Source cancellation profiles of electroencephalography and magnetoencephalography

Recorded electric potentials and magnetic fields due to cortical electrical activity have spatial spread even if their underlying brain sources are focal. Consequently, as a result of source cancellation, loss in signal amplitude and reduction in the effective signal-to-noise ratio can be expected when distributed sources are active simultaneously. Here we investigate the cancellation effects of EEG and MEG through the use of an anatomically correct forward model based on structural MRI acquired from 7 healthy adults. A boundary element model (BEM) with four compartments (brain, cerebrospinal fluid, skull and scalp) and highly accurate cortical meshes (~ 300,000 vertices) were generated. Distributed source activations were simulated using contiguous patches of active dipoles. To investigate cancellation effects in both EEG and MEG, quantitative indices were defined (source enhancement, cortical orientation disparity) and computed for varying values of the patch radius as well as for automatically parcellated gyri and sulci. Results were calculated for each cortical location, averaged over all subjects using a probabilistic atlas, and quantitatively compared between MEG and EEG. As expected, MEG sensors were found to be maximally sensitive to signals due to sources tangential to the scalp, and minimally sensitive to radial sources. Compared to EEG, however, MEG was found to be much more sensitive to signals generated antero-medially, notably in the anterior cingulate gyrus. Given that sources of activation cancel each other according to the orientation disparity of the cortex, this study provides useful methods and results for quantifying the effect of source orientation disparity upon source cancellation.     

Inferring task-related networks using independent component analysis in magnetoencephalography

A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we extend this approach in two ways. First, we systematically investigate optimisation of time-frequency windows for connectivity measurement. This is achieved by estimating the distribution of functional connectivity scores between nodes of known resting-state networks and contrasting it with a distribution of artefactual scores that are entirely due to spatial leakage caused by the inverse problem. We find that functional connectivity, both in the resting-state and during a cognitive task, is best estimated via correlations in the oscillatory envelope in the 8-20 Hz frequency range, temporally down-sampled with windows of 1-4s. Second, we combine ICA with the general linear model (GLM) to incorporate knowledge of task structure into our connectivity analysis. The combination of ICA with the GLM helps overcome problems of these techniques when used independently: namely, the interpretation and separation of interesting independent components from those that represent noise in ICA and the correction for multiple comparisons when applying the GLM. We demonstrate the approach on a 2-back working memory task and show that this novel analysis framework is able to elucidate the functional networks involved in the task beyond that which is achieved using the GLM alone. We find evidence of localised task-related activity in the area of the hippocampus, which is difficult to detect reliably using standard methods. Task-positive ICA, coupled with the GLM, has the potential to be a powerful tool in the analysis of MEG data.     

A review of the current evidence base for significant event analysis

Objective  To review the literature on the perceived benefits and disadvantages associated with significant event analysis (SEA) and identify reported barriers and facilitating factors.
Method  A comprehensive search of electronic databases and peer reviewed journals was conducted during June 2006. Studies which explored or measured perceptions or attitudes in relation to SEA or assessed its impact on health care quality were included.
Results  27 studies were identified with most undertaken in UK general practice. Perceived benefits include: improved communication, enhanced team-working and awareness of others' contributions. SEA has a strong emotional resonance which may lead to a greater commitment to change. Multiple but unverifiable changes in practice and improvements in service quality were reported through participation. Disadvantages include concerns about litigation, reprisal, embarrassment and confidentiality. The reliability of SEA is questioned because it lacks a robust, standard structured method. Evidence of its impact on health care is severely limited. Barriers include a lack of training, poor team dynamics, failings in facilitation and leadership, selective topic choice and associated emotional demands. Facilitating factors include: effective practice in meetings; protected meeting time; a structured methodical approach; and strong team dynamics and leadership.
Conclusion  A chasm exists between the high expectations for SEA and the lack of evidence of its impact. SEA may have some merit as a team-based educational tool. However, it may not be a reliable technique for investigating serious or complex safety issues in general practice. Policy makers need to be more explicit about the actual purpose of SEA.     

经颅多普勒与脑电图对成人与儿童血管性头痛特征分析

INTRODUCTION The headache of common nature as well as headachic epilepsy and hemicrania pianheadache often affects the children.Hemicrania is the result of mental and vascular disorder,which is examined with transcranial Doppler(TCD) and electroencephalography(EEG).In this study,we analyzed the characteristics of vascular headache of children compared with adults.     

A plan for achieving significant improvement in patient safety

Improvement in systems that ensure safety in the provision of care is a high priority to hospital administrators, clinicians, and patients. Research to determine the approaches and methods that will result in the most significant patient safety improvements is underway but more is needed. This article describes the process for improving patient safety adopted at one hospital. Results of these efforts demonstrate significant improvement in staff understanding of patient safety measures. Staff survey results are supported by improvement in clinical indicators. Recommendations for future action and implications for other hospitals are discussed.     

Molecular genetic analysis for the B subgroup revealing two novel alleles in the ABO gene

BACKGROUND: B_x is a very rare ABO blood group phenotype and the molecular mechanism underlying it still remains largely unknown. This study reports two novel B_x alleles in two Chinese individuals. STUDY DESIGN AND METHODS: Serologic investigations including serum transferase activity assay were performed with standard methods. DNA sequences of all seven exons and exon‐intron boundaries of ABO gene were analyzed using genomic DNA by polymerase chain reaction and direct DNA sequencing or sequencing after gene cloning. RESULTS: B_x phenotypes were diagnosed in these two individuals. DNA analysis revealed that the ABO gene of the two B_x individuals was heterozygous of O01/B alleles. Two novel heterozygous mutations 905A>G and 541T>C were identified, respectively, which resulted in the amino acid changes D302G and W181R in the B glycosyltransferases. The mutations were not found in 120 randomly selected samples. CONCLUSION: Amino acid substitutions resulted from novel mutations 905A>G and 541T>C on ABO gene change highly conserved regions of the enzyme and may reduce the activity of the glycosyltransferases, leading to the B_x phenotype.     

A novel node-level structure embedding and alignment representation of structural networks for brain disease analysis

Brain networks based on various neuroimaging technologies, such as diffusion tensor image (DTI) and functional magnetic resonance imaging (fMRI), have been widely applied to brain disease analysis. Currently, there are several node-level structural measures (e.g., local clustering coefficients and node degrees) for representing and analyzing brain networks since they usually can reflect the topological structure of brain regions. However, these measures typically describe specific types of structural information, ignoring important network properties (i.e., small structural changes) that could further improve the performance of brain network analysis. To overcome this problem, in this paper, we first define a novel node-level structure embedding and alignment (nSEA) representation to accurately characterize the node-level structural information of the brain network. Different from existing measures that characterize a specific type of structural properties with a single value, our proposed nSEA method can learn a vector representation for each node, thus contain richer structure information to capture small structural changes. Furthermore, we develop an nSEA representation based learning (nSEAL) framework for brain disease analysis. Specifically, we first perform structural embedding to calculate node vector representations for each brain network and then align vector representations of all brain networks into the common space for two group-level network analyses, including a statistical analysis and brain disease classifications. Experiment results on a real schizophrenia dataset demonstrate that our proposed method not only discover disease-related brain regions that could help to better understand the pathology of brain diseases, but also improve the classification performance of brain diseases, compared with state-of-the-art methods.     

A tutorial in connectome analysis: topological and spatial features of brain networks

High-throughput methods for yielding the set of connections in a neural system, the connectome, are now being developed. This tutorial describes ways to analyze the topological and spatial organizations of the connectome at the macroscopic level of connectivity between brain regions as well as the microscopic level of connectivity between neurons. We will describe topological features at three different levels: the local scale of individual nodes, the regional scale of sets of nodes, and the global scale of the complete set of nodes in a network. Such features can be used to characterize components of a network and to compare different networks, e.g. the connectome of patients and control subjects for clinical studies. At the global scale, different types of networks can be distinguished and we will describe Erd?s-Rényi random, scale-free, small-world, modular, and hierarchical archetypes of networks. Finally, the connectome also has a spatial organization and we describe methods for analyzing wiring lengths of neural systems. As an introduction for new researchers in the field of connectome analysis, we discuss the benefits and limitations of each analysis approach.     

A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity

This paper formulates a novel probabilistic graphical model for noisy stimulus-evoked MEG and EEG sensor data obtained in the presence of large background brain activity. The model describes the observed data in terms of unobserved evoked and background factors with additive sensor noise. We present an expectation maximization (EM) algorithm that estimates the model parameters from data. Using the model, the algorithm cleans the stimulus-evoked data by removing interference from background factors and noise artifacts and separates those data into contributions from independent factors. We demonstrate on real and simulated data that the algorithm outperforms benchmark methods for denoising and separation. We also show that the algorithm improves the performance of localization with beamforming algorithms.     

Differential analysis revealing APOC1 to be a diagnostic and prognostic marker for liver metastases of colorectal cancer

BACKGROUNDColorectal cancer (CRC) is one of the most malignant gastrointestinal cancers worldwide. The liver is the most important metastatic target organ, and liver metastasis is the leading cause of death in patients with CRC. Owing to the lack of sensitive biomarkers and unclear molecular mechanism, the occurrence of liver metastases cannot be predicted and the clinical outcomes are bad for liver metastases. Therefore, it is very important to identify the diagnostic or prognostic markers for liver metastases of CRC.AIMTo investigate the highly differentially expressed genes (HDEGs) and prognostic marker for liver metastases of CRC.METHODSData from three NCBI Gene Expression Omnibus (GEO) datasets were used to show HDEGs between liver metastases of CRC and tumour or normal samples. These significantly HDEGs of the three GEO datasets take the interactions. And these genes were screened through an online tool to explore the prognostic value. Then, TIMER and R package were utilized to investigate the immunity functions of the HDEGs and gene set enrichment analysis was used to explore their potential functions.RESULTSBased on the selection criteria, three CRC datasets for exploration ({"type":"entrez-geo","attrs":{"text":"GSE14297","term_id":"14297"}}GSE14297, {"type":"entrez-geo","attrs":{"text":"GSE41258","term_id":"41258"}}GSE41258, and {"type":"entrez-geo","attrs":{"text":"GSE49355","term_id":"49355"}}GSE49355) were chosen. Venn diagrams were used to show HDEGs common to the six groups and 47 HDEGs were obtained. The HDEGs were shown by using STRING and Cytoscape software. Based on the TCGA database, APOC1 showed significantly different expression between N2 and N0, and N2 and N1. And there was also a significant difference in expression between T2 and T4, and between T2 and T3. In 20 paired CRC and normal tissues, quantitative real-time polymerase chain reaction illustrated that the APOC1 mRNA was strongly upregulated in CRC tissues (P = 0.014). PrognoScan and GEPIA2 revealed the prognostic value of APOC1 for overall survival and disease-free survival in CRC (P < 0.05). TIMER showed that APOC1 has a close relationship with immune infiltration (P < 0.05).CONCLUSIONAPOC1 is a biomarker that is associated with both the diagnosis and prognosis of liver metastases of CRC.     

脑电图对大学生慢性疲劳的诊断效能评价

目的了解大学生慢性疲劳的脑电图特征,评价脑电图对慢性疲劳的诊断效能。方法采用目的抽样（立意抽样）结合自愿原则对山西医科大学临床本硕班四、五年级学生共200人进行慢性疲劳状况调查及脑电图测定。疲劳的评定参考Chalder研制的疲劳量表（ FS）。脑电图测定采用江苏南京韦思公司生产的便携式脑电图仪,按国际10/20系统安置电极,闭眼状态下记录无伪迹脑电波2～3 min,并转化为脑电地形图（BEAM）进行量化分析。采用Epidata 3.0进行数据录入,经核对无误后转入SPSS 11.5进行统计分析。采用χ2检验、t 检验进行单因素分析,线性回归进行多因素分析, ROC 曲线进行诊断效能评价。结果200名大学生中男生66人（33.0％）、女生134人（67.0％）,其中113人（56.5％）没有慢性疲劳症状,87人（43.5％）为慢性疲劳患者。男性慢性疲劳患病率为51.5％,女性慢性疲劳患病率为39.6％,无统计学差异（χ2＝2.575,P＝0.109）。大学生慢性疲劳患者脑电图表现为左侧大脑半球α1功率较非慢性疲劳者低,左前颞和左前额δ功率高（ P＜0.05）。慢性疲劳与α1功率的ROC曲线分析显示曲线下面积为0.602,有统计学意义,但没有较高的灵敏度和特异度。左侧大脑半球α1功率为15.0诊断慢性疲劳,灵敏度为0.63,特异度为0.53。结论脑电图左侧大脑半球α1功率降低可作为慢性疲劳的诊断参考指标之一,但灵敏度和特异度不高。     

Multimodality evoked potentials and electroencephalography in severe coma cases

In a neurosurgical intensive care unit 26 patients with unconciousness and unresponsiveness were investigated by means of multimodality evoked potentials and electroencephalography in order to obtain information on the functional state of the nervous system. Multimodality evoked potential techniques allowed us to differentiate patients with EEG alterations due to drug treatment from those without therapy. The functional state and prognosis can be better evaluated by means of evoked potential techniques. Patients with raised intracranial pressure seem to undergo some characteristic alterations in PEP and far field potential derivations.     

Integrating uncertainty in deep neural networks for MRI based stroke analysis

At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the model's uncertainty. However, a quantification of the reliability of automated image analysis is essential, in particular in medicine when physicians rely on the results for making critical treatment decisions. In this work, we provide an entire framework to diagnose ischemic stroke patients incorporating Bayesian uncertainty into the analysis procedure. We present a Bayesian Convolutional Neural Network (CNN) yielding a probability for a stroke lesion on 2D Magnetic Resonance (MR) images with corresponding uncertainty information about the reliability of the prediction. For patient-level diagnoses, different aggregation methods are proposed and evaluated, which combine the individual image-level predictions. Those methods take advantage of the uncertainty in the image predictions and report model uncertainty at the patient-level. In a cohort of 511 patients, our Bayesian CNN achieved an accuracy of 95.33% at the image-level representing a significant improvement of 2% over a non-Bayesian counterpart. The best patient aggregation method yielded 95.89% of accuracy. Integrating uncertainty information about image predictions in aggregation models resulted in higher uncertainty measures to false patient classifications, which enabled to filter critical patient diagnoses that are supposed to be closer examined by a medical doctor. We therefore recommend using Bayesian approaches not only for improved image-level prediction and uncertainty estimation but also for the detection of uncertain aggregations at the patient-level.     

Hierarchical microimaging for multiscale analysis of large vascular networks

There is a wide range of diseases and normal physiological processes that are associated with alterations of the vascular system in organs. Ex vivo imaging of large vascular networks became feasible with recent developments in microcomputed tomography (microCT). Current methods permit to visualize only limited numbers of physically excised regions of interests (ROIs) from larger samples. We developed a method based on modified vascular corrosion casting (VCC), scanning electron microscopy (SEM), and desktop and synchrotron radiation microCT (SRmicroCT) technologies to image vasculature at increasing levels of resolution, also referred to as hierarchical imaging. This novel approach allows nondestructive 3D visualization and quantification of large microvascular networks, while retaining a precise anatomical context for ROIs scanned at very high resolution. Scans of entire mouse brain VCCs were performed at 16-microm resolution with a desktop microCT system. Custom-made navigation software with a ROI selection tool enabled the identification of anatomical brain structures and precise placement of multiple ROIs. These were then scanned at 1.4-microm voxel size using SRmicroCT and a local tomography setup. A framework was developed for fast sample positioning, precise selection of ROIs, and sequential high-throughput scanning of a large numbers of brain VCCs. Despite the use of local tomography, exceptional image quality was achieved with SRmicroCT. This method enables qualitative and quantitative assessment of vasculature at unprecedented resolution and volume with relatively high throughput, opening new possibilities to study vessel architecture and vascular alterations in models of disease.     

Correlation of sensorimotor activation with functional magnetic resonance imaging and magnetoencephalography in presurgical functional imaging: a spatial analysis.

In this study we investigated the spatial heterotopy of MEG and fMRI localizations after sensory and motor stimulation tasks. Both methods are frequently used to study the topology of the primary and secondary motor cortex, as well as a tool for presurgical brain mapping. fMRI was performed with a 1.5T MR system, using echo-planar imaging with a motor and a sensory task. Somatosensory and motor evoked fields were recorded with a biomagnetometer. fMRI activation was determined with a cross-correlation analysis. MEG source localization was performed with a single equivalent current dipole model and a current density localization approach. Distances between MEG and fMRI activation sites were measured within the same anatomical 3-D-MR image set. The central region could be identified by MEG and fMRI in 33 of 34 cases. However, MEG and fMRI localization results showed significantly different activation sites for the motor and sensory task with a distance of 10 and 15 mm, respectively. This reflects the different neurophysiological mechanisms: direct neuronal current flow (MEG) and secondary changes in cerebral blood flow and oxygenation level of activated versus non activated brain structures (fMRI). The result of our study has clinical implications when MEG and fMRI localizations are used for pre- and intraoperative brain mapping. Although both modalities are useful for the estimation of the motor cortex, a single modality may err in the exact topographical labeling of the motor cortex. In some unclear cases a combination of both methods should be used in order to avoid neurological deficits.