hypothesis that schizophrenia patients show less segregated motor, sensory, cognitive functions and less segregated default mode network activity when engaged with a task. Dynamic FNC, based on sICA, provided additional results which are different than, but complementary to, those of static FNC. For example, it revealed dynamic changes in default mode network connectivities with other regions which were significantly different in schizophrenia in terms of task-modulation, findings which were not possible to discover by static FNC.     

Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity

Analytic tools for addressing spontaneous brain activity, as acquired with fMRI during the "resting-state," have grown dramatically over the past decade. Along with each new technique, novel hypotheses about the functional organization of the brain are also available to researchers. We review six prominent categories of resting-state fMRI data analysis: seed-based functional connectivity, independent component analysis, clustering, pattern classification, graph theory, and two "local" methods. In surveying these methods, we address their underlying assumptions, methodologies, and novel applications.     

In vivo functional connectome of human brainstem nuclei of the ascending arousal,autonomic, and motor systems by high spatial resolution 7-Tesla fMRI

Objective

Our aim was to map the in vivo human functional connectivity of several brainstem nuclei with the rest of the brain by using seed-based correlation of ultra-high magnetic field functional magnetic resonance imaging (fMRI) data.

Materials and methods

We used the recently developed template of 11 brainstem nuclei derived from multi-contrast structural MRI at 7 Tesla as seed regions to determine their connectivity to the rest of the brain. To achieve this, we used the increased contrast-to-noise ratio of 7-Tesla fMRI compared with 3 Tesla and time-efficient simultaneous multi-slice imaging to cover the brain with high spatial resolution (1.1-mm isotropic nominal resolution) while maintaining a short repetition time (2.5 s).

Results

The delineated Pearson’s correlation-based functional connectivity diagrams (connectomes) of 11 brainstem nuclei of the ascending arousal, motor, and autonomic systems from 12 controls are presented and discussed in the context of existing histology and animal work.

Conclusion

Considering that the investigated brainstem nuclei play a crucial role in several vital functions, the delineated preliminary connectomes might prove useful for future in vivo research and clinical studies of human brainstem function and pathology, including disorders of consciousness, sleep disorders, autonomic disorders, Parkinson’s disease, and other motor disorders.

     

A brief introduction to functional MRI.

IEEE Engineering in Medicine and Biology Magazine focuses on modern methods for the analysis of data from functional magnetic resonance imaging (fMRI) studies. Accordingly, the guest editors have seen fit to begin with a brief article on the history, mechanisms and methods behind fMRI. This is followed by the presentation of recent significant progress in paradigm design for fMRI as well as development of other methods for assessing the functional anatomy of the human brain, such as diffusion tensor imaging, for mapping white matter fiber tracts. Thus, the future appears to promise a more integrative approach to functional brain imaging, in which data from multiple modalities are entered into comprehensive analyses of brain function and connectivity.     

Increased sleep pressure reduces resting state functional connectivity

Object

In humans, even a single night of partial sleep deprivation (PSD) can have a negative impact on cognition and affective processing, suggesting that sleep pressure represents a basic physiological constraint of brain function. Among the spontaneously fluctuating resting state networks, the default mode network (DMN) and its anticorrelated network (ACN) hold key functions in segregating internally and externally directed awareness. Task fMRI after sleep deprivation has revealed altered activation patterns in both networks. We hypothesized that effects of PSD in these intrinsically coupled networks can be detected by resting state fMRI.

Methods

We obtained 6-minute echoplanar imaging time series (1.5 Tesla) during eyes-closed, wakeful-resting experiments from 16 healthy volunteers after normal sleep and after PSD. We used independent component and cross-correlation analysis to study functional connectivity (fc), focusing on the DMN and ACN.

Results

After PSD, focal reductions of auto-correlation strength were detected in the posterior and anterior midline node of the DMN and in the lateral parietal and insular nodes of the ACN. Cross-correlation analysis confirmed reduced cortico-cortical connectivity within and between the DMN and ACN.

Conclusions

Increased sleep pressure is reflected in reduced fc of main DMN and ACN nodes during rest. Results have implications for understanding perceptual and cognitive changes after sleep deprivation and are relevant to clinical studies on conditions in which increased sleep propensity is present.     

Real-time direct volume rendering in functional magnetic resonance imaging

Direct volume rendering is a visualization method that allows display of all information hidden in three-dimensional data sets of, for example, computed tomography or magnetic resonance imaging (MRI). In contrast to commonly used surface rendering methods, these algorithms need no preprocessing but suffer from a high computational complexity. A real-time rendering system, VIRIM (Vitec: Visualization Technology GmbH, Mannheim, Germany), cuts down rendering times of minutes on normal workstations to an interactive rate of 1 second or less. The immediate visual feedback allows interactive steering of the visualization process to achieve insight into the internal three-dimensional structure of objects. Additional information is obtained by using an interactive gray-value segmentation tool that both allows segmentation of the data set according to bone, tissue, and liquor and display of multifunctional data sets (e.g., functional MRI [fMRI] data sets). Thus, real-time direct volume rendering allows segmentation and volume data processing of functional and anatomical MR data sets simultaneously. As this method can be integrated in the clinical routine, it is of great importance for real-time motion artifact detection and the interpretation of fMRI data acquired during cognitive experiments with normal subjects and psychiatric patients. Because of the free programmability of VIRIM, more complex matching procedures are currently being investigated for future implementation.     

Increasing the effect size in event-related fMRI studies.

Independent component analysis (ICA) has proved to be a powerful method for exploratory analysis of functional magnetic resonance imaging (fMRI) data. It has been used to uncover unexpected activations in fMRI data derived from brain activation. ICA has been used to characterize other sources of variability in the fMRI signal besides task-related activity, as well as challenging some of the assumptions inherent in other fMRI analysis methods. As a data-driven fMRI analysis technique, the philosophy of ICA is often in disagreement with hypothesis-driven methods. By exploiting the fact that much of fMRI data has deterministic spatial-temporal structure, a scheme employing ICA denoising and least squares (LS) estimation of the evoked hemodynamic response (HDR) is proposed. Simulations suggest that the method is more robust to different noise models compared to naive application of LS. The result is a considerably increased level of significance of activation for a given voxel but still qualitatively similar spatial distribution of activations over all voxels. This suggests that the proposed method has the potential to substantially reduce total scanning time requirements to achieve the same level of statistically significant activation.     

Dimensionality reduction of fMRI time series data using locally linear embedding

Objective

Data-driven methods for fMRI analysis are useful, for example, when an a priori model of signal variations is unavailable. However, activation sources are typically assumed to be linearly mixed, although non-linear properties of fMRI data, including resting-state data, have been observed. In this work, the non-linear locally linear embedding (LLE) algorithm is introduced for dimensionality reduction of fMRI time series data.

Materials and methods

LLE performance was optimised and tested using simulated and volunteer data for task-evoked responses. LLE was compared with principal component analysis (PCA) as a preprocessing step to independent component analysis (ICA). Using an example data set with known non-linear properties, LLE-ICA was compared with PCA-ICA and non-linear PCA-ICA. A resting-state data set was analysed to compare LLE-ICA and PCA-ICA with respect to identifying resting-state networks.

Results

LLE consistently found task-related components as well as known resting-state networks, and the algorithm compared well to PCA. The non-linear example data set demonstrated that LLE, unlike PCA, can separate non-linearly modulated sources in a low-dimensional subspace. Given the same target dimensionality, LLE also performed better than non-linear PCA.

Conclusion

LLE is promising for fMRI data analysis and has potential advantages compared with PCA in terms of its ability to find non-linear relationships.     

Integration of Service-Level Monitoring with Fault Management for End-to-End Multi-Provider Ethernet Services

Assuring end-to-end service quality in a multi- provider Ethernet environment is a challenging task. Operation and maintenance issues have become more and more complex due to the gradual extension of the Ethernet technology from local- to wide-area networks and the increasingly frequent use of layer-2 virtual private networks. End-to-end Ethernet network management is currently under standardization, with a focus on connectivity fault management and performance management. However, none of the tools and research prototypes available to date integrate service-level monitoring with fault management functions such as event correlation or root cause analysis for interconnected Ethernet networks. In this paper, we address the issue by proposing an integrated service-level monitoring and fault management framework. Our event processing module can handle various events generated by network nodes or pollers. We also describe service-level monitoring and fault management methods that are fine-tuned for managing end-to-end multi- provider Ethernet services.     

Imaging birds in a bird cage: in-vivo FSE 3D MRI of bird brain

An in-vivo magnetic resonance imaging (MRI) procedure is described that allows one to obtain three-dimensional high quality images of the entire brain of small birds such as the canary (20 g) and the starling (75 g) with an image resolution of 0.1 mm (58-113 μm, dependent on the size of the imaged bird). The entire imaging procedure took about 2 h after which the birds recovered from anaesthesia uneventfully and could be reused for subsequent additional imaging. This non invasive MRI technique enables to correlate brain measures with behavioural or physiological data that are dynamic in nature and could permit significant progress for bird neurological research. © 1998 Elsevier Science B.V. All rights reserved.     

基于关联矩阵的电网拓扑辨识

提出了基于关联矩阵的电网拓扑辨识算法。该算法使用节点－支路关联矩阵表示电网路的基本拓扑结构,定义了矩阵的“与－或”乘法运算,利用连通性的传递性质,实现对电网络的拓扑辨识。在此基础上,利用节点－支路关联矩阵和节点－节点连通矩阵的对称性,提出了加快计算的技术和实现方法,该算法即可以通过汇编语言或高级语言编程实现,也可以由单片机系统或ASIC等硬件方法实现。     

Open magnetic and electric graphic analysis.

The OMEGA software provides an analysis platform for user-independent, fast, and reproducible multimodal data analysis in one single software environment. Synergetic interactions pursued between the two functional imaging techniques fMRI and MEG use the morphological MRI recording as a basis for a common coordinate frame. In this way, direct interchange, comparison, and integration among the results of the different modalities have become feasible. The fMRI data analysis provides information about the localization of functional activity with low temporal resolution, whereas the MEG recording complements the corresponding time evolution with a high temporal resolution. The implementation of OMEGA allows the analyst to receive comprehensive MEG/fMRI results in a matter of minutes after the measurements have been completed. With OMEGA, the clinical researcher gets comprehensive information in a quick and standardized approach about the sites and the time course of neurological activation, which is useful for clinical applications and diagnostics.     

Contact modeling and prediction-based routing in sparse mobile networks

Mobile ad-hoc networks (MANETs) provide highly robust and self-configuring network capacity required in many critical applications, such as battlefields, disaster relief, and wild life tracking. In this paper, we focus on efficient message forwarding in sparse MANETs, which suffers from frequent and long-duration partitions. Asynchronous contacts become the basic way of communication in such kind of network instead of data links in traditional ad-hoc networks. Current approaches are primarily based on estimation with pure probability calculation. Stochastic forwarding decisions from statistic results can lead to disastrous routing performance when wrong choices are made. This paper introduces a new routing protocol, based on contact modeling and contact prediction, to address the problem. Our contact model focuses on the periodic contact pattern of nodes with actual inter-contact time involved, in order to get an accurate realization of network cooperation and connectivity status. The corresponding contact prediction algorithm makes use of both statistic and time sequence information of contacts and allows choosing the relay that has the earliest contact to the destination, which results in low average latency. Simulation is used to compare the routing performance of our algorithm with three other categories of forwarding algorithm proposed already. The results demonstrate that our scheme is more efficient in both data delivery and energy consumption than previously proposed schemes.     

Brain Network Analysis From High-Resolution EEG Recordings by the Application of Theoretical Graph Indexes

The extraction of the salient characteristics from brain connectivity patterns is an open challenging topic since often the estimated cerebral networks have a relative large size and complex structure. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach would extract significant information from the functional brain networks estimated through different neuroimaging techniques. The present work intends to support the development of the “brain network analysis:” a mathematical tool consisting in a body of indexes based on the graph theory able to improve the comprehension of the complex interactions within the brain. In the present work, we applied for demonstrative purpose some graph indexes to the time-varying networks estimated from a set of high-resolution EEG data in a group of healthy subjects during the performance of a motor task. The comparison with a random benchmark allowed extracting the significant properties of the estimated networks in the representative Alpha (7–12 Hz) band. Altogether, our findings aim at proving how the brain network analysis could reveal important information about the time-frequency dynamics of the functional cortical networks.      

融合多传感器的飞行器电缆网状态在线监测系统

飞行器电缆网承担着电气、信号和数据传输等关键职能。在飞行器超声速飞行过程中电缆网面临诸如高温、振动、电流过载和低气压等挑战,对飞行器电气系统的安全性和可靠性产生影响。本研究设计了电缆网多传感器监测系统和基于多传感器融合的电缆网健康状态监测算法。在监测系统中实现了电压、电流、温度、加速度和气压等数据采集、存储以及无线传输功能。算法在预处理阶段通过归一化的方式,综合考虑了高温、振动、电流过载和低气压等稳态和瞬态值对电缆网健康状态的影响,算法健康状态分类部分设计了多层分类网络对电缆网状态进行分类,在实际实验数据集与仿真数据集中,本文多层分类网络相比于SVM分类网络正确率平均提升6.4%,虚警率平均降低了77.2%;本文的多传感器监测算法相比于单通道监测算法,正确率有显著提升,对比实验结果验证了本文算法在电缆网健康状态分类任务中的有效性。实验结果表明,电缆网多传感器监测系统可以有效监测并识别飞行器电缆网的健康状态,为飞行器电气系统运行提供了有力保障。     

A study of within-subject reliability of the brain’s default-mode network

Objective

Resting-state functional magnetic resonance imaging (fMRI) is promising for Alzheimer’s disease (AD). This study aimed to examine short-term reliability of the default-mode network (DMN), one of the main haemodynamic patterns of the brain.

Materials and methods

Using a 1.5 T Philips Achieva scanner, two consecutive resting-state fMRI runs were acquired on 69 healthy adults, 62 patients with mild cognitive impairment (MCI) due to AD, and 28 patients with AD dementia. The anterior and posterior DMN and, as control, the visual-processing network (VPN) were computed using two different methodologies: connectivity of predetermined seeds (theory-driven) and dual regression (data-driven). Divergence and convergence in network strength and topography were calculated with paired t tests, global correlation coefficients, voxel-based correlation maps, and indices of reliability.

Results

No topographical differences were found in any of the networks. High correlations and reliability were found in the posterior DMN of healthy adults and MCI patients. Lower reliability was found in the anterior DMN and in the VPN, and in the posterior DMN of dementia patients.

Discussion

Strength and topography of the posterior DMN appear relatively stable and reliable over a short-term period of acquisition but with some degree of variability across clinical samples.

     

Approaches for the optimization of MR protocols in clinical hybrid PET/MRI studies

Magnetic resonance imaging (MRI) is the examination method of choice for the diagnosis of a variety of diseases. MRI allows us to obtain not only anatomical information but also identification of physiological and functional parameters such as networks in the brain and tumor cellularity, which plays an increasing role in oncologic imaging, as well as blood flow and tissue perfusion. However, in many cases such as in epilepsy, degenerative neurological diseases and oncological processes, additional metabolic and molecular information obtained by PET can provide essential complementary information for better diagnosis. The combined information obtained from MRI and PET acquired in a single imaging session allows a more accurate localization of pathological findings and better assessment of the underlying physiopathology, thus providing a more powerful diagnostic tool. Two hundred and twenty-one patients were scanned from April 2011 to January 2012 on a Philips Ingenuity TF PET/MRI system. The purpose of this review article is to provide an overview of the techniques used for the optimization of different protocols performed in our hospital by specialists in the following fields: neuroradiology, head and neck, breast, and prostate imaging. This paper also discusses the different problems encountered, such as the length of studies, motion artifacts, and accuracy of image fusion including physical and technical aspects, and the proposed solutions.     

A PMUs placement methodology based on inverse of connectivity and critical measurements

This paper presents a methodology to determine the optimal location of phasor measurement units (PMUs) in any network to make it observable. This proposed methodology is based on network connectivity information and unreachability index (URI), where URI is the difficulty to observe any node in the network and it is computed using the inverse of connectivity. In order to choose the optimal bus, it is basically considered to observe a low connectivity bus from an adjacent bus selected by weighting factors that are based on logical analysis of the observability theory combined with the URI; this process stops until the network is observable. The purpose is minimize the number of PMUs in a network with the optimal location and the aim to get a low number of critical measurements (CM) with a high total redundancy (TR), in order to obtain an optimal distribution of PMUs on the network. The proposal is considered as an easy solver for PMU’s placing on the network due to important reduction in complexity and computational cost, besides comparable results are as good as those papers using recent optimization methods such as metaheuristics and stochastics, without taking into account that the proposal can handle huge networks. The algorithm is applied to the IEEE 14, 30, 57, 118 and 300-bus systems, and also to medium and large power systems of 1006, 3305, 15,000, 20,000 and 30,000 buses with success.     

下一代网络(NGN)与软交换

以现有网络资源为基础，通过引入独立的网络设备及开放的技术标准来承载多种业务，实现传统网络与NGN业务的无缝互通，最终平滑地过渡到以软交换为核心，以综合、开放的网络构架为特征，能够提供语音、数据和视频等多种综合业务的下一代网络(NGN)。简单介绍了NGN和软交换的特点和功能结构。