Magnetoencephalography

The measurement and analysis of the magnetic fields of the brain are discussed. The capabilities of magnetoencephalography are examined, and the measurement techniques are described. The ability of MEG measurements to map neural activity with high resolution is discussed, and analysis techniques that accomplish this are examined. The benefits of combining the functional information provided by MEG with the structural information provided by computed tomography or magnetic resonance imaging are considered     

Imaging neural activity using MEG and EEG

The authors have developed a Bayesian framework for image estimation from combined MEG/EEG data. Their results indicate that performance of their imaging approach is superior to that of weighted minimum norm when the image is sparse and focal. Note however that if the image is not sparse, the authors' method would perform poorly since their prior is specifically designed to give sparse focal sources. This observation serves to emphasize the fact that the use of prior information is crucial in extracting useful spatial information from the data. The authors have also found that combining MEG and EEG gives superior results when compared to using the modalities individually. This improvement is due not only to increasing the number of measurements, but also because of the complimentary nature of MEG/EEG. Even when working with the two modalities in combination, significant limitations to electromagnetic imaging exist. Regardless of the number and placement of sensors, reconstructions are generally only reliable if relatively few source clusters exist. If a large number of distributed sources exist, no imaging technique can hope to reconstruct them accurately strictly from the MEG/EEG data given. Such complex distributions will generally be matched as well or better by simpler solutions. Thus, if used on their own, the authors expect MEG/EEG data to be most useful when the number of activated sites is small. Alternatively, when used in combination with fMRI or PET, it may be possible to produce dynamic images of more complex processes     

Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks

Graph theoretical analysis of structural and functional connectivity MRI data (ie. diffusion tractography or cortical volume correlation and resting-state or task-related (effective) fMRI, respectively) has provided new measures of human brain organization in vivo. The most striking discovery is that the whole-brain network exhibits “small-world” properties shared with many other complex systems (social, technological, information, biological). This topology allows a high efficiency at different spatial and temporal scale with a very low wiring and energy cost. Its modular organization also allows for a high level of adaptation. In addition, degree distribution of brain networks demonstrates highly connected hubs that are crucial for the whole-network functioning. Many of these hubs have been identified in regions previously defined as belonging to the default-mode network (potentially explaining the high basal metabolism of this network) and the attentional networks. This could explain the crucial role of these hub regions in physiology (task-related fMRI data) as well as in pathophysiology. Indeed, such topological definition provides a reliable framework for predicting behavioral consequences of focal or multifocal lesions such as stroke, tumors or multiple sclerosis. It also brings new insights into a better understanding of pathophysiology of many neurological or psychiatric diseases affecting specific local or global brain networks such as epilepsy, Alzheimer’s disease or schizophrenia. Graph theoretical analysis of connectivity MRI data provides an outstanding framework to merge anatomical and functional data in order to better understand brain pathologies.     

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.     

High resolution fMRI of subcortical regions during visual erotic stimulation at 7 T

Object Involvement of distinct subcortical structures during sexual arousal was shown in animals and functional imaging studies gave coarse evidence for a similar organisation in humans. In contrast to previous imaging studies at lower field strengths, we tried to investigate activation in distinguishable subcortical structures at high spatial resolution during a short stimulating paradigm to further account for potential effects of attenuation or adaptation. Materials and methods Seven healthy subjects were investigated using functional magnetic resonance imaging (fMRI) on a 7 T scanner. High resolution EPI images of 1.4 × 1.4 mm² inplane resolution were acquired in a single functional session of 13.6 minutes. During the session erotic and non-erotic pictures were presented in an event-related design. Results In the unsmoothed data with preserved high spatial resolution significant effects were detected in relevant structures, including anterior caudate and mediodorsal thalamus. These effects were restricted to subcortical target structures and their anatomical boundaries. Conclusion This study demonstrates that fMRI at high fields provides an ideal tool to investigate functional anatomy of subcortical structures. Due to an increased signal-to-noise ratio, functional scans of short duration can be acquired at high resolution without the need for further spatial smoothing.     

Optimized EPI for fMRI studies of the orbitofrontal cortex: compensation of susceptibility-induced gradients in the readout direction

Object Most functional magnetic resonance imaging (fMRI) studies record the blood oxygen leveldependent (BOLD) signal using gradient-echo echo-planar imaging (GE EPI). EPI can suffer from substantial BOLD sensitivity loss caused by magnetic field inhomogeneities. Here, BOLD sensitivity losses due to susceptibility- induced gradients in the readout (RO) direction are characterized and a compensation approach is developed. Materials and Methods Based on a theory describing the dropout mechanism, an EPI sequence was optimized for maximal BOLD sensitivity in the orbitofrontal cortex (OFC) using a specific combination of an increased spatial resolution in the RO direction and a reduced echo time. Using measured BOLD sensitivity maps and a breath hold experiment, the model and compensation approach were tested. Results Using typical fMRI EPI parameters, susceptibility-induced gradients in the RO direction caused dropouts in the OFC and the inferior temporal lobe. Optimizing the echo time and spatial resolution effectively reduced the dropout as predicted by the theory. Conclusion The model-based compensation approach effectively reduces BOLD sensitivity losses due to susceptibility-induced gradients in the RO direction. It retains the high temporal resolution of single-shot EPI and can be readily combined with methods for the compensation of susceptibility-induced field gradients in the phase-encoding and through-plane direction.     

Seizing lesions in 3-D

This study adapted the CURRY program and assessed the relationship of the 3-D spike sources to focal lesions evident on MRI scans. The subgroup is selected as it represents an initial step in determining the merit of this technique in the presurgical evaluation of children. Further studies comparing reconstructed spike sources with intracranial electrode recording are planned. CURRY is a software package that provides a variety of methods for accurately localizing the source of electrical activity in the brain. This is achieved by combining EEG and magnetoencephalogram (MEG) signals with structural imaging modalities.     

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.     

Present and future of simultaneous EEG-fMRI

The brain’s activity can be measured in numerous complementary ways, including electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). The simultaneous acquisition of EEG and fMRI was originally developed to make the localization of the generators of often subtle pathological activity commonly observed in EEG recordings of patients with epilepsy more sensitive and spatially accurate by mapping their hemodynamic correlates. Now, the value of the information provided by simultaneous EEG-fMRI is being evaluated in a clinical context, while in parallel, more sophisticated data analysis techniques, e.g. with electrical source imaging or dynamic causal modeling, have begun to be applied to increase the technique’s sensitivity and allow the study of brain network structure. Beyond its clinically oriented application in epilepsy, simultaneous EEG-fMRI recording has now gained interest as a tool for basic and systems human neuroscience, e.g. the study of neuro-vascular coupling and cognitive studies. In this review, we give an overview over the current use of simultaneous EEG-fMRI, its applications to the study of epilepsy as well as human cognition and systems neuroscience and ongoing and anticipated methodological developments.     

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.     

Towards functional spin-echo BOLD line-scanning in humans at 7T

Objective

Neurons cluster into sub-millimeter spatial structures and neural activity occurs at millisecond resolutions; hence, ultimately, high spatial and high temporal resolutions are required for functional MRI. In this work, we implemented a spin-echo line-scanning (SELINE) sequence to use in high spatial and temporal resolution fMRI.

Materials and methods

A line is formed by simply rotating the spin-echo refocusing gradient to a plane perpendicular to the excited slice and by removing the phase-encoding gradient. This technique promises a combination of high spatial and temporal resolution (250 μm, 500 ms) and microvascular specificity of functional responses. We compared SELINE data to a corresponding gradient-echo version (GELINE).

Results

We demonstrate that SELINE showed much-improved line selection (i.e. a sharper line profile) compared to GELINE, albeit at the cost of a significant drop in functional sensitivity.

Discussion

This low functional sensitivity needs to be addressed before SELINE can be applied for neuroscientific purposes.

     

Integration of EEG/MEG with MRI and fMRI.

This article discusses different approaches that have been proposed for multimodal neuroimaging, with special emphasis on the integration of electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging (MRI), and functional MRI (fMRI). Some applications will be shown to illustrate the efficacy and importance of these techniques in clinical and neuroscience studies. Finally, some remaining challenges and problems in the multimodal integration will be discussed.     

新型电力系统故障录波装置的研究

针对电力系统发生严重故障后事故分析的要求,设计一种电力故障录波监测装置。分析系统工作原理,介绍所用到的器件和关键接口电路,列出了软件设计的要点。系统实现了长时间连续记录数据,以及稳态、动态和暂态数据的统一分析;为事故分析提供了可靠的数据来源。应用结果表明,该系统具有低成本、抗干扰性强、操作简单等特点,有广阔的应用前景。     

A nationwide survey of recorded information used for calculating distribution reliability indices

Reliability is one of the most discussed topics in the power industry today. Although many questions are being asked about the reliability of utility systems across the US, there are no standards for recording or measuring interruption minutes, number of customers affected, major events, whether to include transmission and or distribution as well as many other issues. The methods used for calculating the indices and the definitions for the information have a major role in calculating reliability measures as defined in an IEEE standard. This paper provides the results of a nationwide survey on the recording or measuring of data used to calculate reliability indices and associated practices. It points out some of the pitfalls of using differing methodologies in the recording of data behind the measures. These differences often cause discrepancies when comparing reliability indices between utilities.     

Continuous recording and stochastic analysis of PD

We describe the design and use of a digital partial discharge (PD) data recording system capable of continuous real-time recording of PD pulse trains. The recording system consists of a custom two-channel PD digitizer coupled to a personal computer via a 10-bit parallel interface. The digitizer is under software control with the resulting data being stored in binary files on the computer's hard disk. The stored data subsequently are subjected to stochastic analysis using appropriate computer software. Because all data are retained and the computer provides the desired stochastic analysis of data files, the new system is well suited to investigate non-stationary PD behavior such as encountered in aging studies. By way of illustration, the new system was used to determine the time-varying stochastic behavior of ac-generated PD from point-to-dielectric gaps in air where the insulation material was cast epoxy with aluminum oxide filler. The results confirm and extend previous measurements made with an analog stochastic analyser. With these sample results, we demonstrate how the system allows detailed stochastic analyses not possible with data obtained from existing conventional PD measurement systems     

设备状态点检信息网络化管理技术研究

本文结合设备状态的信息分析和实现中小型企业预知维修的需要,通过将点检监测方式应用于设备状态管理,设计了一种设备状态点检信息网络化管理系统.此系统由点检工作站、数据库服务器、客户端组成,点检工作站由状态信息数据库、数采器和掌上电脑抄表仪来完成信息的存储和录入,数据库服务器作为点检信息的载体,客户端来完成信号的诊断分析和设备状态的管理.同时,给出了数采器硬件框图、客户端信号分析软件流程图和分析比较的程序界面.     

The impact of EPI voxel size on SNR and BOLD sensitivity in the anterior medio-temporal lobe: a comparative group study of deactivation of the Default Mode

Objectives  To quantify the gain in time-series SNR that can be achieved in the amygdala by reducing EPI voxel size, and to assess the extent to which this advantage is carried through to statistical significance in a group fMRI study, using a cognitive task to trigger task-independent deactivation of anterior medial temporal structures. Materials and methods  Two groups of seven subjects were posed number-series tasks to induce deactivation of the Default Mode network. This is known from PET work to include the amygdala, which lies in a region of high magnetic field gradient. In 3 T imaging, one group was studied with high resolution EPI with 6 μl voxels, the other with lower resolution EPI with 17 μl voxels. Field maps were acquired to allow field gradients in relevant ROIs to be assessed. Results  Time-series SNR was 45% higher in the amygdala in the high resolution EPI data than in the low resolution data. In activation results, whilst there was good agreement between other areas, the involvement of the amygdala could only be demonstrated in the high resolution data. Conclusion  We find that reduction in signal dephasing afforded by high resolution EPI is realized as a substantial increase in SNR and BOLD sensitivity in group fMRI data. This has allowed the first demonstration of the involvement of the amygdala in the Default Mode in fMRI.     

Diode-pumped Nd:YLF master oscillator for the 30-kJ (UV), 60-beamOMEGA laser facility

The operational conditions of the OMEGA laser system require an extremely reliable and low-maintenance master oscillator. We have developed a diode-pumped, single-frequency, pulsed Nd:YLF laser for this application. The laser generates Q-switched pulses of ~160-ns duration and ~10-μJ energy content at the 1053-nm wavelength with low amplitude fluctuation, (<0.6% rms) and low temporal (build-up time) jitter (<7-ns rms). Amplitude and frequency feedback stabilization systems have been used for high long-term amplitude and frequency stability     

Functional connectivity dynamics among cortical neurons: a dependence analysis

This paper quantifies and comparatively validates functional connectivity between neurons by measuring the statistical dependence between their firing rates. Based on statistical analysis of the pairwise functional connectivity, we estimate, exclusively from neural data, the neural assembly functional connectivity given a behavior task, which provides a quantifiable representation of the dynamic nature during the behavioral task. Because of the time scale of behavior (100-1000 ms), a statistical method that yields robust estimators for this small sample size is desirable. In this work, the temporal resolutions of four estimators of functional connectivity are compared on both simulated data and real neural ensemble recordings. The comparison highlights how the properties and assumptions of statistical-based and phase-based metrics affect the interpretation of connectivity. Simulation results show that mean square contingency (MSC) and mutual information (MI) create more robust quantification of functional connectivity under identical conditions than cross correlation (CC) and phase synchronization (PhS) when the sample size is 1 s. The results of the simulated analysis are extended to real neuronal recordings to assess the functional connectivity in monkey's cortex corresponding to three movement states in a food reaching task and construct the assembly graph given a movement state and the activation degree of a state-related assembly over time using the statistical test exclusively from neural data dependencies. The activation degree of a given state-related assembly reaches the peak repeatedly when the specific movement states occur, which also reveals the network of interactions among the neurons are key for the operation of a specific behavior.     

电力光纤入户的智能小区配用电信息采集系统解决方案

提出基于电力光纤入户的智能小区配用电信息采集系统解决方案,以电力光纤通信方式为主,结合Zigbee通信方式,建立一个实时同步可扩展的通信网络。研制开发了小区配电变压器综合采集仪、三相智能监控终端、多通道采集器、户内智能网关、智能插座、智能空气开关等产品,可以对小区内配电变压器信息、小区公共用电信息、居民电能表信息居民户内分类信息进行完整的信息采集。该小区配用电信息采集系统能够精细化实时采集小区各种用电信息,为小区智能用电提供技术手段,支持供电公司开展居民用户智能用电服务。