fMRI neuroinformatics.

Functional magnetic resonance imaging (fMRI) generates vast amounts of data. The handling, processing, and analysis of fMRI data would be inconceivable without computer-based methods. fMRI neuroinformatics is concerned with research, development, and operation of these methods. Reconstruction, rudimentary analysis, and visualization tools are implemented in software controlling modern MRI scanners. Research in advanced methods for analysis of subtle activation patterns, realistic physiological modeling, or integration of data from multiple subjects is the basis for a lively research field and has led to the development of a large number of tools.     

Multivariate statistical analysis in fMRI.

The paper briefly discussed different statistical analysis in functional magnetic resonance imaging (fMRI). Multivariate regression analysis with multiple comparisons corrections allows the determination of activated voxels that can then be grouped into regions of interest (ROIs). Principal component analysis (PCA) is useful in extracting common temporal response features of an ROI as well as differentiating the temporal response of groups of commonly responding ROI. It can also be used to examine differences in the temporal response of subgroups of subjects in the study. Structural equation modeling (SEM) is a technique that requires a priori knowledge of the connections and their direction between ROIs. It is particularly useful in identifying changes in connectivity that result from different interventions or different classes of patients.     

Dynamic time warping in the analysis of event-related potentials.

The aim of this article is to compute reliable templates of event-related potentials (ERPs) for homogeneous groups of subjects and to automatically quantify the morphological characteristics of the ERPs. We developed a method based on dynamic time warping (DTW). The method was applied to ERPs recorded from normal and dyslexic children during two reading tasks. We found that characteristic latency and amplitude changes of ERP components are due to task and pathology. Our results support the idea that dyslexia involves different and complex cerebral functions aside from the language system. This mathematical approach provides reproducible analysis criteria that are crucial for the reliability of ERP analysis.     

Preattentive change detection using the event-related optical signal.

The current study revealed right STG activation followed by IFG activation in response to temporal deviant events. The sequence and general time course of the activation pattern replicates our earlier findings for omission deviants, but with an SOA that was an order of magnitude larger. It also extends the findings to include early deviants, and thereby it strengthens claims about the spatial localization of the deviance response and the functional roles the STG and the IFG play in that response. Finally, the IFG activity elicited by the early deviants that preceded the STG activity suggests the IFG may play a dual role in preattentive change detection. Future EROS studies using parametric modulations of deviance magnitude will help to provide more information about the functional role of IFG in preattentive change detection.     

Evaluating fMRI preprocessing pipelines.

This article reviews the evaluation and optimization of the preprocessing steps for blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). This technique indirectly measures changes in local neuronal firing rates by measuring associated changes in deoxy-hemoglobin concentrations in nearby blood vessels. Based on the existing literature, it is impossible to make conclusive statements about the optimal algorithm and software implementations for any single preprocessing step, let alone entire pipelines. The author believes that the present focus on the technological testing of preprocessing steps should be balanced by approaches that test the pipeline. This should include all interactions measured using metrics that are closely linked to research and diagnostic questions addressed at the end of the processing pipeline. The goal is to avoid single expedient or default pipelines by developing a framework capable of potentially testing thousands of possible pipeline implementations per dataset. To achieve this goal, researchers depend on recent developments in software tools for managing neuroimaging workflows.     

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.     

Breakdown probability and size effect in liquid helium

Breakdown statistics in liquid helium have been evaluated in order to correlate with size effect degradation, i.e. the area and the volume effect, respectively. Reprocessing large number of data sets yielded the popular two parameter Weibull distribution not being representative; more sophisticated functions, e.g. the Gompertz function had to be used in order to fit the data. A considerable amount of data were found to be not homogeneous, and a sum of 2-parameter Weibull subdistributions had to be defined. These results are discussed in detail. No formal straightforward correlation with the known size effect could be established. Special emphasis has been attributed to the low breakdown probability limit in order to meet engineering design needs. There seems to be a kind of threshold, but size degradation cannot be ruled out, and long term performance has still to be proven     

Unmixing fMRI with independent component analysis.

Independent component analysis (ICA) is a statistical method used to discover hidden factors (sources or features) from a set of measurements or observed data such that the sources are maximally independent. Typically, it assumes a generative model where observations are assumed to be linear mixtures of independent sources and works with higher-order statistics to achieve independence. ICA has recently demonstrated considerable promise in characterizing functional magnetic resonance imaging (fMRI) data, primarily due to its intuitive nature and ability for flexible characterization of the brain function. In this article, ICA is introduced and its application to fMRI data analysis is reviewed.     

Insulator size effect on electron-irradiated quartz

A time-resolved current experiment measuring separately the surface and bulk currents flowing through an insulating sample under electron beam irradiation employing a scanning electron microscope is performed to investigate charging and flashover phenomena. A set of metallic apertures of various values of radius are used to vary the area of the insulator. It is observed that the bulk current decreases monotonically with time until reaching a constant value, while the surface current exhibits two different trends: decreasing monotonically with time, and decreasing with time at first and then increasing. Such observations are related to the establishment of a dynamic equilibrium between electron trapping and detrapping, as well as the flashover behavior, and depends on the insulator size and the experimental conditions     

A direct brain interface based on event-related potentials.

Cross-correlation between a trigger-averaged event-related potential (ERP) template and continuous electrocorticogram was used to detect movement-related ERP's. The accuracy of ERP detection for the five best subjects (of 17 studied), had hit percentages >90% and false positive percentages <10%. These cases were considered appropriate for operation of a direct brain interface.     

Modeling and inference of multisubject fMRI data.

This article reviews four commonly used approaches to group modeling in fMRI. The methods differ in their computational intensity (FSL with its two-level estimation including MCM being the most intense) and assumptions (SPM2 with its assumption of spatially homogeneous covariance V/sub g/ being most restrictive). This study also distinguishes fixed-effects models from mixed-effects models and motivates the importance of a mixed-effects model for group fMRI analysis. The sections following that describe single-subject modeling and show a general method for estimating the group model.     

New methods in fMRI analysis

Hierarchical cluster analysis improves signal-to-noise ratio compared to standard techniques. In this work we present a new approach for improving the SNR in fMRI. The special implementation of the voxel-based motion-correction algorithms allows for very fast and precise correction of head movements in the frame of the selected model. The hierarchical cluster analysis has proven to be a useful tool for the increase of the SNR of the functional results. As these tools and established deterministic and statistical functional analysis tools are implemented in one software platform (FAMIS), direct comparison between the results becomes feasible. As each analysis result can be obtained within seconds and the user can switch between the display for the different results, direct comparison between the methods can be performed. Deterministic analysis leads to an activation concept that is independent from the noise level, and vice versa the statistical analysis leads to an activation concept where the activation is intended as deviation from noise and in such way the activation becomes related to the noise level. The deterministic methods exclude true positive results with increasing noise, and the statistical methods include false positive results with increasing noise. Nevertheless, the decision regarding which of the methods should be the method of choice for fMRI analysis is not the subject of this work     

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.     

Laser tissue welding: laser spot size and beam profile studies

Evaluates the effect of laser spot diameter and beam profile on the shape of the thermal denaturation zone produced during laser tissue welding, 2-cm-long full thickness incisions were made on the epilated backs of guinea pigs in vivo. India ink was used as an absorber and clamps were used to appose the incision edges. Welding was performed using continuous-wave 1.06-μm, Nd:YAG laser radiation scanned over the incisions to produce ~100-ms pulses. Laser spot diameters of 1, 2, 4, and 6 mm were studied, with powers of 1, 4, 16, and 36 W, respectively. The irradiance remained constant at 127 W/cm². Monte Carlo simulations were also conducted to examine the effect of laser spot size and beam profile on the distribution of photons absorbed in the tissue. The laser spot diameter was varied from 1 to 6 mm, Gaussian, flat-top, dual Gaussian, and dual flat-top beam profiles were studied. The experimental results showed that 1-, 2-, 4-, and 6 mm-diameter spots produced thermal denaturation to an average depth of 570, 970, 1470, and 1900 μm, respectively. Monte Carlo simulations demonstrated that the most uniform distribution of photon absorption is achieved using large diameter dual flat-top beams     

DG容量在分布式发电中对数字保护影响的研究

配电网发生故障时,分布式电源(Distributed Generation,DG)对继电保护有重大影响.为了更好地发挥DG的积极作用.减轻DG对电力系统传统保护的不利影响,基于传统电流保护原理,分析了接有DG的配电网保护配合性,并从保护配合性及供电可靠性角度探讨了DG注入容量问题.DG容量的计算原则是:DG下游故障时.保护不能误动,相邻保护之间的动作时间应留有裕度;上游故障时,保护不能拒动,DG容量不宜太小.还要保证最接近故障点的保护最先动作,保持保护配合性.就DG容量对传统过流保护的影响作了MATLAB仿真,仿真结果表明DG容量应在一定范围之内,否则会引起保护误动或拒动.     

自密实混凝土强度尺寸效应的试验与数值仿真

自密实混凝土(SCC)是通过掺加高效减水剂而得到的流动性极佳的混凝土,具有很高的流动性,不离析、不泌水,无须振捣便可达到自密实的效果.本文对边长为70.7mm的立方体小试件在不同边界条件下进行单轴压缩试验,用以研究端部效应对SCC强度及破坏形态的影响.为了避免端部效应对尺寸效应的影响,本文考虑减摩措施,对边长为70.7mm、100mm、150mm、200mm、300mm立方体SCC试件进行单轴压缩试验,试验结果表明,随着试件尺寸的增加,混凝土的单轴抗压强度不断减低,即呈现出明显的尺寸效应现象,符合Weibull强度统计理论.此外,基于混凝土细观概率模型进行相应的数值仿真,通过对比数值模拟结果与试验结果,验证了该模型能较好地模拟混凝土尺寸效应现象.     

张家口发电厂2号发电机增容改造技术

对300 MW汽轮发电机的增容改造的过程、发电机增容前后试验进行了介绍.