Group inference of default‐mode networks from functional magnetic resonance imaging data: comparison of random‐ and mixed‐effects group statistics

Default‐mode network (DMN) activity measured with functional magnetic resonance imaging (fMRI) represents dominant intrinsic neuronal activations of the human brain during rest as opposed to task periods. Previous studies have demonstrated the utility of DMNs in identifying characteristic traits such as hyperactivation and hypoactivation from group‐level fMRI data. However, these group‐level spatial patterns (SPs) were mostly based on random‐effect (RFX) statistics determined using only the intersubject variability. To reduce the potentially significant level of variability in group‐level SPs in RFX due to intrasubject variability, we were motivated to adopt a mixed‐effects (MFX) statistics that is using both intrasubject and intersubject variability. Publicly available group fMRI database during resting state was analyzed using a temporal concatenation‐based group independent component (IC) analysis, and DMN‐related ICs at the group‐level were automatically selected. The individual‐level SPs of these DMN‐related ICs were subsequently estimated using a dual‐regression approach. Using these individual‐level SPs, we evaluated the reproducibility and potential variability of the DMNs from the RFX and MFX statistics using performance measures including (1) neuronal activation levels, (2) percentages of overlap, (3) Pearson's spatial correlation coefficients, and (4) the distances between center‐of‐clusters. The resulting SPs from the MFX‐based group inference showed a significantly greater level of reproducibility than those from the RFX‐based group inference as tested in a bootstrapping framework Family‐wise error (FWE)‐corrected p < 10^?10, one‐way analysis of variance (ANOVA)). The reported findings may provide a valuable supplemental option for investigating the neuropsychiatric group‐ or condition‐dependent characteristic traits implicated in DMNs. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 121–131, 2012     

Time-Resolved and Spatio-Temporal Analysis of Complex Cognitive Processes and their Role in Disorders like Developmental Dyscalculia

The aim of this article is to report on the importance and challenges of a time-resolved and spatio-temporal analysis of fMRI data from complex cognitive processes and associated disorders using a study on developmental dyscalculia (DD). Participants underwent fMRI while judging the incorrectness of multiplication results, and the data were analyzed using a sequence of methods, each of which progressively provided more a detailed picture of the spatio-temporal aspect of this disease. Healthy subjects and subjects with DD performed alike behaviorally though they exhibited parietal disparities using traditional voxel-based group analyses. Further and more detailed differences, however, surfaced with a time-resolved examination of the neural responses during the experiment. While performing inter-group comparisons, a third group of subjects with dyslexia (DL) but with no arithmetic difficulties was included to test the specificity of the analysis and strengthen the statistical base with overall fifty-eight subjects. Surprisingly, the analysis showed a functional dissimilarity during an initial reading phase for the group of dyslexic but otherwise normal subjects, with respect to controls, even though only numerical digits and no alphabetic characters were presented. Thus our results suggest that time-resolved multi-variate analysis of complex experimental paradigms has the ability to yield powerful new clinical insights about abnormal brain function. Similarly, a detailed compilation of aberrations in the functional cascade may have much greater potential to delineate the core processing problems in mental disorders.     

Application of independent component analysis with mixture density model to localize brain alpha activity in fMRI and EEG

Independent component analysis (ICA) is an approach to solve the blind source separation problem. In the original and extended versions of ICA, nonlinearity functions are fixed to have specific density forms such as super‐Gaussian or sub‐Gaussian, thereby limiting their performance when sources with different classes of densities are mixed in multichannel data. In this article, we have incorporated a mixture density model such that no assumption about source density would be required. We show that this leads to better source separation due to increased flexibility in handling source‐ densities with flexible parametric nonlinearity. The algorithm was validated through simulation studies and its performance was compared to other versions of ICA. The modified mixture density ICA was then applied to functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data to localize independent sources of alpha activity in the human brain. A good spatial correlation was found in the spatial distribution of alpha sources derived independently from fMRI and EEG, suggesting that spontaneous alpha rhythm can be imaged by fMRI using ICA without concurrent acquisition of EEG. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 170–180, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20021     

A novel method for detection of voxels for decision making: An fMRI study

This study explores the patterns of activation in brain regions toward classifying decision making voxels from among four major Brodmann areas (BAs) upon stimulus of visual tasks. Toward this goal, a well‐known clustering analysis has been performed on real‐time data of the human brain obtained using functional magnetic resonance imaging (fMRI). The functional connectivity among various brain regions was detected by leveraging a distance correlation graph. Graphical methods have been employed to visualize the clusters elicited in the process. The analysis of the results sheds new light on how four significantly activated BAs of the brain exhibit effective connectively to perform a visual task in the context of decision making.     

Neurofeedback using functional spectroscopy

Neurofeedback based on real‐time measurement of the blood oxygenation level‐dependent (BOLD) signal has potential for treatment of neurological disorders and behavioral enhancement. Commonly used methods are based on functional magnetic resonance imaging (fMRI) sequences that sacrifice speed and accuracy for whole‐brain coverage, which is unnecessary in most applications. We present multivoxel functional spectroscopy (MVFS): a system for computing the BOLD signal from multiple volumes of interest (VOI) in real‐time that improves speed and accuracy of neurofeedback. MVFS consists of a FS pulse sequence, a BOLD reconstruction component, a neural activation estimator, and a stimulus system. The FS pulse sequence is a single‐voxel, magnetic resonance spectroscopy sequence without water suppression that has been extended to allow acquisition of a different VOI at each repetition and real‐time subject head motion compensation. The BOLD reconstruction component determines the T2* decay rate, which is directly related to BOLD signal strength. The neural activation estimator discounts nuisance signals and scales the activation relative to the amount of ROI noise. Finally, the neurofeedback system presents neural activation‐dependent stimuli to experimental subjects with an overall delay of less than 1 s. Here, we present the MVFS system, validation of certain components, examples of its usage in a practical application, and a direct comparison of FS and echo‐planar imaging BOLD measurements. We conclude that in the context of realtime BOLD imaging, MVFS can provide superior accuracy and temporal resolution compared with standard fMRI methods. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 138–148, 2014     

Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes

Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.     

A study of long‐term fMRI reproducibility using data‐driven analysis methods

The reproducibility of functional magnetic resonance imaging (fMRI) is important for fMRI‐based neuroscience research and clinical applications. Previous studies show considerable variation in amplitude and spatial extent of fMRI activation across repeated sessions on individual subjects even using identical experimental paradigms and imaging conditions. Most existing fMRI reproducibility studies were typically limited by time duration and data analysis techniques. Particularly, the assessment of reproducibility is complicated by a fact that fMRI results may depend on data analysis techniques used in reproducibility studies. In this work, the long‐term fMRI reproducibility was investigated with a focus on the data analysis methods. Two spatial smoothing techniques, including a wavelet‐domain Bayesian method and the Gaussian smoothing, were evaluated in terms of their effects on the long‐term reproducibility. A multivariate support vector machine (SVM)‐based method was used to identify active voxels, and compared to a widely used general linear model (GLM)‐based method at the group level. The reproducibility study was performed using multisession fMRI data acquired from eight healthy adults over 1.5 years' period of time. Three regions‐of‐interest (ROI) related to a motor task were defined based upon which the long‐term reproducibility were examined. Experimental results indicate that different spatial smoothing techniques may lead to different reproducibility measures, and the wavelet‐based spatial smoothing and SVM‐based activation detection is a good combination for reproducibility studies. On the basis of the ROIs and multiple numerical criteria, we observed a moderate to substantial within‐subject long‐term reproducibility. A reasonable long‐term reproducibility was also observed from the inter‐subject study. It was found that the short‐term reproducibility is usually higher than the long‐term reproducibility. Furthermore, the results indicate that brain regions with high contrast‐to‐noise ratio do not necessarily exhibit high reproducibility. These findings may provide supportive information for optimal design/implementation of fMRI studies and data interpretation.     

Ideal observer analysis for task normalization of pattern classifier performance applied to EEG and fMRI data

The application of multivariate techniques to neuroimaging and electrophysiological data has greatly enhanced the ability to detect where, when, and how functional neural information is processed during a variety of behavioral tasks. With the extension to single-trial analysis, neuroscientists are able to relate brain states to perceptual, cognitive, and motor processes. Using pattern classification methods, the neuroscientist can extract neural performance measures in a manner analogous to human behavioral performance, allowing for a consistent information content metric across measurement modalities. However, as with behavioral psychophysical performance, pattern classifier performances are a product of both the task-relevant information inherent in the brain and in the task/stimuli. Here, we argue for the use of an ideal observer framework with which the researcher can effectively normalize the observed neural performance given the task's inherent objective difficulty. We use data from a face versus car discrimination task and compare classifier performance applied to electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data with corresponding human behavior through the absolute and relative efficiency metrics. We show that confounding variables that can lead to erroneous interpretations of information content can be accounted for through comparisons to an ideal observer, allowing for more confident interpretation of the neural mechanisms involved in the task of interest. Finally, we discuss limitations of interpretation due to the transduction of indirect measures of neural activity, underlying assumptions in the optimality of the pattern classifiers, and dependence of efficiency results on signal contrast.     

EEG‐based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.     

拉车作业之肌肉疲劳分析与预测

目的本研究的第一个目的是以肌力下降、耐受时间及主观评价来比较在不同负荷水平下拉车作业造成的肌肉疲劳水平;第二个目的是建立数学模型来量化拉车作业造成肌肉疲劳的程度;最终目的为提供拉车作业工作设计的提供依据,以降低劳动者肌肉骨骼伤害的风险。方法通过设计模拟手拉叉车实验,测量两种负荷下被试的实验前后拉力值、持续施力的耐受时间以及身体疲劳主观评价的数据,进行肌肉疲劳分析。结果实验数据显示拉车作业产生肌肉疲劳,性别和负荷对耐受时间、拉力下降速率产生显着影响;性别也显著影响被试对疲劳的主观评价,身体质量指数是影响耐受时间的显著因子。结论拉车作业中肌肉疲劳会导致拉力显著下降;性别是影响肌肉疲劳的重要因子,女性被试比男性被试更易疲劳;根据预测函数模型计算的男、女被试的疲劳速率k值分别为0.071、0.099。     

Tiny Signals from the Human Brain: Acquisition and Processing of Biomagnetic Fields in Magnetoencephalography

Low-temperature superconductivity plays an important role in some specific biomedical applications, and, in particular, in non-invasive imaging methods of human brain activity. Superconducting magnets are indispensable for functional magnetic resonance imaging (fMRI) which allows functional imaging of the brain with high spatial but poor temporal resolution. Superconducting quantum interference devices (SQUIDs) are the most sensitive magnetic field detectors. Up to a few hundreds of SQUIDs are nowdays used in modern whole-head magnetoencephalography (MEG) systems. They allow tracking brain activation with a superior temporal resolution of milliseconds, which is a quintessential condition for the monitoring of brain dynamics and the understanding of information processing in the human brain. We introduce the prerequisites of MEG data acquisition and briefly review two established methods of biomagnetic signal processing: The concept of signal averaging, and the subsequent source identification as a solution of the biomagnetic inverse problem. Beside these standard techniques, we discuss advanced methods for signal processing in MEG, which take into account the frequency content of the recorded signal. We briefly refer to the prospects of Fourier analysis and wavelet transform in MEG data analysis, and suggest matching pursuit as a promising tool for signal decomposition and reconstruction with high resolution in time-frequency plane.     

Source extraction from functional magnetic resonance imaging data using ICA based on fourth-order and exponential contrast functions

Independent component analysis (ICA) is one of the well-known statistical techniques used for blind source separation. It is also used for the extraction of sources from functional magnetic resonance imaging (fMRI) data. Benchmark for different ICA algorithms is speed and accuracy. In this article, we will be focusing on two simple contrast functions along with matrix-based updating rules. Fixed-point iteration is used for optimization of the contrast functions. Application of matrix-based weight updating makes the process converge rapidly. Validity of the algorithms is tested by comparing the speed and accuracy on simulated and actual fMRI data with other conventional ICA approaches.     

Evolution monitoring for innovation sources using patent cluster analysis

According to the increasing importance of advanced technologies for economy growth and the incremental complexity of research and development management, a novel methodology is proposed in this paper to monitor the evolution trace of innovation sources. This approach focuses on the knowledge-transfer among technologies using patent cluster analysis. More specifically, a citation network model, consisting of patents in “Coherent Light Generators” classification, is established with the data collected from the United States Patent and Trademark Office. In addition, dynamical topological structure is investigated to probe into the overview properties and identify key milestones for the expanding citation network from 1976 to 2014. Next, a novel framework for patent clustering is developed to find out knowledge chunks of which internal knowledge-flows are dense while cut edges are sparse. Community detection algorithms are compared with different assessment indices based on citation network and the selected solution is improved using optimization objectives of cluster analysis. Then, the dynamical structure of the detected knowledge chunks is investigated and the evolution of innovation sources, identified by k-core decomposition, is monitored to unveil the technology development trace. Finally, analysis results are discussed and related conclusions are summarized. This article improves approaches for patent cluster analysis and develops a new follow-up investigation methodology for detected knowledge chunks. It is discovered there are not only scale increases, but also the integration for knowledge chunks during the focal period. Identifying the knowledge chunks which obtain rapid growth in both cluster scale and innovation source is useful to detect technology development opportunities.     

基于功能磁共振成像方法的单相抑郁症情绪加工特点研究

运用功能磁共振成像(fMRI)技术,采用适合中国人情绪加工特点的表情图片(愉快、悲伤和中性表情)作为刺激材料,选取15名严格入组的未服药单相抑郁症女性患者与15名条件匹配的正常志愿者,对其情绪加工的大脑活动进行了对比研究。结果发现,与中性表情相比,在加工愉快表情时,抑郁症患者激活了右侧前额叶,而正常对照组激活的是左侧前额叶;在悲伤表情条件下,抑郁症患者激活了双侧颞下回,而正常对照组激活的是双侧前额叶。结果还发现,与正常对照组相比,抑郁症患者在双侧额下回三角区、右侧眶额下回、右侧颞下回等脑区的激活范围和强度均有所减少,尤其在加工悲伤表情时表现得更明显。该结果表明,单相抑郁症病人不仅在情绪加工相关脑区的功能上出现异常,而且加工情绪的脑区也可能出现了转移。     

Optimal data acquisition in fMRI using prolate spheroidal wave functions

Functional magnetic resonance imaging (fMRI) is a technique that can be used to noninvasively study mental activity in a persons brain. fMRI has the potential to answer many interesting questions regarding the way the brain functions. Unfortunately, the drawback to fMRI studies, as they are traditionally performed, is that the temporal resolution is too low to effectively answer questions regarding what happens in an active region of the brain immediately following stimulation. Shepp and Zhang ( 2000 ) introduced a new method that could potentially lead to a significant increase in temporal resolution. Their method suggests a way to improve the time resolution in fMRI studies by sampling only a fraction of the points needed to recreate a full image. Instead of full image reconstruction, an optimal prolate spheroidal wave function filter is used to obtain a measurement over the total activity in a predefined region in the brain, B , at successive time points. The sampling region and filter are chosen in order to minimize the energy loss over the region of interest (ROI). The region they suggested to sample was chosen heuristically and corresponds to the scaled polar set of B . It was shown to be near optimal through extensive computer searches. In this article the optimal sampling region is found for the case when the ROI is circular or spherical and the sampling size is small. Based on this result, a new heuristic is introduced for other ROIs, which improves upon the results obtained using the polar set of B . © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13, 126–132, 2003; Published online in Wiley Inter‐Science (www.interscience.wiley.com). DOI 10.1002/ima.10051     

参与中文配对词语联想学习记忆的脑区和神经机制——脑功能磁共振的研究

通过脑功能磁共振技术,研究健康人参与语言的词语配对联想学习记忆任务的脑区和神经机制。对16名右利手健康志愿者进行一项词语配对联想学习记忆任务作业的同时,进行脑功能磁共振扫描。实验采用组块设计,实验任务（包括记忆编码相和记忆提取相）与对照任务（共两个相）交替进行;数据采用SPM99软件进行数据分析和脑功能区定位。结果表明：左侧额叶,特别是左侧额叶的额中下回和枕叶的18,19区在词语联想学习记忆的编码阶段中起重要作用;而左侧顶上小叶、缘上回和角回则在进行记忆提取阶段起重要作用;左侧纹状体边缘区参与了人脑词语联想学习记忆作业的编码阶段。揭示了人大脑完成语言联想学习记忆任务时,除额、顶、枕和颞叶的皮层结构参与外,还新发现有皮层下结构如纹状体参与了词语联想学习记忆。在配对词语的编码和提取阶段,激活的脑区有所变化,显示了这两个语言阶段的神经活动变化机制。     

Combined Analysis of Overlapping Stratified Random Sample and Simple Random Sample Data

This article provides a methodology to combine overlapping stratified random samples and a simple random sample to estimate subpopulation proportions. That is, all the available data can be used to better estimate the quantities of interest. The methodology based on a Bayesian approach is illustrated with a synthetic data set. WinBUGS code that implements the combined analysis is also presented. Copyright © 2014 John Wiley & Sons, Ltd.     

Charge-transfer matrixes as a tool to desorb intact labile molecules by matrix-assisted laser desorption/ionization. Use of 2,7-dimethoxynaphthalene in the ionization of polymetallic porphyrins

2,7-Dimethoxynaphthalene (DMN) is proposed as matrix to investigate the structure of polymetallic porphyrins through matrix-assisted laser desorption/ionization tandem time-of-flight experiments. The peculiarity of DMN is represented by the formation of molecular radical cations and of some diagnostic fragments only. The traditional matrixes do not afford the expected molecular species. The experiments have been performed on extremely labile species such as zinc porphyirinate complexes with aluminum and gallium quinolinate to prove the softness of the methodology.     

Voxel selection framework based on meta-heuristic search and mutual information for brain decoding

Visual stimulus decoding is an increasingly important challenge in neuroscience. The goal is to classify the activity patterns from the human brain; during the sighting of visual objects. One of the crucial problems in the brain decoder is the selecting informative voxels. We propose a meta-heuristic voxel selection framework for brain decoding. It is composed of four phases: preprocessing of fMRI data; filtering insignificant voxels; postprocessing; and meta-heuristics selection. The main contribution is benefiting a meta-heuristics search algorithm to guide a wrapper voxel selection. The main criterion to nominate a voxel is based on its mutual information with the provided stimulus label. The results show impressive accuracy rates which are 90.66 ± 3.66 and 91.61 ± 8.24 for DS105 and DS107, respectively. This outperforms the most of existing brain decoders in similar validation conditions. The experimental results are very encouraging which can be successfully used in the brain-computer interface.     

An engineering design knowledge reuse methodology using process modelling

This paper describes an approach for reusing engineering design knowledge. Many previous design knowledge reuse systems focus exclusively on geometrical data, which is often not applicable in early design stages. The proposed methodology provides an integrated design knowledge reuse framework, bringing together elements of best practice reuse, design rationale capture and knowledge-based support in a single coherent framework. Best practices are reused through the process model. Rationale is supported by product information, which is retrieved through links to design process tasks. Knowledge-based methods are supported by a common design data model, which serves as a single source of design data to support the design process. By using the design process as the basis for knowledge structuring and retrieval, it serves the dual purpose of design process capture and knowledge reuse: capturing and formalising the rationale that underpins the design process, and providing a framework through which design knowledge can be stored, retrieved and applied. The methodology has been tested with an industrial sponsor producing high vacuum pumps for the semiconductor industry.