Performance comparison of independent component analysis algorithms for fetal cardiac signal reconstruction: a study on synthetic fMCG data

NASA Astrophysics Data System (ADS)

Mantini, D.; Hild, K. E., II; Alleva, G.; Comani, S.

2006-02-01

Independent component analysis (ICA) algorithms have been successfully used for signal extraction tasks in the field of biomedical signal processing. We studied the performances of six algorithms (FastICA, CubICA, JADE, Infomax, TDSEP and MRMI-SIG) for fetal magnetocardiography (fMCG). Synthetic datasets were used to check the quality of the separated components against the original traces. Real fMCG recordings were simulated with linear combinations of typical fMCG source signals: maternal and fetal cardiac activity, ambient noise, maternal respiration, sensor spikes and thermal noise. Clusters of different dimensions (19, 36 and 55 sensors) were prepared to represent different MCG systems. Two types of signal-to-interference ratios (SIR) were measured. The first involves averaging over all estimated components and the second is based solely on the fetal trace. The computation time to reach a minimum of 20 dB SIR was measured for all six algorithms. No significant dependency on gestational age or cluster dimension was observed. Infomax performed poorly when a sub-Gaussian source was included; TDSEP and MRMI-SIG were sensitive to additive noise, whereas FastICA, CubICA and JADE showed the best performances. Of all six methods considered, FastICA had the best overall performance in terms of both separation quality and computation times.

An evaluation of independent component analyses with an application to resting-state fMRI

PubMed Central

Matteson, David S.; Ruppert, David; Eloyan, Ani; Caffo, Brian S.

2013-01-01

Summary We examine differences between independent component analyses (ICAs) arising from different as-sumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA can be viewed as a generalization of principal component analysis (PCA) that takes into account higher-order cross-correlations. Whereas the PCA solution is unique, there are many ICA methods–whose solutions may differ. Infomax, FastICA, and JADE are commonly applied to fMRI studies, with FastICA being arguably the most popular. Hastie and Tibshirani (2003) demonstrated that ProDenICA outperformed FastICA in simulations with two components. We introduce the application of ProDenICA to simulations with more components and to fMRI data. ProDenICA was more accurate in simulations, and we identified differences between biologically meaningful ICs from ProDenICA versus other methods in the fMRI analysis. ICA methods require nonconvex optimization, yet current practices do not recognize the importance of, nor adequately address sensitivity to, initial values. We found that local optima led to dramatically different estimates in both simulations and group ICA of fMRI, and we provide evidence that the global optimum from ProDenICA is the best estimate. We applied a modification of the Hungarian (Kuhn-Munkres) algorithm to match ICs from multiple estimates, thereby gaining novel insights into how brain networks vary in their sensitivity to initial values and ICA method. PMID:24350655

Parallel group independent component analysis for massive fMRI data sets.

PubMed

Chen, Shaojie; Huang, Lei; Qiu, Huitong; Nebel, Mary Beth; Mostofsky, Stewart H; Pekar, James J; Lindquist, Martin A; Eloyan, Ani; Caffo, Brian S

2017-01-01

Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.

Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

PubMed

Leibig, Christian; Wachtler, Thomas; Zeck, Günther

2016-09-15

Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons. Here we present a spike sorting algorithm based on convolutive ICA (cICA) to retrieve a larger number of accurately sorted neurons than with instantaneous ICA while accounting for signal overlaps. Spike sorting was applied to datasets with varying signal-to-noise ratios (SNR: 3-12) and 27% spike overlaps, sampled at either 11.5 or 23kHz on 4365 electrodes. We demonstrate how the instantaneity assumption in ICA-based algorithms has to be relaxed in order to improve the spike sorting performance for high-density microelectrode array recordings. Reformulating the convolutive mixture as an instantaneous mixture by modeling several delayed samples jointly is necessary to increase signal-to-noise ratio. Our results emphasize that different cICA algorithms are not equivalent. Spike sorting performance was assessed with ground-truth data generated from experimentally derived templates. The presented spike sorter was able to extract ≈90% of the true spike trains with an error rate below 2%. It was superior to two alternative (c)ICA methods (≈80% accurately sorted neurons) and comparable to a supervised sorting. Our new algorithm represents a fast solution to overcome the current bottleneck in spike sorting of large datasets generated by simultaneous recording with thousands of electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

NASA Astrophysics Data System (ADS)

Mishra, Puneet; Singla, Sunil Kumar

2013-01-01

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

A general probabilistic model for group independent component analysis and its estimation methods

PubMed Central

Guo, Ying

2012-01-01

SUMMARY Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multi-subject spatio-temporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood method is used for estimating this general group ICA model. We propose two EM algorithms to obtain the ML estimates. The first method is an exact EM algorithm which provides an exact E-step and an explicit noniterative M-step. The second method is an variational approximation EM algorithm which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods. PMID:21517789

[Extraction of evoked related potentials by using the combination of independent component analysis and wavelet analysis].

PubMed

Zou, Ling; Chen, Shuyue; Sun, Yuqiang; Ma, Zhenghua

2010-08-01

In this paper we present a new method of combining Independent Component Analysis (ICA) and Wavelet de-noising algorithm to extract Evoked Related Potentials (ERPs). First, the extended Infomax-ICA algorithm is used to analyze EEG signals and obtain the independent components (Ics); Then, the Wave Shrink (WS) method is applied to the demixed Ics as an intermediate step; the EEG data were rebuilt by using the inverse ICA based on the new Ics; the ERPs were extracted by using de-noised EEG data after being averaged several trials. The experimental results showed that the combined method and ICA method could remove eye artifacts and muscle artifacts mixed in the ERPs, while the combined method could retain the brain neural activity mixed in the noise Ics and could extract the weak ERPs efficiently from strong background artifacts.

FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling.

PubMed

Kim, Chang-Min; Park, Hyung-Min; Kim, Taesu; Choi, Yoon-Kyung; Lee, Soo-Young

2003-01-01

An field programmable gate array (FPGA) implementation of independent component analysis (ICA) algorithm is reported for blind signal separation (BSS) and adaptive noise canceling (ANC) in real time. In order to provide enormous computing power for ICA-based algorithms with multipath reverberation, a special digital processor is designed and implemented in FPGA. The chip design fully utilizes modular concept and several chips may be put together for complex applications with a large number of noise sources. Experimental results with a fabricated test board are reported for ANC only, BSS only, and simultaneous ANC/BSS, which demonstrates successful speech enhancement in real environments in real time.

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

PubMed Central

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method. PMID:18288259

Online artifact removal for brain-computer interfaces using support vector machines and blind source separation.

PubMed

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.

Applications of independent component analysis in SAR images

NASA Astrophysics Data System (ADS)

Huang, Shiqi; Cai, Xinhua; Hui, Weihua; Xu, Ping

2009-07-01

The detection of faint, small and hidden targets in synthetic aperture radar (SAR) image is still an issue for automatic target recognition (ATR) system. How to effectively separate these targets from the complex background is the aim of this paper. Independent component analysis (ICA) theory can enhance SAR image targets and improve signal clutter ratio (SCR), which benefits to detect and recognize faint targets. Therefore, this paper proposes a new SAR image target detection algorithm based on ICA. In experimental process, the fast ICA (FICA) algorithm is utilized. Finally, some real SAR image data is used to test the method. The experimental results verify that the algorithm is feasible, and it can improve the SCR of SAR image and increase the detection rate for the faint small targets.

Application of neural networks with novel independent component analysis methodologies to a Prussian blue modified glassy carbon electrode array.

PubMed

Wang, Liang; Yang, Die; Fang, Cheng; Chen, Zuliang; Lesniewski, Peter J; Mallavarapu, Megharaj; Naidu, Ravendra

2015-01-01

Sodium potassium absorption ratio (SPAR) is an important measure of agricultural water quality, wherein four exchangeable cations (K(+), Na(+), Ca(2+) and Mg(2+)) should be simultaneously determined. An ISE-array is suitable for this application because its simplicity, rapid response characteristics and lower cost. However, cross-interferences caused by the poor selectivity of ISEs need to be overcome using multivariate chemometric methods. In this paper, a solid contact ISE array, based on a Prussian blue modified glassy carbon electrode (PB-GCE), was applied with a novel chemometric strategy. One of the most popular independent component analysis (ICA) methods, the fast fixed-point algorithm for ICA (fastICA), was implemented by the genetic algorithm (geneticICA) to avoid the local maxima problem commonly observed with fastICA. This geneticICA can be implemented as a data preprocessing method to improve the prediction accuracy of the Back-propagation neural network (BPNN). The ISE array system was validated using 20 real irrigation water samples from South Australia, and acceptable prediction accuracies were obtained. Copyright © 2014 Elsevier B.V. All rights reserved.

Task-evoked brain functional magnetic susceptibility mapping by independent component analysis (χICA).

PubMed

Chen, Zikuan; Calhoun, Vince D

2016-03-01

Conventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (χ) functional states. Upon the reconstructed χ dataspace, we propose an ICA-based brain functional χ mapping method (χICA) to extract task-evoked brain functional map. A complex division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase changes (relative to an OFF-state snapshot). A computed inverse MRI (CIMRI) model is used to reconstruct a 4D brain χ response dataset. χICA is implemented by applying a spatial InfoMax ICA algorithm to the reconstructed 4D χ dataspace. With finger-tapping experiments on a 7T system, the χICA-extracted χ-depicted functional map is similar to the SPM-inferred functional χ map by a spatial correlation of 0.67 ± 0.05. In comparison, the AICA-extracted magnitude-depicted map is correlated with the SPM magnitude map by 0.81 ± 0.05. The understanding of the inferiority of χICA to AICA for task-evoked functional map is an ongoing research topic. For task-evoked brain functional mapping, we compare the data-driven ICA method with the task-correlated SPM method. In particular, we compare χICA with AICA for extracting task-correlated timecourses and functional maps. χICA can extract a χ-depicted task-evoked brain functional map from a reconstructed χ dataspace without the knowledge about brain hemodynamic responses. The χICA-extracted brain functional χ map reveals a bidirectional BOLD response pattern that is unavailable (or different) from AICA. Copyright © 2016 Elsevier B.V. All rights reserved.

Mitigation of crosstalk based on CSO-ICA in free space orbital angular momentum multiplexing systems

NASA Astrophysics Data System (ADS)

Xing, Dengke; Liu, Jianfei; Zeng, Xiangye; Lu, Jia; Yi, Ziyao

2018-09-01

Orbital angular momentum (OAM) multiplexing has caused a lot of concerns and researches in recent years because of its great spectral efficiency and many OAM systems in free space channel have been demonstrated. However, due to the existence of atmospheric turbulence, the power of OAM beams will diffuse to beams with neighboring topological charges and inter-mode crosstalk will emerge in these systems, resulting in the system nonavailability in severe cases. In this paper, we introduced independent component analysis (ICA), which is known as a popular method of signal separation, to mitigate inter-mode crosstalk effects; furthermore, aiming at the shortcomings of traditional ICA algorithm's fixed iteration speed, we proposed a joint algorithm, CSO-ICA, to improve the process of solving the separation matrix by taking advantage of fast convergence rate and high convergence precision of chicken swarm algorithm (CSO). We can get the optimal separation matrix by adjusting the step size according to the last iteration in CSO-ICA. Simulation results indicate that the proposed algorithm has a good performance in inter-mode crosstalk mitigation and the optical signal-to-noise ratio (OSNR) requirement of received signals (OAM+2, OAM+4, OAM+6, OAM+8) is reduced about 3.2 dB at bit error ratio (BER) of 3.8 × 10-3. Meanwhile, the convergence speed is much faster than the traditional ICA algorithm by improving about an order of iteration times.

CO Component Estimation Based on the Independent Component Analysis

NASA Astrophysics Data System (ADS)

Ichiki, Kiyotomo; Kaji, Ryohei; Yamamoto, Hiroaki; Takeuchi, Tsutomu T.; Fukui, Yasuo

2014-01-01

Fast Independent Component Analysis (FastICA) is a component separation algorithm based on the levels of non-Gaussianity. Here we apply FastICA to the component separation problem of the microwave background, including carbon monoxide (CO) line emissions that are found to contaminate the PLANCK High Frequency Instrument (HFI) data. Specifically, we prepare 100 GHz, 143 GHz, and 217 GHz mock microwave sky maps, which include galactic thermal dust, NANTEN CO line, and the cosmic microwave background (CMB) emissions, and then estimate the independent components based on the kurtosis. We find that FastICA can successfully estimate the CO component as the first independent component in our deflection algorithm because its distribution has the largest degree of non-Gaussianity among the components. Thus, FastICA can be a promising technique to extract CO-like components without prior assumptions about their distributions and frequency dependences.

CO component estimation based on the independent component analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ichiki, Kiyotomo; Kaji, Ryohei; Yamamoto, Hiroaki

2014-01-01

Fast Independent Component Analysis (FastICA) is a component separation algorithm based on the levels of non-Gaussianity. Here we apply FastICA to the component separation problem of the microwave background, including carbon monoxide (CO) line emissions that are found to contaminate the PLANCK High Frequency Instrument (HFI) data. Specifically, we prepare 100 GHz, 143 GHz, and 217 GHz mock microwave sky maps, which include galactic thermal dust, NANTEN CO line, and the cosmic microwave background (CMB) emissions, and then estimate the independent components based on the kurtosis. We find that FastICA can successfully estimate the CO component as the first independentmore » component in our deflection algorithm because its distribution has the largest degree of non-Gaussianity among the components. Thus, FastICA can be a promising technique to extract CO-like components without prior assumptions about their distributions and frequency dependences.« less

Automatic building detection based on Purposive FastICA (PFICA) algorithm using monocular high resolution Google Earth images

NASA Astrophysics Data System (ADS)

Ghaffarian, Saman; Ghaffarian, Salar

2014-11-01

This paper proposes an improved FastICA model named as Purposive FastICA (PFICA) with initializing by a simple color space transformation and a novel masking approach to automatically detect buildings from high resolution Google Earth imagery. ICA and FastICA algorithms are defined as Blind Source Separation (BSS) techniques for unmixing source signals using the reference data sets. In order to overcome the limitations of the ICA and FastICA algorithms and make them purposeful, we developed a novel method involving three main steps: 1-Improving the FastICA algorithm using Moore-Penrose pseudo inverse matrix model, 2-Automated seeding of the PFICA algorithm based on LUV color space and proposed simple rules to split image into three regions; shadow + vegetation, baresoil + roads and buildings, respectively, 3-Masking out the final building detection results from PFICA outputs utilizing the K-means clustering algorithm with two number of clusters and conducting simple morphological operations to remove noises. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.6% and 85.5% overall pixel-based and object-based precision performances, respectively.

Development, validation, and comparison of ICA-based gradient artifact reduction algorithms for simultaneous EEG-spiral in/out and echo-planar fMRI recordings

PubMed Central

Ryali, S; Glover, GH; Chang, C; Menon, V

2009-01-01

EEG data acquired in an MRI scanner are heavily contaminated by gradient artifacts that can significantly compromise signal quality. We developed two new methods based on Independent Component Analysis (ICA) for reducing gradient artifacts from spiral in-out and echo-planar pulse sequences at 3T, and compared our algorithms with four other commonly used methods: average artifact subtraction (Allen et al. 2000), principal component analysis (Niazy et al. 2005), Taylor series (Wan et al. 2006) and a conventional temporal ICA algorithm. Models of gradient artifacts were derived from simulations as well as a water phantom and performance of each method was evaluated on datasets constructed using visual event-related potentials (ERPs) as well as resting EEG. Our new methods recovered ERPs and resting EEG below the beta band (< 12.5 Hz) with high signal-to-noise ratio (SNR > 4). Our algorithms outperformed all of these methods on resting EEG in the theta- and alpha-bands (SNR > 4); however, for all methods, signal recovery was modest (SNR ~ 1) in the beta-band and poor (SNR < 0.3) in the gamma-band and above. We found that the conventional ICA algorithm performed poorly with uniformly low SNR (< 0.1). Taken together, our new ICA-based methods offer a more robust technique for gradient artifact reduction when scanning at 3T using spiral in-out and echo-planar pulse sequences. We provide new insights into the strengths and weaknesses of each method using a unified subspace framework. PMID:19580873

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

PubMed

Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E

2017-04-15

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (p<0.001) for predicting the task being performed within each scan using artifact-cleaned components. The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy compared to the ICA and sparse coding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p<0.001). Lower classification accuracy occurred when the extracted spatial maps contained more CSF regions (p<0.001). The success of sparse coding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.

Applying different independent component analysis algorithms and support vector regression for IT chain store sales forecasting.

PubMed

Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting

PubMed Central

Dai, Wensheng

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740

Multiple Component Event-Related Potential (mcERP) Estimation

NASA Technical Reports Server (NTRS)

Knuth, K. H.; Clanton, S. T.; Shah, A. S.; Truccolo, W. A.; Ding, M.; Bressler, S. L.; Trejo, L. J.; Schroeder, C. E.; Clancy, Daniel (Technical Monitor)

2002-01-01

We show how model-based estimation of the neural sources responsible for transient neuroelectric signals can be improved by the analysis of single trial data. Previously, we showed that a multiple component event-related potential (mcERP) algorithm can extract the responses of individual sources from recordings of a mixture of multiple, possibly interacting, neural ensembles. McERP also estimated single-trial amplitudes and onset latencies, thus allowing more accurate estimation of ongoing neural activity during an experimental trial. The mcERP algorithm is related to informax independent component analysis (ICA); however, the underlying signal model is more physiologically realistic in that a component is modeled as a stereotypic waveshape varying both in amplitude and onset latency from trial to trial. The result is a model that reflects quantities of interest to the neuroscientist. Here we demonstrate that the mcERP algorithm provides more accurate results than more traditional methods such as factor analysis and the more recent ICA. Whereas factor analysis assumes the sources are orthogonal and ICA assumes the sources are statistically independent, the mcERP algorithm makes no such assumptions thus allowing investigators to examine interactions among components by estimating the properties of single-trial responses.

[Object Separation from Medical X-Ray Images Based on ICA].

PubMed

Li, Yan; Yu, Chun-yu; Miao, Ya-jian; Fei, Bin; Zhuang, Feng-yun

2015-03-01

X-ray medical image can examine diseased tissue of patients and has important reference value for medical diagnosis. With the problems that traditional X-ray images have noise, poor level sense and blocked aliasing organs, this paper proposes a method for the introduction of multi-spectrum X-ray imaging and independent component analysis (ICA) algorithm to separate the target object. Firstly image de-noising preprocessing ensures the accuracy of target extraction based on independent component analysis and sparse code shrinkage. Then according to the main proportion of organ in the images, aliasing thickness matrix of each pixel was isolated. Finally independent component analysis obtains convergence matrix to reconstruct the target object with blind separation theory. In the ICA algorithm, it found that when the number is more than 40, the target objects separate successfully with the aid of subjective evaluation standard. And when the amplitudes of the scale are in the [25, 45] interval, the target images have high contrast and less distortion. The three-dimensional figure of Peak signal to noise ratio (PSNR) shows that the different convergence times and amplitudes have a greater influence on image quality. The contrast and edge information of experimental images achieve better effects with the convergence times 85 and amplitudes 35 in the ICA algorithm.

A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.

PubMed

Zhou, Bangyan; Wu, Xiaopei; Lv, Zhao; Zhang, Lei; Guo, Xiaojin

2016-01-01

Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The "high quality" training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system.

A unified framework for group independent component analysis for multi-subject fMRI data

PubMed Central

Guo, Ying; Pagnoni, Giuseppe

2008-01-01

Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT (Calhoun et al., 2001) and tensor PICA (Beckmann and Smith, 2005), make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation-Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio-temporal processes. We illustrate our group ICA method using data from an fMRI study that investigates changes in neural processing associated with the regular practice of Zen meditation. PMID:18650105

Classification of fMRI resting-state maps using machine learning techniques: A comparative study

NASA Astrophysics Data System (ADS)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA.

PubMed

Zhou, Weidong; Gotman, Jean

2004-01-01

In this study, the methods of wavelet threshold de-noising and independent component analysis (ICA) are introduced. ICA is a novel signal processing technique based on high order statistics, and is used to separate independent components from measurements. The extended ICA algorithm does not need to calculate the higher order statistics, converges fast, and can be used to separate subGaussian and superGaussian sources. A pre-whitening procedure is performed to de-correlate the mixed signals before extracting sources. The experimental results indicate the electromyogram (EMG) and electrocardiograph (ECG) artifacts in electroencephalograph (EEG) can be removed by a combination of wavelet threshold de-noising and ICA.

Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis.

PubMed

Zhang, Han; Zuo, Xi-Nian; Ma, Shuang-Ye; Zang, Yu-Feng; Milham, Michael P; Zhu, Chao-Zhe

2010-07-15

Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data reduction with principal component analysis (PCA: a standard preprocessing step of ICA decomposition) is difficult to achieve for a large dataset. To overcome this, TC-GICA employs multiple-stage PCA data reduction. Such multiple-stage PCA data reduction, however, leads to variable outputs due to different subject concatenation orders. Consequently, the ICA algorithm uses the variable multiple-stage PCA outputs and generates variable decompositions. In this study, a rigorous theoretical analysis was conducted to prove the existence of such variability. Simulated and real fMRI experiments were used to demonstrate the subject-order-induced variability of TC-GICA results using multiple PCA data reductions. To solve this problem, we propose a new subject order-independent group ICA (SOI-GICA). Both simulated and real fMRI data experiments demonstrated the high robustness and accuracy of the SOI-GICA results compared to those of traditional TC-GICA. Accordingly, we recommend SOI-GICA for group ICA-based fMRI studies, especially those with large data sets. Copyright 2010 Elsevier Inc. All rights reserved.

Independent EEG Sources Are Dipolar

PubMed Central

Delorme, Arnaud; Palmer, Jason; Onton, Julie; Oostenveld, Robert; Makeig, Scott

2012-01-01

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison). PMID:22355308

Ranking and averaging independent component analysis by reproducibility (RAICAR).

PubMed

Yang, Zhi; LaConte, Stephen; Weng, Xuchu; Hu, Xiaoping

2008-06-01

Independent component analysis (ICA) is a data-driven approach that has exhibited great utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations, however, do not provide the number and relative importance of the resulting components. In addition, ICA algorithms utilizing gradient-based optimization give decompositions that are dependent on initialization values, which can lead to dramatically different results. In this work, a new method, RAICAR (Ranking and Averaging Independent Component Analysis by Reproducibility), is introduced to address these issues for spatial ICA applied to fMRI. RAICAR utilizes repeated ICA realizations and relies on the reproducibility between them to rank and select components. Different realizations are aligned based on correlations, leading to aligned components. Each component is ranked and thresholded based on between-realization correlations. Furthermore, different realizations of each aligned component are selectively averaged to generate the final estimate of the given component. Reliability and accuracy of this method are demonstrated with both simulated and experimental fMRI data. Copyright 2007 Wiley-Liss, Inc.

Motion artifact removal algorithm by ICA for e-bra: a women ECG measurement system

NASA Astrophysics Data System (ADS)

Kwon, Hyeokjun; Oh, Sechang; Varadan, Vijay K.

2013-04-01

Wearable ECG(ElectroCardioGram) measurement systems have increasingly been developing for people who suffer from CVD(CardioVascular Disease) and have very active lifestyles. Especially, in the case of female CVD patients, several abnormal CVD symptoms are accompanied with CVDs. Therefore, monitoring women's ECG signal is a significant diagnostic method to prevent from sudden heart attack. The E-bra ECG measurement system from our previous work provides more convenient option for women than Holter monitor system. The e-bra system was developed with a motion artifact removal algorithm by using an adaptive filter with LMS(least mean square) and a wandering noise baseline detection algorithm. In this paper, ICA(independent component analysis) algorithms are suggested to remove motion artifact factor for the e-bra system. Firstly, the ICA algorithms are developed with two kinds of statistical theories: Kurtosis, Endropy and evaluated by performing simulations with a ECG signal created by sgolayfilt function of MATLAB, a noise signal including 0.4Hz, 1.1Hz and 1.9Hz, and a weighed vector W estimated by kurtosis or entropy. A correlation value is shown as the degree of similarity between the created ECG signal and the estimated new ECG signal. In the real time E-Bra system, two pseudo signals are extracted by multiplying with a random weighted vector W, the measured ECG signal from E-bra system, and the noise component signal by noise extraction algorithm from our previous work. The suggested ICA algorithm basing on kurtosis or entropy is used to estimate the new ECG signal Y without noise component.

Fetal source extraction from magnetocardiographic recordings by dependent component analysis

NASA Astrophysics Data System (ADS)

de Araujo, Draulio B.; Kardec Barros, Allan; Estombelo-Montesco, Carlos; Zhao, Hui; Roque da Silva Filho, A. C.; Baffa, Oswaldo; Wakai, Ronald; Ohnishi, Noboru

2005-10-01

Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.

A Method for Accurate Group Difference Detection by Constraining the Mixing Coefficients in an ICA Framework

PubMed Central

Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Clark, Vincent P.; Calhoun, Vince D.

2009-01-01

Independent component analysis (ICA) is a promising method that is increasingly used to analyze brain imaging data such as functional magnetic resonance imaging (fMRI), structural MRI, and electroencephalography and has also proved useful for group comparison, e.g., differentiating healthy controls from patients. An advantage of ICA is its ability to identify components that are mixed in an unknown manner. However, ICA is not necessarily robust and optimal in identifying between-group effects, especially in highly noisy situations. Here, we propose a modified ICA framework for multi-group data analysis that incorporates prior information regarding group membership as a constraint into the mixing coefficients. Our approach, called coefficient-constrained ICA (CC-ICA), prioritizes identification of components that show a significant group difference. The performance of CC-ICA via synthetic and hybrid data simulations is evaluated under different hypothesis testing assumptions and signal to noise ratios (SNRs). Group analysis is also conducted on real multitask fMRI data. Results show that CC-ICA improves the estimation accuracy of the independent components greatly, especially those that have different patterns for different groups (e.g., patients vs. controls); In addition, it enhances the data extraction sensitivity to group differences by ranking components with P value or J-divergence more consistently with the ground truth. The proposed algorithm performs quite well for both group-difference detection and multitask fMRI data fusion, which may prove especially important for the identification of relevant disease biomarkers. PMID:19172631

CUDAICA: GPU Optimization of Infomax-ICA EEG Analysis

PubMed Central

Raimondo, Federico; Kamienkowski, Juan E.; Sigman, Mariano; Fernandez Slezak, Diego

2012-01-01

In recent years, Independent Component Analysis (ICA) has become a standard to identify relevant dimensions of the data in neuroscience. ICA is a very reliable method to analyze data but it is, computationally, very costly. The use of ICA for online analysis of the data, used in brain computing interfaces, results are almost completely prohibitive. We show an increase with almost no cost (a rapid video card) of speed of ICA by about 25 fold. The EEG data, which is a repetition of many independent signals in multiple channels, is very suitable for processing using the vector processors included in the graphical units. We profiled the implementation of this algorithm and detected two main types of operations responsible of the processing bottleneck and taking almost 80% of computing time: vector-matrix and matrix-matrix multiplications. By replacing function calls to basic linear algebra functions to the standard CUBLAS routines provided by GPU manufacturers, it does not increase performance due to CUDA kernel launch overhead. Instead, we developed a GPU-based solution that, comparing with the original BLAS and CUBLAS versions, obtains a 25x increase of performance for the ICA calculation. PMID:22811699

Real-time Adaptive EEG Source Separation using Online Recursive Independent Component Analysis

PubMed Central

Hsu, Sheng-Hsiou; Mullen, Tim; Jung, Tzyy-Ping; Cauwenberghs, Gert

2016-01-01

Independent Component Analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and validate an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data. Empirical results of this study demonstrate the algorithm's: (a) suitability for accurate and efficient source identification in high-density (64-channel) realistically-simulated EEG data; (b) capability to detect and adapt to non-stationarity in 64-ch simulated EEG data; and (c) utility for rapidly extracting principal brain and artifact sources in real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment. ORICA was implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces. PMID:26685257

Optics measurement and correction for the Relativistic Heavy Ion Collider

NASA Astrophysics Data System (ADS)

Shen, Xiaozhe

The quality of beam optics is of great importance for the performance of a high energy accelerator like the Relativistic Heavy Ion Collider (RHIC). The turn-by-turn (TBT) beam position monitor (BPM) data can be used to derive beam optics. However, the accuracy of the derived beam optics is often limited by the performance and imperfections of instruments as well as measurement methods and conditions. Therefore, a robust and model-independent data analysis method is highly desired to extract noise-free information from TBT BPM data. As a robust signal-processing technique, an independent component analysis (ICA) algorithm called second order blind identification (SOBI) has been proven to be particularly efficient in extracting physical beam signals from TBT BPM data even in the presence of instrument's noise and error. We applied the SOBI ICA algorithm to RHIC during the 2013 polarized proton operation to extract accurate linear optics from TBT BPM data of AC dipole driven coherent beam oscillation. From the same data, a first systematic estimation of RHIC BPM noise performance was also obtained by the SOBI ICA algorithm, and showed a good agreement with the RHIC BPM configurations. Based on the accurate linear optics measurement, a beta-beat response matrix correction method and a scheme of using horizontal closed orbit bumps at sextupoles for arc beta-beat correction were successfully applied to reach a record-low beam optics error at RHIC. This thesis presents principles of the SOBI ICA algorithm and theory as well as experimental results of optics measurement and correction at RHIC.

A simple iterative independent component analysis algorithm for vibration source signal identification of complex structures

NASA Astrophysics Data System (ADS)

Lee, Dong-Sup; Cho, Dae-Seung; Kim, Kookhyun; Jeon, Jae-Jin; Jung, Woo-Jin; Kang, Myeng-Hwan; Kim, Jae-Ho

2015-01-01

Independent Component Analysis (ICA), one of the blind source separation methods, can be applied for extracting unknown source signals only from received signals. This is accomplished by finding statistical independence of signal mixtures and has been successfully applied to myriad fields such as medical science, image processing, and numerous others. Nevertheless, there are inherent problems that have been reported when using this technique: instability and invalid ordering of separated signals, particularly when using a conventional ICA technique in vibratory source signal identification of complex structures. In this study, a simple iterative algorithm of the conventional ICA has been proposed to mitigate these problems. The proposed method to extract more stable source signals having valid order includes an iterative and reordering process of extracted mixing matrix to reconstruct finally converged source signals, referring to the magnitudes of correlation coefficients between the intermediately separated signals and the signals measured on or nearby sources. In order to review the problems of the conventional ICA technique and to validate the proposed method, numerical analyses have been carried out for a virtual response model and a 30 m class submarine model. Moreover, in order to investigate applicability of the proposed method to real problem of complex structure, an experiment has been carried out for a scaled submarine mockup. The results show that the proposed method could resolve the inherent problems of a conventional ICA technique.

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

PubMed Central

Liu, Quanying; Ganzetti, Marco; Wenderoth, Nicole; Mantini, Dante

2018-01-01

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis. PMID:29551969

An 81.6 μW FastICA processor for epileptic seizure detection.

PubMed

Yang, Chia-Hsiang; Shih, Yi-Hsin; Chiueh, Herming

2015-02-01

To improve the performance of epileptic seizure detection, independent component analysis (ICA) is applied to multi-channel signals to separate artifacts and signals of interest. FastICA is an efficient algorithm to compute ICA. To reduce the energy dissipation, eigenvalue decomposition (EVD) is utilized in the preprocessing stage to reduce the convergence time of iterative calculation of ICA components. EVD is computed efficiently through an array structure of processing elements running in parallel. Area-efficient EVD architecture is realized by leveraging the approximate Jacobi algorithm, leading to a 77.2% area reduction. By choosing proper memory element and reduced wordlength, the power and area of storage memory are reduced by 95.6% and 51.7%, respectively. The chip area is minimized through fixed-point implementation and architectural transformations. Given a latency constraint of 0.1 s, an 86.5% area reduction is achieved compared to the direct-mapped architecture. Fabricated in 90 nm CMOS, the core area of the chip is 0.40 mm(2). The FastICA processor, part of an integrated epileptic control SoC, dissipates 81.6 μW at 0.32 V. The computation delay of a frame of 256 samples for 8 channels is 84.2 ms. Compared to prior work, 0.5% power dissipation, 26.7% silicon area, and 3.4 × computation speedup are achieved. The performance of the chip was verified by human dataset.

Resolving fluorophores by unmixing multispectral fluorescence tomography with independent component analysis

NASA Astrophysics Data System (ADS)

Pu, Huangsheng; Zhang, Guanglei; He, Wei; Liu, Fei; Guang, Huizhi; Zhang, Yue; Bai, Jing; Luo, Jianwen

2014-09-01

It is a challenging problem to resolve and identify drug (or non-specific fluorophore) distribution throughout the whole body of small animals in vivo. In this article, an algorithm of unmixing multispectral fluorescence tomography (MFT) images based on independent component analysis (ICA) is proposed to solve this problem. ICA is used to unmix the data matrix assembled by the reconstruction results from MFT. Then the independent components (ICs) that represent spatial structures and the corresponding spectrum courses (SCs) which are associated with spectral variations can be obtained. By combining the ICs with SCs, the recovered MFT images can be generated and fluorophore concentration can be calculated. Simulation studies, phantom experiments and animal experiments with different concentration contrasts and spectrum combinations are performed to test the performance of the proposed algorithm. Results demonstrate that the proposed algorithm can not only provide the spatial information of fluorophores, but also recover the actual reconstruction of MFT images.

Kmeans-ICA based automatic method for ocular artifacts removal in a motorimagery classification.

PubMed

Bou Assi, Elie; Rihana, Sandy; Sawan, Mohamad

2014-01-01

Electroencephalogram (EEG) recordings aroused as inputs of a motor imagery based BCI system. Eye blinks contaminate the spectral frequency of the EEG signals. Independent Component Analysis (ICA) has been already proved for removing these artifacts whose frequency band overlap with the EEG of interest. However, already ICA developed methods, use a reference lead such as the ElectroOculoGram (EOG) to identify the ocular artifact components. In this study, artifactual components were identified using an adaptive thresholding by means of Kmeans clustering. The denoised EEG signals have been fed into a feature extraction algorithm extracting the band power, the coherence and the phase locking value and inserted into a linear discriminant analysis classifier for a motor imagery classification.

A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

PubMed Central

Marto, Aminaton; Jahed Armaghani, Danial; Tonnizam Mohamad, Edy; Makhtar, Ahmad Mahir

2014-01-01

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches. PMID:25147856

A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network.

PubMed

Marto, Aminaton; Hajihassani, Mohsen; Armaghani, Danial Jahed; Mohamad, Edy Tonnizam; Makhtar, Ahmad Mahir

2014-01-01

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.

Fingerprint separation: an application of ICA

NASA Astrophysics Data System (ADS)

Singh, Meenakshi; Singh, Deepak Kumar; Kalra, Prem Kumar

2008-04-01

Among all existing biometric techniques, fingerprint-based identification is the oldest method, which has been successfully used in numerous applications. Fingerprint-based identification is the most recognized tool in biometrics because of its reliability and accuracy. Fingerprint identification is done by matching questioned and known friction skin ridge impressions from fingers, palms, and toes to determine if the impressions are from the same finger (or palm, toe, etc.). There are many fingerprint matching algorithms which automate and facilitate the job of fingerprint matching, but for any of these algorithms matching can be difficult if the fingerprints are overlapped or mixed. In this paper, we have proposed a new algorithm for separating overlapped or mixed fingerprints so that the performance of the matching algorithms will improve when they are fed with these inputs. Independent Component Analysis (ICA) has been used as a tool to separate the overlapped or mixed fingerprints.

Identification of immiscible NAPL contaminant sources in aquifers by a modified two-level saturation based imperialist competitive algorithm

NASA Astrophysics Data System (ADS)

Ghafouri, H. R.; Mosharaf-Dehkordi, M.; Afzalan, B.

2017-07-01

A simulation-optimization model is proposed for identifying the characteristics of local immiscible NAPL contaminant sources inside aquifers. This model employs the UTCHEM 9.0 software as its simulator for solving the governing equations associated with the multi-phase flow in porous media. As the optimization model, a novel two-level saturation based Imperialist Competitive Algorithm (ICA) is proposed to estimate the parameters of contaminant sources. The first level consists of three parallel independent ICAs and plays as a pre-conditioner for the second level which is a single modified ICA. The ICA in the second level is modified by dividing each country into a number of provinces (smaller parts). Similar to countries in the classical ICA, these provinces are optimized by the assimilation, competition, and revolution steps in the ICA. To increase the diversity of populations, a new approach named knock the base method is proposed. The performance and accuracy of the simulation-optimization model is assessed by solving a set of two and three-dimensional problems considering the effects of different parameters such as the grid size, rock heterogeneity and designated monitoring networks. The obtained numerical results indicate that using this simulation-optimization model provides accurate results at a less number of iterations when compared with the model employing the classical one-level ICA. A model is proposed to identify characteristics of immiscible NAPL contaminant sources. The contaminant is immiscible in water and multi-phase flow is simulated. The model is a multi-level saturation-based optimization algorithm based on ICA. Each answer string in second level is divided into a set of provinces. Each ICA is modified by incorporating a new knock the base model.

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

NASA Astrophysics Data System (ADS)

Safieddine, Doha; Kachenoura, Amar; Albera, Laurent; Birot, Gwénaël; Karfoul, Ahmad; Pasnicu, Anca; Biraben, Arnaud; Wendling, Fabrice; Senhadji, Lotfi; Merlet, Isabelle

2012-12-01

Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals. Eventually, some insights into the numerical complexity of these four algorithms are given.

Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI

PubMed Central

Wang, Kai; Li, Wenjie; Dong, Li; Zou, Ling; Wang, Changming

2018-01-01

Combination of electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) plays a potential role in neuroimaging due to its high spatial and temporal resolution. However, EEG is easily influenced by ballistocardiogram (BCG) artifacts and may cause false identification of the related EEG features, such as epileptic spikes. There are many related methods to remove them, however, they do not consider the time-varying features of BCG artifacts. In this paper, a novel method using clustering algorithm to catch the BCG artifacts' features and together with the constrained ICA (ccICA) is proposed to remove the BCG artifacts. We first applied this method to the simulated data, which was constructed by adding the BCG artifacts to the EEG signal obtained from the conventional environment. Then, our method was tested to demonstrate the effectiveness during EEG and fMRI experiments on 10 healthy subjects. In simulated data analysis, the value of error in signal amplitude (Er) computed by ccICA method was lower than those from other methods including AAS, OBS, and cICA (p < 0.005). In vivo data analysis, the Improvement of Normalized Power Spectrum (INPS) calculated by ccICA method in all electrodes was much higher than AAS, OBS, and cICA methods (p < 0.005). We also used other evaluation index (e.g., power analysis) to compare our method with other traditional methods. In conclusion, our novel method successfully and effectively removed BCG artifacts in both simulated and vivo EEG data tests, showing the potentials of removing artifacts in EEG-fMRI applications. PMID:29487499

Micropower Mixed-signal VLSI Independent Component Analysis for Gradient Flow Acoustic Source Separation.

PubMed

Stanaćević, Milutin; Li, Shuo; Cauwenberghs, Gert

2016-07-01

A parallel micro-power mixed-signal VLSI implementation of independent component analysis (ICA) with reconfigurable outer-product learning rules is presented. With the gradient sensing of the acoustic field over a miniature microphone array as a pre-processing method, the proposed ICA implementation can separate and localize up to 3 sources in mild reverberant environment. The ICA processor is implemented in 0.5 µm CMOS technology and occupies 3 mm × 3 mm area. At 16 kHz sampling rate, ASIC consumes 195 µW power from a 3 V supply. The outer-product implementation of natural gradient and Herault-Jutten ICA update rules demonstrates comparable performance to benchmark FastICA algorithm in ideal conditions and more robust performance in noisy and reverberant environment. Experiments demonstrate perceptually clear separation and precise localization over wide range of separation angles of two speech sources presented through speakers positioned at 1.5 m from the array on a conference room table. The presented ASIC leads to a extreme small form factor and low power consumption microsystem for source separation and localization required in applications like intelligent hearing aids and wireless distributed acoustic sensor arrays.

An algorithm for separation of mixed sparse and Gaussian sources

PubMed Central

Akkalkotkar, Ameya

2017-01-01

Independent component analysis (ICA) is a ubiquitous method for decomposing complex signal mixtures into a small set of statistically independent source signals. However, in cases in which the signal mixture consists of both nongaussian and Gaussian sources, the Gaussian sources will not be recoverable by ICA and will pollute estimates of the nongaussian sources. Therefore, it is desirable to have methods for mixed ICA/PCA which can separate mixtures of Gaussian and nongaussian sources. For mixtures of purely Gaussian sources, principal component analysis (PCA) can provide a basis for the Gaussian subspace. We introduce a new method for mixed ICA/PCA which we call Mixed ICA/PCA via Reproducibility Stability (MIPReSt). Our method uses a repeated estimations technique to rank sources by reproducibility, combined with decomposition of multiple subsamplings of the original data matrix. These multiple decompositions allow us to assess component stability as the size of the data matrix changes, which can be used to determinine the dimension of the nongaussian subspace in a mixture. We demonstrate the utility of MIPReSt for signal mixtures consisting of simulated sources and real-word (speech) sources, as well as mixture of unknown composition. PMID:28414814

An algorithm for separation of mixed sparse and Gaussian sources.

PubMed

Akkalkotkar, Ameya; Brown, Kevin Scott

2017-01-01

Independent component analysis (ICA) is a ubiquitous method for decomposing complex signal mixtures into a small set of statistically independent source signals. However, in cases in which the signal mixture consists of both nongaussian and Gaussian sources, the Gaussian sources will not be recoverable by ICA and will pollute estimates of the nongaussian sources. Therefore, it is desirable to have methods for mixed ICA/PCA which can separate mixtures of Gaussian and nongaussian sources. For mixtures of purely Gaussian sources, principal component analysis (PCA) can provide a basis for the Gaussian subspace. We introduce a new method for mixed ICA/PCA which we call Mixed ICA/PCA via Reproducibility Stability (MIPReSt). Our method uses a repeated estimations technique to rank sources by reproducibility, combined with decomposition of multiple subsamplings of the original data matrix. These multiple decompositions allow us to assess component stability as the size of the data matrix changes, which can be used to determinine the dimension of the nongaussian subspace in a mixture. We demonstrate the utility of MIPReSt for signal mixtures consisting of simulated sources and real-word (speech) sources, as well as mixture of unknown composition.

Hybrid EEG--Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal.

PubMed

Mannan, Malik M Naeem; Kim, Shinjung; Jeong, Myung Yung; Kamran, M Ahmad

2016-02-19

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.

A soft computing-based approach to optimise queuing-inventory control problem

NASA Astrophysics Data System (ADS)

Alaghebandha, Mohammad; Hajipour, Vahid

2015-04-01

In this paper, a multi-product continuous review inventory control problem within batch arrival queuing approach (MQr/M/1) is developed to find the optimal quantities of maximum inventory. The objective function is to minimise summation of ordering, holding and shortage costs under warehouse space, service level and expected lost-sales shortage cost constraints from retailer and warehouse viewpoints. Since the proposed model is Non-deterministic Polynomial-time hard, an efficient imperialist competitive algorithm (ICA) is proposed to solve the model. To justify proposed ICA, both ganetic algorithm and simulated annealing algorithm are utilised. In order to determine the best value of algorithm parameters that result in a better solution, a fine-tuning procedure is executed. Finally, the performance of the proposed ICA is analysed using some numerical illustrations.

INVESTIGATING DIFFERENCES IN BRAIN FUNCTIONAL NETWORKS USING HIERARCHICAL COVARIATE-ADJUSTED INDEPENDENT COMPONENT ANALYSIS.

PubMed

Shi, Ran; Guo, Ying

2016-12-01

Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks. Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks, we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder (PTSD).

Diagnostic Performance of a Novel Coronary CT Angiography Algorithm: Prospective Multicenter Validation of an Intracycle CT Motion Correction Algorithm for Diagnostic Accuracy.

PubMed

Andreini, Daniele; Lin, Fay Y; Rizvi, Asim; Cho, Iksung; Heo, Ran; Pontone, Gianluca; Bartorelli, Antonio L; Mushtaq, Saima; Villines, Todd C; Carrascosa, Patricia; Choi, Byoung Wook; Bloom, Stephen; Wei, Han; Xing, Yan; Gebow, Dan; Gransar, Heidi; Chang, Hyuk-Jae; Leipsic, Jonathon; Min, James K

2018-06-01

Motion artifact can reduce the diagnostic accuracy of coronary CT angiography (CCTA) for coronary artery disease (CAD). The purpose of this study was to compare the diagnostic performance of an algorithm dedicated to correcting coronary motion artifact with the performance of standard reconstruction methods in a prospective international multicenter study. Patients referred for clinically indicated invasive coronary angiography (ICA) for suspected CAD prospectively underwent an investigational CCTA examination free from heart rate-lowering medications before they underwent ICA. Blinded core laboratory interpretations of motion-corrected and standard reconstructions for obstructive CAD (≥ 50% stenosis) were compared with ICA findings. Segments unevaluable owing to artifact were considered obstructive. The primary endpoint was per-subject diagnostic accuracy of the intracycle motion correction algorithm for obstructive CAD found at ICA. Among 230 patients who underwent CCTA with the motion correction algorithm and standard reconstruction, 92 (40.0%) had obstructive CAD on the basis of ICA findings. At a mean heart rate of 68.0 ± 11.7 beats/min, the motion correction algorithm reduced the number of nondiagnostic scans compared with standard reconstruction (20.4% vs 34.8%; p < 0.001). Diagnostic accuracy for obstructive CAD with the motion correction algorithm (62%; 95% CI, 56-68%) was not significantly different from that of standard reconstruction on a per-subject basis (59%; 95% CI, 53-66%; p = 0.28) but was superior on a per-vessel basis: 77% (95% CI, 74-80%) versus 72% (95% CI, 69-75%) (p = 0.02). The motion correction algorithm was superior in subgroups of patients with severely obstructive (≥ 70%) stenosis, heart rate ≥ 70 beats/min, and vessels in the atrioventricular groove. The motion correction algorithm studied reduces artifacts and improves diagnostic performance for obstructive CAD on a per-vessel basis and in selected subgroups on a per-subject basis.

[Detection of the main quality indicators in red wine with infrared spectroscopy based on FastICA and neural network].

PubMed

Fang, Li-Min; Lin, Min

2009-08-01

For the rapid detection of the ethanol, pH and rest sugar in red wine, infrared (IR) spectra of 44 wine samples were analyzed. The algorithm of fast independent component analysis (FastICA) was used to decompose the data of IR spectra, and their independent components and the mixing matrix were obtained. Then, the ICA-NNR calibration model with three-level artificial neural network (ANN) structure was built by using back-propagation (BP) algorithm. The models were used to estimate the contents of ethanol, pH and rest sugar in red wine samples for both in calibration set and predicted set. Correlation coefficient (r) of prediction and root mean square error of prediction (RMSEP) were used as the evaluation indexes. The results indicate that the r and RMSEP for the prediction of ethanol content, pH and rest sugar content are 0.953, 0.983 and 0.994, and 0.161, 0.017 and 0.181, respectively. The maximum relative deviations between the ICA-NNR method predicted value and referenced value of the 22 samples in predicted set are less than 4%. The results of this paper provide a foundation for the application and further development of IR on-line red wine analyzer.

Single-channel mixed signal blind source separation algorithm based on multiple ICA processing

NASA Astrophysics Data System (ADS)

Cheng, Xiefeng; Li, Ji

2017-01-01

Take separating the fetal heart sound signal from the mixed signal that get from the electronic stethoscope as the research background, the paper puts forward a single-channel mixed signal blind source separation algorithm based on multiple ICA processing. Firstly, according to the empirical mode decomposition (EMD), the single-channel mixed signal get multiple orthogonal signal components which are processed by ICA. The multiple independent signal components are called independent sub component of the mixed signal. Then by combining with the multiple independent sub component into single-channel mixed signal, the single-channel signal is expanded to multipath signals, which turns the under-determined blind source separation problem into a well-posed blind source separation problem. Further, the estimate signal of source signal is get by doing the ICA processing. Finally, if the separation effect is not very ideal, combined with the last time's separation effect to the single-channel mixed signal, and keep doing the ICA processing for more times until the desired estimated signal of source signal is get. The simulation results show that the algorithm has good separation effect for the single-channel mixed physiological signals.

Speckle noise reduction technique for Lidar echo signal based on self-adaptive pulse-matching independent component analysis

NASA Astrophysics Data System (ADS)

Xu, Fan; Wang, Jiaxing; Zhu, Daiyin; Tu, Qi

2018-04-01

Speckle noise has always been a particularly tricky problem in improving the ranging capability and accuracy of Lidar system especially in harsh environment. Currently, effective speckle de-noising techniques are extremely scarce and should be further developed. In this study, a speckle noise reduction technique has been proposed based on independent component analysis (ICA). Since normally few changes happen in the shape of laser pulse itself, the authors employed the laser source as a reference pulse and executed the ICA decomposition to find the optimal matching position. In order to achieve the self-adaptability of algorithm, local Mean Square Error (MSE) has been defined as an appropriate criterion for investigating the iteration results. The obtained experimental results demonstrated that the self-adaptive pulse-matching ICA (PM-ICA) method could effectively decrease the speckle noise and recover the useful Lidar echo signal component with high quality. Especially, the proposed method achieves 4 dB more improvement of signal-to-noise ratio (SNR) than a traditional homomorphic wavelet method.

A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies

PubMed Central

Tang, Li

2014-01-01

Summary An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this paper, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, e.g., subjects with mental disorders or neurodegenerative diseases such as Parkinson’s as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation. PMID:24033125

Blind source separation problem in GPS time series

NASA Astrophysics Data System (ADS)

Gualandi, A.; Serpelloni, E.; Belardinelli, M. E.

2016-04-01

A critical point in the analysis of ground displacement time series, as those recorded by space geodetic techniques, is the development of data-driven methods that allow the different sources of deformation to be discerned and characterized in the space and time domains. Multivariate statistic includes several approaches that can be considered as a part of data-driven methods. A widely used technique is the principal component analysis (PCA), which allows us to reduce the dimensionality of the data space while maintaining most of the variance of the dataset explained. However, PCA does not perform well in finding the solution to the so-called blind source separation (BSS) problem, i.e., in recovering and separating the original sources that generate the observed data. This is mainly due to the fact that PCA minimizes the misfit calculated using an L2 norm (χ 2), looking for a new Euclidean space where the projected data are uncorrelated. The independent component analysis (ICA) is a popular technique adopted to approach the BSS problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we test the use of a modified variational Bayesian ICA (vbICA) method to recover the multiple sources of ground deformation even in the presence of missing data. The vbICA method models the probability density function (pdf) of each source signal using a mix of Gaussian distributions, allowing for more flexibility in the description of the pdf of the sources with respect to standard ICA, and giving a more reliable estimate of them. Here we present its application to synthetic global positioning system (GPS) position time series, generated by simulating deformation near an active fault, including inter-seismic, co-seismic, and post-seismic signals, plus seasonal signals and noise, and an additional time-dependent volcanic source. We evaluate the ability of the PCA and ICA decomposition techniques in explaining the data and in recovering the original (known) sources. Using the same number of components, we find that the vbICA method fits the data almost as well as a PCA method, since the χ 2 increase is less than 10 % the value calculated using a PCA decomposition. Unlike PCA, the vbICA algorithm is found to correctly separate the sources if the correlation of the dataset is low (<0.67) and the geodetic network is sufficiently dense (ten continuous GPS stations within a box of side equal to two times the locking depth of a fault where an earthquake of Mw >6 occurred). We also provide a cookbook for the use of the vbICA algorithm in analyses of position time series for tectonic and non-tectonic applications.

Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features.

PubMed

Radüntz, Thea; Scouten, Jon; Hochmuth, Olaf; Meffert, Beate

2017-08-01

Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.

Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features

NASA Astrophysics Data System (ADS)

Radüntz, Thea; Scouten, Jon; Hochmuth, Olaf; Meffert, Beate

2017-08-01

Objective. Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. Approach. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. Main results. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Significance. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.

Hybrid EEG—Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal

PubMed Central

Mannan, Malik M. Naeem; Kim, Shinjung; Jeong, Myung Yung; Kamran, M. Ahmad

2016-01-01

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data. PMID:26907276

Design of FPGA ICA for hyperspectral imaging processing

NASA Astrophysics Data System (ADS)

Nordin, Anis; Hsu, Charles C.; Szu, Harold H.

2001-03-01

The remote sensing problem which uses hyperspectral imaging can be transformed into a blind source separation problem. Using this model, hyperspectral imagery can be de-mixed into sub-pixel spectra which indicate the different material present in the pixel. This can be further used to deduce areas which contain forest, water or biomass, without even knowing the sources which constitute the image. This form of remote sensing allows previously blurred images to show the specific terrain involved in that region. The blind source separation problem can be implemented using an Independent Component Analysis algorithm. The ICA Algorithm has previously been successfully implemented using software packages such as MATLAB, which has a downloadable version of FastICA. The challenge now lies in implementing it in a form of hardware, or firmware in order to improve its computational speed. Hardware implementation also solves insufficient memory problem encountered by software packages like MATLAB when employing ICA for high resolution images and a large number of channels. Here, a pipelined solution of the firmware, realized using FPGAs are drawn out and simulated using C. Since C code can be translated into HDLs or be used directly on the FPGAs, it can be used to simulate its actual implementation in hardware. The simulated results of the program is presented here, where seven channels are used to model the 200 different channels involved in hyperspectral imaging.

Performance Analysis of ICA in Sensor Array

PubMed Central

Cai, Xin; Wang, Xiang; Huang, Zhitao; Wang, Fenghua

2016-01-01

As the best-known scheme in the field of Blind Source Separation (BSS), Independent Component Analysis (ICA) has been intensively used in various domains, including biomedical and acoustics applications, cooperative or non-cooperative communication, etc. While sensor arrays are involved in most of the applications, the influence on the performance of ICA of practical factors therein has not been sufficiently investigated yet. In this manuscript, the issue is researched by taking the typical antenna array as an illustrative example. Factors taken into consideration include the environment noise level, the properties of the array and that of the radiators. We analyze the analytic relationship between the noise variance, the source variance, the condition number of the mixing matrix and the optimal signal to interference-plus-noise ratio, as well as the relationship between the singularity of the mixing matrix and practical factors concerned. The situations where the mixing process turns (nearly) singular have been paid special attention to, since such circumstances are critical in applications. Results and conclusions obtained should be instructive when applying ICA algorithms on mixtures from sensor arrays. Moreover, an effective countermeasure against the cases of singular mixtures has been proposed, on the basis of previous analysis. Experiments validating the theoretical conclusions as well as the effectiveness of the proposed scheme have been included. PMID:27164100

Application of the Statistical ICA Technique in the DANCE Data Analysis

NASA Astrophysics Data System (ADS)

Baramsai, Bayarbadrakh; Jandel, M.; Bredeweg, T. A.; Rusev, G.; Walker, C. L.; Couture, A.; Mosby, S.; Ullmann, J. L.; Dance Collaboration

2015-10-01

The Detector for Advanced Neutron Capture Experiments (DANCE) at the Los Alamos Neutron Science Center is used to improve our understanding of the neutron capture reaction. DANCE is a highly efficient 4 π γ-ray detector array consisting of 160 BaF2 crystals which make it an ideal tool for neutron capture experiments. The (n, γ) reaction Q-value equals to the sum energy of all γ-rays emitted in the de-excitation cascades from the excited capture state to the ground state. The total γ-ray energy is used to identify reactions on different isotopes as well as the background. However, it's challenging to identify contribution in the Esum spectra from different isotopes with the similar Q-values. Recently we have tested the applicability of modern statistical methods such as Independent Component Analysis (ICA) to identify and separate different (n, γ) reaction yields on different isotopes that are present in the target material. ICA is a recently developed computational tool for separating multidimensional data into statistically independent additive subcomponents. In this conference talk, we present some results of the application of ICA algorithms and its modification for the DANCE experimental data analysis. This research is supported by the U. S. Department of Energy, Office of Science, Nuclear Physics under the Early Career Award No. LANL20135009.

Spatiotemporal patterns of ERP based on combined ICA-LORETA analysis

NASA Astrophysics Data System (ADS)

Zhang, Jiacai; Guo, Taomei; Xu, Yaqin; Zhao, Xiaojie; Yao, Li

2007-03-01

In contrast to the FMRI methods widely used up to now, this method try to understand more profoundly how the brain systems work under sentence processing task map accurately the spatiotemporal patterns of activity of the large neuronal populations in the human brain from the analysis of ERP data recorded on the brain scalp. In this study, an event-related brain potential (ERP) paradigm to record the on-line responses to the processing of sentences is chosen as an example. In order to give attention to both utilizing the ERPs' temporal resolution of milliseconds and overcoming the insensibility of cerebral location ERP sources, we separate these sources in space and time based on a combined method of independent component analysis (ICA) and low-resolution tomography (LORETA) algorithms. ICA blindly separate the input ERP data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. And then the spatial maps associated with each ICA component are analyzed, with use of LORETA to uniquely locate its cerebral sources throughout the full brain according to the assumption that neighboring neurons are simultaneously and synchronously activated. Our results show that the cerebral computation mechanism underlies content words reading is mediated by the orchestrated activity of several spatially distributed brain sources located in the temporal, frontal, and parietal areas, and activate at distinct time intervals and are grouped into different statistically independent components. Thus ICA-LORETA analysis provides an encouraging and effective method to study brain dynamics from ERP.

Identification of immiscible NAPL contaminant sources in aquifers by a modified two-level saturation based imperialist competitive algorithm.

PubMed

Ghafouri, H R; Mosharaf-Dehkordi, M; Afzalan, B

2017-07-01

A simulation-optimization model is proposed for identifying the characteristics of local immiscible NAPL contaminant sources inside aquifers. This model employs the UTCHEM 9.0 software as its simulator for solving the governing equations associated with the multi-phase flow in porous media. As the optimization model, a novel two-level saturation based Imperialist Competitive Algorithm (ICA) is proposed to estimate the parameters of contaminant sources. The first level consists of three parallel independent ICAs and plays as a pre-conditioner for the second level which is a single modified ICA. The ICA in the second level is modified by dividing each country into a number of provinces (smaller parts). Similar to countries in the classical ICA, these provinces are optimized by the assimilation, competition, and revolution steps in the ICA. To increase the diversity of populations, a new approach named knock the base method is proposed. The performance and accuracy of the simulation-optimization model is assessed by solving a set of two and three-dimensional problems considering the effects of different parameters such as the grid size, rock heterogeneity and designated monitoring networks. The obtained numerical results indicate that using this simulation-optimization model provides accurate results at a less number of iterations when compared with the model employing the classical one-level ICA. Copyright © 2017 Elsevier B.V. All rights reserved.

Three-way parallel independent component analysis for imaging genetics using multi-objective optimization.

PubMed

Ulloa, Alvaro; Jingyu Liu; Vergara, Victor; Jiayu Chen; Calhoun, Vince; Pattichis, Marios

2014-01-01

In the biomedical field, current technology allows for the collection of multiple data modalities from the same subject. In consequence, there is an increasing interest for methods to analyze multi-modal data sets. Methods based on independent component analysis have proven to be effective in jointly analyzing multiple modalities, including brain imaging and genetic data. This paper describes a new algorithm, three-way parallel independent component analysis (3pICA), for jointly identifying genomic loci associated with brain function and structure. The proposed algorithm relies on the use of multi-objective optimization methods to identify correlations among the modalities and maximally independent sources within modality. We test the robustness of the proposed approach by varying the effect size, cross-modality correlation, noise level, and dimensionality of the data. Simulation results suggest that 3p-ICA is robust to data with SNR levels from 0 to 10 dB and effect-sizes from 0 to 3, while presenting its best performance with high cross-modality correlations, and more than one subject per 1,000 variables. In an experimental study with 112 human subjects, the method identified links between a genetic component (pointing to brain function and mental disorder associated genes, including PPP3CC, KCNQ5, and CYP7B1), a functional component related to signal decreases in the default mode network during the task, and a brain structure component indicating increases of gray matter in brain regions of the default mode region. Although such findings need further replication, the simulation and in-vivo results validate the three-way parallel ICA algorithm presented here as a useful tool in biomedical data decomposition applications.

Synthesis of blind source separation algorithms on reconfigurable FPGA platforms

NASA Astrophysics Data System (ADS)

Du, Hongtao; Qi, Hairong; Szu, Harold H.

2005-03-01

Recent advances in intelligence technology have boosted the development of micro- Unmanned Air Vehicles (UAVs) including Sliver Fox, Shadow, and Scan Eagle for various surveillance and reconnaissance applications. These affordable and reusable devices have to fit a series of size, weight, and power constraints. Cameras used on such micro-UAVs are therefore mounted directly at a fixed angle without any motion-compensated gimbals. This mounting scheme has resulted in the so-called jitter effect in which jitter is defined as sub-pixel or small amplitude vibrations. The jitter blur caused by the jitter effect needs to be corrected before any other processing algorithms can be practically applied. Jitter restoration has been solved by various optimization techniques, including Wiener approximation, maximum a-posteriori probability (MAP), etc. However, these algorithms normally assume a spatial-invariant blur model that is not the case with jitter blur. Szu et al. developed a smart real-time algorithm based on auto-regression (AR) with its natural generalization of unsupervised artificial neural network (ANN) learning to achieve restoration accuracy at the sub-pixel level. This algorithm resembles the capability of the human visual system, in which an agreement between the pair of eyes indicates "signal", otherwise, the jitter noise. Using this non-statistical method, for each single pixel, a deterministic blind sources separation (BSS) process can then be carried out independently based on a deterministic minimum of the Helmholtz free energy with a generalization of Shannon's information theory applied to open dynamic systems. From a hardware implementation point of view, the process of jitter restoration of an image using Szu's algorithm can be optimized by pixel-based parallelization. In our previous work, a parallelly structured independent component analysis (ICA) algorithm has been implemented on both Field Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC) using standard-height cells. ICA is an algorithm that can solve BSS problems by carrying out the all-order statistical, decorrelation-based transforms, in which an assumption that neighborhood pixels share the same but unknown mixing matrix A is made. In this paper, we continue our investigation on the design challenges of firmware approaches to smart algorithms. We think two levels of parallelization can be explored, including pixel-based parallelization and the parallelization of the restoration algorithm performed at each pixel. This paper focuses on the latter and we use ICA as an example to explain the design and implementation methods. It is well known that the capacity constraints of single FPGA have limited the implementation of many complex algorithms including ICA. Using the reconfigurability of FPGA, we show, in this paper, how to manipulate the FPGA-based system to provide extra computing power for the parallelized ICA algorithm with limited FPGA resources. The synthesis aiming at the pilchard re-configurable FPGA platform is reported. The pilchard board is embedded with single Xilinx VIRTEX 1000E FPGA and transfers data directly to CPU on the 64-bit memory bus at the maximum frequency of 133MHz. Both the feasibility performance evaluations and experimental results validate the effectiveness and practicality of this synthesis, which can be extended to the spatial-variant jitter restoration for micro-UAV deployment.

Novel near-infrared spectrum analysis tool: Synergy adaptive moving window model based on immune clone algorithm.

PubMed

Wang, Shenghao; Zhang, Yuyan; Cao, Fuyi; Pei, Zhenying; Gao, Xuewei; Zhang, Xu; Zhao, Yong

2018-02-13

This paper presents a novel spectrum analysis tool named synergy adaptive moving window modeling based on immune clone algorithm (SA-MWM-ICA) considering the tedious and inconvenient labor involved in the selection of pre-processing methods and spectral variables by prior experience. In this work, immune clone algorithm is first introduced into the spectrum analysis field as a new optimization strategy, covering the shortage of the relative traditional methods. Based on the working principle of the human immune system, the performance of the quantitative model is regarded as antigen, and a special vector corresponding to the above mentioned antigen is regarded as antibody. The antibody contains a pre-processing method optimization region which is created by 11 decimal digits, and a spectrum variable optimization region which is formed by some moving windows with changeable width and position. A set of original antibodies are created by modeling with this algorithm. After calculating the affinity of these antibodies, those with high affinity will be selected to clone. The regulation for cloning is that the higher the affinity, the more copies will be. In the next step, another import operation named hyper-mutation is applied to the antibodies after cloning. Moreover, the regulation for hyper-mutation is that the lower the affinity, the more possibility will be. Several antibodies with high affinity will be created on the basis of these steps. Groups of simulated dataset, gasoline near-infrared spectra dataset, and soil near-infrared spectra dataset are employed to verify and illustrate the performance of SA-MWM-ICA. Analysis results show that the performance of the quantitative models adopted by SA-MWM-ICA are better especially for structures with relatively complex spectra than traditional models such as partial least squares (PLS), moving window PLS (MWPLS), genetic algorithm PLS (GAPLS), and pretreatment method classification and adjustable parameter changeable size moving window PLS (CA-CSMWPLS). The selected pre-processing methods and spectrum variables are easily explained. The proposed method will converge in few generations and can be used not only for near-infrared spectroscopy analysis but also for other similar spectral analysis, such as infrared spectroscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

Neural spike sorting using iterative ICA and a deflation-based approach.

PubMed

Tiganj, Z; Mboup, M

2012-12-01

We propose a spike sorting method for multi-channel recordings. When applied in neural recordings, the performance of the independent component analysis (ICA) algorithm is known to be limited, since the number of recording sites is much lower than the number of neurons. The proposed method uses an iterative application of ICA and a deflation technique in two nested loops. In each iteration of the external loop, the spiking activity of one neuron is singled out and then deflated from the recordings. The internal loop implements a sequence of ICA and sorting for removing the noise and all the spikes that are not fired by the targeted neuron. Then a final step is appended to the two nested loops in order to separate simultaneously fired spikes. We solve this problem by taking all possible pairs of the sorted neurons and apply ICA only on the segments of the signal during which at least one of the neurons in a given pair was active. We validate the performance of the proposed method on simulated recordings, but also on a specific type of real recordings: simultaneous extracellular-intracellular. We quantify the sorting results on the extracellular recordings for the spikes that come from the neurons recorded intracellularly. The results suggest that the proposed solution significantly improves the performance of ICA in spike sorting.

Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia

PubMed Central

Castro, Eduardo; Martínez-Ramón, Manel; Pearlson, Godfrey; Sui, Jing; Calhoun, Vince D.

2011-01-01

Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICA. PMID:21723948

Auditory steady state responses and cochlear implants: Modeling the artifact-response mixture in the perspective of denoising

PubMed Central

Mina, Faten; Attina, Virginie; Duroc, Yvan; Veuillet, Evelyne; Truy, Eric; Thai-Van, Hung

2017-01-01

Auditory steady state responses (ASSRs) in cochlear implant (CI) patients are contaminated by the spread of a continuous CI electrical stimulation artifact. The aim of this work was to model the electrophysiological mixture of the CI artifact and the corresponding evoked potentials on scalp electrodes in order to evaluate the performance of denoising algorithms in eliminating the CI artifact in a controlled environment. The basis of the proposed computational framework is a neural mass model representing the nodes of the auditory pathways. Six main contributors to auditory evoked potentials from the cochlear level and up to the auditory cortex were taken into consideration. The simulated dynamics were then projected into a 3-layer realistic head model. 32-channel scalp recordings of the CI artifact-response were then generated by solving the electromagnetic forward problem. As an application, the framework’s simulated 32-channel datasets were used to compare the performance of 4 commonly used Independent Component Analysis (ICA) algorithms: infomax, extended infomax, jade and fastICA in eliminating the CI artifact. As expected, two major components were detectable in the simulated datasets, a low frequency component at the modulation frequency and a pulsatile high frequency component related to the stimulation frequency. The first can be attributed to the phase-locked ASSR and the second to the stimulation artifact. Among the ICA algorithms tested, simulations showed that infomax was the most efficient and reliable in denoising the CI artifact-response mixture. Denoising algorithms can induce undesirable deformation of the signal of interest in real CI patient recordings. The proposed framework is a valuable tool for evaluating these algorithms in a controllable environment ahead of experimental or clinical applications. PMID:28350887

Auditory steady state responses and cochlear implants: Modeling the artifact-response mixture in the perspective of denoising.

PubMed

Mina, Faten; Attina, Virginie; Duroc, Yvan; Veuillet, Evelyne; Truy, Eric; Thai-Van, Hung

2017-01-01

Auditory steady state responses (ASSRs) in cochlear implant (CI) patients are contaminated by the spread of a continuous CI electrical stimulation artifact. The aim of this work was to model the electrophysiological mixture of the CI artifact and the corresponding evoked potentials on scalp electrodes in order to evaluate the performance of denoising algorithms in eliminating the CI artifact in a controlled environment. The basis of the proposed computational framework is a neural mass model representing the nodes of the auditory pathways. Six main contributors to auditory evoked potentials from the cochlear level and up to the auditory cortex were taken into consideration. The simulated dynamics were then projected into a 3-layer realistic head model. 32-channel scalp recordings of the CI artifact-response were then generated by solving the electromagnetic forward problem. As an application, the framework's simulated 32-channel datasets were used to compare the performance of 4 commonly used Independent Component Analysis (ICA) algorithms: infomax, extended infomax, jade and fastICA in eliminating the CI artifact. As expected, two major components were detectable in the simulated datasets, a low frequency component at the modulation frequency and a pulsatile high frequency component related to the stimulation frequency. The first can be attributed to the phase-locked ASSR and the second to the stimulation artifact. Among the ICA algorithms tested, simulations showed that infomax was the most efficient and reliable in denoising the CI artifact-response mixture. Denoising algorithms can induce undesirable deformation of the signal of interest in real CI patient recordings. The proposed framework is a valuable tool for evaluating these algorithms in a controllable environment ahead of experimental or clinical applications.

Cost component analysis.

PubMed

Lörincz, András; Póczos, Barnabás

2003-06-01

In optimizations the dimension of the problem may severely, sometimes exponentially increase optimization time. Parametric function approximatiors (FAPPs) have been suggested to overcome this problem. Here, a novel FAPP, cost component analysis (CCA) is described. In CCA, the search space is resampled according to the Boltzmann distribution generated by the energy landscape. That is, CCA converts the optimization problem to density estimation. Structure of the induced density is searched by independent component analysis (ICA). The advantage of CCA is that each independent ICA component can be optimized separately. In turn, (i) CCA intends to partition the original problem into subproblems and (ii) separating (partitioning) the original optimization problem into subproblems may serve interpretation. Most importantly, (iii) CCA may give rise to high gains in optimization time. Numerical simulations illustrate the working of the algorithm.

EEG artifact elimination by extraction of ICA-component features using image processing algorithms.

PubMed

Radüntz, T; Scouten, J; Hochmuth, O; Meffert, B

2015-03-30

Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Constrained Null Space Component Analysis for Semiblind Source Separation Problem.

PubMed

Hwang, Wen-Liang; Lu, Keng-Shih; Ho, Jinn

2018-02-01

The blind source separation (BSS) problem extracts unknown sources from observations of their unknown mixtures. A current trend in BSS is the semiblind approach, which incorporates prior information on sources or how the sources are mixed. The constrained independent component analysis (ICA) approach has been studied to impose constraints on the famous ICA framework. We introduced an alternative approach based on the null space component (NCA) framework and referred to the approach as the c-NCA approach. We also presented the c-NCA algorithm that uses signal-dependent semidefinite operators, which is a bilinear mapping, as signatures for operator design in the c-NCA approach. Theoretically, we showed that the source estimation of the c-NCA algorithm converges with a convergence rate dependent on the decay of the sequence, obtained by applying the estimated operators on corresponding sources. The c-NCA can be formulated as a deterministic constrained optimization method, and thus, it can take advantage of solvers developed in optimization society for solving the BSS problem. As examples, we demonstrated electroencephalogram interference rejection problems can be solved by the c-NCA with proximal splitting algorithms by incorporating a sparsity-enforcing separation model and considering the case when reference signals are available.

Improved separability of dipole sources by tripolar versus conventional disk electrodes: a modeling study using independent component analysis.

PubMed

Cao, H; Besio, W; Jones, S; Medvedev, A

2009-01-01

Tripolar electrodes have been shown to have less mutual information and higher spatial resolution than disc electrodes. In this work, a four-layer anisotropic concentric spherical head computer model was programmed, then four configurations of time-varying dipole signals were used to generate the scalp surface signals that would be obtained with tripolar and disc electrodes, and four important EEG artifacts were tested: eye blinking, cheek movements, jaw movements, and talking. Finally, a fast fixed-point algorithm was used for signal independent component analysis (ICA). The results show that signals from tripolar electrodes generated better ICA separation results than from disc electrodes for EEG signals with these four types of artifacts.

Identification of faulty sensor using relative partial decomposition via independent component analysis

NASA Astrophysics Data System (ADS)

Wang, Z.; Quek, S. T.

2015-07-01

Performance of any structural health monitoring algorithm relies heavily on good measurement data. Hence, it is necessary to employ robust faulty sensor detection approaches to isolate sensors with abnormal behaviour and exclude the highly inaccurate data in the subsequent analysis. The independent component analysis (ICA) is implemented to detect the presence of sensors showing abnormal behaviour. A normalized form of the relative partial decomposition contribution (rPDC) is proposed to identify the faulty sensor. Both additive and multiplicative types of faults are addressed and the detectability illustrated using a numerical and an experimental example. An empirical method to establish control limits for detecting and identifying the type of fault is also proposed. The results show the effectiveness of the ICA and rPDC method in identifying faulty sensor assuming that baseline cases are available.

A K-means multivariate approach for clustering independent components from magnetoencephalographic data.

PubMed

Spadone, Sara; de Pasquale, Francesco; Mantini, Dante; Della Penna, Stefania

2012-09-01

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are confirmed by a comparison with a MEG tailored version of the self-organizing group ICA, which is largely used for fMRI IC clustering. Copyright © 2012 Elsevier Inc. All rights reserved.

On signals faint and sparse: The ACICA algorithm for blind de-trending of exoplanetary transits with low signal-to-noise

DOE Office of Scientific and Technical Information (OSTI.GOV)

Waldmann, I. P., E-mail: ingo@star.ucl.ac.uk

2014-01-01

Independent component analysis (ICA) has recently been shown to be a promising new path in data analysis and de-trending of exoplanetary time series signals. Such approaches do not require or assume any prior or auxiliary knowledge about the data or instrument in order to de-convolve the astrophysical light curve signal from instrument or stellar systematic noise. These methods are often known as 'blind-source separation' (BSS) algorithms. Unfortunately, all BSS methods suffer from an amplitude and sign ambiguity of their de-convolved components, which severely limits these methods in low signal-to-noise (S/N) observations where their scalings cannot be determined otherwise. Here wemore » present a novel approach to calibrate ICA using sparse wavelet calibrators. The Amplitude Calibrated Independent Component Analysis (ACICA) allows for the direct retrieval of the independent components' scalings and the robust de-trending of low S/N data. Such an approach gives us an unique and unprecedented insight in the underlying morphology of a data set, which makes this method a powerful tool for exoplanetary data de-trending and signal diagnostics.« less

Time course based artifact identification for independent components of resting-state FMRI.

PubMed

Rummel, Christian; Verma, Rajeev Kumar; Schöpf, Veronika; Abela, Eugenio; Hauf, Martinus; Berruecos, José Fernando Zapata; Wiest, Roland

2013-01-01

In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis

PubMed Central

Cassani, Raymundo; Falk, Tiago H.; Fraga, Francisco J.; Kanda, Paulo A. M.; Anghinah, Renato

2014-01-01

Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment. PMID:24723886

Detection and Characterization of Ground Displacement Sources from Variational Bayesian Independent Component Analysis of GPS Time Series

NASA Astrophysics Data System (ADS)

Gualandi, A.; Serpelloni, E.; Belardinelli, M. E.

2014-12-01

A critical point in the analysis of ground displacements time series is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies. Indeed, PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem, i.e. in recovering and separating the original sources that generated the observed data. This is mainly due to the assumptions on which PCA relies: it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here we present the application of the vbICA technique to GPS position time series. First, we use vbICA on synthetic data that simulate a seismic cycle (interseismic + coseismic + postseismic + seasonal + noise), and study the ability of the algorithm to recover the original (known) sources of deformation. Secondly, we apply vbICA to different tectonically active scenarios, such as earthquakes in central and northern Italy, as well as the study of slow slip events in Cascadia.

Blind ICA detection based on second-order cone programming for MC-CDMA systems

NASA Astrophysics Data System (ADS)

Jen, Chih-Wei; Jou, Shyh-Jye

2014-12-01

The multicarrier code division multiple access (MC-CDMA) technique has received considerable interest for its potential application to future wireless communication systems due to its high data rate. A common problem regarding the blind multiuser detectors used in MC-CDMA systems is that they are extremely sensitive to the complex channel environment. Besides, the perturbation of colored noise may negatively affect the performance of the system. In this paper, a new coherent detection method will be proposed, which utilizes the modified fast independent component analysis (FastICA) algorithm, based on approximate negentropy maximization that is subject to the second-order cone programming (SOCP) constraint. The aim of the proposed coherent detection is to provide robustness against small-to-medium channel estimation mismatch (CEM) that may arise from channel frequency response estimation error in the MC-CDMA system, which is modulated by downlink binary phase-shift keying (BPSK) under colored noise. Noncoherent demodulation schemes are preferable to coherent demodulation schemes, as the latter are difficult to implement over time-varying fading channels. Differential phase-shift keying (DPSK) is therefore the natural choice for an alternative modulation scheme. Furthermore, the new blind differential SOCP-based ICA (SOCP-ICA) detection without channel estimation and compensation will be proposed to combat Doppler spread caused by time-varying fading channels in the DPSK-modulated MC-CDMA system under colored noise. In this paper, numerical simulations are used to illustrate the robustness of the proposed blind coherent SOCP-ICA detector against small-to-medium CEM and to emphasize the advantage of the blind differential SOCP-ICA detector in overcoming Doppler spread.

An ICA based MIMO-OFDM VLC scheme

NASA Astrophysics Data System (ADS)

Jiang, Fangqing; Deng, Honggui; Xiao, Wei; Tao, Shaohua; Zhu, Kaicheng

2015-07-01

In this paper, we propose a novel ICA based MIMO-OFDM VLC scheme, where ICA is applied to convert the MIMO-OFDM channel into several SISO-OFDM channels to reduce computational complexity in channel estimation, without any spectral overhead. Besides, the FM is first investigated to further modulate the OFDM symbols to eliminate the correlation of the signals, so as to improve the separation performance of the ICA algorithm. In the 4×4MIMO-OFDM VLC simulation experiment, LOS path and NLOS paths are both considered, each transmitting signal at 100 Mb/s. Simulation results show that the BER of the proposed scheme reaches the 10-5 level at SNR=20 dB, which is a large improvement compared to the traditional schemes.

Validating the performance of one-time decomposition for fMRI analysis using ICA with automatic target generation process.

PubMed

Yao, Shengnan; Zeng, Weiming; Wang, Nizhuan; Chen, Lei

2013-07-01

Independent component analysis (ICA) has been proven to be effective for functional magnetic resonance imaging (fMRI) data analysis. However, ICA decomposition requires to optimize the unmixing matrix iteratively whose initial values are generated randomly. Thus the randomness of the initialization leads to different ICA decomposition results. Therefore, just one-time decomposition for fMRI data analysis is not usually reliable. Under this circumstance, several methods about repeated decompositions with ICA (RDICA) were proposed to reveal the stability of ICA decomposition. Although utilizing RDICA has achieved satisfying results in validating the performance of ICA decomposition, RDICA cost much computing time. To mitigate the problem, in this paper, we propose a method, named ATGP-ICA, to do the fMRI data analysis. This method generates fixed initial values with automatic target generation process (ATGP) instead of being produced randomly. We performed experimental tests on both hybrid data and fMRI data to indicate the effectiveness of the new method and made a performance comparison of the traditional one-time decomposition with ICA (ODICA), RDICA and ATGP-ICA. The proposed method demonstrated that it not only could eliminate the randomness of ICA decomposition, but also could save much computing time compared to RDICA. Furthermore, the ROC (Receiver Operating Characteristic) power analysis also denoted the better signal reconstruction performance of ATGP-ICA than that of RDICA. Copyright © 2013 Elsevier Inc. All rights reserved.

Avoiding hard chromatographic segmentation: A moving window approach for the automated resolution of gas chromatography-mass spectrometry-based metabolomics signals by multivariate methods.

PubMed

Domingo-Almenara, Xavier; Perera, Alexandre; Brezmes, Jesus

2016-11-25

Gas chromatography-mass spectrometry (GC-MS) produces large and complex datasets characterized by co-eluted compounds and at trace levels, and with a distinct compound ion-redundancy as a result of the high fragmentation by the electron impact ionization. Compounds in GC-MS can be resolved by taking advantage of the multivariate nature of GC-MS data by applying multivariate resolution methods. However, multivariate methods have to be applied in small regions of the chromatogram, and therefore chromatograms are segmented prior to the application of the algorithms. The automation of this segmentation process is a challenging task as it implies separating between informative data and noise from the chromatogram. This study demonstrates the capabilities of independent component analysis-orthogonal signal deconvolution (ICA-OSD) and multivariate curve resolution-alternating least squares (MCR-ALS) with an overlapping moving window implementation to avoid the typical hard chromatographic segmentation. Also, after being resolved, compounds are aligned across samples by an automated alignment algorithm. We evaluated the proposed methods through a quantitative analysis of GC-qTOF MS data from 25 serum samples. The quantitative performance of both moving window ICA-OSD and MCR-ALS-based implementations was compared with the quantification of 33 compounds by the XCMS package. Results shown that most of the R 2 coefficients of determination exhibited a high correlation (R 2 >0.90) in both ICA-OSD and MCR-ALS moving window-based approaches. Copyright © 2016 Elsevier B.V. All rights reserved.

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

A Combined Independent Source Separation and Quality Index Optimization Method for Fetal ECG Extraction from Abdominal Maternal Leads

PubMed Central

Billeci, Lucia; Varanini, Maurizio

2017-01-01

The non-invasive fetal electrocardiogram (fECG) technique has recently received considerable interest in monitoring fetal health. The aim of our paper is to propose a novel fECG algorithm based on the combination of the criteria of independent source separation and of a quality index optimization (ICAQIO-based). The algorithm was compared with two methods applying the two different criteria independently—the ICA-based and the QIO-based methods—which were previously developed by our group. All three methods were tested on the recently implemented Fetal ECG Synthetic Database (FECGSYNDB). Moreover, the performance of the algorithm was tested on real data from the PhysioNet fetal ECG Challenge 2013 Database. The proposed combined method outperformed the other two algorithms on the FECGSYNDB (ICAQIO-based: 98.78%, QIO-based: 97.77%, ICA-based: 97.61%). Significant differences were obtained in particular in the conditions when uterine contractions and maternal and fetal ectopic beats occurred. On the real data, all three methods obtained very high performances, with the QIO-based method proving slightly better than the other two (ICAQIO-based: 99.38%, QIO-based: 99.76%, ICA-based: 99.37%). The findings from this study suggest that the proposed method could potentially be applied as a novel algorithm for accurate extraction of fECG, especially in critical recording conditions. PMID:28509860

Improving Video Based Heart Rate Monitoring.

PubMed

Lin, Jian; Rozado, David; Duenser, Andreas

2015-01-01

Non-contact measurements of cardiac pulse can provide robust measurement of heart rate (HR) without the annoyance of attaching electrodes to the body. In this paper we explore a novel and reliable method to carry out video-based HR estimation and propose various performance improvement over existing approaches. The investigated method uses Independent Component Analysis (ICA) to detect the underlying HR signal from a mixed source signal present in the RGB channels of the image. The original ICA algorithm was implemented and several modifications were explored in order to determine which one could be optimal for accurate HR estimation. Using statistical analysis, we compared the cardiac pulse rate estimation from the different methods under comparison on the extracted videos to a commercially available oximeter. We found that some of these methods are quite effective and efficient in terms of improving accuracy and latency of the system. We have made the code of our algorithms openly available to the scientific community so that other researchers can explore how to integrate video-based HR monitoring in novel health technology applications. We conclude by noting that recent advances in video-based HR monitoring permit computers to be aware of a user's psychophysiological status in real time.

Concurrent fNIRS-fMRI measurement to validate a method for separating deep and shallow fNIRS signals by using multidistance optodes

PubMed Central

Funane, Tsukasa; Sato, Hiroki; Yahata, Noriaki; Takizawa, Ryu; Nishimura, Yukika; Kinoshita, Akihide; Katura, Takusige; Atsumori, Hirokazu; Fukuda, Masato; Kasai, Kiyoto; Koizumi, Hideaki; Kiguchi, Masashi

2015-01-01

Abstract. It has been reported that a functional near-infrared spectroscopy (fNIRS) signal can be contaminated by extracerebral contributions. Many algorithms using multidistance separations to address this issue have been proposed, but their spatial separation performance has rarely been validated with simultaneous measurements of fNIRS and functional magnetic resonance imaging (fMRI). We previously proposed a method for discriminating between deep and shallow contributions in fNIRS signals, referred to as the multidistance independent component analysis (MD-ICA) method. In this study, to validate the MD-ICA method from the spatial aspect, multidistance fNIRS, fMRI, and laser-Doppler-flowmetry signals were simultaneously obtained for 12 healthy adult males during three tasks. The fNIRS signal was separated into deep and shallow signals by using the MD-ICA method, and the correlation between the waveforms of the separated fNIRS signals and the gray matter blood oxygenation level–dependent signals was analyzed. A three-way analysis of variance (signal depth×Hb kind×task) indicated that the main effect of fNIRS signal depth on the correlation is significant [F(1,1286)=5.34, p<0.05]. This result indicates that the MD-ICA method successfully separates fNIRS signals into spatially deep and shallow signals, and the accuracy and reliability of the fNIRS signal will be improved with the method. PMID:26157983

Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method.

PubMed

Wong, Chung-Ki; Luo, Qingfei; Zotev, Vadim; Phillips, Raquel; Chan, Kam Wai Clifford; Bodurka, Jerzy

2018-03-31

In simultaneous EEG-fMRI, identification of the period of cardioballistic artifact (BCG) in EEG is required for the artifact removal. Recording the electrocardiogram (ECG) waveform during fMRI is difficult, often causing inaccurate period detection. Since the waveform of the BCG extracted by independent component analysis (ICA) is relatively invariable compared to the ECG waveform, we propose a multiple-scale peak-detection algorithm to determine the BCG cycle directly from the EEG data. The algorithm first extracts the high contrast BCG component from the EEG data by ICA. The BCG cycle is then estimated by band-pass filtering the component around the fundamental frequency identified from its energy spectral density, and the peak of BCG artifact occurrence is selected from each of the estimated cycle. The algorithm is shown to achieve a high accuracy on a large EEG-fMRI dataset. It is also adaptive to various heart rates without the needs of adjusting the threshold parameters. The cycle detection remains accurate with the scan duration reduced to half a minute. Additionally, the algorithm gives a figure of merit to evaluate the reliability of the detection accuracy. The algorithm is shown to give a higher detection accuracy than the commonly used cycle detection algorithm fmrib_qrsdetect implemented in EEGLAB. The achieved high cycle detection accuracy of our algorithm without using the ECG waveforms makes possible to create and automate pipelines for processing large EEG-fMRI datasets, and virtually eliminates the need for ECG recordings for BCG artifact removal. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal.

PubMed

Oosugi, Naoya; Kitajo, Keiichi; Hasegawa, Naomi; Nagasaka, Yasuo; Okanoya, Kazuo; Fujii, Naotaka

2017-09-01

Blind source separation (BSS) algorithms extract neural signals from electroencephalography (EEG) data. However, it is difficult to quantify source separation performance because there is no criterion to dissociate neural signals and noise in EEG signals. This study develops a method for evaluating BSS performance. The idea is neural signals in EEG can be estimated by comparison with simultaneously measured electrocorticography (ECoG). Because the ECoG electrodes cover the majority of the lateral cortical surface and should capture most of the original neural sources in the EEG signals. We measured real EEG and ECoG data and developed an algorithm for evaluating BSS performance. First, EEG signals are separated into EEG components using the BSS algorithm. Second, the EEG components are ranked using the correlation coefficients of the ECoG regression and the components are grouped into subsets based on their ranks. Third, canonical correlation analysis estimates how much information is shared between the subsets of the EEG components and the ECoG signals. We used our algorithm to compare the performance of BSS algorithms (PCA, AMUSE, SOBI, JADE, fastICA) via the EEG and ECoG data of anesthetized nonhuman primates. The results (Best case >JADE = fastICA >AMUSE = SOBI ≥ PCA >random separation) were common to the two subjects. To encourage the further development of better BSS algorithms, our EEG and ECoG data are available on our Web site (http://neurotycho.org/) as a common testing platform. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Optimal model of PDIG based microgrid and design of complementary stabilizer using ICA.

PubMed

Amini, R Mohammad; Safari, A; Ravadanegh, S Najafi

2016-09-01

The generalized Heffron-Phillips model (GHPM) for a microgrid containing a photovoltaic (PV)-diesel machine (DM)-induction motor (IM)-governor (GV) (PDIG) has been developed at the low voltage level. A GHPM is calculated by linearization method about a loading condition. An effective Maximum Power Point Tracking (MPPT) approach for PV network has been done using sliding mode control (SMC) to maximize output power. Additionally, to improve stability of microgrid for more penetration of renewable energy resources with nonlinear load, a complementary stabilizer has been presented. Imperialist competitive algorithm (ICA) is utilized to design of gains for the complementary stabilizer with the multiobjective function. The stability analysis of the PDIG system has been completed with eigenvalues analysis and nonlinear simulations. Robustness and validity of the proposed controllers on damping of electromechanical modes examine through time domain simulation under input mechanical torque disturbances. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

PubMed Central

Smolinski, Tomasz G; Buchanan, Roger; Boratyn, Grzegorz M; Milanova, Mariofanna; Prinz, Astrid A

2006-01-01

Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself. PMID:17118151

Sequential Dictionary Learning From Correlated Data: Application to fMRI Data Analysis.

PubMed

Seghouane, Abd-Krim; Iqbal, Asif

2017-03-22

Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of spatio-temporal correlation and temporal smoothness. This prior information has not been included in the K-SVD algorithm when applied to fMRI data analysis. In this paper we propose three variants of the K-SVD algorithm dedicated to fMRI data analysis by accounting for this prior information. The proposed algorithms differ from the K-SVD in their sparse coding and dictionary update stages. The first two algorithms account for the known correlation structure in the fMRI data by using the squared Q, R-norm instead of the Frobenius norm for matrix approximation. The third and last algorithm account for both the known correlation structure in the fMRI data and the temporal smoothness. The temporal smoothness is incorporated in the dictionary update stage via regularization of the dictionary atoms obtained with penalization. The performance of the proposed dictionary learning algorithms are illustrated through simulations and applications on real fMRI data.

Independent component analysis-based source-level hyperlink analysis for two-person neuroscience studies

NASA Astrophysics Data System (ADS)

Zhao, Yang; Dai, Rui-Na; Xiao, Xiang; Zhang, Zong; Duan, Lian; Li, Zheng; Zhu, Chao-Zhe

2017-02-01

Two-person neuroscience, a perspective in understanding human social cognition and interaction, involves designing immersive social interaction experiments as well as simultaneously recording brain activity of two or more subjects, a process termed "hyperscanning." Using newly developed imaging techniques, the interbrain connectivity or hyperlink of various types of social interaction has been revealed. Functional near-infrared spectroscopy (fNIRS)-hyperscanning provides a more naturalistic environment for experimental paradigms of social interaction and has recently drawn much attention. However, most fNIRS-hyperscanning studies have computed hyperlinks using sensor data directly while ignoring the fact that the sensor-level signals contain confounding noises, which may lead to a loss of sensitivity and specificity in hyperlink analysis. In this study, on the basis of independent component analysis (ICA), a source-level analysis framework is proposed to investigate the hyperlinks in a fNIRS two-person neuroscience study. The performance of five widely used ICA algorithms in extracting sources of interaction was compared in simulative datasets, and increased sensitivity and specificity of hyperlink analysis by our proposed method were demonstrated in both simulative and real two-person experiments.

Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe.

PubMed

Ebtehaj, Isa; Bonakdari, Hossein

2014-01-01

The existence of sediments in wastewater greatly affects the performance of the sewer and wastewater transmission systems. Increased sedimentation in wastewater collection systems causes problems such as reduced transmission capacity and early combined sewer overflow. The article reviews the performance of the genetic algorithm (GA) and imperialist competitive algorithm (ICA) in minimizing the target function (mean square error of observed and predicted Froude number). To study the impact of bed load transport parameters, using four non-dimensional groups, six different models have been presented. Moreover, the roulette wheel selection method is used to select the parents. The ICA with root mean square error (RMSE) = 0.007, mean absolute percentage error (MAPE) = 3.5% show better results than GA (RMSE = 0.007, MAPE = 5.6%) for the selected model. All six models return better results than the GA. Also, the results of these two algorithms were compared with multi-layer perceptron and existing equations.

Independent component analysis of EEG dipole source localization in resting and action state of brain

NASA Astrophysics Data System (ADS)

Almurshedi, Ahmed; Ismail, Abd Khamim

2015-04-01

EEG source localization was studied in order to determine the location of the brain sources that are responsible for the measured potentials at the scalp electrodes using EEGLAB with Independent Component Analysis (ICA) algorithm. Neuron source locations are responsible in generating current dipoles in different states of brain through the measured potentials. The current dipole sources localization are measured by fitting an equivalent current dipole model using a non-linear optimization technique with the implementation of standardized boundary element head model. To fit dipole models to ICA components in an EEGLAB dataset, ICA decomposition is performed and appropriate components to be fitted are selected. The topographical scalp distributions of delta, theta, alpha, and beta power spectrum and cross coherence of EEG signals are observed. In close eyes condition it shows that during resting and action states of brain, alpha band was activated from occipital (O1, O2) and partial (P3, P4) area. Therefore, parieto-occipital area of brain are active in both resting and action state of brain. However cross coherence tells that there is more coherence between right and left hemisphere in action state of brain than that in the resting state. The preliminary result indicates that these potentials arise from the same generators in the brain.

An imperialist competitive algorithm for virtual machine placement in cloud computing

NASA Astrophysics Data System (ADS)

Jamali, Shahram; Malektaji, Sepideh; Analoui, Morteza

2017-05-01

Cloud computing, the recently emerged revolution in IT industry, is empowered by virtualisation technology. In this paradigm, the user's applications run over some virtual machines (VMs). The process of selecting proper physical machines to host these virtual machines is called virtual machine placement. It plays an important role on resource utilisation and power efficiency of cloud computing environment. In this paper, we propose an imperialist competitive-based algorithm for the virtual machine placement problem called ICA-VMPLC. The base optimisation algorithm is chosen to be ICA because of its ease in neighbourhood movement, good convergence rate and suitable terminology. The proposed algorithm investigates search space in a unique manner to efficiently obtain optimal placement solution that simultaneously minimises power consumption and total resource wastage. Its final solution performance is compared with several existing methods such as grouping genetic and ant colony-based algorithms as well as bin packing heuristic. The simulation results show that the proposed method is superior to other tested algorithms in terms of power consumption, resource wastage, CPU usage efficiency and memory usage efficiency.

Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers

PubMed Central

Salimi-Khorshidi, Gholamreza; Douaud, Gwenaëlle; Beckmann, Christian F; Glasser, Matthew F; Griffanti, Ludovica; Smith, Stephen M

2014-01-01

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject “at rest”). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing “signal” (brain activity) can be distinguished form the “noise” components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX (“FMRIB’s ICA-based X-noiseifier”), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different Classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL. PMID:24389422

Robust demarcation of basal cell carcinoma by dependent component analysis-based segmentation of multi-spectral fluorescence images.

PubMed

Kopriva, Ivica; Persin, Antun; Puizina-Ivić, Neira; Mirić, Lina

2010-07-02

This study was designed to demonstrate robust performance of the novel dependent component analysis (DCA)-based approach to demarcation of the basal cell carcinoma (BCC) through unsupervised decomposition of the red-green-blue (RGB) fluorescent image of the BCC. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms, which exploit spectral and spatial diversities between the BCC and the surrounding tissue. Used filtering-based DCA approach represents an extension of the independent component analysis (ICA) and is necessary in order to account for statistical dependence that is induced by spectral similarity between the BCC and surrounding tissue. This generates weak edges what represents a challenge for other segmentation methods as well. By comparative performance analysis with state-of-the-art image segmentation methods such as active contours (level set), K-means clustering, non-negative matrix factorization, ICA and ratio imaging we experimentally demonstrate good performance of DCA-based BCC demarcation in two demanding scenarios where intensity of the fluorescent image has been varied almost two orders of magnitude. Copyright 2010 Elsevier B.V. All rights reserved.

Application and Evaluation of Independent Component Analysis Methods to Generalized Seizure Disorder Activities Exhibited in the Brain.

PubMed

George, S Thomas; Balakrishnan, R; Johnson, J Stanly; Jayakumar, J

2017-07-01

EEG records the spontaneous electrical activity of the brain using multiple electrodes placed on the scalp, and it provides a wealth of information related to the functions of brain. Nevertheless, the signals from the electrodes cannot be directly applied to a diagnostic tool like brain mapping as they undergo a "mixing" process because of the volume conduction effect in the scalp. A pervasive problem in neuroscience is determining which regions of the brain are active, given voltage measurements at the scalp. Because of which, there has been a surge of interest among the biosignal processing community to investigate the process of mixing and unmixing to identify the underlying active sources. According to the assumptions of independent component analysis (ICA) algorithms, the resultant mixture obtained from the scalp can be closely approximated by a linear combination of the "actual" EEG signals emanating from the underlying sources of electrical activity in the brain. As a consequence, using these well-known ICA techniques in preprocessing of the EEG signals prior to clinical applications could result in development of diagnostic tool like quantitative EEG which in turn can assist the neurologists to gain noninvasive access to patient-specific cortical activity, which helps in treating neuropathologies like seizure disorders. The popular and proven ICA schemes mentioned in various literature and applications were selected (which includes Infomax, JADE, and SOBI) and applied on generalized seizure disorder samples using EEGLAB toolbox in MATLAB environment to see their usefulness in source separations; and they were validated by the expert neurologist for clinical relevance in terms of pathologies on brain functionalities. The performance of Infomax method was found to be superior when compared with other ICA schemes applied on EEG and it has been established based on the validations carried by expert neurologist for generalized seizure and its clinical correlation. The results are encouraging for furthering the studies in the direction of developing useful brain mapping tools using ICA methods.

Gravity Tides Extracted from Relative Gravimeter Data by Combining Empirical Mode Decomposition and Independent Component Analysis

NASA Astrophysics Data System (ADS)

Yu, Hongjuan; Guo, Jinyun; Kong, Qiaoli; Chen, Xiaodong

2018-04-01

The static observation data from a relative gravimeter contain noise and signals such as gravity tides. This paper focuses on the extraction of the gravity tides from the static relative gravimeter data for the first time applying the combined method of empirical mode decomposition (EMD) and independent component analysis (ICA), called the EMD-ICA method. The experimental results from the CG-5 gravimeter (SCINTREX Limited Ontario Canada) data show that the gravity tides time series derived by EMD-ICA are consistent with the theoretical reference (Longman formula) and the RMS of their differences only reaches 4.4 μGal. The time series of the gravity tides derived by EMD-ICA have a strong correlation with the theoretical time series and the correlation coefficient is greater than 0.997. The accuracy of the gravity tides estimated by EMD-ICA is comparable to the theoretical model and is slightly higher than that of independent component analysis (ICA). EMD-ICA could overcome the limitation of ICA having to process multiple observations and slightly improve the extraction accuracy and reliability of gravity tides from relative gravimeter data compared to that estimated with ICA.

How Many Separable Sources? Model Selection In Independent Components Analysis

PubMed Central

Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

2015-01-01

Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988

Blind source separation in retinal videos

NASA Astrophysics Data System (ADS)

Barriga, Eduardo S.; Truitt, Paul W.; Pattichis, Marios S.; Tüso, Dan; Kwon, Young H.; Kardon, Randy H.; Soliz, Peter

2003-05-01

An optical imaging device of retina function (OID-RF) has been developed to measure changes in blood oxygen saturation due to neural activity resulting from visual stimulation of the photoreceptors in the human retina. The video data that are collected represent a mixture of the functional signal in response to the retinal activation and other signals from undetermined physiological activity. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1% to 1.0% of the total reflected intensity level which makes the functional signal difficult to detect by standard methods since it is masked by the other signals that are present. In this paper, we apply principal component analysis (PCA), blind source separation (BSS), using Extended Spatial Decorrelation (ESD) and independent component analysis (ICA) using the Fast-ICA algorithm to extract the functional signal from the retinal videos. The results revealed that the functional signal in a stimulated retina can be detected through the application of some of these techniques.

A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.

PubMed

Lu, Chi-Jie; Chang, Chi-Chang

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System.

PubMed

Chai, Rifai; Naik, Ganesh R; Nguyen, Tuan Nghia; Ling, Sai Ho; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-05-01

This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.

A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

PubMed Central

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting. PMID:25045738

A new algorithm for ECG interference removal from single channel EMG recording.

PubMed

Yazdani, Shayan; Azghani, Mahmood Reza; Sedaaghi, Mohammad Hossein

2017-09-01

This paper presents a new method to remove electrocardiogram (ECG) interference from electromyogram (EMG). This interference occurs during the EMG acquisition from trunk muscles. The proposed algorithm employs progressive image denoising (PID) algorithm and ensembles empirical mode decomposition (EEMD) to remove this type of interference. PID is a very recent method that is being used for denoising digital images mixed with white Gaussian noise. It detects white Gaussian noise by deterministic annealing. To the best of our knowledge, PID has never been used before, in the case of EMG and ECG separation or in other 1D signal denoising applications. We have used it according to this fact that amplitude of the EMG signal can be modeled as white Gaussian noise using a filter with time-variant properties. The proposed algorithm has been compared to the other well-known methods such as HPF, EEMD-ICA, Wavelet-ICA and PID. The results show that the proposed algorithm outperforms the others, on the basis of three evaluation criteria used in this paper: Normalized mean square error, Signal to noise ratio and Pearson correlation.

Detecting intention to execute the next movement while performing current movement from EEG using global optimal constrained ICA.

PubMed

Eilbeigi, Elnaz; Setarehdan, Seyed Kamaledin

2018-05-26

Brain-computer interfaces (BCIs) are a promising tool in neurorehabilitation. The intention to perform a motor action can be detected from brain signals and used to control robotic devices. Most previous studies have focused on the starting of movements from a resting state, while in daily life activities, motions occur continuously and the neural activities correlated to the evolving movements are yet to be investigated. First we investigate the existence of neural correlates of intention to replace an object on the table during a holding phase. Next, we present a new method to extract the movement-related cortical potentials (MRCP) from a single-trial EEG. A novel method called Global optimal constrained ICA (GocICA) is proposed to overcome the limitations of cICA which is implemented based on Particle Swarm Optimization (PSO) and Charged System Search (CSS) techniques. GocICA is then utilized for decoding the intention to grasp and lift and intention to replace movements where the results were compared. It was found that GocICA significantly improves the intention detection performance. Best results in offline detection were obtained with CSS-cICA for both kinds of intentions. Furthermore, pseudo-online decoding showed that GocICA was able to predict both intentions before the onset of related movements with the highest probability. Decoding of the next movement intention during current movement is possible, which can be used to create more natural neuroprostheses. The results demonstrate that GocICA is a promising new algorithm for single-trial MRCP detection which can be used for detecting other types of ERPs such as P300. Copyright © 2018 Elsevier Ltd. All rights reserved.

Data analysis using a combination of independent component analysis and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lin, Shih-Lin; Tung, Pi-Cheng; Huang, Norden E.

2009-06-01

A combination of independent component analysis and empirical mode decomposition (ICA-EMD) is proposed in this paper to analyze low signal-to-noise ratio data. The advantages of ICA-EMD combination are these: ICA needs few sensory clues to separate the original source from unwanted noise and EMD can effectively separate the data into its constituting parts. The case studies reported here involve original sources contaminated by white Gaussian noise. The simulation results show that the ICA-EMD combination is an effective data analysis tool.

Grouping individual independent BOLD effects: a new way to ICA group analysis

NASA Astrophysics Data System (ADS)

Duann, Jeng-Ren; Jung, Tzyy-Ping; Sejnowski, Terrence J.; Makeig, Scott

2009-04-01

A new group analysis method to summarize the task-related BOLD responses based on independent component analysis (ICA) was presented. As opposite to the previously proposed group ICA (gICA) method, which first combined multi-subject fMRI data in either temporal or spatial domain and applied ICA decomposition only once to the combined fMRI data to extract the task-related BOLD effects, the method presented here applied ICA decomposition to the individual subjects' fMRI data to first find the independent BOLD effects specifically for each individual subject. Then, the task-related independent BOLD component was selected among the resulting independent components from the single-subject ICA decomposition and hence grouped across subjects to derive the group inference. In this new ICA group analysis (ICAga) method, one does not need to assume that the task-related BOLD time courses are identical across brain areas and subjects as used in the grand ICA decomposition on the spatially concatenated fMRI data. Neither does one need to assume that after spatial normalization, the voxels at the same coordinates represent exactly the same functional or structural brain anatomies across different subjects. These two assumptions have been problematic given the recent BOLD activation evidences. Further, since the independent BOLD effects were obtained from each individual subject, the ICAga method can better account for the individual differences in the task-related BOLD effects. Unlike the gICA approach whereby the task-related BOLD effects could only be accounted for by a single unified BOLD model across multiple subjects. As a result, the newly proposed method, ICAga, was able to better fit the task-related BOLD effects at individual level and thus allow grouping more appropriate multisubject BOLD effects in the group analysis.

Hybrid ICA-Bayesian Network approach reveals distinct effective connectivity differences in schizophrenia

PubMed Central

Kim, D.; Burge, J.; Lane, T.; Pearlson, G. D; Kiehl, K. A; Calhoun, V. D.

2008-01-01

We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge et al., 2007) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge and Lane, 2005). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, 1991). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal and frontal cortices, plus cerebellum during an auditory paradigm. PMID:18602482

Source-Modeling Auditory Processes of EEG Data Using EEGLAB and Brainstorm.

PubMed

Stropahl, Maren; Bauer, Anna-Katharina R; Debener, Stefan; Bleichner, Martin G

2018-01-01

Electroencephalography (EEG) source localization approaches are often used to disentangle the spatial patterns mixed up in scalp EEG recordings. However, approaches differ substantially between experiments, may be strongly parameter-dependent, and results are not necessarily meaningful. In this paper we provide a pipeline for EEG source estimation, from raw EEG data pre-processing using EEGLAB functions up to source-level analysis as implemented in Brainstorm. The pipeline is tested using a data set of 10 individuals performing an auditory attention task. The analysis approach estimates sources of 64-channel EEG data without the prerequisite of individual anatomies or individually digitized sensor positions. First, we show advanced EEG pre-processing using EEGLAB, which includes artifact attenuation using independent component analysis (ICA). ICA is a linear decomposition technique that aims to reveal the underlying statistical sources of mixed signals and is further a powerful tool to attenuate stereotypical artifacts (e.g., eye movements or heartbeat). Data submitted to ICA are pre-processed to facilitate good-quality decompositions. Aiming toward an objective approach on component identification, the semi-automatic CORRMAP algorithm is applied for the identification of components representing prominent and stereotypic artifacts. Second, we present a step-wise approach to estimate active sources of auditory cortex event-related processing, on a single subject level. The presented approach assumes that no individual anatomy is available and therefore the default anatomy ICBM152, as implemented in Brainstorm, is used for all individuals. Individual noise modeling in this dataset is based on the pre-stimulus baseline period. For EEG source modeling we use the OpenMEEG algorithm as the underlying forward model based on the symmetric Boundary Element Method (BEM). We then apply the method of dynamical statistical parametric mapping (dSPM) to obtain physiologically plausible EEG source estimates. Finally, we show how to perform group level analysis in the time domain on anatomically defined regions of interest (auditory scout). The proposed pipeline needs to be tailored to the specific datasets and paradigms. However, the straightforward combination of EEGLAB and Brainstorm analysis tools may be of interest to others performing EEG source localization.

An alternative ionospheric correction model for global navigation satellite systems

NASA Astrophysics Data System (ADS)

Hoque, M. M.; Jakowski, N.

2015-04-01

The ionosphere is recognized as a major error source for single-frequency operations of global navigation satellite systems (GNSS). To enhance single-frequency operations the global positioning system (GPS) uses an ionospheric correction algorithm (ICA) driven by 8 coefficients broadcasted in the navigation message every 24 h. Similarly, the global navigation satellite system Galileo uses the electron density NeQuick model for ionospheric correction. The Galileo satellite vehicles (SVs) transmit 3 ionospheric correction coefficients as driver parameters of the NeQuick model. In the present work, we propose an alternative ionospheric correction algorithm called Neustrelitz TEC broadcast model NTCM-BC that is also applicable for global satellite navigation systems. Like the GPS ICA or Galileo NeQuick, the NTCM-BC can be optimized on a daily basis by utilizing GNSS data obtained at the previous day at monitor stations. To drive the NTCM-BC, 9 ionospheric correction coefficients need to be uploaded to the SVs for broadcasting in the navigation message. Our investigation using GPS data of about 200 worldwide ground stations shows that the 24-h-ahead prediction performance of the NTCM-BC is better than the GPS ICA and comparable to the Galileo NeQuick model. We have found that the 95 percentiles of the prediction error are about 16.1, 16.1 and 13.4 TECU for the GPS ICA, Galileo NeQuick and NTCM-BC, respectively, during a selected quiet ionospheric period, whereas the corresponding numbers are found about 40.5, 28.2 and 26.5 TECU during a selected geomagnetic perturbed period. However, in terms of complexity the NTCM-BC is easier to handle than the Galileo NeQuick and in this respect comparable to the GPS ICA.

A method for the automatic reconstruction of fetal cardiac signals from magnetocardiographic recordings

NASA Astrophysics Data System (ADS)

Mantini, D.; Alleva, G.; Comani, S.

2005-10-01

Fetal magnetocardiography (fMCG) allows monitoring the fetal heart function through algorithms able to retrieve the fetal cardiac signal, but no standardized automatic model has become available so far. In this paper, we describe an automatic method that restores the fetal cardiac trace from fMCG recordings by means of a weighted summation of fetal components separated with independent component analysis (ICA) and identified through dedicated algorithms that analyse the frequency content and temporal structure of each source signal. Multichannel fMCG datasets of 66 healthy and 4 arrhythmic fetuses were used to validate the automatic method with respect to a classical procedure requiring the manual classification of fetal components by an expert investigator. ICA was run with input clusters of different dimensions to simulate various MCG systems. Detection rates, true negative and false positive component categorization, QRS amplitude, standard deviation and signal-to-noise ratio of reconstructed fetal signals, and real and per cent QRS differences between paired fetal traces retrieved automatically and manually were calculated to quantify the performances of the automatic method. Its robustness and reliability, particularly evident with the use of large input clusters, might increase the diagnostic role of fMCG during the prenatal period.

Hierarchical clustering using mutual information

NASA Astrophysics Data System (ADS)

Kraskov, A.; Stögbauer, H.; Andrzejak, R. G.; Grassberger, P.

2005-04-01

We present a conceptually simple method for hierarchical clustering of data called mutual information clustering (MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits its grouping property: The MI between three objects X, Y, and Z is equal to the sum of the MI between X and Y, plus the MI between Z and the combined object (XY). We use this both in the Shannon (probabilistic) version of information theory and in the Kolmogorov (algorithmic) version. We apply our method to the construction of phylogenetic trees from mitochondrial DNA sequences and to the output of independent components analysis (ICA) as illustrated with the ECG of a pregnant woman.

Multimodal integration of fMRI and EEG data for high spatial and temporal resolution analysis of brain networks

PubMed Central

Mantini, D.; Marzetti, L.; Corbetta, M.; Romani, G.L.; Del Gratta, C.

2017-01-01

Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes. PMID:20052528

A review of multivariate methods in brain imaging data fusion

NASA Astrophysics Data System (ADS)

Sui, Jing; Adali, Tülay; Li, Yi-Ou; Yang, Honghui; Calhoun, Vince D.

2010-03-01

On joint analysis of multi-task brain imaging data sets, a variety of multivariate methods have shown their strengths and been applied to achieve different purposes based on their respective assumptions. In this paper, we provide a comprehensive review on optimization assumptions of six data fusion models, including 1) four blind methods: joint independent component analysis (jICA), multimodal canonical correlation analysis (mCCA), CCA on blind source separation (sCCA) and partial least squares (PLS); 2) two semi-blind methods: parallel ICA and coefficient-constrained ICA (CC-ICA). We also propose a novel model for joint blind source separation (BSS) of two datasets using a combination of sCCA and jICA, i.e., 'CCA+ICA', which, compared with other joint BSS methods, can achieve higher decomposition accuracy as well as the correct automatic source link. Applications of the proposed model to real multitask fMRI data are compared to joint ICA and mCCA; CCA+ICA further shows its advantages in capturing both shared and distinct information, differentiating groups, and interpreting duration of illness in schizophrenia patients, hence promising applicability to a wide variety of medical imaging problems.

The interruption of PKC-ι signaling and TRAIL combination therapy against glioblastoma cells.

PubMed

McCray, Andrea N; Desai, Shraddha; Acevedo-Duncan, Mildred

2014-09-01

Glioblastoma is a highly aggressive type of brain cancer which currently has limited options for treatment. It is imperative to develop combination therapies that could cause apoptosis in glioblastoma. The aim of this study was to characterize the affect of modified ICA-1, a PKC-iota inhibitor, on the growth pattern of various glioblastoma cell lines. T98G and U87 glioblastoma cells were treated with ICA-1 alone and the absolute cell numbers of each group were determined for cell growth expansion analysis, cell viability analysis, and cell death analysis. Low dose ICA-1 treatment alone significantly inhibited cell growth expansion of high density glioblastoma cells without inducing cell death. However, the high dose ICA-1 treatment regimen provided significant apoptosis for glioblastoma cells. Furthermore, this study was conducted to use a two layer molecular level approach for treating glioblastoma cells with ICA-1 plus an apoptosis agent, tumor-necrosis factor-related apoptosis-inducing ligand (TRAIL), to induce apoptosis in such chemo-refractory cancer cells. Following ICA-1 plus TRAIL treatment, apoptosis was detected in glioblastoma cells via the TUNEL assay and via flow cytometric analysis using Annexin-V FITC/PI. This study offers the first evidence for ICA-1 alone to inhibit glioblastoma cell proliferation as well as the novel combination of ICA-1 with TRAIL to cause robust apoptosis in a caspase-3 mediated mechanism. Furthermore, ICA-1 plus TRAIL simultaneously modulates down-regulation of PKC-iota and c-Jun.

ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features.

PubMed

Mognon, Andrea; Jovicich, Jorge; Bruzzone, Lorenzo; Buiatti, Marco

2011-02-01

A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal. Copyright © 2010 Society for Psychophysiological Research.

Independent Component Analysis of Textures

NASA Technical Reports Server (NTRS)

Manduchi, Roberto; Portilla, Javier

2000-01-01

A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on Independent Components Analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximates the joint probability density function of the filter outputs. The algorithm is implemented using a steerable filter space. Experiments involving both texture classification and synthesis show that compared to Principal Components Analysis, ICA provides superior performance for modeling of natural and synthetic textures.

Separation and reconstruction of high pressure water-jet reflective sound signal based on ICA

NASA Astrophysics Data System (ADS)

Yang, Hongtao; Sun, Yuling; Li, Meng; Zhang, Dongsu; Wu, Tianfeng

2011-12-01

The impact of high pressure water-jet on the different materials target will produce different reflective mixed sound. In order to reconstruct the reflective sound signals distribution on the linear detecting line accurately and to separate the environment noise effectively, the mixed sound signals acquired by linear mike array were processed by ICA. The basic principle of ICA and algorithm of FASTICA were described in detail. The emulation experiment was designed. The environment noise signal was simulated by using band-limited white noise and the reflective sound signal was simulated by using pulse signal. The reflective sound signal attenuation produced by the different distance transmission was simulated by weighting the sound signal with different contingencies. The mixed sound signals acquired by linear mike array were synthesized by using the above simulated signals and were whitened and separated by ICA. The final results verified that the environment noise separation and the reconstruction of the detecting-line sound distribution can be realized effectively.

Non-Inferential Multi-Subject Study of Functional Connectivity during Visual Stimulation.

PubMed

Esposito, F; Cirillo, M; Aragri, A; Caranci, F; Cirillo, L; Di Salle, F; Cirillo, S

2007-01-31

Independent component analysis (ICA) is a powerful technique for the multivariate, non-inferential, data-driven analysis of functional magnetic resonance imaging (fMRI) data-sets. The non-inferential nature of ICA makes this a suitable technique for the study of complex mental states whose temporal evolution would be difficult to describe analytically in terms of classical statistical regressors. Taking advantage of this feature, ICA can extract a number of functional connectivity patterns regardless of the task executed by the subject. The technique is so powerful that functional connectivity patterns can be derived even when the subject is just resting in the scanner, opening the opportunity for functional investigation of the human mind at its basal "default" state, which has been proposed to be altered in several brain disorders. However, one major drawback of ICA consists in the difficulty of managing its results, which are not represented by a single functional image as in inferential studies. This produces the need for a classification of ICA results and exacerbates the difficulty of obtaining group "averaged" functional connectivity patterns, while preserving the interpretation of individual differences. Addressing the subject-level variability in the very same framework of "grouping" appears to be a favourable approach towards the clinical evaluation and application of ICA-based methodologies. Here we present a novel strategy for group-level ICA analyses, namely the self-organizing group-level ICA (sog-ICA), which is used on visual activation fMRI data from a block-design experiment repeated on six subjects. We propose the sog-ICA as a multi-subject analysis tool for grouping ICA data while assessing the similarity and variability of the fMRI results of individual subject decompositions.

Independent component analysis based channel equalization for 6 × 6 MIMO-OFDM transmission over few-mode fiber.

PubMed

He, Zhixue; Li, Xiang; Luo, Ming; Hu, Rong; Li, Cai; Qiu, Ying; Fu, Songnian; Yang, Qi; Yu, Shaohua

2016-05-02

We propose and experimentally demonstrate two independent component analysis (ICA) based channel equalizers (CEs) for 6 × 6 MIMO-OFDM transmission over few-mode fiber. Compared with the conventional channel equalizer based on training symbols (TSs-CE), the proposed two ICA-based channel equalizers (ICA-CE-I and ICA-CE-II) can achieve comparable performances, while requiring much less training symbols. Consequently, the overheads for channel equalization can be substantially reduced from 13.7% to 0.4% and 2.6%, respectively. Meanwhile, we also experimentally investigate the convergence speed of the proposed ICA-based CEs.

Appliance of Independent Component Analysis to System Intrusion Analysis

NASA Astrophysics Data System (ADS)

Ishii, Yoshikazu; Takagi, Tarou; Nakai, Kouji

In order to analyze the output of the intrusion detection system and the firewall, we evaluated the applicability of ICA(independent component analysis). We developed a simulator for evaluation of intrusion analysis method. The simulator consists of the network model of an information system, the service model and the vulnerability model of each server, and the action model performed on client and intruder. We applied the ICA for analyzing the audit trail of simulated information system. We report the evaluation result of the ICA on intrusion analysis. In the simulated case, ICA separated two attacks correctly, and related an attack and the abnormalities of the normal application produced under the influence of the attach.

Hybrid ICA-Bayesian network approach reveals distinct effective connectivity differences in schizophrenia.

PubMed

Kim, D; Burge, J; Lane, T; Pearlson, G D; Kiehl, K A; Calhoun, V D

2008-10-01

We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P., 2007. Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp.) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge, J., Lane, T., 2005. Learning Class-Discriminative Dynamic Bayesian Networks. Proceedings of the International Conference on Machine Learning, Bonn, Germany, pp. 97-104.). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, A., 1991. Probability, random variables, and stochastic processes. McGraw-Hill, New York.). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal, frontal, and cerebellar regions during an auditory paradigm.

ECG-based gating in ultra high field cardiovascular magnetic resonance using an independent component analysis approach.

PubMed

Krug, Johannes W; Rose, Georg; Clifford, Gari D; Oster, Julien

2013-11-19

In Cardiovascular Magnetic Resonance (CMR), the synchronization of image acquisition with heart motion is performed in clinical practice by processing the electrocardiogram (ECG). The ECG-based synchronization is well established for MR scanners with magnetic fields up to 3 T. However, this technique is prone to errors in ultra high field environments, e.g. in 7 T MR scanners as used in research applications. The high magnetic fields cause severe magnetohydrodynamic (MHD) effects which disturb the ECG signal. Image synchronization is thus less reliable and yields artefacts in CMR images. A strategy based on Independent Component Analysis (ICA) was pursued in this work to enhance the ECG contribution and attenuate the MHD effect. ICA was applied to 12-lead ECG signals recorded inside a 7 T MR scanner. An automatic source identification procedure was proposed to identify an independent component (IC) dominated by the ECG signal. The identified IC was then used for detecting the R-peaks. The presented ICA-based method was compared to other R-peak detection methods using 1) the raw ECG signal, 2) the raw vectorcardiogram (VCG), 3) the state-of-the-art gating technique based on the VCG, 4) an updated version of the VCG-based approach and 5) the ICA of the VCG. ECG signals from eight volunteers were recorded inside the MR scanner. Recordings with an overall length of 87 min accounting for 5457 QRS complexes were available for the analysis. The records were divided into a training and a test dataset. In terms of R-peak detection within the test dataset, the proposed ICA-based algorithm achieved a detection performance with an average sensitivity (Se) of 99.2%, a positive predictive value (+P) of 99.1%, with an average trigger delay and jitter of 5.8 ms and 5.0 ms, respectively. Long term stability of the demixing matrix was shown based on two measurements of the same subject, each being separated by one year, whereas an averaged detection performance of Se = 99.4% and +P = 99.7% was achieved.Compared to the state-of-the-art VCG-based gating technique at 7 T, the proposed method increased the sensitivity and positive predictive value within the test dataset by 27.1% and 42.7%, respectively. The presented ICA-based method allows the estimation and identification of an IC dominated by the ECG signal. R-peak detection based on this IC outperforms the state-of-the-art VCG-based technique in a 7 T MR scanner environment.

Joint approximate diagonalization of eigenmatrices as a high-throughput approach for analysis of hyphenated and comprehensive two-dimensional gas chromatographic data.

PubMed

Zarghani, Maryam; Parastar, Hadi

2017-11-17

The objective of the present work is development of joint approximate diagonalization of eigenmatrices (JADE) as a member of independent component analysis (ICA) family, for the analysis of gas chromatography-mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) data to address incomplete separation problem occurred during the analysis of complex sample matrices. In this regard, simulated GC-MS and GC×GC-MS data sets with different number of components, different degree of overlap and noise were evaluated. In the case of simultaneous analysis of multiple samples, column-wise augmentation for GC-MS and column-wise super-augmentation for GC×GC-MS was used before JADE analysis. The performance of JADE was evaluated in terms of statistical parameters of lack of fit (LOF), mutual information (MI) and Amari index as well as analytical figures of merit (AFOMs) obtained from calibration curves. In addition, the area of feasible solutions (AFSs) was calculated by two different approaches of MCR-BANDs and polygon inflation algorithm (FACPACK). Furthermore, JADE performance was compared with multivariate curve resolution-alternating least squares (MCR-ALS) and other ICA algorithms of mean-field ICA (MFICA) and mutual information least dependent component analysis (MILCA). In all cases, JADE could successfully resolve the elution and spectral profiles in GC-MS and GC×GC-MS data with acceptable statistical and calibration parameters and their solutions were in AFSs. To check the applicability of JADE in real cases, JADE was used for resolution and quantification of phenanthrene and anthracene in aromatic fraction of heavy fuel oil (HFO) analyzed by GC×GC-MS. Surprisingly, pure elution and spectral profiles of target compounds were properly resolved in the presence of baseline and interferences using JADE. Once more, the performance of JADE was compared with MCR-ALS in real case. On this matter, the mutual information (MI) values were 1.01 and 1.13 for resolved profiles by JADE and MCR-ALS, respectively. In addition, LOD values (μg/mL) were respectively 1.36 and 1.24 for phenanthrene and 1.26 and 1.09 for anthracene using MCR-ALS and JADE which showed outperformance of JADE over MCR-ALS. Copyright © 2017 Elsevier B.V. All rights reserved.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine.

PubMed

Monakhova, Yulia B; Godelmann, Rolf; Kuballa, Thomas; Mushtakova, Svetlana P; Rutledge, Douglas N

2015-08-15

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements. Copyright © 2015 Elsevier B.V. All rights reserved.

Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings.

PubMed

Zou, Yuan; Nathan, Viswam; Jafari, Roozbeh

2016-01-01

Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from independent component analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event-related potential (ERP)-related independent components. However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g., identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by nonbiological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature-based clustering algorithm used to identify artifacts which have physiological origins; and 2) the electrode-scalp impedance information employed for identifying nonbiological artifacts. The results on EEG data collected from ten subjects show that our algorithm can effectively detect, separate, and remove both physiological and nonbiological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods.

Automatic Identification of Artifact-related Independent Components for Artifact Removal in EEG Recordings

PubMed Central

Zou, Yuan; Nathan, Viswam; Jafari, Roozbeh

2017-01-01

Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from Independent Component Analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event related potential (ERP)-related independent components (ICs). However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g. identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by non-biological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature based clustering algorithm used to identify artifacts which have physiological origins and 2) the electrode-scalp impedance information employed for identifying non-biological artifacts. The results on EEG data collected from 10 subjects show that our algorithm can effectively detect, separate, and remove both physiological and non-biological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods. PMID:25415992

Spatial-time-state fusion algorithm for defect detection through eddy current pulsed thermography

NASA Astrophysics Data System (ADS)

Xiao, Xiang; Gao, Bin; Woo, Wai Lok; Tian, Gui Yun; Xiao, Xiao Ting

2018-05-01

Eddy Current Pulsed Thermography (ECPT) has received extensive attention due to its high sensitive of detectability on surface and subsurface cracks. However, it remains as a difficult challenge in unsupervised detection as to identify defects without knowing any prior knowledge. This paper presents a spatial-time-state features fusion algorithm to obtain fully profile of the defects by directional scanning. The proposed method is intended to conduct features extraction by using independent component analysis (ICA) and automatic features selection embedding genetic algorithm. Finally, the optimal feature of each step is fused to obtain defects reconstruction by applying common orthogonal basis extraction (COBE) method. Experiments have been conducted to validate the study and verify the efficacy of the proposed method on blind defect detection.

Decreased muscle mass in Korean subjects with intracranial arterial stenosis: The Kangbuk Samsung Health Study.

PubMed

Jung, Ho-Jung; Jung, Hwanseok; Lee, Taeyoung; Kim, Jongho; Park, Jongsin; Kim, Hacsoo; Cho, Junghwan; Lee, Won-Young; Park, Sung-Woo; Rhee, Eun-Jung; Oh, Hyung-Geun

2017-01-01

Intracranial arterial stenosis (ICAS) is a common cause of ischemic stroke in Asians. Decreased muscle mass is one of the major causes of chronic disease in adults. The purpose of this study was to analyze the relationship between muscle mass and ICAS in Korean adults. For this study, we selected a total of 10,530 participants (mean age, 43.3 years; 8558 men) in a health screening program, for whom transcranial Doppler (TCD) ultrasound was used to detect >50% ICAS based on criteria modified from the stroke outcomes and neuroimaging of intracranial atherosclerosis trial. Body composition was evaluated by bioelectrical impedance analysis (BIA). Skeletal muscle index (SMI) was calculated with muscle mass/weight (kg) * 100. Among the total patient population, 322 (3.1%) subjects had ICAS. Subjects with ICAS were older, and had higher mean values for fasting glucose, body mass index and blood pressure compared with those without ICAS. Subjects with ICAS had significantly lower muscle mass, SMI and higher percent body fat compared with those without ICAS. In logistic regression analysis, the subjects in the highest tertile of muscle mass had the lowest odds ratio for ICAS with the lowest tertile group of muscle mass as the reference group even after adjusting for age, systolic blood pressure, fasting blood glucose, sex, smoking and exercise (OR 0.650, 95% CI 0.442-0.955). Subjects with ICAS had significantly decreased muscle mass compared with those without ICAS in Korean adults. The risk for ICAS was lower in subjects with higher muscle mass. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Artifacts and noise removal in electrocardiograms using independent component analysis.

PubMed

Chawla, M P S; Verma, H K; Kumar, Vinod

2008-09-26

Independent component analysis (ICA) is a novel technique capable of separating independent components from electrocardiogram (ECG) complex signals. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG recordings. ICA is applied to remove artifacts and noise in ECG segments of either an individual ECG CSE data base file or all files. The reconstructed ECGs are compared with the original ECG signal. For the four special cases discussed, the R-Peak magnitudes of the CSE data base ECG waveforms before and after applying ICA are also found. In the results, it is shown that in most of the cases, the percentage error in reconstruction is very small. The results show that there is a significant improvement in signal quality, i.e. SNR. All the ECG recording cases dealt showed an improved ECG appearance after the use of ICA. This establishes the efficacy of ICA in elimination of noise and artifacts in electrocardiograms.

Secretome analysis of rat osteoblasts during icariin treatment induced osteogenesis

PubMed Central

Qian, Weiqing; Su, Yan; Zhang, Yajie; Yao, Nianwei; Gu, Nin; Zhang, Xu; Yin, Hong

2018-01-01

Osteoporosis is a serious public health problem and icariin (ICA) is the active component of the Epimedium sagittatum, a traditional Chinese medicinal herb. The present study aimed to investigate the effects and underlying mechanisms of ICA as a potential therapy for osteoporosis. Calvaria osteoblasts were isolated from newborn rats and treated with ICA. Cell viability, apoptosis, alkaline phosphatase activity and calcium deposition were analyzed. Bioinformatics analyses were performed to identify differentially expressed proteins (DEPs) in response to ICA treatment. Western blot analysis was performed to validate the expression of DEPs. ICA administration promoted osteoblast viability, alkaline phosphatase activity, calcium deposition and inhibited osteoblast apoptosis. Secretome analysis of ICA-treated cells was performed using two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. A total of 56 DEPs were identified, including serpin family F member 1 (PEDF), protein disulfide isomerase family A, member 3 (PDIA3), nuclear protein, co-activator of histone transcription (NPAT), c-Myc and heat shock protein 70 (HSP70). These proteins were associated with signaling pathways, including Fas and p53. Bioinformatics and western blot analyses confirmed that the expression levels of the six DEPs were upregulated following ICA treatment. These genes may be directly or indirectly involved in ICA-mediated osteogenic differentiation and osteogenesis. It was demonstrated that ICA treatment promoted osteogenesis by modulating the expression of PEDF, PDIA3, NPAT and HSP70 through signaling pathways, including Fas and p53. PMID:29532868

Combining EEG and eye tracking: identification, characterization, and correction of eye movement artifacts in electroencephalographic data

PubMed Central

Plöchl, Michael; Ossandón, José P.; König, Peter

2012-01-01

Eye movements introduce large artifacts to electroencephalographic recordings (EEG) and thus render data analysis difficult or even impossible. Trials contaminated by eye movement and blink artifacts have to be discarded, hence in standard EEG-paradigms subjects are required to fixate on the screen. To overcome this restriction, several correction methods including regression and blind source separation have been proposed. Yet, there is no automated standard procedure established. By simultaneously recording eye movements and 64-channel-EEG during a guided eye movement paradigm, we investigate and review the properties of eye movement artifacts, including corneo-retinal dipole changes, saccadic spike potentials and eyelid artifacts, and study their interrelations during different types of eye- and eyelid movements. In concordance with earlier studies our results confirm that these artifacts arise from different independent sources and that depending on electrode site, gaze direction, and choice of reference these sources contribute differently to the measured signal. We assess the respective implications for artifact correction methods and therefore compare the performance of two prominent approaches, namely linear regression and independent component analysis (ICA). We show and discuss that due to the independence of eye artifact sources, regression-based correction methods inevitably over- or under-correct individual artifact components, while ICA is in principle suited to address such mixtures of different types of artifacts. Finally, we propose an algorithm, which uses eye tracker information to objectively identify eye-artifact related ICA-components (ICs) in an automated manner. In the data presented here, the algorithm performed very similar to human experts when those were given both, the topographies of the ICs and their respective activations in a large amount of trials. Moreover it performed more reliable and almost twice as effective than human experts when those had to base their decision on IC topographies only. Furthermore, a receiver operating characteristic (ROC) analysis demonstrated an optimal balance of false positive and false negative at an area under curve (AUC) of more than 0.99. Removing the automatically detected ICs from the data resulted in removal or substantial suppression of ocular artifacts including microsaccadic spike potentials, while the relevant neural signal remained unaffected. In conclusion the present work aims at a better understanding of individual eye movement artifacts, their interrelations and the respective implications for eye artifact correction. Additionally, the proposed ICA-procedure provides a tool for optimized detection and correction of eye movement-related artifact components. PMID:23087632

Directional connectivity of resting state human fMRI data using cascaded ICA-PDC analysis.

PubMed

Silfverhuth, Minna J; Remes, Jukka; Starck, Tuomo; Nikkinen, Juha; Veijola, Juha; Tervonen, Osmo; Kiviniemi, Vesa

2011-11-01

Directional connectivity measures, such as partial directed coherence (PDC), give us means to explore effective connectivity in the human brain. By utilizing independent component analysis (ICA), the original data-set reduction was performed for further PDC analysis. To test this cascaded ICA-PDC approach in causality studies of human functional magnetic resonance imaging (fMRI) data. Resting state group data was imaged from 55 subjects using a 1.5 T scanner (TR 1800 ms, 250 volumes). Temporal concatenation group ICA in a probabilistic ICA and further repeatability runs (n = 200) were overtaken. The reduced data-set included the time series presentation of the following nine ICA components: secondary somatosensory cortex, inferior temporal gyrus, intracalcarine cortex, primary auditory cortex, amygdala, putamen and the frontal medial cortex, posterior cingulate cortex and precuneus, comprising the default mode network components. Re-normalized PDC (rPDC) values were computed to determine directional connectivity at the group level at each frequency. The integrative role was suggested for precuneus while the role of major divergence region may be proposed to primary auditory cortex and amygdala. This study demonstrates the potential of the cascaded ICA-PDC approach in directional connectivity studies of human fMRI.

Automatic classification of artifactual ICA-components for artifact removal in EEG signals.

PubMed

Winkler, Irene; Haufe, Stefan; Tangermann, Michael

2011-08-02

Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.

Acoustic transient classification with a template correlation processor.

PubMed

Edwards, R T

1999-10-01

I present an architecture for acoustic pattern classification using trinary-trinary template correlation. In spite of its computational simplicity, the algorithm and architecture represent a method which greatly reduces bandwidth of the input, storage requirements of the classifier memory, and power consumption of the system without compromising classification accuracy. The linear system should be amenable to training using recently-developed methods such as Independent Component Analysis (ICA), and we predict that behavior will be qualitatively similar to that of structures in the auditory cortex.

Gaussian process based independent analysis for temporal source separation in fMRI.

PubMed

Hald, Ditte Høvenhoff; Henao, Ricardo; Winther, Ole

2017-05-15

Functional Magnetic Resonance Imaging (fMRI) gives us a unique insight into the processes of the brain, and opens up for analyzing the functional activation patterns of the underlying sources. Task-inferred supervised learning with restrictive assumptions in the regression set-up, restricts the exploratory nature of the analysis. Fully unsupervised independent component analysis (ICA) algorithms, on the other hand, can struggle to detect clear classifiable components on single-subject data. We attribute this shortcoming to inadequate modeling of the fMRI source signals by failing to incorporate its temporal nature. fMRI source signals, biological stimuli and non-stimuli-related artifacts are all smooth over a time-scale compatible with the sampling time (TR). We therefore propose Gaussian process ICA (GPICA), which facilitates temporal dependency by the use of Gaussian process source priors. On two fMRI data sets with different sampling frequency, we show that the GPICA-inferred temporal components and associated spatial maps allow for a more definite interpretation than standard temporal ICA methods. The temporal structures of the sources are controlled by the covariance of the Gaussian process, specified by a kernel function with an interpretable and controllable temporal length scale parameter. We propose a hierarchical model specification, considering both instantaneous and convolutive mixing, and we infer source spatial maps, temporal patterns and temporal length scale parameters by Markov Chain Monte Carlo. A companion implementation made as a plug-in for SPM can be downloaded from https://github.com/dittehald/GPICA. Copyright © 2017 Elsevier Inc. All rights reserved.

A nonlinear circuit architecture for magnetoencephalographic signal analysis.

PubMed

Bucolo, M; Fortuna, L; Frasca, M; La Rosa, M; Virzì, M C; Shannahoff-Khalsa, D

2004-01-01

The objective of this paper was to face the complex spatio-temporal dynamics shown by Magnetoencephalography (MEG) data by applying a nonlinear distributed approach for the Blind Sources Separation. The effort was to characterize and differ-entiate the phases of a yogic respiratory exercise used in the treatment of obsessive compulsive disorders. The patient performed a precise respiratory protocol, at one breath per minute for 31 minutes, with 10 minutes resting phase before and after. The two steps of classical Independent Component Approach have been performed by using a Cellular Neural Network with two sets of templates. The choice of the couple of suitable templates has been carried out using genetic algorithm optimization techniques. Performing BSS with a nonlinear distributed approach, the outputs of the CNN have been compared to the ICA ones. In all the protocol phases, the main components founded with CNN have similar trends compared with that ones obtained with ICA. Moreover, using this distributed approach, a spatial location has been associated to each component. To underline the spatio-temporal and the nonlinearly of the neural process a distributed nonlinear architecture has been proposed. This strategy has been designed in order to overcome the hypothesis of linear combination among the sources signals, that is characteristic of the ICA approach, taking advantage of the spatial information.

Comparison of multi-subject ICA methods for analysis of fMRI data

PubMed Central

Erhardt, Erik Barry; Rachakonda, Srinivas; Bedrick, Edward; Allen, Elena; Adali, Tülay; Calhoun, Vince D.

2010-01-01

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however there has not yet been a full comparison of the implications of their use. Here, we provide extensive comparisons of four multi-subject ICA approaches in combination with data reduction methods for simulated and fMRI task data. For multi-subject ICA, the data first undergo reduction at the subject and group levels using principal component analysis (PCA). Comparisons of subject-specific, spatial concatenation, and group data mean subject-level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject-specific and group data mean subject-level PCA are preferred because of well-estimated TCs and SMs. Second, aggregate independent components are estimated using either noise free ICA or probabilistic ICA (PICA). Third, subject-specific SMs and TCs are estimated using back-reconstruction. We compare several direct group ICA (GICA) back-reconstruction approaches (GICA1-GICA3) and an indirect back-reconstruction approach, spatio-temporal regression (STR, or dual regression). Results show the earlier group ICA (GICA1) approximates STR, however STR has contradictory assumptions and may show mixed-component artifacts in estimated SMs. Our evidence-based recommendation is to use GICA3, introduced here, with subject-specific PCA and noise-free ICA, providing the most robust and accurate estimated SMs and TCs in addition to offering an intuitive interpretation. PMID:21162045

Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis.

PubMed

Cong, Fengyu; Puoliväli, Tuomas; Alluri, Vinoo; Sipola, Tuomo; Burunat, Iballa; Toiviainen, Petri; Nandi, Asoke K; Brattico, Elvira; Ristaniemi, Tapani

2014-02-15

Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA. For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated with musical features were selected. Finally, for individual ICA, common components across majority of participants were found by diffusion map and spectral clustering. The extracted spatial maps (by the new ICA approach) common across most participants evidenced slightly right-lateralized activity within and surrounding the auditory cortices. Meanwhile, they were found associated with the musical features. Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model order selection methods underestimated the true number of sources in the conventionally pre-processed fMRI data for the individual ICA. Pre-processing the fMRI data by using a reasonable band-pass digital filter can greatly benefit the following model order selection and ICA with fMRI data by naturalistic paradigms. Diffusion map and spectral clustering are straightforward tools to find common ICA spatial maps. Copyright © 2013 Elsevier B.V. All rights reserved.

Effect of ionized serum calcium on outcomes in acute kidney injury needing renal replacement therapy: Secondary analysis of the Acute Renal Failure Trial Network Study

PubMed Central

Afshinnia, Farsad; Belanger, Karen; Palevsky, Paul M.; Young, Eric W.

2014-01-01

Background Hypocalcemia is very common in critically ill patients. While the effect of ionized calcium (iCa) on outcome is not well understood, manipulation of iCa in critically ill patients is a common practice. We analyzed all-cause mortality and several secondary outcomes in patients with acute kidney injury (AKI) by categories of serum iCa among participants in the Acute Renal Failure Trial Network (ATN) Study. Methods This is a post hoc secondary analysis of the ATN Study which was not preplanned in the original trial. Risk of mortality and renal recovery by categories of iCa were compared using multiple fixed and adjusted time-varying Cox regression models. Multiple linear regression models were used to explore the impact of baseline iCa on days free from ICU and hospital. Results A total of 685 patients were included in the analysis. Mean age was 60 (SD=15) years. There were 502 male patients (73.3%). Sixty-day all-cause mortality was 57.0%, 54.8%, and 54.4%, in patients with an iCa <1, 1–1.14, and ≥1.15 mmol/L, respectively (P=0.87). Mean of days free from ICU or hospital in all patients and the 28-day renal recovery in survivors to day 28 were not significantly different by categories of iCa. The hazard for death in a fully adjusted time-varying Cox regression survival model was 1.7 (95% CI: 1.3–2.4) comparing iCa <1 to iCa ≥1.15 mmol/L. No outcome was different for levels of iCa >1 mmol/L. Conclusion Severe hypocalcemia with iCa <1 mmol/L independently predicted mortality in patients with AKI needing renal replacement therapy. PMID:23992422

Strategies for reducing large fMRI data sets for independent component analysis.

PubMed

Wang, Ze; Wang, Jiongjiong; Calhoun, Vince; Rao, Hengyi; Detre, John A; Childress, Anna R

2006-06-01

In independent component analysis (ICA), principal component analysis (PCA) is generally used to reduce the raw data to a few principal components (PCs) through eigenvector decomposition (EVD) on the data covariance matrix. Although this works for spatial ICA (sICA) on moderately sized fMRI data, it is intractable for temporal ICA (tICA), since typical fMRI data have a high spatial dimension, resulting in an unmanageable data covariance matrix. To solve this problem, two practical data reduction methods are presented in this paper. The first solution is to calculate the PCs of tICA from the PCs of sICA. This approach works well for moderately sized fMRI data; however, it is highly computationally intensive, even intractable, when the number of scans increases. The second solution proposed is to perform PCA decomposition via a cascade recursive least squared (CRLS) network, which provides a uniform data reduction solution for both sICA and tICA. Without the need to calculate the covariance matrix, CRLS extracts PCs directly from the raw data, and the PC extraction can be terminated after computing an arbitrary number of PCs without the need to estimate the whole set of PCs. Moreover, when the whole data set becomes too large to be loaded into the machine memory, CRLS-PCA can save data retrieval time by reading the data once, while the conventional PCA requires numerous data retrieval steps for both covariance matrix calculation and PC extractions. Real fMRI data were used to evaluate the PC extraction precision, computational expense, and memory usage of the presented methods.

In-silico and in-vitro anti-cancer potential of a curcumin analogue (1E, 6E)-1, 7-di (1H-indol-3-yl) hepta-1, 6-diene-3, 5-dione.

PubMed

Sufi, Shamim Akhtar; Adigopula, Lakshmi Narayana; Syed, Safiulla Basha; Mukherjee, Victor; Coumar, Mohane S; Rao, H Surya Prakash; Rajagopalan, Rukkumani

2017-01-01

Previously we showed that BDMC, an analogue of curcumin suppresses growth of human breast and laryngeal cancer cell line by causing apoptosis. Here, we demonstrate the enhanced anti-cancer activity of a heterocyclic ring (indole) incorporated curcumin analogue ((1E, 6E)-1, 7-di (1H-indol-3-yl) hepta-1, 6-diene-3, 5-Dione), ICA in short, in comparison to curcumin. ICA was synthesized by a one pot condensation reaction. Anti-cancer potential of ICA was assessed in three human cancer cell lines of different origin (Lung adenocarcinoma (A549), leukemia (K562) and colon cancer (SW480)) by MTT assay. Mode of cell death was determined by acridine orange-ethidium bromide (Ao-Eb) staining. Putative cellular targets of ICA were investigated by molecular docking studies. Cell cycle analysis following curcumin or ICA treatment in SW480 cell line was carried out by flow cytometry. Expression levels of Cyclin D1 and apoptotic markers, such as Caspase 3, 8 and 9 were studied by western blot analysis in SW480 cell line treated with or without ICA and curcumin. The yield of ICA synthesis was found to be 69% with a purity of 98%. ICA demonstrated promising anti-cancer activity compared to curcumin alone, as discerned by MTT assay. ICA was non-toxic to the cell line of normal origin. We further observed that ICA is ∼2 fold more potent than curcumin in inhibiting the growth of SW480 cells. Ao-Eb staining revealed that ICA could induce apoptosis in all the cell lines tested. Molecular docking studies suggest that ICA may possibly exhibit its anticancer effect by inhibiting EGFR in A549, Bcr-Abl in K562 and GSK-3β kinase in SW480 cell line. Moreover, ICA showed strong binding avidity for Bcl-2 protein in silico, which could result in induction of apoptosis. Cell cycle analysis revealed that both curcumin and ICA induced concomitant cell cycle arrest at G0/G1 and G2/M phase. Western blot shows that ICA could effectively down regulate the expression of cell cycle protein cyclin D1, while promoting the activation of Caspase 3, 8 and 9 when compared to curcumin in human colon cancer cell line SW480. The result of this study indicates that ICA could hold promise to be a potential anti-cancer agent. Since ICA has shown encouraging results in terms of its anti-cancer activity compared to curcumin, further research is necessary to fully delineate the underlying molecular mechanism of its anticancer potential. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Role of diversity in ICA and IVA: theory and applications

NASA Astrophysics Data System (ADS)

Adalı, Tülay

2016-05-01

Independent component analysis (ICA) has been the most popular approach for solving the blind source separation problem. Starting from a simple linear mixing model and the assumption of statistical independence, ICA can recover a set of linearly-mixed sources to within a scaling and permutation ambiguity. It has been successfully applied to numerous data analysis problems in areas as diverse as biomedicine, communications, finance, geo- physics, and remote sensing. ICA can be achieved using different types of diversity—statistical property—and, can be posed to simultaneously account for multiple types of diversity such as higher-order-statistics, sample dependence, non-circularity, and nonstationarity. A recent generalization of ICA, independent vector analysis (IVA), generalizes ICA to multiple data sets and adds the use of one more type of diversity, statistical dependence across the data sets, for jointly achieving independent decomposition of multiple data sets. With the addition of each new diversity type, identification of a broader class of signals become possible, and in the case of IVA, this includes sources that are independent and identically distributed Gaussians. We review the fundamentals and properties of ICA and IVA when multiple types of diversity are taken into account, and then ask the question whether diversity plays an important role in practical applications as well. Examples from various domains are presented to demonstrate that in many scenarios it might be worthwhile to jointly account for multiple statistical properties. This paper is submitted in conjunction with the talk delivered for the "Unsupervised Learning and ICA Pioneer Award" at the 2016 SPIE Conference on Sensing and Analysis Technologies for Biomedical and Cognitive Applications.

Lattice Independent Component Analysis for Mobile Robot Localization

NASA Astrophysics Data System (ADS)

Villaverde, Ivan; Fernandez-Gauna, Borja; Zulueta, Ekaitz

This paper introduces an approach to appearance based mobile robot localization using Lattice Independent Component Analysis (LICA). The Endmember Induction Heuristic Algorithm (EIHA) is used to select a set of Strong Lattice Independent (SLI) vectors, which can be assumed to be Affine Independent, and therefore candidates to be the endmembers of the data. Selected endmembers are used to compute the linear unmixing of the robot's acquired images. The resulting mixing coefficients are used as feature vectors for view recognition through classification. We show on a sample path experiment that our approach can recognise the localization of the robot and we compare the results with the Independent Component Analysis (ICA).

Real-space analysis of radiation-induced specific changes with independent component analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Borek, Dominika; Bromberg, Raquel; Hattne, Johan

A method of analysis is presented that allows for the separation of specific radiation-induced changes into distinct components in real space. The method relies on independent component analysis (ICA) and can be effectively applied to electron density maps and other types of maps, provided that they can be represented as sets of numbers on a grid. Here, for glucose isomerase crystals, ICA was used in a proof-of-concept analysis to separate temperature-dependent and temperature-independent components of specific radiation-induced changes for data sets acquired from multiple crystals across multiple temperatures. ICA identified two components, with the temperature-independent component being responsible for themore » majority of specific radiation-induced changes at temperatures below 130 K. The patterns of specific temperature-independent radiation-induced changes suggest a contribution from the tunnelling of electron holes as a possible explanation. In the second case, where a group of 22 data sets was collected on a single thaumatin crystal, ICA was used in another type of analysis to separate specific radiation-induced effects happening on different exposure-level scales. Here, ICA identified two components of specific radiation-induced changes that likely result from radiation-induced chemical reactions progressing with different rates at different locations in the structure. In addition, ICA unexpectedly identified the radiation-damage state corresponding to reduced disulfide bridges rather than the zero-dose extrapolated state as the highest contrast structure. The application of ICA to the analysis of specific radiation-induced changes in real space and the data pre-processing for ICA that relies on singular value decomposition, which was used previously in data space to validate a two-component physical model of X-ray radiation-induced changes, are discussed in detail. This work lays a foundation for a better understanding of protein-specific radiation chemistries and provides a framework for analysing effects of specific radiation damage in crystallographic and cryo-EM experiments.« less

Independent component analysis applied to long bunch beams in the Los Alamos Proton Storage Ring

NASA Astrophysics Data System (ADS)

Kolski, Jeffrey S.; Macek, Robert J.; McCrady, Rodney C.; Pang, Xiaoying

2012-11-01

Independent component analysis (ICA) is a powerful blind source separation (BSS) method. Compared to the typical BSS method, principal component analysis, ICA is more robust to noise, coupling, and nonlinearity. The conventional ICA application to turn-by-turn position data from multiple beam position monitors (BPMs) yields information about cross-BPM correlations. With this scheme, multi-BPM ICA has been used to measure the transverse betatron phase and amplitude functions, dispersion function, linear coupling, sextupole strength, and nonlinear beam dynamics. We apply ICA in a new way to slices along the bunch revealing correlations of particle motion within the beam bunch. We digitize beam signals of the long bunch at the Los Alamos Proton Storage Ring with a single device (BPM or fast current monitor) for an entire injection-extraction cycle. ICA of the digitized beam signals results in source signals, which we identify to describe varying betatron motion along the bunch, locations of transverse resonances along the bunch, measurement noise, characteristic frequencies of the digitizing oscilloscopes, and longitudinal beam structure.

Detection of Partial Discharge Sources Using UHF Sensors and Blind Signal Separation

PubMed Central

Boya, Carlos; Parrado-Hernández, Emilio

2017-01-01

The measurement of the emitted electromagnetic energy in the UHF region of the spectrum allows the detection of partial discharges and, thus, the on-line monitoring of the condition of the insulation of electrical equipment. Unfortunately, determining the affected asset is difficult when there are several simultaneous insulation defects. This paper proposes the use of an independent component analysis (ICA) algorithm to separate the signals coming from different partial discharge (PD) sources. The performance of the algorithm has been tested using UHF signals generated by test objects. The results are validated by two automatic classification techniques: support vector machines and similarity with class mean. Both methods corroborate the suitability of the algorithm to separate the signals emitted by each PD source even when they are generated by the same type of insulation defect. PMID:29140267

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

PubMed Central

Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M.

2016-01-01

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover. PMID:27438600

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks.

PubMed

Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M

2016-01-01

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.

Use seismic colored inversion and power law committee machine based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir

NASA Astrophysics Data System (ADS)

Ansari, Hamid Reza

2014-09-01

In this paper we propose a new method for predicting rock porosity based on a combination of several artificial intelligence systems. The method focuses on one of the Iranian carbonate fields in the Persian Gulf. Because there is strong heterogeneity in carbonate formations, estimation of rock properties experiences more challenge than sandstone. For this purpose, seismic colored inversion (SCI) and a new approach of committee machine are used in order to improve porosity estimation. The study comprises three major steps. First, a series of sample-based attributes is calculated from 3D seismic volume. Acoustic impedance is an important attribute that is obtained by the SCI method in this study. Second, porosity log is predicted from seismic attributes using common intelligent computation systems including: probabilistic neural network (PNN), radial basis function network (RBFN), multi-layer feed forward network (MLFN), ε-support vector regression (ε-SVR) and adaptive neuro-fuzzy inference system (ANFIS). Finally, a power law committee machine (PLCM) is constructed based on imperial competitive algorithm (ICA) to combine the results of all previous predictions in a single solution. This technique is called PLCM-ICA in this paper. The results show that PLCM-ICA model improved the results of neural networks, support vector machine and neuro-fuzzy system.

Variational Bayesian Learning for Wavelet Independent Component Analysis

NASA Astrophysics Data System (ADS)

Roussos, E.; Roberts, S.; Daubechies, I.

2005-11-01

In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or "sources" via a generally unknown mapping. For the noisy overcomplete case, where we have more sources than observations, the problem becomes extremely ill-posed. Solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian methodology, allowing us to incorporate "soft" constraints in a natural manner. The work described in this paper is mainly driven by problems in functional magnetic resonance imaging of the brain, for the neuro-scientific goal of extracting relevant "maps" from the data. This can be stated as a `blind' source separation problem. Recent experiments in the field of neuroscience show that these maps are sparse, in some appropriate sense. The separation problem can be solved by independent component analysis (ICA), viewed as a technique for seeking sparse components, assuming appropriate distributions for the sources. We derive a hybrid wavelet-ICA model, transforming the signals into a domain where the modeling assumption of sparsity of the coefficients with respect to a dictionary is natural. We follow a graphical modeling formalism, viewing ICA as a probabilistic generative model. We use hierarchical source and mixing models and apply Bayesian inference to the problem. This allows us to perform model selection in order to infer the complexity of the representation, as well as automatic denoising. Since exact inference and learning in such a model is intractable, we follow a variational Bayesian mean-field approach in the conjugate-exponential family of distributions, for efficient unsupervised learning in multi-dimensional settings. The performance of the proposed algorithm is demonstrated on some representative experiments.

Lattice modeling and application of independent component analysis to high power, long bunch beams in the Los Alamos Proton Storage Ring

NASA Astrophysics Data System (ADS)

Kolski, Jeffrey

The linear lattice properties of the Proton Storage Ring (PSR) at the Los Alamos Neutron Science Center (LANSCE) in Los Alamos, NM were measured and applied to determine a better linear accelerator model. We found that the initial model was deficient in predicting the vertical focusing strength. The additional vertical focusing was located through fundamental understanding of experiment and statistically rigorous analysis. An improved model was constructed and compared against the initial model and measurement at operation set points and set points far away from nominal and was shown to indeed be an enhanced model. Independent component analysis (ICA) is a tool for data mining in many fields of science. Traditionally, ICA is applied to turn-by-turn beam position data as a means to measure the lattice functions of the real machine. Due to the diagnostic setup for the PSR, this method is not applicable. A new application method for ICA is derived, ICA applied along the length of the bunch. The ICA modes represent motions within the beam pulse. Several of the dominate ICA modes are experimentally identified.

Least-dependent-component analysis based on mutual information

NASA Astrophysics Data System (ADS)

Stögbauer, Harald; Kraskov, Alexander; Astakhov, Sergey A.; Grassberger, Peter

2004-12-01

We propose to use precise estimators of mutual information (MI) to find the least dependent components in a linearly mixed signal. On the one hand, this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand, it has the advantage, compared to other implementations of “independent” component analysis (ICA), some of which are based on crude approximations for MI, that the numerical values of the MI can be used for (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output by comparing the pairwise MIs with those of remixed components; and (iii) clustering the output according to the residual interdependencies. For the MI estimator, we use a recently proposed k -nearest-neighbor-based algorithm. For time sequences, we combine this with delay embedding, in order to take into account nontrivial time correlations. After several tests with artificial data, we apply the resulting MILCA (mutual-information-based least dependent component analysis) algorithm to a real-world dataset, the ECG of a pregnant woman.

Face recognition using an enhanced independent component analysis approach.

PubMed

Kwak, Keun-Chang; Pedrycz, Witold

2007-03-01

This paper is concerned with an enhanced independent component analysis (ICA) and its application to face recognition. Typically, face representations obtained by ICA involve unsupervised learning and high-order statistics. In this paper, we develop an enhancement of the generic ICA by augmenting this method by the Fisher linear discriminant analysis (LDA); hence, its abbreviation, FICA. The FICA is systematically developed and presented along with its underlying architecture. A comparative analysis explores four distance metrics, as well as classification with support vector machines (SVMs). We demonstrate that the FICA approach leads to the formation of well-separated classes in low-dimension subspace and is endowed with a great deal of insensitivity to large variation in illumination and facial expression. The comprehensive experiments are completed for the facial-recognition technology (FERET) face database; a comparative analysis demonstrates that FICA comes with improved classification rates when compared with some other conventional approaches such as eigenface, fisherface, and the ICA itself.

Effect of contrast leakage on the detection of abnormal brain tumor vasculature in high-grade glioma.

PubMed

LaViolette, Peter S; Daun, Mitchell K; Paulson, Eric S; Schmainda, Kathleen M

2014-02-01

Abnormal brain tumor vasculature has recently been highlighted by a dynamic susceptibility contrast (DSC) MRI processing technique. The technique uses independent component analysis (ICA) to separate arterial and venous perfusion. The overlap of the two, i.e. arterio-venous overlap or AVOL, preferentially occurs in brain tumors and predicts response to anti-angiogenic therapy. The effects of contrast agent leakage on the AVOL biomarker have yet to be established. DSC was acquired during two separate contrast boluses in ten patients undergoing clinical imaging for brain tumor diagnosis. Three components were modeled with ICA, which included the arterial and venous components. The percentage of each component as well as a third component were determined within contrast enhancing tumor and compared. AVOL within enhancing tumor was also compared between doses. The percentage of enhancing tumor classified as not arterial or venous and instead into a third component with contrast agent leakage apparent in the time-series was significantly greater for the first contrast dose compared to the second. The amount of AVOL detected within enhancing tumor was also significantly greater with the second dose compared to the first. Contrast leakage results in large signal variance classified as a separate component by the ICA algorithm. The use of a second dose mitigates the effect and allows measurement of AVOL within enhancement.

Artifact removal in the context of group ICA: a comparison of single-subject and group approaches

PubMed Central

Du, Yuhui; Allen, Elena A.; He, Hao; Sui, Jing; Wu, Lei; Calhoun, Vince D.

2018-01-01

Independent component analysis (ICA) has been widely applied to identify intrinsic brain networks from fMRI data. Group ICA computes group-level components from all data and subsequently estimates individual-level components to recapture inter-subject variability. However, the best approach to handle artifacts, which may vary widely among subjects, is not yet clear. In this work, we study and compare two ICA approaches for artifacts removal. One approach, recommended in recent work by the Human Connectome Project, first performs ICA on individual subject data to remove artifacts, and then applies a group ICA on the cleaned data from all subjects. We refer to this approach as Individual ICA based artifacts Removal Plus Group ICA (IRPG). A second proposed approach, called Group Information Guided ICA (GIG-ICA), performs ICA on group data, then removes the group-level artifact components, and finally performs subject-specific ICAs using the group-level non-artifact components as spatial references. We used simulations to evaluate the two approaches with respect to the effects of data quality, data quantity, variable number of sources among subjects, and spatially unique artifacts. Resting-state test-retest datasets were also employed to investigate the reliability of functional networks. Results from simulations demonstrate GIG-ICA has greater performance compared to IRPG, even in the case when single-subject artifacts removal is perfect and when individual subjects have spatially unique artifacts. Experiments using test-retest data suggest that GIG-ICA provides more reliable functional networks. Based on high estimation accuracy, ease of implementation, and high reliability of functional networks, we find GIG-ICA to be a promising approach. PMID:26859308

Source-space ICA for MEG source imaging.

PubMed

Jonmohamadi, Yaqub; Jones, Richard D

2016-02-01

One of the most widely used approaches in electroencephalography/magnetoencephalography (MEG) source imaging is application of an inverse technique (such as dipole modelling or sLORETA) on the component extracted by independent component analysis (ICA) (sensor-space ICA + inverse technique). The advantage of this approach over an inverse technique alone is that it can identify and localize multiple concurrent sources. Among inverse techniques, the minimum-variance beamformers offer a high spatial resolution. However, in order to have both high spatial resolution of beamformer and be able to take on multiple concurrent sources, sensor-space ICA + beamformer is not an ideal combination. We propose source-space ICA for MEG as a powerful alternative approach which can provide the high spatial resolution of the beamformer and handle multiple concurrent sources. The concept of source-space ICA for MEG is to apply the beamformer first and then singular value decomposition + ICA. In this paper we have compared source-space ICA with sensor-space ICA both in simulation and real MEG. The simulations included two challenging scenarios of correlated/concurrent cluster sources. Source-space ICA provided superior performance in spatial reconstruction of source maps, even though both techniques performed equally from a temporal perspective. Real MEG from two healthy subjects with visual stimuli were also used to compare performance of sensor-space ICA and source-space ICA. We have also proposed a new variant of minimum-variance beamformer called weight-normalized linearly-constrained minimum-variance with orthonormal lead-field. As sensor-space ICA-based source reconstruction is popular in EEG and MEG imaging, and given that source-space ICA has superior spatial performance, it is expected that source-space ICA will supersede its predecessor in many applications.

Characterization of Ground Displacement Sources from Variational Bayesian Independent Component Analysis of Space Geodetic Time Series

NASA Astrophysics Data System (ADS)

Gualandi, Adriano; Serpelloni, Enrico; Elina Belardinelli, Maria; Bonafede, Maurizio; Pezzo, Giuseppe; Tolomei, Cristiano

2015-04-01

A critical point in the analysis of ground displacement time series, as those measured by modern space geodetic techniques (primarly continuous GPS/GNSS and InSAR) is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies, since PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem. The recovering and separation of the different sources that generate the observed ground deformation is a fundamental task in order to provide a physical meaning to the possible different sources. PCA fails in the BSS problem since it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the displacement time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient deformation signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here we introduce the vbICA technique and present its application on synthetic data that simulate a GPS network recording ground deformation in a tectonically active region, with synthetic time-series containing interseismic, coseismic, and postseismic deformation, plus seasonal deformation, and white and coloured noise. We study the ability of the algorithm to recover the original (known) sources of deformation, and then apply it to a real scenario: the Emilia seismic sequence (2012, northern Italy), which is an example of seismic sequence occurred in a slowly converging tectonic setting, characterized by several local to regional anthropogenic or natural sources of deformation, mainly subsidence due to fluid withdrawal and sediments compaction. We apply both PCA and vbICA to displacement time-series recorded by continuous GPS and InSAR (Pezzo et al., EGU2015-8950).

Independent component analysis based digital signal processing in coherent optical fiber communication systems

NASA Astrophysics Data System (ADS)

Li, Xiang; Luo, Ming; Qiu, Ying; Alphones, Arokiaswami; Zhong, Wen-De; Yu, Changyuan; Yang, Qi

2018-02-01

In this paper, channel equalization techniques for coherent optical fiber transmission systems based on independent component analysis (ICA) are reviewed. The principle of ICA for blind source separation is introduced. The ICA based channel equalization after both single-mode fiber and few-mode fiber transmission for single-carrier and orthogonal frequency division multiplexing (OFDM) modulation formats are investigated, respectively. The performance comparisons with conventional channel equalization techniques are discussed.

Danish study of Non-Invasive testing in Coronary Artery Disease (Dan-NICAD): study protocol for a randomised controlled trial.

PubMed

Nissen, Louise; Winther, Simon; Isaksen, Christin; Ejlersen, June Anita; Brix, Lau; Urbonaviciene, Grazina; Frost, Lars; Madsen, Lene Helleskov; Knudsen, Lars Lyhne; Schmidt, Samuel Emil; Holm, Niels Ramsing; Maeng, Michael; Nyegaard, Mette; Bøtker, Hans Erik; Bøttcher, Morten

2016-05-26

Coronary computed tomography angiography (CCTA) is an established method for ruling out coronary artery disease (CAD). Most patients referred for CCTA do not have CAD and only approximately 20-30 % of patients are subsequently referred to further testing by invasive coronary angiography (ICA) or non-invasive perfusion evaluation due to suspected obstructive CAD. In cases with severe calcifications, a discrepancy between CCTA and ICA often occurs, leading to the well-described, low-diagnostic specificity of CCTA. As ICA is cost consuming and involves a risk of complications, an optimized algorithm would be valuable and could decrease the number of ICAs that do not lead to revascularization. The primary objective of the Dan-NICAD study is to determine the diagnostic accuracy of cardiac magnetic resonance imaging (CMRI) and myocardial perfusion scintigraphy (MPS) as secondary tests after a primary CCTA where CAD could not be ruled out. The secondary objective includes an evaluation of the diagnostic precision of an acoustic technology that analyses the sound of coronary blood flow. It may potentially provide better stratification prior to CCTA than clinical risk stratification scores alone. Dan-NICAD is a multi-centre, randomised, cross-sectional trial, which will include approximately 2,000 patients without known CAD, who were referred to CCTA due to a history of symptoms suggestive of CAD and a low-risk to intermediate-risk profile, as evaluated by a cardiologist. Patient interview, sound recordings, and blood samples are obtained in connection with the CCTA. All patients with suspected obstructive CAD by CCTA are randomised to either stress CMRI or stress MPS, followed by ICA with fractional flow reserve (FFR) measurements. Obstructive CAD is defined as an FFR below 0.80 or as high-grade stenosis (>90 % diameter stenosis) by visual assessment. Diagnostic performance is evaluated as sensitivity, specificity, predictive values, likelihood ratios, and C statistics. Enrolment commenced in September 2014 and is expected to be complete in May 2016. Dan-NICAD is designed to assess whether a secondary perfusion examination after CCTA could safely reduce the number of ICAs where revascularization is not required. The results are expected to add knowledge about the optimal algorithm for diagnosing CAD. Clinicaltrials.gov identifier, NCT02264717 . Registered on 26 September 2014.

Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.

PubMed

Jahed Armaghani, Danial; Hajihassani, Mohsen; Marto, Aminaton; Shirani Faradonbeh, Roohollah; Mohamad, Edy Tonnizam

2015-11-01

Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.

Combined ICA-LORETA analysis of mismatch negativity.

PubMed

Marco-Pallarés, J; Grau, C; Ruffini, G

2005-04-01

A major challenge for neuroscience is to map accurately the spatiotemporal patterns of activity of the large neuronal populations that are believed to underlie computing in the human brain. To study a specific example, we selected the mismatch negativity (MMN) brain wave (an event-related potential, ERP) because it gives an electrophysiological index of a "primitive intelligence" capable of detecting changes, even abstract ones, in a regular auditory pattern. ERPs have a temporal resolution of milliseconds but appear to result from mixed neuronal contributions whose spatial location is not fully understood. Thus, it is important to separate these sources in space and time. To tackle this problem, a two-step approach was designed combining the independent component analysis (ICA) and low-resolution tomography (LORETA) algorithms. Here we implement this approach to analyze the subsecond spatiotemporal dynamics of MMN cerebral sources using trial-by-trial experimental data. We show evidence that a cerebral computation mechanism underlies MMN. This mechanism is mediated by the orchestrated activity of several spatially distributed brain sources located in the temporal, frontal, and parietal areas, which activate at distinct time intervals and are grouped in six main statistically independent components.

Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals

PubMed Central

2011-01-01

Background Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. Methods We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Results Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. Conclusions We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies. PMID:21810266

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data

PubMed Central

Calhoun, Vince D.; Liu, Jingyu; Adalı, Tülay

2009-01-01

Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain. PMID:19059344

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data.

PubMed

Calhoun, Vince D; Liu, Jingyu; Adali, Tülay

2009-03-01

Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain.

Comparison of conventional filtering and independent component analysis for artifact reduction in simultaneous gastric EMG and magnetogastrography from porcines.

PubMed

Irimia, Andrei; Richards, William O; Bradshaw, L Alan

2009-11-01

In this study, we perform a comparative study of independent component analysis (ICA) and conventional filtering (CF) for the purpose of artifact reduction from simultaneous gastric EMG and magnetogastrography (MGG). EMG/MGG data were acquired from ten anesthetized pigs by obtaining simultaneous recordings using serosal electrodes (EMG) as well as with a superconducting quantum interference device biomagnetometer (MGG). The analysis of MGG waveforms using ICA and CF indicates that ICA is superior to the CF method in its ability to extract respiration and cardiac artifacts from MGG recordings. A signal frequency analysis of ICA- and CF-processed data was also undertaken using waterfall plots, and it was determined that the two methods produce qualitatively comparable results. Through the use of simultaneous EMG/MGG, we were able to demonstrate the accuracy and trustworthiness of our results by comparison and cross-validation within the framework of a porcine model.

Overview of evidence on emergency carotid stenting in patients with acute ischemic stroke due to tandem occlusions: a systematic review and meta-analysis.

PubMed

Coelho, Andreia; Lobo, Miguel; Gouveia, Ricardo; Silveira, Diogo; Campos, Jacinta; Augusto, Rita; Coelho, Nuno; Canedo, Alexandra

2018-01-23

Endovascular intracranial thrombectomy (IT) has established itself as the standard of care in treating large-vessel anterior circulation acute ischemic stroke (AIS). However, internal carotid artery (ICA) stenosis/occlusion hampers distal access and controversy about simultaneous emergency ICA stenting ensues. The purpose of this review was to evaluate the safety of emergency ICA stenting in combination with IT for AIS with tandem occlusions. To our knowledge this is the first meta-analysis to evaluate emergency ICA stenting in tandem occlusions, combining results from studies with a control group. A systematic review and meta-analysis was conducted according to the recommendations of the Preferred Reporting Items for Systematic reviews and Meta- Analyses (PRISMA) statement. A total of 649 potentially relevant articles were initially selected. After reviewing at title or abstract level, 87 articles were read in full and 23 were included. These studies recruited 1000 patients, 220 submitted to IT with no emergency ICA stenting and 780 to IT and emergency ICA stenting. Successful revascularization (Thrombolysis in cerebral infarction scale - TICI≥2b) was achieved in 48.6-100%. Good outcome (modified Rankin scale - mRS≤2) ranged from 18.2-100%. Symptomatic intracranial haemorrhage (sICH) ranged from 0-45.7% (overall n=168; 17.2%). Mortality at 90 days ranged from 0-45.4% (overall n=114; 11.7%). Time to recanalization was significantly longer in the stenting group with an overall mean difference of 1.76 (95% Confidence Interval: 1.59-1.93). In this meta-analysis time to recanalization was significantly longer in the emergency ICA stenting group. There was no benefit from emergency stenting in parameters such as successful revascularization (TICI≥2b), clinical outcome (mRS≤2) or 90-day mortality. Data on sICH were scarce. Emergency ICA stenting appears to increase time to revascularization and increase the risk of complications with no demonstrated clinical benefit. Furthermore, no prospective, randomized controlled trials demonstrating relative efficacy and safety of concomitant ICA stenting have been published to date. Additional studies must be undertaken to define the role of angioplasty and stenting of the extracranial carotid arteries in the early management of acute stroke in tandem occlusions. Until then, we recommend that ICA stenting concommintant to thrombectomy in acute stroke patients should be avoided.

Blind extraction of exoplanetary spectra

NASA Astrophysics Data System (ADS)

Morello, Giuseppe; Waldmann, Ingo P.; Tinetti, Giovanna

2016-06-01

In the last decade, remote sensing spectroscopy enabled characterization of the atmospheres of extrasolar planets. Transmission and emission spectra of tens of transiting exoplanets have been measured with multiple instruments aboard Spitzer and Hubble Space Telescopes as well as ground-based facilities, revealing the presence of atomic, ionic and molecular species in their atmospheres, and constraining their temperature and pressure profiles.Early analyses were somehow heuristic both in measuring the spectra and in their interpretation, leading to some controversies in the literature.A photometric precision of 0.01% is necessary to detect the atmospheric spectral modulations. Current observatories, except Kepler, were not designed to achieve this precision. Data reduction is necessary to minimize the effect of instrument systematics in order to achieve the target precision. In the past, parametric models have extensively been used by most teams to remove correlated noise with the aid of auxiliary information of the instrument, the so-called optical state vectors (OSVs). Such OSVs can include inter- and intra-pixel position of the star or its spectrum, instrument temperatures and inclinations, and/or other parameters. In some cases, different parameterizations led to discrepant results.We recommend the use of blind non-parametric data detrending techniques to overcome those issues. In particular, we adopt Independent Component Analysis (ICA), i.e. a blind source separation (BSS) technique to disentangle the multiple instrument systematics and astrophysical signals in transit/eclipse light curves. ICA does not require a model for the systematics, and for this reason, it can be applied to any instrument with little changes, if any. ICA-based algorithms have been applied to Spitzer/IRAC and synthetic observations in photometry (Morello et al. 2014, 2015, 2016; Morello 2015) and to Hubble/NICMOS and Spitzer/IRS in spectroscopy (Waldmann 2012, 2014, Waldmann et al. 2013) with excellent results. In this conference, I will illustrate the detrending algorithms optimized to specific instruments and the results obtained over different observations, in addition the already published ones.

Non-contact cardiac pulse rate estimation based on web-camera

NASA Astrophysics Data System (ADS)

Wang, Yingzhi; Han, Tailin

2015-12-01

In this paper, we introduce a new methodology of non-contact cardiac pulse rate estimation based on the imaging Photoplethysmography (iPPG) and blind source separation. This novel's approach can be applied to color video recordings of the human face and is based on automatic face tracking along with blind source separation of the color channels into RGB three-channel component. First of all, we should do some pre-processings of the data which can be got from color video such as normalization and sphering. We can use spectrum analysis to estimate the cardiac pulse rate by Independent Component Analysis (ICA) and JADE algorithm. With Bland-Altman and correlation analysis, we compared the cardiac pulse rate extracted from videos recorded by a basic webcam to a Commercial pulse oximetry sensors and achieved high accuracy and correlation. Root mean square error for the estimated results is 2.06bpm, which indicates that the algorithm can realize the non-contact measurements of cardiac pulse rate.

Independent component analysis for cochlear implant artifacts attenuation from electrically evoked auditory steady-state response measurements

NASA Astrophysics Data System (ADS)

Deprez, Hanne; Gransier, Robin; Hofmann, Michael; van Wieringen, Astrid; Wouters, Jan; Moonen, Marc

2018-02-01

Objective. Electrically evoked auditory steady-state responses (EASSRs) are potentially useful for objective cochlear implant (CI) fitting and follow-up of the auditory maturation in infants and children with a CI. EASSRs are recorded in the electro-encephalogram (EEG) in response to electrical stimulation with continuous pulse trains, and are distorted by significant CI artifacts related to this electrical stimulation. The aim of this study is to evaluate a CI artifacts attenuation method based on independent component analysis (ICA) for three EASSR datasets. Approach. ICA has often been used to remove CI artifacts from the EEG to record transient auditory responses, such as cortical evoked auditory potentials. Independent components (ICs) corresponding to CI artifacts are then often manually identified. In this study, an ICA based CI artifacts attenuation method was developed and evaluated for EASSR measurements with varying CI artifacts and EASSR characteristics. Artifactual ICs were automatically identified based on their spectrum. Main results. For 40 Hz amplitude modulation (AM) stimulation at comfort level, in high SNR recordings, ICA succeeded in removing CI artifacts from all recording channels, without distorting the EASSR. For lower SNR recordings, with 40 Hz AM stimulation at lower levels, or 90 Hz AM stimulation, ICA either distorted the EASSR or could not remove all CI artifacts in most subjects, except for two of the seven subjects tested with low level 40 Hz AM stimulation. Noise levels were reduced after ICA was applied, and up to 29 ICs were rejected, suggesting poor ICA separation quality. Significance. We hypothesize that ICA is capable of separating CI artifacts and EASSR in case the contralateral hemisphere is EASSR dominated. For small EASSRs or large CI artifact amplitudes, ICA separation quality is insufficient to ensure complete CI artifacts attenuation without EASSR distortion.

Visual target modulation of functional connectivity networks revealed by self-organizing group ICA.

PubMed

van de Ven, Vincent; Bledowski, Christoph; Prvulovic, David; Goebel, Rainer; Formisano, Elia; Di Salle, Francesco; Linden, David E J; Esposito, Fabrizio

2008-12-01

We applied a data-driven analysis based on self-organizing group independent component analysis (sogICA) to fMRI data from a three-stimulus visual oddball task. SogICA is particularly suited to the investigation of the underlying functional connectivity and does not rely on a predefined model of the experiment, which overcomes some of the limitations of hypothesis-driven analysis. Unlike most previous applications of ICA in functional imaging, our approach allows the analysis of the data at the group level, which is of particular interest in high order cognitive studies. SogICA is based on the hierarchical clustering of spatially similar independent components, derived from single subject decompositions. We identified four main clusters of components, centered on the posterior cingulate, bilateral insula, bilateral prefrontal cortex, and right posterior parietal and prefrontal cortex, consistently across all participants. Post hoc comparison of time courses revealed that insula, prefrontal cortex and right fronto-parietal components showed higher activity for targets than for distractors. Activation for distractors was higher in the posterior cingulate cortex, where deactivation was observed for targets. While our results conform to previous neuroimaging studies, they also complement conventional results by showing functional connectivity networks with unique contributions to the task that were consistent across subjects. SogICA can thus be used to probe functional networks of active cognitive tasks at the group-level and can provide additional insights to generate new hypotheses for further study. Copyright 2007 Wiley-Liss, Inc.

Effect of electrocardiogram interference on cortico-cortical connectivity analysis and a possible solution.

PubMed

Govindan, R B; Kota, Srinivas; Al-Shargabi, Tareq; Massaro, An N; Chang, Taeun; du Plessis, Adre

2016-09-01

Electroencephalogram (EEG) signals are often contaminated by the electrocardiogram (ECG) interference, which affects quantitative characterization of EEG. We propose null-coherence, a frequency-based approach, to attenuate the ECG interference in EEG using simultaneously recorded ECG as a reference signal. After validating the proposed approach using numerically simulated data, we apply this approach to EEG recorded from six newborns receiving therapeutic hypothermia for neonatal encephalopathy. We compare our approach with an independent component analysis (ICA), a previously proposed approach to attenuate ECG artifacts in the EEG signal. The power spectrum and the cortico-cortical connectivity of the ECG attenuated EEG was compared against the power spectrum and the cortico-cortical connectivity of the raw EEG. The null-coherence approach attenuated the ECG contamination without leaving any residual of the ECG in the EEG. We show that the null-coherence approach performs better than ICA in attenuating the ECG contamination without enhancing cortico-cortical connectivity. Our analysis suggests that using ICA to remove ECG contamination from the EEG suffers from redistribution problems, whereas the null-coherence approach does not. We show that both the null-coherence and ICA approaches attenuate the ECG contamination. However, the EEG obtained after ICA cleaning displayed higher cortico-cortical connectivity compared with that obtained using the null-coherence approach. This suggests that null-coherence is superior to ICA in attenuating the ECG interference in EEG for cortico-cortical connectivity analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.

PubMed

Bai, Ou; Lin, Peter; Vorbach, Sherry; Li, Jiang; Furlani, Steve; Hallett, Mark

2007-12-01

To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

Icariin and icaritin stimulate the proliferation of SKBr3 cells through the GPER1-mediated modulation of the EGFR-MAPK signaling pathway.

PubMed

Ma, Hai-Rong; Wang, Jie; Chen, Yiu-Fai; Chen, Hua; Wang, Wei-Shan; Aisa, Haji Akber

2014-06-01

Icariin (ICA) and icaritin (ICT), with a similar structure to genistein, are the important bioactive components of the genus Epimedium, and regulate many cellular processes. In the present study, using the estrogen receptor (ER)-negative breast cancer cell line, SKBr3, as a model, we examined the hypothesis that ICA and ICT at low concentrations stimulate SKBr3 cell proliferation in vitro through the functional membrane, G protein‑coupled estrogen receptor 1 (GPER1), mediated by the epithelial growth factor receptor (EGFR)‑mitogen-activated protein kinase (MAPK) signaling pathway. MTT assay revealed that ICA and ICT at doses of 1 nM to 1 µM markedly stimulated SKBr3 cell proliferation in a dose-dependent manner. The ICA- and ICT-stimulated cell growth was completely suppressed by the GPER1 antagonist, G-15, indicating that the ICA‑ and ICT-stimulated cell proliferation was mediated by GPER1 activation. Semi-quantitative RT-PCR analysis revealed that treatment with ICA and ICT enhanced the transcription of c-fos, a proliferation-related early gene. The ICA- and ICT-stimulated mRNA expression was markedly attenuated by G-15, AG-1478 (an EGFR antagonist) or PD98059 (a MAPK inhibitor). Our data also demonstrated that ICA and ICT increased the phosphorylation of ERK1/2. The ICA- and ICT-stimulated ERK1/2 phosphorylation was blocked by pre-treatment of the cells with G-15 and AG-1478 or PD 98059. Flow cytometric analysis confirmed that the ICA- and ICT-stimulated SKBr3 cell proliferation involved the GPER1-mediated modulation of the EGFR‑MAPK signaling pathway. To the best of our knowledge, our current findings demonstrate for the first time that ICA and ICT promote the progression of ER-negative breast cancer through the activation of membrane GPER1.

The Comparison Between Nmf and Ica in Pigment Mixture Identification of Ancient Chinese Paintings

NASA Astrophysics Data System (ADS)

Liu, Y.; Lyu, S.; Hou, M.; Yin, Q.

2018-04-01

Since the colour in painting cultural relics observed by our naked eyes or hyperspectral cameras is usually a mixture of several kinds of pigments, the mixed pigments analysis will be an important subject in the field of ancient painting conservation and restoration. This paper aims to find a more effective method to confirm the types of every pure pigment from mixture on the surface of paintings. Firstly, we adopted two kinds of blind source separation algorithms, which are independent component analysis and non-negative matrix factorization, to extract the pure pigment component from mixed spectrum respectively. Moreover, we matched the separated pure spectrum with the pigments spectra library built by our team to determine the pigment type. Furthermore, three kinds of data including simulation data, mixed pigments spectral data measured in laboratory, and the spectral data of an ancient painting were chosen to evaluate the performance of the different algorithms. And the accuracy was compared between the two algorithms. Finally, the experimental results show that non-negative matrix factorization method is more suitable for endmember extraction in the field of ancient painting conservation and restoration.

EEG Artifact Removal Using a Wavelet Neural Network

NASA Technical Reports Server (NTRS)

Nguyen, Hoang-Anh T.; Musson, John; Li, Jiang; McKenzie, Frederick; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom

2011-01-01

!n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

Single-trial event-related potential extraction through one-unit ICA-with-reference

NASA Astrophysics Data System (ADS)

Lih Lee, Wee; Tan, Tele; Falkmer, Torbjörn; Leung, Yee Hong

2016-12-01

Objective. In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed. Approach. In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly. Main results. Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction. Significance. In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application.

Single-trial event-related potential extraction through one-unit ICA-with-reference.

PubMed

Lee, Wee Lih; Tan, Tele; Falkmer, Torbjörn; Leung, Yee Hong

2016-12-01

In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed. In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly. Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction. In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application.

The Research of Multiple Attenuation Based on Feedback Iteration and Independent Component Analysis

NASA Astrophysics Data System (ADS)

Xu, X.; Tong, S.; Wang, L.

2017-12-01

How to solve the problem of multiple suppression is a difficult problem in seismic data processing. The traditional technology for multiple attenuation is based on the principle of the minimum output energy of the seismic signal, this criterion is based on the second order statistics, and it can't achieve the multiple attenuation when the primaries and multiples are non-orthogonal. In order to solve the above problems, we combine the feedback iteration method based on the wave equation and the improved independent component analysis (ICA) based on high order statistics to suppress the multiple waves. We first use iterative feedback method to predict the free surface multiples of each order. Then, in order to predict multiples from real multiple in amplitude and phase, we design an expanded pseudo multi-channel matching filtering method to get a more accurate matching multiple result. Finally, we present the improved fast ICA algorithm which is based on the maximum non-Gauss criterion of output signal to the matching multiples and get better separation results of the primaries and the multiples. The advantage of our method is that we don't need any priori information to the prediction of the multiples, and can have a better separation result. The method has been applied to several synthetic data generated by finite-difference model technique and the Sigsbee2B model multiple data, the primaries and multiples are non-orthogonal in these models. The experiments show that after three to four iterations, we can get the perfect multiple results. Using our matching method and Fast ICA adaptive multiple subtraction, we can not only effectively preserve the effective wave energy in seismic records, but also can effectively suppress the free surface multiples, especially the multiples related to the middle and deep areas.

Hybrid ICA-Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals.

PubMed

Mannan, Malik M Naeem; Jeong, Myung Y; Kamran, Muhammad A

2016-01-01

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

PubMed

Aragri, Adriana; Scarabino, Tommaso; Seifritz, Erich; Comani, Silvia; Cirillo, Sossio; Tedeschi, Gioacchino; Esposito, Fabrizio; Di Salle, Francesco

2006-09-01

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity. (c) 2006 Wiley-Liss, Inc.

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP.

PubMed

Winkler, Irene; Debener, Stefan; Müller, Klaus-Robert; Tangermann, Michael

2015-01-01

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets.

PubMed

Wang, Nizhuan; Chang, Chunqi; Zeng, Weiming; Shi, Yuhu; Yan, Hongjie

2017-01-01

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.

Selection of suitable reference genes for gene expression studies in Staphylococcus capitis during growth under erythromycin stress.

PubMed

Cui, Bintao; Smooker, Peter M; Rouch, Duncan A; Deighton, Margaret A

2016-08-01

Accurate and reproducible measurement of gene transcription requires appropriate reference genes, which are stably expressed under different experimental conditions to provide normalization. Staphylococcus capitis is a human pathogen that produces biofilm under stress, such as imposed by antimicrobial agents. In this study, a set of five commonly used staphylococcal reference genes (gyrB, sodA, recA, tuf and rpoB) were systematically evaluated in two clinical isolates of Staphylococcus capitis (S. capitis subspecies urealyticus and capitis, respectively) under erythromycin stress in mid-log and stationary phases. Two public software programs (geNorm and NormFinder) and two manual calculation methods, reference residue normalization (RRN) and relative quantitative (RQ), were applied. The potential reference genes selected by the four algorithms were further validated by comparing the expression of a well-studied biofilm gene (icaA) with phenotypic biofilm formation in S. capitis under four different experimental conditions. The four methods differed considerably in their ability to predict the most suitable reference gene or gene combination for comparing icaA expression under different conditions. Under the conditions used here, the RQ method provided better selection of reference genes than the other three algorithms; however, this finding needs to be confirmed with a larger number of isolates. This study reinforces the need to assess the stability of reference genes for analysis of target gene expression under different conditions and the use of more than one algorithm in such studies. Although this work was conducted using a specific human pathogen, it emphasizes the importance of selecting suitable reference genes for accurate normalization of gene expression more generally.

PROKR2 and PROK2 mutations cause isolated congenital anosmia without gonadotropic deficiency.

PubMed

Moya-Plana, Antoine; Villanueva, Carine; Laccourreye, Ollivier; Bonfils, Pierre; de Roux, Nicolas

2013-01-01

Isolated congenital anosmia (ICA) is a rare phenotype defined as absent recall of any olfactory sensations since birth and the absence of any disease known to cause anosmia. Although most cases of ICA are sporadic, reports of familial cases suggest a genetic cause. ICA due to olfactory bulb agenesis and associated to hypogonadotropic hypogonadism defines Kallmann syndrome (KS), in which several gene defects have been described. In KS families, the phenotype may be restricted to ICA. We therefore hypothesized that mutations in KS genes cause ICA in patients, even in the absence of family history of reproduction disorders. In 25 patients with ICA and olfactory bulb agenesis, a detailed phenotype analysis was conducted and the coding sequences of KAL1, FGFR1, FGF8, PROKR2, and PROK2 were sequenced. Three PROKR2 mutations previously described in KS and one new PROK2 mutation were found. Investigation of the families showed incomplete penetrance of these mutations. This study is the first to report genetic causes of ICA and indicates that KS genes must be screened in patients with ICA. It also confirms the considerable complexity of GNRH neuron development in humans.

Model-free fMRI group analysis using FENICA.

PubMed

Schöpf, V; Windischberger, C; Robinson, S; Kasess, C H; Fischmeister, F PhS; Lanzenberger, R; Albrecht, J; Kleemann, A M; Kopietz, R; Wiesmann, M; Moser, E

2011-03-01

Exploratory analysis of functional MRI data allows activation to be detected even if the time course differs from that which is expected. Independent Component Analysis (ICA) has emerged as a powerful approach, but current extensions to the analysis of group studies suffer from a number of drawbacks: they can be computationally demanding, results are dominated by technical and motion artefacts, and some methods require that time courses be the same for all subjects or that templates be defined to identify common components. We have developed a group ICA (gICA) method which is based on single-subject ICA decompositions and the assumption that the spatial distribution of signal changes in components which reflect activation is similar between subjects. This approach, which we have called Fully Exploratory Network Independent Component Analysis (FENICA), identifies group activation in two stages. ICA is performed on the single-subject level, then consistent components are identified via spatial correlation. Group activation maps are generated in a second-level GLM analysis. FENICA is applied to data from three studies employing a wide range of stimulus and presentation designs. These are an event-related motor task, a block-design cognition task and an event-related chemosensory experiment. In all cases, the group maps identified by FENICA as being the most consistent over subjects correspond to task activation. There is good agreement between FENICA results and regions identified in prior GLM-based studies. In the chemosensory task, additional regions are identified by FENICA and temporal concatenation ICA that we show is related to the stimulus, but exhibit a delayed response. FENICA is a fully exploratory method that allows activation to be identified without assumptions about temporal evolution, and isolates activation from other sources of signal fluctuation in fMRI. It has the advantage over other gICA methods that it is computationally undemanding, spotlights components relating to activation rather than artefacts, allows the use of familiar statistical thresholding through deployment of a higher level GLM analysis and can be applied to studies where the paradigm is different for all subjects. Copyright © 2010 Elsevier Inc. All rights reserved.

Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

NASA Astrophysics Data System (ADS)

E, Jianwei; Bao, Yanling; Ye, Jimin

2017-10-01

As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.

Slow dynamics of a protein backbone in molecular dynamics simulation revealed by time-structure based independent component analysis

NASA Astrophysics Data System (ADS)

Naritomi, Yusuke; Fuchigami, Sotaro

2013-12-01

We recently proposed the method of time-structure based independent component analysis (tICA) to examine the slow dynamics involved in conformational fluctuations of a protein as estimated by molecular dynamics (MD) simulation [Y. Naritomi and S. Fuchigami, J. Chem. Phys. 134, 065101 (2011)]. Our previous study focused on domain motions of the protein and examined its dynamics by using rigid-body domain analysis and tICA. However, the protein changes its conformation not only through domain motions but also by various types of motions involving its backbone and side chains. Some of these motions might occur on a slow time scale: we hypothesize that if so, we could effectively detect and characterize them using tICA. In the present study, we investigated slow dynamics of the protein backbone using MD simulation and tICA. The selected target protein was lysine-, arginine-, ornithine-binding protein (LAO), which comprises two domains and undergoes large domain motions. MD simulation of LAO in explicit water was performed for 1 μs, and the obtained trajectory of Cα atoms in the backbone was analyzed by tICA. This analysis successfully provided us with slow modes for LAO that represented either domain motions or local movements of the backbone. Further analysis elucidated the atomic details of the suggested local motions and confirmed that these motions truly occurred on the expected slow time scale.

Determining the optimal number of independent components for reproducible transcriptomic data analysis.

PubMed

Kairov, Ulykbek; Cantini, Laura; Greco, Alessandro; Molkenov, Askhat; Czerwinska, Urszula; Barillot, Emmanuel; Zinovyev, Andrei

2017-09-11

Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data. Here we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile. Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets. We suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.

A projection pursuit algorithm to classify individuals using fMRI data: Application to schizophrenia.

PubMed

Demirci, Oguz; Clark, Vincent P; Calhoun, Vince D

2008-02-15

Schizophrenia is diagnosed based largely upon behavioral symptoms. Currently, no quantitative, biologically based diagnostic technique has yet been developed to identify patients with schizophrenia. Classification of individuals into patient with schizophrenia and healthy control groups based on quantitative biologically based data is of great interest to support and refine psychiatric diagnoses. We applied a novel projection pursuit technique on various components obtained with independent component analysis (ICA) of 70 subjects' fMRI activation maps obtained during an auditory oddball task. The validity of the technique was tested with a leave-one-out method and the detection performance varied between 80% and 90%. The findings suggest that the proposed data reduction algorithm is effective in classifying individuals into schizophrenia and healthy control groups and may eventually prove useful as a diagnostic tool.

Estimates of the atmospheric parameters of M-type stars: a machine-learning perspective

NASA Astrophysics Data System (ADS)

Sarro, L. M.; Ordieres-Meré, J.; Bello-García, A.; González-Marcos, A.; Solano, E.

2018-05-01

Estimating the atmospheric parameters of M-type stars has been a difficult task due to the lack of simple diagnostics in the stellar spectra. We aim at uncovering good sets of predictive features of stellar atmospheric parameters (Teff, log (g), [M/H]) in spectra of M-type stars. We define two types of potential features (equivalent widths and integrated flux ratios) able to explain the atmospheric physical parameters. We search the space of feature sets using a genetic algorithm that evaluates solutions by their prediction performance in the framework of the BT-Settl library of stellar spectra. Thereafter, we construct eight regression models using different machine-learning techniques and compare their performances with those obtained using the classical χ2 approach and independent component analysis (ICA) coefficients. Finally, we validate the various alternatives using two sets of real spectra from the NASA Infrared Telescope Facility (IRTF) and Dwarf Archives collections. We find that the cross-validation errors are poor measures of the performance of regression models in the context of physical parameter prediction in M-type stars. For R ˜ 2000 spectra with signal-to-noise ratios typical of the IRTF and Dwarf Archives, feature selection with genetic algorithms or alternative techniques produces only marginal advantages with respect to representation spaces that are unconstrained in wavelength (full spectrum or ICA). We make available the atmospheric parameters for the two collections of observed spectra as online material.

Recovering fNIRS brain signals: physiological interference suppression with independent component analysis

NASA Astrophysics Data System (ADS)

Zhang, Y.; Shi, M.; Sun, J.; Yang, C.; Zhang, Yajuan; Scopesi, F.; Makobore, P.; Chin, C.; Serra, G.; Wickramasinghe, Y. A. B. D.; Rolfe, P.

2015-02-01

Brain activity can be monitored non-invasively by functional near-infrared spectroscopy (fNIRS), which has several advantages in comparison with other methods, such as flexibility, portability, low cost and fewer physical restrictions. However, in practice fNIRS measurements are often contaminated by physiological interference arising from cardiac contraction, breathing and blood pressure fluctuations, thereby severely limiting the utility of the method. Hence, further improvement is necessary to reduce or eliminate such interference in order that the evoked brain activity information can be extracted reliably from fNIRS data. In the present paper, the multi-distance fNIRS probe configuration has been adopted. The short-distance fNIRS measurement is treated as the virtual channel and the long-distance fNIRS measurement is treated as the measurement channel. Independent component analysis (ICA) is employed for the fNIRS recordings to separate the brain signals and the interference. Least-absolute deviation (LAD) estimator is employed to recover the brain activity signals. We also utilized Monte Carlo simulations based on a five-layer model of the adult human head to evaluate our methodology. The results demonstrate that the ICA algorithm has the potential to separate physiological interference in fNIRS data and the LAD estimator could be a useful criterion to recover the brain activity signals.

Evaluation of algorithm methods for fluorescence spectra of cancerous and normal human tissues

NASA Astrophysics Data System (ADS)

Pu, Yang; Wang, Wubao; Alfano, Robert R.

2016-03-01

The paper focus on the various algorithms on to unravel the fluorescence spectra by unmixing methods to identify cancerous and normal human tissues from the measured fluorescence spectroscopy. The biochemical or morphologic changes that cause fluorescence spectra variations would appear earlier than the histological approach; therefore, fluorescence spectroscopy holds a great promise as clinical tool for diagnosing early stage of carcinomas and other deceases for in vivo use. The method can further identify tissue biomarkers by decomposing the spectral contributions of different fluorescent molecules of interest. In this work, we investigate the performance of blind source un-mixing methods (backward model) and spectral fitting approaches (forward model) in decomposing the contributions of key fluorescent molecules from the tissue mixture background when certain selected excitation wavelength is applied. Pairs of adenocarcinoma as well as normal tissues confirmed by pathologist were excited by selective wavelength of 340 nm. The emission spectra of resected fresh tissue were used to evaluate the relative changes of collagen, reduced nicotinamide adenine dinucleotide (NADH), and Flavin by various spectral un-mixing methods. Two categories of algorithms: forward methods and Blind Source Separation [such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), and Nonnegative Matrix Factorization (NMF)] will be introduced and evaluated. The purpose of the spectral analysis is to discard the redundant information which conceals the difference between these two types of tissues, but keep their diagnostically significance. The facts predicted by different methods were compared to the gold standard of histopathology. The results indicate that these key fluorophores within tissue, e.g. tryptophan, collagen, and NADH, and flavin, show differences of relative contents of fluorophores among different types of human cancer and normal tissues. The sensitivity, specificity, and receiver operating characteristic (ROC) are finally employed as the criteria to evaluate the efficacy of these methods in cancer detection. The underlying physical and biological basis for these optical approaches will be discussed with examples. This ex vivo preliminary trial demonstrates that these different criteria from different methods can distinguish carcinoma from normal tissues with good sensitivity and specificity while among them, we found that ICA appears to be the superior method in predication accuracy.

Association of multiple infarctions and ICAS with outcomes of minor stroke and TIA.

PubMed

Pan, Yuesong; Meng, Xia; Jing, Jing; Li, Hao; Zhao, Xingquan; Liu, Liping; Wang, David; Johnston, S Claiborne; Wang, Yilong; Wang, Yongjun

2017-03-14

To estimate the association of different patterns of infarction and intracranial arterial stenosis (ICAS) with the prognosis of acute minor ischemic stroke and TIA. We derived data from the Clopidogrel in High-risk Patients with Acute Nondisabling Cerebrovascular Events (CHANCE) trial. A total of 1,089 patients from 45 of 114 participating sites of the trial undergoing baseline MRI/angiography were included in this subgroup analysis. Patterns of infarction and ICAS were recorded for each individual. The primary efficacy outcome was an ischemic stroke at the 90-day follow-up. We assessed the associations between imaging patterns and prognosis of patients using multivariable Cox regression models. Among the 1,089 patients included in this subgroup analysis, 93 (8.5%) patients had a recurrent ischemic stroke at 90 days. Compared with those without infarction or ICAS, patients with single infarction with ICAS (11.9% vs 1.3%, hazard ratio [HR] 6.25, 95% confidence intervals [CIs] 1.40-27.86, p = 0.02) and single infarction without ICAS (6.8% vs 1.3%, HR 4.65, 95% CI 1.05-20.64, p = 0.04) were all associated with an increased risk of ischemic stroke at 90 days. Patients with both multiple infarctions and ICAS were associated with approximately 13-fold risk of ischemic stroke at 90 days (18.0% vs 1.3%, HR 13.14, 95% CI 2.96-58.36, p < 0.001). The presence of multiple infarctions and ICAS were both associated with an increased risk of 90-day ischemic stroke in patients with minor stroke or TIA, while the presence of both imaging features had a combined effect. NCT00979589. © 2017 American Academy of Neurology.

Three-dimensional localization and optical imaging of objects in turbid media with independent component analysis.

PubMed

Xu, M; Alrubaiee, M; Gayen, S K; Alfano, R R

2005-04-01

A new approach for optical imaging and localization of objects in turbid media that makes use of the independent component analysis (ICA) from information theory is demonstrated. Experimental arrangement realizes a multisource illumination of a turbid medium with embedded objects and a multidetector acquisition of transmitted light on the medium boundary. The resulting spatial diversity and multiple angular observations provide robust data for three-dimensional localization and characterization of absorbing and scattering inhomogeneities embedded in a turbid medium. ICA of the perturbations in the spatial intensity distribution on the medium boundary sorts out the embedded objects, and their locations are obtained from Green's function analysis based on any appropriate light propagation model. Imaging experiments were carried out on two highly scattering samples of thickness approximately 50 times the transport mean-free path of the respective medium. One turbid medium had two embedded absorptive objects, and the other had four scattering objects. An independent component separation of the signal, in conjunction with diffusive photon migration theory, was used to locate the embedded inhomogeneities. In both cases, improved lateral and axial localizations of the objects over the result obtained by use of common photon migration reconstruction algorithms were achieved. The approach is applicable to different medium geometries, can be used with any suitable photon propagation model, and is amenable to near-real-time imaging applications.

Wavelet-based fMRI analysis: 3-D denoising, signal separation, and validation metrics

PubMed Central

Khullar, Siddharth; Michael, Andrew; Correa, Nicolle; Adali, Tulay; Baum, Stefi A.; Calhoun, Vince D.

2010-01-01

We present a novel integrated wavelet-domain based framework (w-ICA) for 3-D de-noising functional magnetic resonance imaging (fMRI) data followed by source separation analysis using independent component analysis (ICA) in the wavelet domain. We propose the idea of a 3-D wavelet-based multi-directional de-noising scheme where each volume in a 4-D fMRI data set is sub-sampled using the axial, sagittal and coronal geometries to obtain three different slice-by-slice representations of the same data. The filtered intensity value of an arbitrary voxel is computed as an expected value of the de-noised wavelet coefficients corresponding to the three viewing geometries for each sub-band. This results in a robust set of de-noised wavelet coefficients for each voxel. Given the decorrelated nature of these de-noised wavelet coefficients; it is possible to obtain more accurate source estimates using ICA in the wavelet domain. The contributions of this work can be realized as two modules. First, the analysis module where we combine a new 3-D wavelet denoising approach with better signal separation properties of ICA in the wavelet domain, to yield an activation component that corresponds closely to the true underlying signal and is maximally independent with respect to other components. Second, we propose and describe two novel shape metrics for post-ICA comparisons between activation regions obtained through different frameworks. We verified our method using simulated as well as real fMRI data and compared our results against the conventional scheme (Gaussian smoothing + spatial ICA: s-ICA). The results show significant improvements based on two important features: (1) preservation of shape of the activation region (shape metrics) and (2) receiver operating characteristic (ROC) curves. It was observed that the proposed framework was able to preserve the actual activation shape in a consistent manner even for very high noise levels in addition to significant reduction in false positives voxels. PMID:21034833

An EEMD-ICA Approach to Enhancing Artifact Rejection for Noisy Multivariate Neural Data.

PubMed

Zeng, Ke; Chen, Dan; Ouyang, Gaoxiang; Wang, Lizhe; Liu, Xianzeng; Li, Xiaoli

2016-06-01

As neural data are generally noisy, artifact rejection is crucial for data preprocessing. It has long been a grand research challenge for an approach which is able: 1) to remove the artifacts and 2) to avoid loss or disruption of the structural information at the same time, thus the risk of introducing bias to data interpretation may be minimized. In this study, an approach (namely EEMD-ICA) was proposed to first decompose multivariate neural data that are possibly noisy into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD). Independent component analysis (ICA) was then applied to the IMFs to separate the artifactual components. The approach was tested against the classical ICA and the automatic wavelet ICA (AWICA) methods, which were dominant methods for artifact rejection. In order to evaluate the effectiveness of the proposed approach in handling neural data possibly with intensive noises, experiments on artifact removal were performed using semi-simulated data mixed with a variety of noises. Experimental results indicate that the proposed approach continuously outperforms the counterparts in terms of both normalized mean square error (NMSE) and Structure SIMilarity (SSIM). The superiority becomes even greater with the decrease of SNR in all cases, e.g., SSIM of the EEMD-ICA can almost double that of AWICA and triple that of ICA. To further examine the potentials of the approach in sophisticated applications, the approach together with the counterparts were used to preprocess a real-life epileptic EEG with absence seizure. Experiments were carried out with the focus on characterizing the dynamics of the data after artifact rejection, i.e., distinguishing seizure-free, pre-seizure and seizure states. Using multi-scale permutation entropy to extract feature and linear discriminant analysis for classification, the EEMD-ICA performed the best for classifying the states (87.4%, about 4.1% and 8.7% higher than that of AWICA and ICA respectively), which was closest to the results of the manually selected dataset (89.7%).

Histology of the distal dural ring.

PubMed

Graffeo, Christopher S; Perry, Avital; Copeland, William R; Raghunathan, Aditya; Link, Michael J

2017-09-01

The distal dural ring (DDR) is a conserved intracranial anatomic structure marking the boundary point at which the internal carotid artery (ICA) exits the cavernous sinus (CS) and enters the subarachnoid space. Although the CS has been well described in a range of anatomic studies, to our knowledge no prior study has analyzed the histologic relationship between the ICA and DDR. Correspondingly, our objective was to assess the relationship of the DDR to the ICA and determine whether the DDR can be dissected from the ICA and thus divided, or can only be circumferentially trimmed around the artery. The authors examined ten fresh-frozen, adult cadaveric specimens. A standard frontotemporal craniotomy, orbito-optic osteotomy, and extradural anterior clinoidectomy was performed bilaterally. The cavernous ICA, DDR, and supraclinoid ICA were harvested as an en bloc specimen. Specimens formalin-fixed and paraffin-embedded prior to routine histochemical staining with hematoxylin and eosin and Masson trichrome. In all specimens, marked microscopic investment of the DDR throughout the ICA adventitia was noted. Dural collagen fibers extensively permeated the arterial layers superficial to the muscularis propria, with no evidence of a clear separation between the DDR and arterial adventitia. Histologic analysis suggests that the ICA and DDR are highly interrelated, continuous structures, and therefore attempted intraoperative dissection between these structures may carry an elevated risk of injury to the ICA. We correspondingly recommend careful circumferential trimming of the DDR in lieu of direct dissection in cases requiring mobilization of the clinoidal ICA. Clin. Anat. 30:742-746, 2017. © 2017Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Highly Conductive and Reliable Copper-Filled Isotropically Conductive Adhesives Using Organic Acids for Oxidation Prevention

NASA Astrophysics Data System (ADS)

Chen, Wenjun; Deng, Dunying; Cheng, Yuanrong; Xiao, Fei

2015-07-01

The easy oxidation of copper is one critical obstacle to high-performance copper-filled isotropically conductive adhesives (ICAs). In this paper, a facile method to prepare highly reliable, highly conductive, and low-cost ICAs is reported. The copper fillers were treated by organic acids for oxidation prevention. Compared with ICA filled with untreated copper flakes, the ICA filled with copper flakes treated by different organic acids exhibited much lower bulk resistivity. The lowest bulk resistivity achieved was 4.5 × 10-5 Ω cm, which is comparable to that of commercially available Ag-filled ICA. After 500 h of 85°C/85% relative humidity (RH) aging, the treated ICAs showed quite stable bulk resistivity and relatively stable contact resistance. Through analyzing the results of x-ray diffraction, x-ray photoelectron spectroscopy, and thermogravimetric analysis, we found that, with the assistance of organic acids, the treated copper flakes exhibited resistance to oxidation, thus guaranteeing good performance.

Four-Dimensional Flow MRI Analysis of Cerebral Blood Flow Before and After High-Flow Extracranial-Intracranial Bypass Surgery With Internal Carotid Artery Ligation.

PubMed

Orita, Erika; Murai, Yasuo; Sekine, Tetsuro; Takagi, Ryo; Amano, Yasuo; Ando, Takahiro; Iwata, Kotomi; Obara, Makoto; Kumita, Shinichiro

2018-05-11

The hemodynamic changes that occur after high-flow (extracranial-intracranial) EC-IC bypass surgery with internal carotid artery (ICA) ligation are not well known. To assess blood flow changes after high-flow EC-IC bypass with ICA ligation by time-resolved 3-dimensional phase-contrast (4D Flow) magnetic resonance imaging (MRI). We enrolled 11 patients who underwent high-flow EC-IC bypass. 4D Flow MRI was performed before and after surgery to quantify the blood flow volume (BFV) of the ipsilateral ICA (BFViICA), bypass artery (BFVbypass), contralateral ICA (BFVcICA), and basilar artery (BFVBA). Subsequently, we calculated the total BFV (BFVtotal = BFViICA + BFVcICA + BFVBA [before surgery], BFVcICA + BFVBA + BFVbypass [after surgery]). The BFV changes after bypass was statistically analyzed. BFVbypass was slightly lower than BFViICA, but the difference was not statistically significant (3.84 ± 0.94 vs 4.42 ± 1.38 mL/s). The BFVcICA and BFVBA significantly increased after bypass surgery (BFVcICA 5.89 ± 1.44 vs 7.22 ± 1.37 mL/s [P = .0018], BFVBA 3.06 ± 0.41 vs 4.12 ± 0.38 mL/s [P < .001]). The BFVtotal significantly increased after surgery (13.37 ± 2.58 vs 15.18 ± 1.77 mL/s [P = .015]). There was no evidence of hyperperfusion syndrome in any cases. After high-flow EC-IC bypass with permanent ICA ligation, the bypass artery could partially compensate for the loss of BFV of the sacrificed ICA. The increased flow of the contralateral ICA and BA supply collateral blood flow. Clinically irrelevant hyperperfusion was observed.

Detection of methicillin resistance and slime factor production of Staphylococcus aureus in bovine mastitis

PubMed Central

Ciftci, Alper; Findik, Arzu; Onuk, Ertan Emek; Savasan, Serap

2009-01-01

This study aimed to detect methicillin resistant and slime producing Staphylococcus aureus in cases of bovine mastitis. A triplex PCR was optimized targetting 16S rRNA, nuc and mecA genes for detection of Staphylococcus species, S. aureus and methicillin resistance, respectively. Furthermore, for detection of slime producing strains, a PCR assay targetting icaA and icaD genes was performed. In this study, 59 strains were detected as S. aureus by both conventional tests and PCR, and 13 of them were found to be methicillin resistant and 4 (30.7%) were positive for mecA gene. Although 22 of 59 (37.2%) S. aureus isolates were slime-producing in Congo Red Agar, in PCR analysis only 15 were positive for both icaA and icaD genes. Sixteen and 38 out of 59 strains were positive for icaA and icaD gene, respectively. Only 2 of 59 strains were positive for both methicillin resistance and slime producing, phenotypically, suggesting lack of correlation between methicillin resistance and slime production in these isolates. In conclusion, the optimized triplex PCR in this study was useful for rapid and reliable detection of methicillin resistant S. aureus. Furthermore, only PCR targetting icaA and icaD may not sufficient to detect slime production and further studies targetting other ica genes should be conducted for accurate evaluation of slime production characters of S. aureus strains. PMID:24031354

Instrumentation, control, and automation for submerged anaerobic membrane bioreactors.

PubMed

Robles, Ángel; Durán, Freddy; Ruano, María Victoria; Ribes, Josep; Rosado, Alfredo; Seco, Aurora; Ferrer, José

2015-01-01

A submerged anaerobic membrane bioreactor (AnMBR) demonstration plant with two commercial hollow-fibre ultrafiltration systems (PURON®, Koch Membrane Systems, PUR-PSH31) was designed and operated for urban wastewater treatment. An instrumentation, control, and automation (ICA) system was designed and implemented for proper process performance. Several single-input-single-output (SISO) feedback control loops based on conventional on-off and PID algorithms were implemented to control the following operating variables: flow-rates (influent, permeate, sludge recycling and wasting, and recycled biogas through both reactor and membrane tanks), sludge wasting volume, temperature, transmembrane pressure, and gas sparging. The proposed ICA for AnMBRs for urban wastewater treatment enables the optimization of this new technology to be achieved with a high level of process robustness towards disturbances.

Evaluating Internal Communication: The ICA Communication Audit.

ERIC Educational Resources Information Center

Goldhaber, Gerald M.

1978-01-01

The ICA Communication Audit is described in detail as an effective measurement procedure that can help an academic institution to evaluate its internal communication system. Tools, computer programs, analysis, and feedback procedures are described and illustrated. (JMF)

Algorithms for Spectral Decomposition with Applications to Optical Plume Anomaly Detection

NASA Technical Reports Server (NTRS)

Srivastava, Askok N.; Matthews, Bryan; Das, Santanu

2008-01-01

The analysis of spectral signals for features that represent physical phenomenon is ubiquitous in the science and engineering communities. There are two main approaches that can be taken to extract relevant features from these high-dimensional data streams. The first set of approaches relies on extracting features using a physics-based paradigm where the underlying physical mechanism that generates the spectra is used to infer the most important features in the data stream. We focus on a complementary methodology that uses a data-driven technique that is informed by the underlying physics but also has the ability to adapt to unmodeled system attributes and dynamics. We discuss the following four algorithms: Spectral Decomposition Algorithm (SDA), Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Principal Components Analysis (PCA) and compare their performance on a spectral emulator which we use to generate artificial data with known statistical properties. This spectral emulator mimics the real-world phenomena arising from the plume of the space shuttle main engine and can be used to validate the results that arise from various spectral decomposition algorithms and is very useful for situations where real-world systems have very low probabilities of fault or failure. Our results indicate that methods like SDA and NMF provide a straightforward way of incorporating prior physical knowledge while NMF with a tuning mechanism can give superior performance on some tests. We demonstrate these algorithms to detect potential system-health issues on data from a spectral emulator with tunable health parameters.

Electrically evoked compound action potentials artefact rejection by independent component analysis: procedure automation.

PubMed

Akhoun, Idrick; McKay, Colette; El-Deredy, Wael

2015-01-15

Independent-components-analysis (ICA) successfully separated electrically-evoked compound action potentials (ECAPs) from the stimulation artefact and noise (ECAP-ICA, Akhoun et al., 2013). This paper shows how to automate the ECAP-ICA artefact cancellation process. Raw-ECAPs without artefact rejection were consecutively recorded for each stimulation condition from at least 8 intra-cochlear electrodes. Firstly, amplifier-saturated recordings were discarded, and the data from different stimulus conditions (different current-levels) were concatenated temporally. The key aspect of the automation procedure was the sequential deductive source categorisation after ICA was applied with a restriction to 4 sources. The stereotypical aspect of the 4 sources enables their automatic classification as two artefact components, a noise and the sought ECAP based on theoretical and empirical considerations. The automatic procedure was tested using 8 cochlear implant (CI) users and one to four stimulus electrodes. The artefact and noise sources were successively identified and discarded, leaving the ECAP as the remaining source. The automated ECAP-ICA procedure successfully extracted the correct ECAPs compared to standard clinical forward masking paradigm in 22 out of 26 cases. ECAP-ICA does not require extracting the ECAP from a combination of distinct buffers as it is the case with regular methods. It is an alternative that does not have the possible bias of traditional artefact rejections such as alternate-polarity or forward-masking paradigms. The ECAP-ICA procedure bears clinical relevance, for example as the artefact rejection sub-module of automated ECAP-threshold detection techniques, which are common features of CI clinical fitting software. Copyright © 2014. Published by Elsevier B.V.

Determination of the optimal number of components in independent components analysis.

PubMed

Kassouf, Amine; Jouan-Rimbaud Bouveresse, Delphine; Rutledge, Douglas N

2018-03-01

Independent components analysis (ICA) may be considered as one of the most established blind source separation techniques for the treatment of complex data sets in analytical chemistry. Like other similar methods, the determination of the optimal number of latent variables, in this case, independent components (ICs), is a crucial step before any modeling. Therefore, validation methods are required in order to decide about the optimal number of ICs to be used in the computation of the final model. In this paper, three new validation methods are formally presented. The first one, called Random_ICA, is a generalization of the ICA_by_blocks method. Its specificity resides in the random way of splitting the initial data matrix into two blocks, and then repeating this procedure several times, giving a broader perspective for the selection of the optimal number of ICs. The second method, called KMO_ICA_Residuals is based on the computation of the Kaiser-Meyer-Olkin (KMO) index of the transposed residual matrices obtained after progressive extraction of ICs. The third method, called ICA_corr_y, helps to select the optimal number of ICs by computing the correlations between calculated proportions and known physico-chemical information about samples, generally concentrations, or between a source signal known to be present in the mixture and the signals extracted by ICA. These three methods were tested using varied simulated and experimental data sets and compared, when necessary, to ICA_by_blocks. Results were relevant and in line with expected ones, proving the reliability of the three proposed methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Developing a complex independent component analysis technique to extract non-stationary patterns from geophysical time-series

NASA Astrophysics Data System (ADS)

Forootan, Ehsan; Kusche, Jürgen

2016-04-01

Geodetic/geophysical observations, such as the time series of global terrestrial water storage change or sea level and temperature change, represent samples of physical processes and therefore contain information about complex physical interactionswith many inherent time scales. Extracting relevant information from these samples, for example quantifying the seasonality of a physical process or its variability due to large-scale ocean-atmosphere interactions, is not possible by rendering simple time series approaches. In the last decades, decomposition techniques have found increasing interest for extracting patterns from geophysical observations. Traditionally, principal component analysis (PCA) and more recently independent component analysis (ICA) are common techniques to extract statistical orthogonal (uncorrelated) and independent modes that represent the maximum variance of observations, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the auto-covariance matrix or diagonalizing higher (than two)-order statistical tensors from centered time series. However, the stationary assumption is obviously not justifiable for many geophysical and climate variables even after removing cyclic components e.g., the seasonal cycles. In this paper, we present a new decomposition method, the complex independent component analysis (CICA, Forootan, PhD-2014), which can be applied to extract to non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA (Forootan and Kusche, JoG-2012), where we (i) define a new complex data set using a Hilbert transformation. The complex time series contain the observed values in their real part, and the temporal rate of variability in their imaginary part. (ii) An ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex data set in (i). (iii) Dominant non-stationary patterns are recognized as independent complex patterns that can be used to represent the space and time amplitude and phase propagations. We present the results of CICA on simulated and real cases e.g., for quantifying the impact of large-scale ocean-atmosphere interaction on global mass changes. Forootan (PhD-2014) Statistical signal decomposition techniques for analyzing time-variable satellite gravimetry data, PhD Thesis, University of Bonn, http://hss.ulb.uni-bonn.de/2014/3766/3766.htm Forootan and Kusche (JoG-2012) Separation of global time-variable gravity signals into maximally independent components, Journal of Geodesy 86 (7), 477-497, doi: 10.1007/s00190-011-0532-5

Linked independent component analysis for multimodal data fusion.

PubMed

Groves, Adrian R; Beckmann, Christian F; Smith, Steve M; Woolrich, Mark W

2011-02-01

In recent years, neuroimaging studies have increasingly been acquiring multiple modalities of data and searching for task- or disease-related changes in each modality separately. A major challenge in analysis is to find systematic approaches for fusing these differing data types together to automatically find patterns of related changes across multiple modalities, when they exist. Independent Component Analysis (ICA) is a popular unsupervised learning method that can be used to find the modes of variation in neuroimaging data across a group of subjects. When multimodal data is acquired for the subjects, ICA is typically performed separately on each modality, leading to incompatible decompositions across modalities. Using a modular Bayesian framework, we develop a novel "Linked ICA" model for simultaneously modelling and discovering common features across multiple modalities, which can potentially have completely different units, signal- and contrast-to-noise ratios, voxel counts, spatial smoothnesses and intensity distributions. Furthermore, this general model can be configured to allow tensor ICA or spatially-concatenated ICA decompositions, or a combination of both at the same time. Linked ICA automatically determines the optimal weighting of each modality, and also can detect single-modality structured components when present. This is a fully probabilistic approach, implemented using Variational Bayes. We evaluate the method on simulated multimodal data sets, as well as on a real data set of Alzheimer's patients and age-matched controls that combines two very different types of structural MRI data: morphological data (grey matter density) and diffusion data (fractional anisotropy, mean diffusivity, and tensor mode). Copyright © 2010 Elsevier Inc. All rights reserved.

Structural Basis for the De-N-acetylation of Poly-β-1,6-N-acetyl-d-glucosamine in Gram-positive Bacteria*

PubMed Central

Little, Dustin J.; Bamford, Natalie C.; Pokrovskaya, Varvara; Robinson, Howard; Nitz, Mark; Howell, P. Lynne

2014-01-01

Exopolysaccharides are required for the development and integrity of biofilms produced by a wide variety of bacteria. In staphylococci, partial de-N-acetylation of the exopolysaccharide poly-β-1,6-N-acetyl-d-glucosamine (PNAG) by the extracellular protein IcaB is required for biofilm formation. To understand the molecular basis for PNAG de-N-acetylation, the structure of IcaB from Ammonifex degensii (IcaBAd) has been determined to 1.7 Å resolution. The structure of IcaBAd reveals a (β/α)7 barrel common to the family four carbohydrate esterases (CE4s) with the canonical motifs circularly permuted. The metal dependence of IcaBAd is similar to most CE4s showing the maximum rates of de-N-acetylation with Ni2+, Co2+, and Zn2+. From docking studies with β-1,6-GlcNAc oligomers and structural comparison to PgaB from Escherichia coli, the Gram-negative homologue of IcaB, we identify Arg-45, Tyr-67, and Trp-180 as key residues for PNAG binding during catalysis. The absence of these residues in PgaB provides a rationale for the requirement of a C-terminal domain for efficient deacetylation of PNAG in Gram-negative species. Mutational analysis of conserved active site residues suggests that IcaB uses an altered catalytic mechanism in comparison to other characterized CE4 members. Furthermore, we identified a conserved surface-exposed hydrophobic loop found only in Gram-positive homologues of IcaB. Our data suggest that this loop is required for membrane association and likely anchors IcaB to the membrane during polysaccharide biosynthesis. The work presented herein will help guide the design of IcaB inhibitors to combat biofilm formation by staphylococci. PMID:25359777

ICA model order selection of task co-activation networks.

PubMed

Ray, Kimberly L; McKay, D Reese; Fox, Peter M; Riedel, Michael C; Uecker, Angela M; Beckmann, Christian F; Smith, Stephen M; Fox, Peter T; Laird, Angela R

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

ICA model order selection of task co-activation networks

PubMed Central

Ray, Kimberly L.; McKay, D. Reese; Fox, Peter M.; Riedel, Michael C.; Uecker, Angela M.; Beckmann, Christian F.; Smith, Stephen M.; Fox, Peter T.; Laird, Angela R.

2013-01-01

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders. PMID:24339802

Nonstationary signal analysis in episodic memory retrieval

NASA Astrophysics Data System (ADS)

Ku, Y. G.; Kawasumi, Masashi; Saito, Masao

2004-04-01

The problem of blind source separation from a mixture that has nonstationarity can be seen in signal processing, speech processing, spectral analysis and so on. This study analyzed EEG signal during episodic memory retrieval using ICA and TVAR. This paper proposes a method which combines ICA and TVAR. The signal from the brain not only exhibits the nonstationary behavior, but also contain artifacts. EEG data at the frontal lobe (F3) from the scalp is collected during the episodic memory retrieval task. The method is applied to EEG data for analysis. The artifact (eye movement) is removed by ICA, and a single burst (around 6Hz) is obtained by TVAR, suggesting that the single burst is related to the brain activity during the episodic memory retrieval.

Carotid artery and lower cranial nerve exposure with increasing surgical complexity to the parapharyngeal space.

PubMed

Lemos-Rodriguez, Ana M; Sreenath, Satyan B; Rawal, Rounak B; Overton, Lewis J; Farzal, Zainab; Zanation, Adam M

2017-03-01

To investigate the extent of carotid artery exposure attained, including the identification of the external carotid branches and lower cranial nerves in five sequential external approaches to the parapharyngeal space, and to provide an anatomical algorithm. Anatomical study. Six latex-injected adult cadaver heads were dissected in five consecutive approaches: transcervical approach with submandibular gland removal, posterior extension of the transcervical approach, transcervical approach with parotidectomy, parotidectomy with lateral mandibulotomy, and parotidectomy with mandibulectomy. The degree of carotid artery exposure attained, external carotid branches, and lower cranial nerves visualized was documented. The transcervical approach exposed 1.5 cm (Standard Deviation (SD) 0.5) of internal carotid artery (ICA) and 1.25 cm (SD 0.25) of external carotid artery (ECA). The superior thyroid and facial arteries and cranial nerve XII and XI were identified. The posterior extension exposed 2.9 cm (SD 0.7) of ICA and 2.7 cm (SD 1.0) of ECA. Occipital and ascending pharyngeal arteries were visualized. The transparotid approach exposed 4.0 cm (SD 1.1) of ICA and 3.98 cm (SD 1.8) of ECA. Lateral mandibulotomy exposed the internal maxillary artery, cranial nerve X, the sympathetic trunk, and 4.6 cm (SD 2.4) of ICA. Mandibulectomy allowed for complete ECA exposure, cranial nerve IX, lingual nerve, and 6.9 cm (SD 1.3) of ICA. Approaches for the parapharyngeal space must be based on anatomic and biological patient factors. This study provides a guide for the skull base surgeon for an extended approach based on the desired anatomic exposure. N/A. Laryngoscope, 127:585-591, 2017. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Extraction of phase information in daily stock prices

NASA Astrophysics Data System (ADS)

Fujiwara, Yoshi; Maekawa, Satoshi

2000-06-01

It is known that, in an intermediate time-scale such as days, stock market fluctuations possess several statistical properties that are common to different markets. Namely, logarithmic returns of an asset price have (i) truncated Pareto-Lévy distribution, (ii) vanishing linear correlation, (iii) volatility clustering and its power-law autocorrelation. The fact (ii) is a consequence of nonexistence of arbitragers with simple strategies, but this does not mean statistical independence of market fluctuations. Little attention has been paid to temporal structure of higher-order statistics, although it contains some important information on market dynamics. We applied a signal separation technique, called Independent Component Analysis (ICA), to actual data of daily stock prices in Tokyo and New York Stock Exchange (TSE/NYSE). ICA does a linear transformation of lag vectors from time-series to find independent components by a nonlinear algorithm. We obtained a similar impulse response for these dataset. If it were a Martingale process, it can be shown that impulse response should be a delta-function under a few conditions that could be numerically checked and as was verified by surrogate data. This result would provide information on the market dynamics including speculative bubbles and arbitrating processes. .

Diagnostic Accuracy of Coronary Computed Tomography Before Aortic Valve Replacement: Systematic Review and Meta-Analysis.

PubMed

Chaikriangkrai, Kongkiat; Jhun, Hye Yeon; Shantha, Ghanshyam Palamaner Subash; Abdulhak, Aref Bin; Tandon, Rudhir; Alqasrawi, Musab; Klappa, Anthony; Pancholy, Samir; Deshmukh, Abhishek; Bhama, Jay; Sigurdsson, Gardar

2018-07-01

In aortic stenosis patients referred for surgical and transcatheter aortic valve replacement (AVR), the evidence of diagnostic accuracy of coronary computed tomography angiography (CCTA) has been limited. The objective of this study was to investigate the diagnostic accuracy of CCTA for significant coronary artery disease (CAD) in patients referred for AVR using invasive coronary angiography (ICA) as the gold standard. We searched databases for all diagnostic studies of CCTA in patients referred for AVR, which reported diagnostic testing characteristics on patient-based analysis required to pool summary sensitivity, specificity, positive-likelihood ratio, and negative-likelihood ratio. Significant CAD in both CCTA and ICA was defined by >50% stenosis in any coronary artery, coronary stent, or bypass graft. Thirteen studies evaluated 1498 patients (mean age, 74 y; 47% men; 76% transcatheter AVR). The pooled prevalence of significant stenosis determined by ICA was 43%. Hierarchical summary receiver-operating characteristic analysis demonstrated a summary area under curve of 0.96. The pooled sensitivity, specificity, and positive-likelihood and negative-likelihood ratios of CCTA in identifying significant stenosis determined by ICA were 95%, 79%, 4.48, and 0.06, respectively. In subgroup analysis, the diagnostic profiles of CCTA were comparable between surgical and transcatheter AVR. Despite the higher prevalence of significant CAD in patients with aortic stenosis than with other valvular heart diseases, our meta-analysis has shown that CCTA has a suitable diagnostic accuracy profile as a gatekeeper test for ICA. Our study illustrates a need for further study of the potential role of CCTA in preoperative planning for AVR.

Hybrid ICA—Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals

PubMed Central

Mannan, Malik M. Naeem; Jeong, Myung Y.; Kamran, Muhammad A.

2016-01-01

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG. PMID:27199714

Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: The case of domain motions

NASA Astrophysics Data System (ADS)

Naritomi, Yusuke; Fuchigami, Sotaro

2011-02-01

Protein dynamics on a long time scale was investigated using all-atom molecular dynamics (MD) simulation and time-structure based independent component analysis (tICA). We selected the lysine-, arginine-, ornithine-binding protein (LAO) as a target protein and focused on its domain motions in the open state. A MD simulation of the LAO in explicit water was performed for 600 ns, in which slow and large-amplitude domain motions of the LAO were observed. After extracting domain motions by rigid-body domain analysis, the tICA was applied to the obtained rigid-body trajectory, yielding slow modes of the LAO's domain motions in order of decreasing time scale. The slowest mode detected by the tICA represented not a closure motion described by a largest-amplitude mode determined by the principal component analysis but a twist motion with a time scale of tens of nanoseconds. The slow dynamics of the LAO were well described by only the slowest mode and were characterized by transitions between two basins. The results show that tICA is promising for describing and analyzing slow dynamics of proteins.

Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: the case of domain motions.

PubMed

Naritomi, Yusuke; Fuchigami, Sotaro

2011-02-14

Protein dynamics on a long time scale was investigated using all-atom molecular dynamics (MD) simulation and time-structure based independent component analysis (tICA). We selected the lysine-, arginine-, ornithine-binding protein (LAO) as a target protein and focused on its domain motions in the open state. A MD simulation of the LAO in explicit water was performed for 600 ns, in which slow and large-amplitude domain motions of the LAO were observed. After extracting domain motions by rigid-body domain analysis, the tICA was applied to the obtained rigid-body trajectory, yielding slow modes of the LAO's domain motions in order of decreasing time scale. The slowest mode detected by the tICA represented not a closure motion described by a largest-amplitude mode determined by the principal component analysis but a twist motion with a time scale of tens of nanoseconds. The slow dynamics of the LAO were well described by only the slowest mode and were characterized by transitions between two basins. The results show that tICA is promising for describing and analyzing slow dynamics of proteins.

Applying independent component analysis to detect silent speech in magnetic resonance imaging signals.

PubMed

Abe, Kazuhiro; Takahashi, Toshimitsu; Takikawa, Yoriko; Arai, Hajime; Kitazawa, Shigeru

2011-10-01

Independent component analysis (ICA) can be usefully applied to functional imaging studies to evaluate the spatial extent and temporal profile of task-related brain activity. It requires no a priori assumptions about the anatomical areas that are activated or the temporal profile of the activity. We applied spatial ICA to detect a voluntary but hidden response of silent speech. To validate the method against a standard model-based approach, we used the silent speech of a tongue twister as a 'Yes' response to single questions that were delivered at given times. In the first task, we attempted to estimate one number that was chosen by a participant from 10 possibilities. In the second task, we increased the possibilities to 1000. In both tasks, spatial ICA was as effective as the model-based method for determining the number in the subject's mind (80-90% correct per digit), but spatial ICA outperformed the model-based method in terms of time, especially in the 1000-possibility task. In the model-based method, calculation time increased by 30-fold, to 15 h, because of the necessity of testing 1000 possibilities. In contrast, the calculation time for spatial ICA remained as short as 30 min. In addition, spatial ICA detected an unexpected response that occurred by mistake. This advantage was validated in a third task, with 13 500 possibilities, in which participants had the freedom to choose when to make one of four responses. We conclude that spatial ICA is effective for detecting the onset of silent speech, especially when it occurs unexpectedly. © 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Independent Component Analysis applied to Ground-based observations

NASA Astrophysics Data System (ADS)

Martins-Filho, Walter; Griffith, Caitlin; Pearson, Kyle; Waldmann, Ingo; Alvarez-Candal, Alvaro; Zellem, Robert Thomas

2018-01-01

Transit measurements of Jovian-sized exoplanetary atmospheres allow one to study the composition of exoplanets, largely independent of the planet’s temperature profile. However, measurements of hot-Jupiter transits must archive a level of accuracy in the flux to determine the spectral modulation of the exoplanetary atmosphere. To accomplish this level of precision, we need to extract systematic errors, and, for ground-based measurements, the effects of Earth’s atmosphere, from signal due to the exoplanet, which is several orders of magnitude smaller. The effects of the terrestrial atmosphere and some of the time-dependent systematic errors of ground-based transit measurements are treated mainly by dividing the host star by a reference star at each wavelength and time step of the transit. Recently, Independent Component Analysis (ICA) have been used to remove systematics effects from the raw data of space-based observations (Waldmann, 2014, 2012; Morello et al., 2016, 2015). ICA is a statistical method born from the ideas of the blind-source separations studies, which can be used to de-trend several independent source signals of a data set (Hyvarinen and Oja, 2000). This technique requires no additional prior knowledge of the data set. In addition, this technique has the advantage of requiring no reference star. Here we apply the ICA to ground-based photometry of the exoplanet XO-2b recorded by the 61” Kuiper Telescope and compare the results of the ICA to those of a previous analysis from Zellem et al. (2015), which does not use ICA. We also simulate the effects of various conditions (concerning the systematic errors, noise and the stability of object on the detector) to determine the conditions under which an ICA can be used with high precision to extract the light curve of exoplanetary photometry measurements

Multi-modal data fusion using source separation: Two effective models based on ICA and IVA and their properties

PubMed Central

Adali, Tülay; Levin-Schwartz, Yuri; Calhoun, Vince D.

2015-01-01

Fusion of information from multiple sets of data in order to extract a set of features that are most useful and relevant for the given task is inherent to many problems we deal with today. Since, usually, very little is known about the actual interaction among the datasets, it is highly desirable to minimize the underlying assumptions. This has been the main reason for the growing importance of data-driven methods, and in particular of independent component analysis (ICA) as it provides useful decompositions with a simple generative model and using only the assumption of statistical independence. A recent extension of ICA, independent vector analysis (IVA) generalizes ICA to multiple datasets by exploiting the statistical dependence across the datasets, and hence, as we discuss in this paper, provides an attractive solution to fusion of data from multiple datasets along with ICA. In this paper, we focus on two multivariate solutions for multi-modal data fusion that let multiple modalities fully interact for the estimation of underlying features that jointly report on all modalities. One solution is the Joint ICA model that has found wide application in medical imaging, and the second one is the the Transposed IVA model introduced here as a generalization of an approach based on multi-set canonical correlation analysis. In the discussion, we emphasize the role of diversity in the decompositions achieved by these two models, present their properties and implementation details to enable the user make informed decisions on the selection of a model along with its associated parameters. Discussions are supported by simulation results to help highlight the main issues in the implementation of these methods. PMID:26525830

An automated method for identifying artifact in independent component analysis of resting-state FMRI.

PubMed

Bhaganagarapu, Kaushik; Jackson, Graeme D; Abbott, David F

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available.

An Automated Method for Identifying Artifact in Independent Component Analysis of Resting-State fMRI

PubMed Central

Bhaganagarapu, Kaushik; Jackson, Graeme D.; Abbott, David F.

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available. PMID:23847511

Improved application of independent component analysis to functional magnetic resonance imaging study via linear projection techniques.

PubMed

Long, Zhiying; Chen, Kewei; Wu, Xia; Reiman, Eric; Peng, Danling; Yao, Li

2009-02-01

Spatial Independent component analysis (sICA) has been widely used to analyze functional magnetic resonance imaging (fMRI) data. The well accepted implicit assumption is the spatially statistical independency of intrinsic sources identified by sICA, making the sICA applications difficult for data in which there exist interdependent sources and confounding factors. This interdependency can arise, for instance, from fMRI studies investigating two tasks in a single session. In this study, we introduced a linear projection approach and considered its utilization as a tool to separate task-related components from two-task fMRI data. The robustness and feasibility of the method are substantiated through simulation on computer data and fMRI real rest data. Both simulated and real two-task fMRI experiments demonstrated that sICA in combination with the projection method succeeded in separating spatially dependent components and had better detection power than pure model-based method when estimating activation induced by each task as well as both tasks.

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

PubMed

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

2016-09-01

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

Comparison of common components analysis with principal components analysis and independent components analysis: Application to SPME-GC-MS volatolomic signatures.

PubMed

Bouhlel, Jihéne; Jouan-Rimbaud Bouveresse, Delphine; Abouelkaram, Said; Baéza, Elisabeth; Jondreville, Catherine; Travel, Angélique; Ratel, Jérémy; Engel, Erwan; Rutledge, Douglas N

2018-02-01

The aim of this work is to compare a novel exploratory chemometrics method, Common Components Analysis (CCA), with Principal Components Analysis (PCA) and Independent Components Analysis (ICA). CCA consists in adapting the multi-block statistical method known as Common Components and Specific Weights Analysis (CCSWA or ComDim) by applying it to a single data matrix, with one variable per block. As an application, the three methods were applied to SPME-GC-MS volatolomic signatures of livers in an attempt to reveal volatile organic compounds (VOCs) markers of chicken exposure to different types of micropollutants. An application of CCA to the initial SPME-GC-MS data revealed a drift in the sample Scores along CC2, as a function of injection order, probably resulting from time-related evolution in the instrument. This drift was eliminated by orthogonalization of the data set with respect to CC2, and the resulting data are used as the orthogonalized data input into each of the three methods. Since the first step in CCA is to norm-scale all the variables, preliminary data scaling has no effect on the results, so that CCA was applied only to orthogonalized SPME-GC-MS data, while, PCA and ICA were applied to the "orthogonalized", "orthogonalized and Pareto-scaled", and "orthogonalized and autoscaled" data. The comparison showed that PCA results were highly dependent on the scaling of variables, contrary to ICA where the data scaling did not have a strong influence. Nevertheless, for both PCA and ICA the clearest separations of exposed groups were obtained after autoscaling of variables. The main part of this work was to compare the CCA results using the orthogonalized data with those obtained with PCA and ICA applied to orthogonalized and autoscaled variables. The clearest separations of exposed chicken groups were obtained by CCA. CCA Loadings also clearly identified the variables contributing most to the Common Components giving separations. The PCA Loadings did not highlight the most influencing variables for each separation, whereas the ICA Loadings highlighted the same variables as did CCA. This study shows the potential of CCA for the extraction of pertinent information from a data matrix, using a procedure based on an original optimisation criterion, to produce results that are complementary, and in some cases may be superior, to those of PCA and ICA. Copyright © 2017 Elsevier B.V. All rights reserved.

Internal Carotid Artery Stenosis and Collateral Recruitment in Stroke Patients.

PubMed

Dankbaar, Jan W; Kerckhoffs, Kelly G P; Horsch, Alexander D; van der Schaaf, Irene C; Kappelle, L Jaap; Velthuis, Birgitta K

2017-04-24

Leptomeningeal collaterals improve outcome in stroke patients. There is great individual variability in their extent. Internal carotid artery (ICA) stenosis may lead to more extensive recruitment of leptomeningeal collaterals. The purpose of this study was to evaluate the association of pre-existing ICA stenosis with leptomeningeal collateral filling visualized with computed tomography perfusion (CTP). From a prospective acute ischemic stroke cohort, patients were included with an M1 middle cerebral artery (MCA) occlusion and absent ipsilateral, extracranial ICA occlusion. ICA stenosis was determined on admission CT angiography (CTA). Leptomeningeal collaterals were graded as good (>50%) or poor (≤50%) collateral filling in the affected MCA territory on CTP-derived vessel images of the admission scan. The association between ipsilateral ICA stenosis ≥70% and extent of collateral filling was analyzed using logistic regression. In a multivariable analysis the odds ratio (OR) of ICA stenosis ≥70% was adjusted for complete circle of Willis, gender and age. We included 188 patients in our analyses, 50 (26.6%) patients were classified as having poor collateral filling and 138 (73.4%) as good. Of the patients 4 with poor collateral filling had an ICA stenosis ≥70% and 14 with good collateral filling. Unadjusted and adjusted ORs of ICA stenosis ≥70% for good collateral filling were 1.30 (0.41-4.15) and 2.67 (0.81-8.77), respectively. Patients with poor collateral filling had a significantly worse outcome (90-day modified Rankin scale 3-6; 80% versus 52%, p = 0.001). No association was found between pre-existing ICA stenosis and extent of CTP derived collateral filling in patients with an M1 occlusion.

Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data.

PubMed

Glasser, Matthew F; Coalson, Timothy S; Bijsterbosch, Janine D; Harrison, Samuel J; Harms, Michael P; Anticevic, Alan; Van Essen, David C; Smith, Stephen M

2018-06-02

Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal "noise" from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread "global" structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary-to do or not to do global signal regression-given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a "best of both worlds" solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data. Copyright © 2018 Elsevier Inc. All rights reserved.

Dual antiplatelet therapy in stroke and ICAS: Subgroup analysis of CHANCE.

PubMed

Liu, Liping; Wong, Ka Sing Lawrence; Leng, Xinyi; Pu, Yuehua; Wang, Yilong; Jing, Jing; Zou, Xinying; Pan, Yuesong; Wang, Anxin; Meng, Xia; Wang, Chunxue; Zhao, Xingquan; Soo, Yannie; Johnston, S Claiborne; Wang, Yongjun

2015-09-29

AB OBJECTIVE: We aimed to investigate whether the efficacy and safety of clopidogrel plus aspirin vs aspirin alone were consistent between patients with and without intracranial arterial stenosis (ICAS), in the Clopidogrel in High-Risk Patients with Acute Non-disabling Cerebrovascular Events (CHANCE) trial. We assessed the interaction of the treatment effects of the 2 antiplatelet therapies among patients with and without ICAS, identified by magnetic resonance angiography (MRA) in CHANCE (ClinicalTrials.gov identifier NCT00979589). Overall, 1,089 patients with MRA images available in CHANCE were included in this subanalysis, 608 patients (55.8%) with ICAS and 481 (44.2%) without. Patients with ICAS had higher rates of recurrent stroke (12.5% vs 5.4%; p<0.0001) at 90 days than those without. But there was no statistically significant treatment by presence of ICAS interaction on either the primary outcome of any stroke (hazard ratio for clopidogrel plus aspirin vs aspirin alone: 0.79 [0.47-1.32] vs 1.12 [0.56-2.25]; interaction p=0.522) or the safety outcome of any bleeding event (interaction p=0.277). The results indicated higher rate of recurrent stroke in minor stroke or high-risk TIA patients with ICAS than in those without. However, there was no significant difference in the response to the 2 antiplatelet therapies between patients with and without ICAS in the CHANCE trial. Classification of evidence: This study provides Class II evidence that for patients with acute minor stroke or TIA with and without ICAS identified by MRA, clopidogrel plus aspirin is not significantly different than aspirin alone in preventing recurrent stroke. © 2015 American Academy of Neurology.

Extracting intrinsic functional networks with feature-based group independent component analysis.

PubMed

Calhoun, Vince D; Allen, Elena

2013-04-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.

Effect of framework design on crown failure.

PubMed

Bonfante, Estevam A; da Silva, Nelson R F A; Coelho, Paulo G; Bayardo-González, Daniel E; Thompson, Van P; Bonfante, Gerson

2009-04-01

This study evaluated the effect of core-design modification on the characteristic strength and failure modes of glass-infiltrated alumina (In-Ceram) (ICA) compared with porcelain fused to metal (PFM). Premolar crowns of a standard design (PFMs and ICAs) or with a modified framework design (PFMm and ICAm) were fabricated, cemented on dies, and loaded until failure. The crowns were loaded at 0.5 mm min(-1) using a 6.25 mm tungsten-carbide ball at the central fossa. Fracture load values were recorded and fracture analysis of representative samples were evaluated using scanning electron microscopy. Probability Weibull curves with two-sided 90% confidence limits were calculated for each group and a contour plot of the characteristic strength was obtained. Design modification showed an increase in the characteristic strength of the PFMm and ICAm groups, with PFM groups showing higher characteristic strength than ICA groups. The PFMm group showed the highest characteristic strength among all groups. Fracture modes of PFMs and of PFMm frequently reached the core interface at the lingual cusp, whereas ICA exhibited bulk fracture through the alumina core. Core-design modification significantly improved the characteristic strength for PFM and for ICA. The PFM groups demonstrated higher characteristic strength than both ICA groups combined.

Secure Communication Based on a Hybrid of Chaos and Ica Encryptions

NASA Astrophysics Data System (ADS)

Chen, Wei Ching; Yuan, John

Chaos and independent component analysis (ICA) encryptions are two novel schemes for secure communications. In this paper, a new scheme combining chaos and ICA techniques is proposed to enhance the security level during communication. In this scheme, a master chaotic system is embedded at the transmitter. The message signal is mixed with a chaotic signal and a Gaussian white noise into two mixed signals and then transmitted to the receiver through the public channels. A signal for synchronization is transmitted through another public channel to the receiver where a slave chaotic system is embedded to reproduce the chaotic signal. A modified ICA is used to recover the message signal at the receiver. Since only two of the three transmitted signals contain the information of message signal, a hacker would not be able to retrieve the message signal by using ICA even though all the transmitted signals are intercepted. Spectrum analyses are used to prove that the message signal can be securely hidden under this scheme.

Evaluating the efficacy of fully automated approaches for the selection of eye blink ICA components

PubMed Central

Pontifex, Matthew B.; Miskovic, Vladimir; Laszlo, Sarah

2017-01-01

Independent component analysis (ICA) offers a powerful approach for the isolation and removal of eye blink artifacts from EEG signals. Manual identification of the eye blink ICA component by inspection of scalp map projections, however, is prone to error, particularly when non-artifactual components exhibit topographic distributions similar to the blink. The aim of the present investigation was to determine the extent to which automated approaches for selecting eye blink related ICA components could be utilized to replace manual selection. We evaluated popular blink selection methods relying on spatial features [EyeCatch()], combined stereotypical spatial and temporal features [ADJUST()], and a novel method relying on time-series features alone [icablinkmetrics()] using both simulated and real EEG data. The results of this investigation suggest that all three methods of automatic component selection are able to accurately identify eye blink related ICA components at or above the level of trained human observers. However, icablinkmetrics(), in particular, appears to provide an effective means of automating ICA artifact rejection while at the same time eliminating human errors inevitable during manual component selection and false positive component identifications common in other automated approaches. Based upon these findings, best practices for 1) identifying artifactual components via automated means and 2) reducing the accidental removal of signal-related ICA components are discussed. PMID:28191627

Spatiotemporal analysis of single-trial EEG of emotional pictures based on independent component analysis and source location

NASA Astrophysics Data System (ADS)

Liu, Jiangang; Tian, Jie

2007-03-01

The present study combined the Independent Component Analysis (ICA) and low-resolution brain electromagnetic tomography (LORETA) algorithms to identify the spatial distribution and time course of single-trial EEG record differences between neural responses to emotional stimuli vs. the neutral. Single-trial multichannel (129-sensor) EEG records were collected from 21 healthy, right-handed subjects viewing the emotion emotional (pleasant/unpleasant) and neutral pictures selected from International Affective Picture System (IAPS). For each subject, the single-trial EEG records of each emotional pictures were concatenated with the neutral, and a three-step analysis was applied to each of them in the same way. First, the ICA was performed to decompose each concatenated single-trial EEG records into temporally independent and spatially fixed components, namely independent components (ICs). The IC associated with artifacts were isolated. Second, the clustering analysis classified, across subjects, the temporally and spatially similar ICs into the same clusters, in which nonparametric permutation test for Global Field Power (GFP) of IC projection scalp maps identified significantly different temporal segments of each emotional condition vs. neutral. Third, the brain regions accounted for those significant segments were localized spatially with LORETA analysis. In each cluster, a voxel-by-voxel randomization test identified significantly different brain regions between each emotional condition vs. the neutral. Compared to the neutral, both emotional pictures elicited activation in the visual, temporal, ventromedial and dorsomedial prefrontal cortex and anterior cingulated gyrus. In addition, the pleasant pictures activated the left middle prefrontal cortex and the posterior precuneus, while the unpleasant pictures activated the right orbitofrontal cortex, posterior cingulated gyrus and somatosensory region. Our results were well consistent with other functional imaging studies, while revealed temporal dynamics of emotional processing of specific brain structure with high temporal resolution.

Common mode error in Antarctic GPS coordinate time series on its effect on bedrock-uplift estimates

NASA Astrophysics Data System (ADS)

Liu, Bin; King, Matt; Dai, Wujiao

2018-05-01

Spatially-correlated common mode error always exists in regional, or-larger, GPS networks. We applied independent component analysis (ICA) to GPS vertical coordinate time series in Antarctica from 2010 to 2014 and made a comparison with the principal component analysis (PCA). Using PCA/ICA, the time series can be decomposed into a set of temporal components and their spatial responses. We assume the components with common spatial responses are common mode error (CME). An average reduction of ˜40% about the RMS values was achieved in both PCA and ICA filtering. However, the common mode components obtained from the two approaches have different spatial and temporal features. ICA time series present interesting correlations with modeled atmospheric and non-tidal ocean loading displacements. A white noise (WN) plus power law noise (PL) model was adopted in the GPS velocity estimation using maximum likelihood estimation (MLE) analysis, with ˜55% reduction of the velocity uncertainties after filtering using ICA. Meanwhile, spatiotemporal filtering reduces the amplitude of PL and periodic terms in the GPS time series. Finally, we compare the GPS uplift velocities, after correction for elastic effects, with recent models of glacial isostatic adjustment (GIA). The agreements of the GPS observed velocities and four GIA models are generally improved after the spatiotemporal filtering, with a mean reduction of ˜0.9 mm/yr of the WRMS values, possibly allowing for more confident separation of various GIA model predictions.

Application of the Intuitionistic Fuzzy InterCriteria Analysis Method with Triples to a Neural Network Preprocessing Procedure

PubMed Central

Atanassova, Vassia; Sotirova, Evdokia; Doukovska, Lyubka; Bureva, Veselina; Mavrov, Deyan; Tomov, Jivko

2017-01-01

The approach of InterCriteria Analysis (ICA) was applied for the aim of reducing the set of variables on the input of a neural network, taking into account the fact that their large number increases the number of neurons in the network, thus making them unusable for hardware implementation. Here, for the first time, with the help of the ICA method, correlations between triples of the input parameters for training of the neural networks were obtained. In this case, we use the approach of ICA for data preprocessing, which may yield reduction of the total time for training the neural networks, hence, the time for the network's processing of data and images. PMID:28874908

A novel method for device-related electroencephalography artifact suppression to explore cochlear implant-related cortical changes in single-sided deafness.

PubMed

Kim, Kyungsoo; Punte, Andrea Kleine; Mertens, Griet; Van de Heyning, Paul; Park, Kyung-Joon; Choi, Hongsoo; Choi, Ji-Woong; Song, Jae-Jin

2015-11-30

Quantitative electroencephalography (qEEG) is effective when used to analyze ongoing cortical oscillations in cochlear implant (CI) users. However, localization of cortical activity in such users via qEEG is confounded by the presence of artifacts produced by the device itself. Typically, independent component analysis (ICA) is used to remove CI artifacts in auditory evoked EEG signals collected upon brief stimulation and it is effective for auditory evoked potentials (AEPs). However, AEPs do not reflect the daily environments of patients, and thus, continuous EEG data that are closer to such environments are desirable. In this case, device-related artifacts in EEG data are difficult to remove selectively via ICA due to over-completion of EEG data removal in the absence of preprocessing. EEGs were recorded for a long time under conditions of continuous auditory stimulation. To obviate the over-completion problem, we limited the frequency of CI artifacts to a significant characteristic peak and apply ICA artifact removal. Topographic brain mapping results analyzed via band-limited (BL)-ICA exhibited a better energy distribution, matched to the CI location, than data obtained using conventional ICA. Also, source localization data verified that BL-ICA effectively removed CI artifacts. The proposed method selectively removes CI artifacts from continuous EEG recordings, while ICA removal method shows residual peak and removes important brain activity signals. CI artifacts in EEG data obtained during continuous passive listening can be effectively removed with the aid of BL-ICA, opening up new EEG research possibilities in subjects with CIs. Copyright © 2015 Elsevier B.V. All rights reserved.

A first application of independent component analysis to extracting structure from stock returns.

PubMed

Back, A D; Weigend, A S

1997-08-01

This paper explores the application of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).

Receiver Diversity Combining Using Evolutionary Algorithms in Rayleigh Fading Channel

PubMed Central

Akbari, Mohsen; Manesh, Mohsen Riahi

2014-01-01

In diversity combining at the receiver, the output signal-to-noise ratio (SNR) is often maximized by using the maximal ratio combining (MRC) provided that the channel is perfectly estimated at the receiver. However, channel estimation is rarely perfect in practice, which results in deteriorating the system performance. In this paper, an imperialistic competitive algorithm (ICA) is proposed and compared with two other evolutionary based algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA), for diversity combining of signals travelling across the imperfect channels. The proposed algorithm adjusts the combiner weights of the received signal components in such a way that maximizes the SNR and minimizes the bit error rate (BER). The results indicate that the proposed method eliminates the need of channel estimation and can outperform the conventional diversity combining methods. PMID:25045725

Multicomponent quantitative spectroscopic analysis without reference substances based on ICA modelling.

PubMed

Monakhova, Yulia B; Mushtakova, Svetlana P

2017-05-01

A fast and reliable spectroscopic method for multicomponent quantitative analysis of targeted compounds with overlapping signals in complex mixtures has been established. The innovative analytical approach is based on the preliminary chemometric extraction of qualitative and quantitative information from UV-vis and IR spectral profiles of a calibration system using independent component analysis (ICA). Using this quantitative model and ICA resolution results of spectral profiling of "unknown" model mixtures, the absolute analyte concentrations in multicomponent mixtures and authentic samples were then calculated without reference solutions. Good recoveries generally between 95% and 105% were obtained. The method can be applied to any spectroscopic data that obey the Beer-Lambert-Bouguer law. The proposed method was tested on analysis of vitamins and caffeine in energy drinks and aromatic hydrocarbons in motor fuel with 10% error. The results demonstrated that the proposed method is a promising tool for rapid simultaneous multicomponent analysis in the case of spectral overlap and the absence/inaccessibility of reference materials.

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

NASA Astrophysics Data System (ADS)

Forootan, Ehsan; Kusche, Jürgen; Talpe, Matthieu; Shum, C. K.; Schmidt, Michael

2017-12-01

In recent decades, decomposition techniques have enabled increasingly more applications for dimension reduction, as well as extraction of additional information from geophysical time series. Traditionally, the principal component analysis (PCA)/empirical orthogonal function (EOF) method and more recently the independent component analysis (ICA) have been applied to extract, statistical orthogonal (uncorrelated), and independent modes that represent the maximum variance of time series, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the autocovariance matrix and diagonalizing higher (than two) order statistical tensors from centered time series, respectively. However, the stationarity assumption in these techniques is not justified for many geophysical and climate variables even after removing cyclic components, e.g., the commonly removed dominant seasonal cycles. In this paper, we present a novel decomposition method, the complex independent component analysis (CICA), which can be applied to extract non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA, where (a) we first define a new complex dataset that contains the observed time series in its real part, and their Hilbert transformed series as its imaginary part, (b) an ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex dataset in (a), and finally, (c) the dominant independent complex modes are extracted and used to represent the dominant space and time amplitudes and associated phase propagation patterns. The performance of CICA is examined by analyzing synthetic data constructed from multiple physically meaningful modes in a simulation framework, with known truth. Next, global terrestrial water storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) gravimetry mission (2003-2016), and satellite radiometric sea surface temperature (SST) data (1982-2016) over the Atlantic and Pacific Oceans are used with the aim of demonstrating signal separations of the North Atlantic Oscillation (NAO) from the Atlantic Multi-decadal Oscillation (AMO), and the El Niño Southern Oscillation (ENSO) from the Pacific Decadal Oscillation (PDO). CICA results indicate that ENSO-related patterns can be extracted from the Gravity Recovery And Climate Experiment Terrestrial Water Storage (GRACE TWS) with an accuracy of 0.5-1 cm in terms of equivalent water height (EWH). The magnitude of errors in extracting NAO or AMO from SST data using the complex EOF (CEOF) approach reaches up to 50% of the signal itself, while it is reduced to 16% when applying CICA. Larger errors with magnitudes of 100% and 30% of the signal itself are found while separating ENSO from PDO using CEOF and CICA, respectively. We thus conclude that the CICA is more effective than CEOF in separating non-stationary patterns.

Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI

PubMed Central

Maneshi, Mona; Vahdat, Shahabeddin; Gotman, Jean; Grova, Christophe

2016-01-01

Independent component analysis (ICA) has been widely used to study functional magnetic resonance imaging (fMRI) connectivity. However, the application of ICA in multi-group designs is not straightforward. We have recently developed a new method named “shared and specific independent component analysis” (SSICA) to perform between-group comparisons in the ICA framework. SSICA is sensitive to extract those components which represent a significant difference in functional connectivity between groups or conditions, i.e., components that could be considered “specific” for a group or condition. Here, we investigated the performance of SSICA on realistic simulations, and task fMRI data and compared the results with one of the state-of-the-art group ICA approaches to infer between-group differences. We examined SSICA robustness with respect to the number of allowable extracted specific components and between-group orthogonality assumptions. Furthermore, we proposed a modified formulation of the back-reconstruction method to generate group-level t-statistics maps based on SSICA results. We also evaluated the consistency and specificity of the extracted specific components by SSICA. The results on realistic simulated and real fMRI data showed that SSICA outperforms the regular group ICA approach in terms of reconstruction and classification performance. We demonstrated that SSICA is a powerful data-driven approach to detect patterns of differences in functional connectivity across groups/conditions, particularly in model-free designs such as resting-state fMRI. Our findings in task fMRI show that SSICA confirms results of the general linear model (GLM) analysis and when combined with clustering analysis, it complements GLM findings by providing additional information regarding the reliability and specificity of networks. PMID:27729843

Adjustment of ionized calcium concentration for serum pH is not a valid marker of calcium homeostasis: implications for identifying individuals at risk of calcium metabolic disorders.

PubMed

Lam, Virginie; Dhaliwal, Satvinder S; Mamo, John C

2013-05-01

Ionized calcium (iCa) is the biologically active form of this micronutrient. Serum determination of iCa is measured via ion-electrode potentiometry (IEP) and reporting iCa relative to pH 7.4 is normally utilized to avoid the potential confounding effects of ex vivo changes to serum pH. Adjustment of iCa for pH has not been adequately justified. In this study, utilizing carefully standardized protocols for blood collection, the preparation of serum and controlling time of collection-to-analysis, we determined serum iCa and pH utilizing an IEP-analyser hosted at an accredited diagnostic laboratory. Regression analysis of unadjusted-iCa (iCa(raw)) concentration versus pH was described by linear regression and accounted for 37% of serum iCa(raw) variability. iCa(raw) was then expressed at pH 7.4 by either adjusting iCa(raw) based on the linear regression equation describing the association of iCa with serum pH (iCa(regr)) or using IEP coded published normative equations (iCa(pub)). iCa(regr) was comparable to iCa(raw), indicating that blood collection and processing methodologies were sound. However, iCa(pub) yielded values that were significantly lower than iCa(raw). iCa(pub) did not identify 15% subjects who had greater than desirable serum concentration of iCa based on iCa(raw). Sixty percent of subjects with low levels of iCa(raw) were also not detected by iCa(pub). Determination of the kappa value measure of agreement for iCa(raw) versus iCa(pub) showed relatively poor concordance (κ = 0.42). With simple protocols that avoid sampling artefacts, expressing iCa(raw) is likely to be a more valid and physiologically relevant marker of calcium homeostasis than is iCa(pub).

Limitations and applications of ICA for surface electromyogram.

PubMed

Djuwari, D; Kumar, D K; Naik, G R; Arjunan, S P; Palaniswami, M

2006-09-01

Surface electromyogram (SEMG) has numerous applications, but the presence of artefacts and noise, especially at low level of muscle activity make the recordings unreliable. Spectral and temporal overlap can make the removal of artefacts and noise, or separation of relevant signals from other bioelectric signals extremely difficult. Individual muscles may be considered as independent at the local level and this makes an argument for separating the signals using independent component analysis (ICA). In the recent past, due to the easy availability of ICA tools, numbers of researchers have attempted to use ICA for this application. This paper reports research conducted to evaluate the use of ICA for the separation of muscle activity and removal of the artefacts from SEMG. It discusses some of the conditions that could affect the reliability of the separation and evaluates issues related to the properties of the signals and number of sources. The paper also identifies the lack of suitable measure of quality of separation for bioelectric signals and it recommends and tests a more robust measure of separation. The paper also reports tests using Zibulevsky's technique of temporal plotting to identify number of independent sources in SEMG recordings. The theoretical analysis and experimental results demonstrate that ICA is suitable for SEMG signals. The results identify the unsuitability of ICA when the number of sources is greater than the number of recording channels. The results also demonstrate the limitations of such applications due to the inability of the system to identify the correct order and magnitude of the signals. The paper determines the suitability of the use of error measure using simulated mixing matrix and the estimated unmixing matrix as a means identifying the quality of separation of the output. The work demonstrates that even at extremely low level of muscle contraction, and with filtering using wavelets and band pass filters, it is not possible to get the data sparse enough to identify number of independent sources using Zibulevs.ky's technique.

A combined cICA-EEMD analysis of EEG recordings from depressed or schizophrenic patients during olfactory stimulation

NASA Astrophysics Data System (ADS)

Götz, Th; Stadler, L.; Fraunhofer, G.; Tomé, A. M.; Hausner, H.; Lang, E. W.

2017-02-01

Objective. We propose a combination of a constrained independent component analysis (cICA) with an ensemble empirical mode decomposition (EEMD) to analyze electroencephalographic recordings from depressed or schizophrenic subjects during olfactory stimulation. Approach. EEMD serves to extract intrinsic modes (IMFs) underlying the recorded EEG time. The latter then serve as reference signals to extract the most similar underlying independent component within a constrained ICA. The extracted modes are further analyzed considering their power spectra. Main results. The analysis of the extracted modes reveals clear differences in the related power spectra between the disease characteristics of depressed and schizophrenic patients. Such differences appear in the high frequency γ-band in the intrinsic modes, but also in much more detail in the low frequency range in the α-, θ- and δ-bands. Significance. The proposed method provides various means to discriminate both disease pictures in a clinical environment.

Unsupervised detection and removal of muscle artifacts from scalp EEG recordings using canonical correlation analysis, wavelets and random forests.

PubMed

Anastasiadou, Maria N; Christodoulakis, Manolis; Papathanasiou, Eleftherios S; Papacostas, Savvas S; Mitsis, Georgios D

2017-09-01

This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert neurophysiologists, EMG signal recording and user visual inspection. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

One-step immunochromatographic visual assay for anti-transglutaminase detection in organ culture system: An easy and prompt method to simplify the in vitro diagnosis of celiac disease.

PubMed

Di Tola, Marco; Marino, Mariacatia; Casale, Rossella; Borghini, Raffaele; Tiberti, Antonio; Donato, Giuseppe; Occhiuzzi, Umberto; Picarelli, Antonio

2018-01-01

Anti-tissue transglutaminase (anti-tTG) and endomysium antibodies (EMA) are detectable in duodenal culture media of celiac disease (CD) patients. To improve the management of this organ culture system, we evaluated the anti-tTG occurrence by immunochromatographic assay (ICA). A total of 103 CD patients and 41 disease controls underwent duodenal biopsy for the organ culture. In culture supernatants, IgA anti-tTG were tested by both enzyme-linked immunosorbent assay (ELISA) and ICA, IgA EMA were searched by indirect immunofluorescence analysis (iIFA). Endomysium antibodies and anti-tTG measured by ELISA were positive in culture media of all CD patients, while anti-tTG detected by ICA were positive in culture media of 87/103 CD patients. Anti-tTG ICA scores significantly correlated with anti-tTG ELISA values (r=.71, P<.0001). Sensitivity, specificity and diagnostic accuracy of anti-tTG detected by ICA were 84.5%, 100% and 88.9%, respectively. Using ICA, anti-tTG are detectable in duodenal culture media of most CD patients and the intensity of indicative lines depends on the anti-tTG concentration. Sensitivity and diagnostic accuracy achieved with ICA are lower than those obtained with ELISA but, given that the first is a more easy and prompt method, data suggest the possibility of utilizing it in the in vitro diagnosis of CD. © 2017 Wiley Periodicals, Inc.

Group in-course assessment promotes cooperative learning and increases performance.

PubMed

Pratten, Margaret K; Merrick, Deborah; Burr, Steven A

2014-01-01

The authors describe and evaluate a method to motivate medical students to maximize the effectiveness of dissection opportunities by using In-Course-Assessments (ICAs) to encourage teamwork. A student's final mark was derived by combining the group dissection mark, group mark for questions, and their individual question mark. An analysis of the impact of the ICA was performed by comparing end of module practical summative marks in student cohorts who had, or had not, participated in the ICAs. Summative marks were compared by two-way ANOVA followed by Dunnets test, or by repeated measures ANOVA, as appropriate. A cohort of medical students was selected that had experienced both practical classes without (year one) and with the new ICA structure (year two). Comparison of summative year one and year two marks illustrated an increased improvement in year two performance in this cohort. A significant increase was also noted when comparing this cohort with five preceding year two cohorts who had not experienced the ICAs (P <0.0001). To ensure that variation in the practical summative examination was not impacting on the data, a comparison was made between three cohorts who had performed the same summative examination. Results show that students who had undertook weekly ICAs showed significantly improved summative marks, compared with those who did not (P <0.0001). This approach to ICA promotes engagement with learning resources in an active, team-based, cooperative learning environment. © 2013 American Association of Anatomists.

A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification.

PubMed

LeVan, P; Urrestarazu, E; Gotman, J

2006-04-01

To devise an automated system to remove artifacts from ictal scalp EEG, using independent component analysis (ICA). A Bayesian classifier was used to determine the probability that 2s epochs of seizure segments decomposed by ICA represented EEG activity, as opposed to artifact. The classifier was trained using numerous statistical, spectral, and spatial features. The system's performance was then assessed using separate validation data. The classifier identified epochs representing EEG activity in the validation dataset with a sensitivity of 82.4% and a specificity of 83.3%. An ICA component was considered to represent EEG activity if the sum of the probabilities that its epochs represented EEG exceeded a threshold predetermined using the training data. Otherwise, the component represented artifact. Using this threshold on the validation set, the identification of EEG components was performed with a sensitivity of 87.6% and a specificity of 70.2%. Most misclassified components were a mixture of EEG and artifactual activity. The automated system successfully rejected a good proportion of artifactual components extracted by ICA, while preserving almost all EEG components. The misclassification rate was comparable to the variability observed in human classification. Current ICA methods of artifact removal require a tedious visual classification of the components. The proposed system automates this process and removes simultaneously multiple types of artifacts.

Immunochromatographic assay of cadmium levels in oysters.

PubMed

Nishi, Kosuke; Kim, In-Hae; Itai, Takaaki; Sugahara, Takuya; Takeyama, Haruko; Ohkawa, Hideo

2012-08-15

Oysters are one of foodstuffs containing a relatively high amount of cadmium. Here we report on establishment of an immunochromatographic assay (ICA) method of cadmium levels in oysters. Cadmium was extracted with 0.l mol L(-1) HCl from oysters and cleaned up from other metals by the use of an anion-exchange column. The behavior of five metals Mn, Fe, Cu, Zn, and Cd was monitored at each step of extraction and clean-up procedure for the ICA method in an inductively coupled plasma-mass spectrometry (ICP-MS) analysis. The results revealed that a simple extraction method with the HCl solution was efficient enough to extract almost all of cadmium from oysters. Clean-up with an anion-exchange column presented almost no loss of cadmium adsorbed on the column and an efficient removal of metals other than cadmium. When a spiked recovery test was performed in the ICA method, the recovery ranged from 98% to 112% with relative standard deviations between 5.9% and 9.2%. The measured values of cadmium in various oyster samples in the ICA method were favorably correlated with those in ICP-MS analysis (r(2)=0.97). Overall results indicate that the ICA method established in the present study is an adequate and reliable detection method for cadmium levels in oysters. Copyright © 2012 Elsevier B.V. All rights reserved.

Functional subdivision of group-ICA results of fMRI data collected during cinema viewing.

PubMed

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film ("At land" by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.

Gray matter alteration in isolated congenital anosmia patient: a voxel-based morphometry study.

PubMed

Yao, Linyin; Yi, Xiaoli; Wei, Yongxiang

2013-09-01

Decreased volume of gray matter (GM) was observed in olfactory loss in patients with neurodegenerative disorder. However, GM volume has not yet been investigated in isolated congenital anosmia (ICA) people. We herewith investigated the volume change of gray matter of an ICA boy by morphometric analysis of magnetic resonance images (voxel-based morphometry), and compared with that of 20 age-matched healthy controls. ICA boy presented a significant decrease in GM volume in the orbitofrontal cortex, anterior cingulate cortex, middle cingulate cortex, thalamus, insular cortex, cerebellum, precuneus, gyrus rectus, subcallosal gyrus, middle temporal gyrus, fusiform gyrus and piriform cortex. No significant GM volume increase was detected in other brain areas. The pattern of GM atrophy was similar as previous literature reported. Our results identified similar GM volume alterations regardless of the causes of olfactory impairment. Decreased GM volume was not only shown in olfactory bulbs, olfactory tracts and olfactory sulcus, also in primary olfactory cortex and the secondary cerebral olfactory areas in ICA people. This is the first study to evaluate GM volume alterations in ICA people.

Blood Pressure Is the Determinant for the Increased Risk for Intracranial Arterial Stenosis in Subjects with Elevated Glycated Hemoglobin Levels: The Kangbuk Samsung Health Study.

PubMed

Oh, Hyung-Geun; Rhee, Eun-Jung

2016-11-01

Ischemic stroke is known to be an important vascular complication of diabetes. Intracranial arterial stenosis (ICAS) is considered as an important cause of stroke in Asians. We aimed to analyze the risk for ICAS assessed by transcranial Doppler (TCD) ultrasonography in different groups of young Korean subjects divided by glycated hemoglobin (HbA1c) levels. This study included 10,437 participants without history of cardiovascular diseases (81.3% men, mean age 43 years) from a health screening program, in whom TCD ultrasonography was used to detect greater than 50% ICAS based on criteria modified from the SONIA (Stroke Outcomes and Neuroimaging of Intracranial Atherosclerosis) trial. The subjects were divided into 3 groups according to HbA1c levels: HbA1c < 5.7%, 5.7 ≤ HbA1c < 6.5%, and HbA1c ≥ 6.5% or under medication for diabetes. Among the participants, 3.0% of the subjects had ICAS. The subjects with ICAS tended to have higher mean HbA1c level compared with those without ICAS (5.8 ± .8 versus 5.7 ± .6, P = .063). The proportion of subjects with ICAS significantly increased as the HbA1c increased from the first to the third group (2.8%, 3.0%, 4.6%, P for linear trend = .022). In logistic regression analysis with ICAS as the dependent variable, the group with HbA1c ≥ 6.5% showed significantly increased odds ratio for ICAS with subjects with HbA1c < 5.7% as the reference after adjustment for confounding variables (1.575, 95% confidence interval 1.056-2.347). However, this significance disappeared with inclusion of presence of hypertension in the model. The risk for ICAS assessed by TCD was increased in young Korean subjects with HbA1c ≥ 6.5%. However, this significance was attenuated after adjustment for presence of hypertension, suggesting the importance of hypertension in ICAS. Copyright © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Technical Note: Independent component analysis for quality assurance in functional MRI.

PubMed

Astrakas, Loukas G; Kallistis, Nikolaos S; Kalef-Ezra, John A

2016-02-01

Independent component analysis (ICA) is an established method of analyzing human functional MRI (fMRI) data. Here, an ICA-based fMRI quality control (QC) tool was developed and used. ICA-based fMRI QC tool to be used with a commercial phantom was developed. In an attempt to assess the performance of the tool relative to preexisting alternative tools, it was used seven weeks before and eight weeks after repair of a faulty gradient amplifier of a non-state-of-the-art MRI unit. More specifically, its performance was compared with the AAPM 100 acceptance testing and quality assurance protocol and two fMRI QC protocols, proposed by Freidman et al. ["Report on a multicenter fMRI quality assurance protocol," J. Magn. Reson. Imaging 23, 827-839 (2006)] and Stocker et al. ["Automated quality assurance routines for fMRI data applied to a multicenter study," Hum. Brain Mapp. 25, 237-246 (2005)], respectively. The easily developed and applied ICA-based QC protocol provided fMRI QC indices and maps equally sensitive to fMRI instabilities with the indices and maps of other established protocols. The ICA fMRI QC indices were highly correlated with indices of other fMRI QC protocols and in some cases theoretically related to them. Three or four independent components with slow varying time series are detected under normal conditions. ICA applied on phantom measurements is an easy and efficient tool for fMRI QC. Additionally, it can protect against misinterpretations of artifact components as human brain activations. Evaluating fMRI QC indices in the central region of a phantom is not always the optimal choice.

Independent Component Analysis applied to Ground-based observations

NASA Astrophysics Data System (ADS)

Martins-Filho, Walter; Griffith, Caitlin Ann; Pearson, Kyle; Waldmann, Ingo; Alvarez-Candal, Alvaro; Zellem, Robert

2017-10-01

Transit measurements of Jovian-sized exoplanetary atmospheres allow one to study the composition of exoplanets, largely independent of the planet’s temperature profile. However, measurements of hot-Jupiter transits must archive a level of accuracy in the flux to determine the spectral modulations of the exoplanetary atmosphere. To accomplish this level of precision, we need to extract systematic errors, and, for ground-based measurements, the effects of Earth’s atmosphere, from signal due to the exoplanet, which is several orders of magnitudes smaller.The effects of the terrestrial atmosphere and some of the time dependent systematic errors of ground-based transit measurements are treated mainly by dividing the host star by a reference star at each wavelength and time step of the transit. Recently, Independent Component Analyses (ICA) have been used to remove systematics effects from the raw data of space-based observations (Waldmann, 2014, 2012; Morello et al., 2016, 2015). ICA is a statistical method born from the ideas of the blind-source separations studies, which can be used to de-trend several independent source signals of a data set (Hyvarinen and Oja, 2000). This technique requires no additional prior knowledge of the data set. In addition this technique has the advantage of requiring no reference star.Here we apply the ICA to ground-based photometry of the exoplanet XO-2b recorded by the 61” Kuiper Telescope and compare the results of the ICA to those of a previous analysis from Zellem et al. (2015), which does not use ICA. We also simulate the effects of various conditions (concerning the systematic errors, noise and the stability of object on the detector) to determine the conditions under which an ICA can be used with high precision to extract the light curve of exoplanetary photometry measurements.

Vascular geometry of the extracranial carotid arteries: an analysis of length, diameter, and tortuosity.

PubMed

Choudhry, Farooq A; Grantham, John T; Rai, Ansaar T; Hogg, Jeffery P

2016-05-01

Stable access is essential for successful intracranial interventions. Quantifying variations in extracranial carotid arteries may help in the selection and development of access catheters. This study describes the vascular dimensions from the aortic arch to the skull base. CT angiography analysis was performed on 100 patients. The lengths, diameters, and tortuosity of the common carotid artery (CCA) and internal carotid artery (ICA) were measured from the aortic arch to the skull base. The mean±SD length of the carotid artery from the aortic arch to the skull base was 22.2±2.2 cm for the right side and 20.8±1.9 cm for the left side (p<0.0001). The length of the right CCA was 13.6±1.2 cm and the length of the left CCA was 12.4±1.4 cm (p<0.0001). The length of the right ICA was 8.6±1.4 cm compared with 8.4±1.4 cm for the left ICA (p=0.3). The ICA length in men and women was 8.9±1.3 cm and 8.2±1.3 cm, respectively (p=0.0001), and the CCA length in men and women was 13.6±1.5 cm and 12.3±1.6 cm, respectively (p<0.0001). The lengths of the CCA and ICA in patients aged ≥60 years were 13.3±1.7 cm and 8.9±1.5 cm, respectively compared with 12.8±1.7 cm and 8.2±1.1 cm, respectively, for patients aged <60 years (p=0.04 for CCA, p=0.0002 for ICA). Tortuosity of the CCA and ICA was 1.2±0.2 and 1.3±0.1, respectively, in patients aged ≥60 years compared with 1.1±0.1 for both the ICA and CCA in patients aged <60 years (p<0.0001 for both). There was a consistent ratio of CCA/ICA length of 1.6±0.3 on the right and 1.5±0.3 on the left (p<0.0001). The arterial diameters did not show any significant difference. The distance from the aortic arch to the skull base is longer on the right than on the left side. Both the CCA and ICA are longer in men and in patients aged ≥60 years. The tortuosity of both segments significantly increases with age. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Intelligent fuzzy approach for fast fractal image compression

NASA Astrophysics Data System (ADS)

Nodehi, Ali; Sulong, Ghazali; Al-Rodhaan, Mznah; Al-Dhelaan, Abdullah; Rehman, Amjad; Saba, Tanzila

2014-12-01

Fractal image compression (FIC) is recognized as a NP-hard problem, and it suffers from a high number of mean square error (MSE) computations. In this paper, a two-phase algorithm was proposed to reduce the MSE computation of FIC. In the first phase, based on edge property, range and domains are arranged. In the second one, imperialist competitive algorithm (ICA) is used according to the classified blocks. For maintaining the quality of the retrieved image and accelerating algorithm operation, we divided the solutions into two groups: developed countries and undeveloped countries. Simulations were carried out to evaluate the performance of the developed approach. Promising results thus achieved exhibit performance better than genetic algorithm (GA)-based and Full-search algorithms in terms of decreasing the number of MSE computations. The number of MSE computations was reduced by the proposed algorithm for 463 times faster compared to the Full-search algorithm, although the retrieved image quality did not have a considerable change.

Finessing filter scarcity problem in face recognition via multi-fold filter convolution

NASA Astrophysics Data System (ADS)

Low, Cheng-Yaw; Teoh, Andrew Beng-Jin

2017-06-01

The deep convolutional neural networks for face recognition, from DeepFace to the recent FaceNet, demand a sufficiently large volume of filters for feature extraction, in addition to being deep. The shallow filter-bank approaches, e.g., principal component analysis network (PCANet), binarized statistical image features (BSIF), and other analogous variants, endure the filter scarcity problem that not all PCA and ICA filters available are discriminative to abstract noise-free features. This paper extends our previous work on multi-fold filter convolution (ℳ-FFC), where the pre-learned PCA and ICA filter sets are exponentially diversified by ℳ folds to instantiate PCA, ICA, and PCA-ICA offspring. The experimental results unveil that the 2-FFC operation solves the filter scarcity state. The 2-FFC descriptors are also evidenced to be superior to that of PCANet, BSIF, and other face descriptors, in terms of rank-1 identification rate (%).

Internal Carotid Artery Sacrifice for Radical Resection of Skull Base Tumors

PubMed Central

Lawton, Michael T.; Spetzler, Robert F.

1996-01-01

When dealing with skull base tumors that encase the internal carotid artery (ICA), the surgeon must decide between ICA preservation and incomplete tumor resection, or radical resection with ICA sacrifice. In our experience with more than 300 anterior skull base tumors, the ICA was sacrificed in only 10 patients. These tumors were malignant, except for one meningioma that occluded the ICA and produced translent ischemic symptoms. All patients had the ICA resected with the tumor, and all patients underwent revascularization (cervical ICA-MCA saphenous bypass, n = 4; cervical-to-supraclinoid bypass, n = 1; petrous-to-supraclinoid bypass, n = 3; bonnet bypass, n = 2). This small patient series reflects our practice of preserving the ICA whenever possible. We recommend preserving the ICA with benign tumors because they do not invade the artery, or do so only to a limited extent. In addition, similar rates of tumor recurrence are seen after aggressive resection with or without ICA sacrifice. In contrast, we recommend radical tumor resection and sacrifice of the ICA with malignant tumors because they directly threaten the integrity of the ICA and the patient's survival. The ICA should not be considered a limitation to radical tumor resection because the ICA can be reconstructed safely with an appropriate bypass procedure. PMID:17170986

Hand classification of fMRI ICA noise components.

PubMed

Griffanti, Ludovica; Douaud, Gwenaëlle; Bijsterbosch, Janine; Evangelisti, Stefania; Alfaro-Almagro, Fidel; Glasser, Matthew F; Duff, Eugene P; Fitzgibbon, Sean; Westphal, Robert; Carone, Davide; Beckmann, Christian F; Smith, Stephen M

2017-07-01

We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Use of sEMG in identification of low level muscle activities: features based on ICA and fractal dimension.

PubMed

Naik, Ganesh R; Kumar, Dinesh K; Arjunan, Sridhar

2009-01-01

This paper has experimentally verified and compared features of sEMG (Surface Electromyogram) such as ICA (Independent Component Analysis) and Fractal Dimension (FD) for identification of low level forearm muscle activities. The fractal dimension was used as a feature as reported in the literature. The normalized feature values were used as training and testing vectors for an Artificial neural network (ANN), in order to reduce inter-experimental variations. The identification accuracy using FD of four channels sEMG was 58%, and increased to 96% when the signals are separated to their independent components using ICA.

Preparation of chitosan/nano hydroxyapatite organic-inorganic hybrid microspheres for bone repair.

PubMed

Chen, Jingdi; Pan, Panpan; Zhang, Yujue; Zhong, Shengnan; Zhang, Qiqing

2015-10-01

In this work, we encapsulated icariin (ICA) into chitosan (CS)/nano hydroxyapatite (nHAP) composite microspheres to form organic-inorganic hybrid microspheres for drug delivery carrier. The composition and morphology of composite microspheres were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) and differential scanning calorimetry- thermogravimetric analysis (DSC-TGA). Moreover, we further studied the performance of swelling properties, degradation properties and drug release behavior of the microspheres. ICA, the extract of traditional Chinese medicine-epimedium, was combined to study drug release properties of the microspheres. ICA loaded microspheres take on a sustained release behavior, which can be not only ascribed to electrostatic interaction between reactive negative hydroxyl (OH) of ICA and positive amine groups (NH₂) of CS, but also depended on the homogeneous dispersion of HAP nanoparticles inside CS organic matrix. In addition, the adhesion and morphology of osteoblasts were detected by inverted fluorescence microscopy. The biocompatibility of CS/nHAP/ICA microspheres was evaluated by the MTT cytotoxicity assay, Hoechst 33258 and PI fluorescence staining. These studies demonstrate that composite microspheres provide a suitable microenvironment for osteoblast attachment and proliferation. It can be speculated that the ICA loaded CS-based organic-inorganic hybrid microspheres might have potential applications in drug delivery systems. Copyright © 2015 Elsevier B.V. All rights reserved.

Independent component analysis separates spikes of different origin in the EEG.

PubMed

Urrestarazu, Elena; Iriarte, Jorge; Artieda, Julio; Alegre, Manuel; Valencia, Miguel; Viteri, César

2006-02-01

Independent component analysis (ICA) is a novel system that finds independent sources in recorded signals. Its usefulness in separating epileptiform activity of different origin has not been determined. The goal of this study was to demonstrate that ICA is useful for separating different spikes using samples of EEG of patients with focal epilepsy. Digital EEG samples from four patients with focal epilepsy were included. The patients had temporal (n = 2), centrotemporal (n = 1) or frontal spikes (n = 1). Twenty-six samples with two (or more) spikes from two different patients were created. The selection of the two spikes for each mixed EEG was performed randomly, trying to have all the different combinations and rejecting the mixture of two spikes from the same patient. Two different examiners studied the EEGs using ICA with JADE paradigm in Matlab platform, trying to separate and to identify the spikes. They agreed in the correct separation of the spikes in 24 of the 26 samples, classifying the spikes as frontal, temporal or centrotemporal, left or right sided. The demonstration of the possibility of detecting different artificially mixed spikes confirms that ICA may be useful in separating spikes or other elements in real EEGs.

Phosphorylation of Icariin Can Alleviate the Oxidative Stress Caused by the Duck Hepatitis Virus A through Mitogen-Activated Protein Kinases Signaling Pathways.

PubMed

Xiong, Wen; Zhang, Wei; Yuan, Wenjuan; Du, Hongxu; Ming, Ke; Yao, Fangke; Bai, Jingying; Chen, Yun; Liu, Jiaguo; Wang, Deyun; Hu, Yuanliang; Wu, Yi

2017-01-01

The duck virus hepatitis (DVH) caused by the duck hepatitis virus A (DHAV) has produced extensive economic losses to the duck industry. The currently licensed commercial vaccine has shown some defects and does not completely prevent the DVH. Accordingly, a new alternative treatment for this disease is urgently needed. Previous studies have shown that icariin (ICA) and its phosphorylated derivative (pICA) possessed good anti-DHAV effects through direct and indirect antiviral pathways, such as antioxidative stress. But the antioxidant activity showed some differences between ICA and pICA. The aim of this study is to prove that ICA and pICA attenuate oxidative stress caused by DHAV in vitro and in vivo , and to investigate their mechanism of action to explain their differences in antioxidant activities. In vivo , the dynamic deaths, oxidative evaluation indexes and hepatic pathological change scores were detected. When was added the hinokitiol which showed the pro-oxidative effect as an intervention method, pICA still possessed more treatment effect than ICA. The strong correlation between mortality and oxidative stress proves that ICA and pICA alleviate oxidative stress caused by DHAV. This was also demonstrated by the addition of hydrogen peroxide (H2O2) as an intervention method in vitro . pICA can be more effective than ICA to improve duck embryonic hepatocytes (DEHs) viability and reduce the virulence of DHAV. The strong correlation between TCID50 and oxidative stress demonstrates that ICA and pICA can achieve anti-DHAV effects by inhibiting oxidative stress. In addition, the superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) of ICA and pICA showed significant difference. pICA could significantly inhibit the phosphorylation of p38, extra cellular signal regulated Kinase (ERK 1/2) and c-Jun N-terminal kinase (JNK), which were related to mitogen-activated protein kinases (MAPKs) signaling pathways. Ultimately, compared to ICA, pICA exhibited more antioxidant activity that could regulate oxidative stress-related indicators, and inhibited the phosphorylation of MAPKs signaling pathway.

Properties of the low threshold Ca current in single frog atrial cardiomyocytes. A comparison with the high threshold Ca current

PubMed Central

1992-01-01

The properties of the low threshold Ca current (ICaT) in bullfrog (Rana catesbeiana) isolated atrial cardiomyocytes were studied using the whole-cell recording patch-clamp technique and compared with those of the high threshold Ca current (ICaL). In 91% of atrial cells we observed both ICaT and ICaL when collagenase and trypsin were used to dissociate the cells. But when pronase was used, only 30% of the cells exhibited ICaT. ICaT was never found in ventricular cells. ICaT could be investigated more easily when ICaL was inhibited by Cd ions (50 microM). Its kinetics were unchanged by substituting Ba for Ca, or in the presence of high concentrations of Ba. Both ICaT and ICaL exhibited reduced inactivation after high depolarizing prepulses. ICaT was found to be sensitive to dihydropyridines: 1 microM nifedipine decreased this current while 1 microM BAY K 8644 increased it; this occurred without significant variations in the steady-state inactivation curve. ICaT was more sensitive than ICaL to alpha 1-adrenergic and P2-purinergic stimulations, while ICaL was more sensitive to beta-adrenergic stimulation. Isoproterenol was still able to increase ICaT in the presence of high intracellular cAMP. Both currents were increased by 1 microM ouabain (although ICaL only transiently) and decreased by 10 microM ouabain. It is concluded that the two types of Ca channels can be observed in bullfrog atrial cells and that they are specifically altered by pharmacological agents and neuromediators. This may have implications for cardiac behavior. PMID:1279097

Functional Subdivision of Group-ICA Results of fMRI Data Collected during Cinema Viewing

PubMed Central

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film (“At land” by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative. PMID:22860044

Is fetal-type posterior cerebral artery a risk factor for intracranial aneurysm as analyzed by multislice CT angiography?

PubMed

He, Zhen; Wan, Yeda

2018-01-01

Fetal-type posterior cerebral artery (FTP) is a common anatomic variation that is closely associated with intracranial aneurysm. In the present study, multislice computed tomography angiography (CTA) was performed to assess whether FTP is a risk factor for intracranial aneurysm. CTA data of 364 consecutive cases of patients who were suspected with cerebrovascular disease or intracranial aneurysm of intracranial artery from 2013 to 2016 were reviewed and the incidence rates of FTP, other variations of the circle of Willis, intracranial aneurysm and FTP with intracranial aneurysm were evaluated. The χ 2 test was used to assess the influence of FTP and gender on the incidence rates of other variations of the circle of Willis, intracranial aneurysm and internal carotid artery-posterior communicating artery (ICA-PComA) aneurysm. Binary logistic regression analysis was performed to assess the associations of FTP and gender with intracranial aneurysm and ICA-PComA aneurysm. Compared with non-FTP patients, FTP cases exhibited significantly higher rates of other variations of the circle of Willis (χ 2 =80.173, P<0.001) and ICA-PComA aneurysm (χ 2 =4.437, P=0.035). Among patients with FTP and bilateral FTP, more female than male patients with intracranial aneurysm were identified. However, among all patients with intracranial aneurysm, no statistically significant differences in the prevalence of FTP (χ 2 =2.577, P=0.108) and bilateral FTP (χ 2 =2.199, P=0.159) between males and females were identified. Binary logistic regression analysis revealed that FTP and gender were risk factors for intracranial aneurysm and ICA-PComA aneurysm. A moderate association between FTP and ICA-PComA aneurysm (OR=2.762) were identified, although there was a weak association between FTP and intracranial aneurysm [odds ratio (OR)=1.365]. Furthermore, a strong association was identified between gender and intracranial aneurysm (OR=0.328), and a moderate association existed between gender and ICA-PComA aneurysm (OR=0.357). In conclusion, female gender is an independent risk factor for intracranial aneurysm, and FTP and female gender are independent risk factors for ICA-PComA aneurysm.

Is fetal-type posterior cerebral artery a risk factor for intracranial aneurysm as analyzed by multislice CT angiography?

PubMed Central

He, Zhen; Wan, Yeda

2018-01-01

Fetal-type posterior cerebral artery (FTP) is a common anatomic variation that is closely associated with intracranial aneurysm. In the present study, multislice computed tomography angiography (CTA) was performed to assess whether FTP is a risk factor for intracranial aneurysm. CTA data of 364 consecutive cases of patients who were suspected with cerebrovascular disease or intracranial aneurysm of intracranial artery from 2013 to 2016 were reviewed and the incidence rates of FTP, other variations of the circle of Willis, intracranial aneurysm and FTP with intracranial aneurysm were evaluated. The χ2 test was used to assess the influence of FTP and gender on the incidence rates of other variations of the circle of Willis, intracranial aneurysm and internal carotid artery-posterior communicating artery (ICA-PComA) aneurysm. Binary logistic regression analysis was performed to assess the associations of FTP and gender with intracranial aneurysm and ICA-PComA aneurysm. Compared with non-FTP patients, FTP cases exhibited significantly higher rates of other variations of the circle of Willis (χ2=80.173, P<0.001) and ICA-PComA aneurysm (χ2=4.437, P=0.035). Among patients with FTP and bilateral FTP, more female than male patients with intracranial aneurysm were identified. However, among all patients with intracranial aneurysm, no statistically significant differences in the prevalence of FTP (χ2=2.577, P=0.108) and bilateral FTP (χ2=2.199, P=0.159) between males and females were identified. Binary logistic regression analysis revealed that FTP and gender were risk factors for intracranial aneurysm and ICA-PComA aneurysm. A moderate association between FTP and ICA-PComA aneurysm (OR=2.762) were identified, although there was a weak association between FTP and intracranial aneurysm [odds ratio (OR)=1.365]. Furthermore, a strong association was identified between gender and intracranial aneurysm (OR=0.328), and a moderate association existed between gender and ICA-PComA aneurysm (OR=0.357). In conclusion, female gender is an independent risk factor for intracranial aneurysm, and FTP and female gender are independent risk factors for ICA-PComA aneurysm. PMID:29434687

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA.

PubMed

Labounek, René; Bridwell, David A; Mareček, Radek; Lamoš, Martin; Mikl, Michal; Slavíček, Tomáš; Bednařík, Petr; Baštinec, Jaromír; Hluštík, Petr; Brázdil, Milan; Jan, Jiří

2018-01-01

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Stabilizing bidirectional associative memory with Principles in Independent Component Analysis and Null Space (PICANS)

NASA Astrophysics Data System (ADS)

LaRue, James P.; Luzanov, Yuriy

2013-05-01

A new extension to the way in which the Bidirectional Associative Memory (BAM) algorithms are implemented is presented here. We will show that by utilizing the singular value decomposition (SVD) and integrating principles of independent component analysis (ICA) into the nullspace (NS) we have created a novel approach to mitigating spurious attractors. We demonstrate this with two applications. The first application utilizes a one-layer association while the second application is modeled after the several hierarchal associations of ventral pathways. The first application will detail the way in which we manage the associations in terms of matrices. The second application will take what we have learned from the first example and apply it to a cascade of a convolutional neural network (CNN) and perceptron this being our signal processing model of the ventral pathways, i.e., visual systems.

Regulation of neuromuscular junction organization by Rab2 and its effector ICA69 in Drosophila.

PubMed

Mallik, Bhagaban; Dwivedi, Manish Kumar; Mushtaq, Zeeshan; Kumari, Manisha; Verma, Praveen Kumar; Kumar, Vimlesh

2017-06-01

The mechanisms underlying synaptic differentiation, which involves neuronal membrane and cytoskeletal remodeling, are not completely understood. We performed a targeted RNAi-mediated screen of Drosophila BAR-domain proteins and identified islet cell autoantigen 69 kDa (ICA69) as one of the key regulators of morphological differentiation of the larval neuromuscular junction (NMJ). We show that Drosophila ICA69 colocalizes with α-Spectrin at the NMJ. The conserved N-BAR domain of ICA69 deforms liposomes in vitro Full-length ICA69 and the ICAC but not the N-BAR domain of ICA69 induce filopodia in cultured cells. Consistent with its cytoskeleton regulatory role, ICA69 mutants show reduced α-Spectrin immunoreactivity at the larval NMJ. Manipulating levels of ICA69 or its interactor PICK1 alters the synaptic level of ionotropic glutamate receptors (iGluRs). Moreover, reducing PICK1 or Rab2 levels phenocopies ICA69 mutation. Interestingly, Rab2 regulates not only synaptic iGluR but also ICA69 levels. Thus, our data suggest that: (1) ICA69 regulates NMJ organization through a pathway that involves PICK1 and Rab2, and (2) Rab2 functions genetically upstream of ICA69 and regulates NMJ organization and targeting/retention of iGluRs by regulating ICA69 levels. © 2017. Published by The Company of Biologists Ltd.

Intrinsic Resting-State Functional Connectivity in the Human Spinal Cord at 3.0 T.

PubMed

San Emeterio Nateras, Oscar; Yu, Fang; Muir, Eric R; Bazan, Carlos; Franklin, Crystal G; Li, Wei; Li, Jinqi; Lancaster, Jack L; Duong, Timothy Q

2016-04-01

To apply resting-state functional magnetic resonance (MR) imaging to map functional connectivity of the human spinal cord. Studies were performed in nine self-declared healthy volunteers with informed consent and institutional review board approval. Resting-state functional MR imaging was performed to map functional connectivity of the human cervical spinal cord from C1 to C4 at 1 × 1 × 3-mm resolution with a 3.0-T clinical MR imaging unit. Independent component analysis (ICA) was performed to derive resting-state functional MR imaging z-score maps rendered on two-dimensional and three-dimensional images. Seed-based analysis was performed for cross validation with ICA networks by using Pearson correlation. Reproducibility analysis of resting-state functional MR imaging maps from four repeated trials in a single participant yielded a mean z score of 6 ± 1 (P < .0001). The centroid coordinates across the four trials deviated by 2 in-plane voxels ± 2 mm (standard deviation) and up to one adjacent image section ± 3 mm. ICA of group resting-state functional MR imaging data revealed prominent functional connectivity patterns within the spinal cord gray matter. There were statistically significant (z score > 3, P < .001) bilateral, unilateral, and intersegmental correlations in the ventral horns, dorsal horns, and central spinal cord gray matter. Three-dimensional surface rendering provided visualization of these components along the length of the spinal cord. Seed-based analysis showed that many ICA components exhibited strong and significant (P < .05) correlations, corroborating the ICA results. Resting-state functional MR imaging connectivity networks are qualitatively consistent with known neuroanatomic and functional structures in the spinal cord. Resting-state functional MR imaging of the human cervical spinal cord with a 3.0-T clinical MR imaging unit and standard MR imaging protocols and hardware reveals prominent functional connectivity patterns within the spinal cord gray matter, consistent with known functional and anatomic layouts of the spinal cord.

Genotyping of methicillin-resistant Staphylococcus aureus (MRSA) isolated from milk and dairy products in South Italy.

PubMed

Basanisi, M G; La Bella, G; Nobili, G; Franconieri, I; La Salandra, G

2017-04-01

Methicillin-resistant Staphylococcus aureus (MRSA) is a pathogen emerging in hospitals as well as community and livestock. MRSA is a significant and costly public health concern because it may enter the human food chain and contaminate milk and dairy products causing foodborne illness. This study aimed to determine the occurrence and the characteristics of MRSA isolated from 3760 samples of milk and dairy products in a previous survey conducted in southern Italy during 2008-2014. Overall out of 484 S. aureus strains isolated, 40 (8.3%) were MRSA and were characterized by spa-typing, Multi-Locus Sequence Typing, SCCmec typing, Staphylococcal enterotoxins (SEs) genes, Panton-Valentine Leukocidin (PVL) genes and ability to form biofilm. The most frequently recovered STs were ST152 (t355-67.5%), followed by ST398 (t899, t108-25%), ST1 (t127-5%) and ST5 (t688-2.5%). All isolates harboured the SCCmec type V (92.5%) or IVa (25%). In one isolate (2.5%), ST398/t899, the SCCmec resulted not detected. Three isolates (7.5%) carried one or more enterotoxin encoding genes (one strain had seg, sei, sem, sen and seo genes; two strains had seh gene). The 50% of isolated strains harboured PVL-encoding genes. Molecular analysis for icaA and icaD genes showed: 72.5% icaA and icaD positive, 25% only icaD gene and one icaA and icaD negative. The detection of MRSA in food of animal origin is a potential health hazard, thus it is necessary monitoring of food-producing animals and improving hygiene standards in food practices in order to reduce the microbiological risk to minimum. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Impact of MCA stenosis on the early outcome in acute ischemic stroke patients

PubMed Central

Jeng, Jiann-Shing; Hsieh, Fang-I; Yeh, Hsu-Ling; Chen, Wei-Hung; Chiu, Hou-Chang; Tang, Sung-Chun; Liu, Chung-Hsiang; Lin, Huey-Juan; Hsu, Shih-Pin; Lo, Yuk-Keung; Chan, Lung; Chen, Chih-Hung; Lin, Ruey-Tay; Chen, Yu-Wei; Lee, Jiunn-Tay; Yeh, Chung-Hsin; Sun, Ming-Hui; Lai, Ta-Chang; Sun, Yu; Sun, Mu-Chien; Chen, Po-Lin; Chiang, Tsuey-Ru; Lin, Shinn-Kuang; Yip, Bak-Sau; Chen, Chin-I; Bai, Chi-Huey; Chen, Sien-Tsong; Chiou, Hung-Yi; Lien, Li-Ming; Hsu, Chung Y.

2017-01-01

Background Asians have higher frequency of intracranial arterial stenosis. The present study aimed to compare the clinical features and outcomes of ischemic stroke patients with and without middle cerebral artery (MCA) stenosis, assessed by transcranial sonography (TCS), based on the Taiwan Stroke Registry (TSR). Methods Patients with acute ischemic stroke or transient ischemic attack registered in the TSR, and received both carotid duplex and TCS assessment were categorized into those with stenosis (≥50%) and without (<50%) in the extracranial internal carotid artery (ICA) and MCA, respectively. Logistic regression analysis, Kaplan-Meier method and Cox proportional hazard model were applied to assess relevant variables between groups. Results Of 6003 patients, 23.3% had MCA stenosis, 10.1% ICA stenosis, and 3.9% both MCA and ICA stenosis. Patients with MCA stenosis had greater initial NIHSS, higher likelihood of stroke-in-evolution, and more severe disability than those without (all p<0.001). Patients with MCA stenosis had higher prevalence of hypertension, diabetes and hypercholesterolemia. Patients with combined MCA and extracranial ICA stenosis had even higher NIHSS, worse functional outcome, higher risk of stroke recurrence or death (hazard ratio, 2.204; 95% confidence intervals, 1.440–3.374; p<0.001) at 3 months after stroke than those without MCA stenosis. Conclusions In conclusion, MCA stenosis was more prevalent than extracranial ICA stenosis in ischemic stroke patients in Taiwan. Patients with MCA stenosis, especially combined extracranial ICA stenosis, had more severe neurological deficit and worse outcome. PMID:28388675

Detection of Movement Related Cortical Potentials from EEG Using Constrained ICA for Brain-Computer Interface Applications.

PubMed

Karimi, Fatemeh; Kofman, Jonathan; Mrachacz-Kersting, Natalie; Farina, Dario; Jiang, Ning

2017-01-01

The movement related cortical potential (MRCP), a slow cortical potential from the scalp electroencephalogram (EEG), has been used in real-time brain-computer-interface (BCI) systems designed for neurorehabilitation. Detecting MPCPs in real time with high accuracy and low latency is essential in these applications. In this study, we propose a new MRCP detection method based on constrained independent component analysis (cICA). The method was tested for MRCP detection during executed and imagined ankle dorsiflexion of 24 healthy participants, and compared with four commonly used spatial filters for MRCP detection in an offline experiment. The effect of cICA and the compared spatial filters on the morphology of the extracted MRCP was evaluated by two indices quantifying the signal-to-noise ratio and variability of the extracted MRCP. The performance of the filters for detection was then directly compared for accuracy and latency. The latency obtained with cICA (-34 ± 29 ms motor execution (ME) and 28 ± 16 ms for motor imagery (MI) dataset) was significantly smaller than with all other spatial filters. Moreover, cICA resulted in greater true positive rates (87.11 ± 11.73 for ME and 86.66 ± 6.96 for MI dataset) and lower false positive rates (20.69 ± 13.68 for ME and 19.31 ± 12.60 for MI dataset) compared to the other methods. These results confirm the superiority of cICA in MRCP detection with respect to previously proposed EEG filtering approaches.

A method for functional network connectivity among spatially independent resting-state components in schizophrenia.

PubMed

Jafri, Madiha J; Pearlson, Godfrey D; Stevens, Michael; Calhoun, Vince D

2008-02-15

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in healthy individuals as well as in patients with brain disorders. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject's ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients.

A Method for Functional Network Connectivity Among Spatially Independent Resting-State Components in Schizophrenia

PubMed Central

Jafri, Madiha J; Pearlson, Godfrey D; Stevens, Michael; Calhoun, Vince D

2011-01-01

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in patients versus controls. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject’s ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients. PMID:18082428

Characterization of Strombolian events by using independent component analysis

NASA Astrophysics Data System (ADS)

Ciaramella, A.; de Lauro, E.; de Martino, S.; di Lieto, B.; Falanga, M.; Tagliaferri, R.

2004-10-01

We apply Independent Component Analysis (ICA) to seismic signals recorded at Stromboli volcano. Firstly, we show how ICA works considering synthetic signals, which are generated by dynamical systems. We prove that Strombolian signals, both tremor and explosions, in the high frequency band (>0.5 Hz), are similar in time domain. This seems to give some insights to the organ pipe model generation for the source of these events. Moreover, we are able to recognize in the tremor signals a low frequency component (<0.5 Hz), with a well defined peak corresponding to 30s.

Comparison of multivariate analysis methods for extracting the paraffin component from the paraffin-embedded cancer tissue spectra for Raman imaging

NASA Astrophysics Data System (ADS)

Meksiarun, Phiranuphon; Ishigaki, Mika; Huck-Pezzei, Verena A. C.; Huck, Christian W.; Wongravee, Kanet; Sato, Hidetoshi; Ozaki, Yukihiro

2017-03-01

This study aimed to extract the paraffin component from paraffin-embedded oral cancer tissue spectra using three multivariate analysis (MVA) methods; Independent Component Analysis (ICA), Partial Least Squares (PLS) and Independent Component - Partial Least Square (IC-PLS). The estimated paraffin components were used for removing the contribution of paraffin from the tissue spectra. These three methods were compared in terms of the efficiency of paraffin removal and the ability to retain the tissue information. It was found that ICA, PLS and IC-PLS could remove the paraffin component from the spectra at almost the same level while Principal Component Analysis (PCA) was incapable. In terms of retaining cancer tissue spectral integrity, effects of PLS and IC-PLS on the non-paraffin region were significantly less than that of ICA where cancer tissue spectral areas were deteriorated. The paraffin-removed spectra were used for constructing Raman images of oral cancer tissue and compared with Hematoxylin and Eosin (H&E) stained tissues for verification. This study has demonstrated the capability of Raman spectroscopy together with multivariate analysis methods as a diagnostic tool for the paraffin-embedded tissue section.

Discrepant post filter ionized calcium concentrations by common blood gas analyzers in CRRT using regional citrate anticoagulation.

PubMed

Schwarzer, Patrik; Kuhn, Sven-Olaf; Stracke, Sylvia; Gründling, Matthias; Knigge, Stephan; Selleng, Sixten; Helm, Maximilian; Friesecke, Sigrun; Abel, Peter; Kallner, Anders; Nauck, Matthias; Petersmann, Astrid

2015-09-08

Ionized calcium (iCa) concentration is often used in critical care and measured using blood gas analyzers at the point of care. Controlling and adjusting regional citrate anticoagulation (RCA) for continuous renal replacement therapy (CRRT) involves measuring the iCa concentration in two samples: systemic with physiological iCa concentrations and post filter samples with very low iCa concentrations. However, modern blood gas analyzers are optimized for physiological iCa concentrations which might make them less suitable for measuring low iCa in blood with a high concentration of citrate. We present results of iCa measurements from six different blood gas analyzers and the impact on clinical decisions based on the recommendations of the dialysis' device manufacturer. The iCa concentrations of systemic and post filter samples were measured using six distinct, frequently used blood gas analyzers. We obtained iCa results of 74 systemic and 84 post filter samples from patients undergoing RCA for CRRT at the University Medicine of Greifswald. The systemic samples showed concordant results on all analyzers with median iCa concentrations ranging from 1.07 to 1.16 mmol/L. The medians of iCa concentrations for post filter samples ranged from 0.21 to 0.50 mmol/L. Results of >70% of the post filter samples would lead to major differences in decisions regarding citrate flow depending on the instrument used. Measurements of iCa in post filter samples may give misleading information in monitoring the RCA. Recommendations of the dialysis manufacturer need to be revised. Meanwhile, little weight should be given to post filter iCa. Reference methods for low iCa in whole blood containing citrate should be established.

Dimension reduction: additional benefit of an optimal filter for independent component analysis to extract event-related potentials.

PubMed

Cong, Fengyu; Leppänen, Paavo H T; Astikainen, Piia; Hämäläinen, Jarmo; Hietanen, Jari K; Ristaniemi, Tapani

2011-09-30

The present study addresses benefits of a linear optimal filter (OF) for independent component analysis (ICA) in extracting brain event-related potentials (ERPs). A filter such as the digital filter is usually considered as a denoising tool. Actually, in filtering ERP recordings by an OF, the ERP' topography should not be changed by the filter, and the output should also be able to be modeled by the linear transformation. Moreover, an OF designed for a specific ERP source or component may remove noise, as well as reduce the overlap of sources and even reject some non-targeted sources in the ERP recordings. The OF can thus accomplish both the denoising and dimension reduction (reducing the number of sources) simultaneously. We demonstrated these effects using two datasets, one containing visual and the other auditory ERPs. The results showed that the method including OF and ICA extracted much more reliable components than the sole ICA without OF did, and that OF removed some non-targeted sources and made the underdetermined model of EEG recordings approach to the determined one. Thus, we suggest designing an OF based on the properties of an ERP to filter recordings before using ICA decomposition to extract the targeted ERP component. Copyright © 2011 Elsevier B.V. All rights reserved.

Big Data in Reciprocal Space: Sliding Fast Fourier Transforms for Determining Periodicity

DOE PAGES

Vasudevan, Rama K.; Belianinov, Alex; Gianfrancesco, Anthony G.; ...

2015-03-03

Significant advances in atomically resolved imaging of crystals and surfaces have occurred in the last decade allowing unprecedented insight into local crystal structures and periodicity. Yet, the analysis of the long-range periodicity from the local imaging data, critical to correlation of functional properties and chemistry to the local crystallography, remains a challenge. Here, we introduce a Sliding Fast Fourier Transform (FFT) filter to analyze atomically resolved images of in-situ grown La5/8Ca3/8MnO3 films. We demonstrate the ability of sliding FFT algorithm to differentiate two sub-lattices, resulting from a mixed-terminated surface. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) of themore » Sliding FFT dataset reveal the distinct changes in crystallography, step edges and boundaries between the multiple sub-lattices. The method is universal for images with any periodicity, and is especially amenable to atomically resolved probe and electron-microscopy data for rapid identification of the sub-lattices present.« less

Big Data in Reciprocal Space: Sliding Fast Fourier Transforms for Determining Periodicity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vasudevan, Rama K.; Belianinov, Alex; Gianfrancesco, Anthony G.

Significant advances in atomically resolved imaging of crystals and surfaces have occurred in the last decade allowing unprecedented insight into local crystal structures and periodicity. Yet, the analysis of the long-range periodicity from the local imaging data, critical to correlation of functional properties and chemistry to the local crystallography, remains a challenge. Here, we introduce a Sliding Fast Fourier Transform (FFT) filter to analyze atomically resolved images of in-situ grown La5/8Ca3/8MnO3 films. We demonstrate the ability of sliding FFT algorithm to differentiate two sub-lattices, resulting from a mixed-terminated surface. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) of themore » Sliding FFT dataset reveal the distinct changes in crystallography, step edges and boundaries between the multiple sub-lattices. The method is universal for images with any periodicity, and is especially amenable to atomically resolved probe and electron-microscopy data for rapid identification of the sub-lattices present.« less

tICA-Metadynamics: Accelerating Metadynamics by Using Kinetically Selected Collective Variables.

PubMed

M Sultan, Mohammad; Pande, Vijay S

2017-06-13

Metadynamics is a powerful enhanced molecular dynamics sampling method that accelerates simulations by adding history-dependent multidimensional Gaussians along selective collective variables (CVs). In practice, choosing a small number of slow CVs remains challenging due to the inherent high dimensionality of biophysical systems. Here we show that time-structure based independent component analysis (tICA), a recent advance in Markov state model literature, can be used to identify a set of variationally optimal slow coordinates for use as CVs for Metadynamics. We show that linear and nonlinear tICA-Metadynamics can complement existing MD studies by explicitly sampling the system's slowest modes and can even drive transitions along the slowest modes even when no such transitions are observed in unbiased simulations.

An advanced algorithm for fetal heart rate estimation from non-invasive low electrode density recordings.

PubMed

Dessì, Alessia; Pani, Danilo; Raffo, Luigi

2014-08-01

Non-invasive fetal electrocardiography is still an open research issue. The recent publication of an annotated dataset on Physionet providing four-channel non-invasive abdominal ECG traces promoted an international challenge on the topic. Starting from that dataset, an algorithm for the identification of the fetal QRS complexes from a reduced number of electrodes and without any a priori information about the electrode positioning has been developed, entering into the top ten best-performing open-source algorithms presented at the challenge.In this paper, an improved version of that algorithm is presented and evaluated exploiting the same challenge metrics. It is mainly based on the subtraction of the maternal QRS complexes in every lead, obtained by synchronized averaging of morphologically similar complexes, the filtering of the maternal P and T waves and the enhancement of the fetal QRS through independent component analysis (ICA) applied on the processed signals before a final fetal QRS detection stage. The RR time series of both the mother and the fetus are analyzed to enhance pseudoperiodicity with the aim of correcting wrong annotations. The algorithm has been designed and extensively evaluated on the open dataset A (N = 75), and finally evaluated on datasets B (N = 100) and C (N = 272) to have the mean scores over data not used during the algorithm development. Compared to the results achieved by the previous version of the algorithm, the current version would mark the 5th and 4th position in the final ranking related to the events 1 and 2, reserved to the open-source challenge entries, taking into account both official and unofficial entrants. On dataset A, the algorithm achieves 0.982 median sensitivity and 0.976 median positive predictivity.

Within-subject joint independent component analysis of simultaneous fMRI/ERP in an auditory oddball paradigm

PubMed Central

MANGALATHU-ARUMANA, J.; BEARDSLEY, S. A.; LIEBENTHAL, E.

2012-01-01

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. This research aimed to determine the sensitivity and limitations of applying joint independent component analysis (jICA) within-subjects, for ERP and fMRI data collected simultaneously in a parametric auditory frequency oddball paradigm. In a group of 20 subjects, an increase in ERP peak amplitude ranging 1–8 μV in the time window of the P300 (350–700ms), and a correlated increase in fMRI signal in a network of regions including the right superior temporal and supramarginal gyri, was observed with the increase in deviant frequency difference. JICA of the same ERP and fMRI group data revealed activity in a similar network, albeit with stronger amplitude and larger extent. In addition, activity in the left pre- and post- central gyri, likely associated with right hand somato-motor response, was observed only with the jICA approach. Within-subject, the jICA approach revealed significantly stronger and more extensive activity in the brain regions associated with the auditory P300 than the P300 linear regression analysis. The results suggest that with the incorporation of spatial and temporal information from both imaging modalities, jICA may be a more sensitive method for extracting common sources of activity between ERP and fMRI. PMID:22377443

Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra.

PubMed

Mantini, Dante; Petrucci, Francesca; Del Boccio, Piero; Pieragostino, Damiana; Di Nicola, Marta; Lugaresi, Alessandra; Federici, Giorgio; Sacchetta, Paolo; Di Ilio, Carmine; Urbani, Andrea

2008-01-01

Independent component analysis (ICA) is a signal processing technique that can be utilized to recover independent signals from a set of their linear mixtures. We propose ICA for the analysis of signals obtained from large proteomics investigations such as clinical multi-subject studies based on MALDI-TOF MS profiling. The method is validated on simulated and experimental data for demonstrating its capability of correctly extracting protein profiles from MALDI-TOF mass spectra. The comparison on peak detection with an open-source and two commercial methods shows its superior reliability in reducing the false discovery rate of protein peak masses. Moreover, the integration of ICA and statistical tests for detecting the differences in peak intensities between experimental groups allows to identify protein peaks that could be indicators of a diseased state. This data-driven approach demonstrates to be a promising tool for biomarker-discovery studies based on MALDI-TOF MS technology. The MATLAB implementation of the method described in the article and both simulated and experimental data are freely available at http://www.unich.it/proteomica/bioinf/.

Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI

PubMed Central

Mangalathu-Arumana, Jain; Liebenthal, Einat; Beardsley, Scott A.

2018-01-01

Joint independent component analysis (jICA) can be applied within subject for fusion of multi-channel event-related potentials (ERP) and functional magnetic resonance imaging (fMRI), to measure brain function at high spatiotemporal resolution (Mangalathu-Arumana et al., 2012). However, the impact of experimental design choices on jICA performance has not been systematically studied. Here, the sensitivity of jICA for recovering neural sources in individual data was evaluated as a function of imaging SNR, number of independent representations of the ERP/fMRI data, relationship between instantiations of the joint ERP/fMRI activity (linear, non-linear, uncoupled), and type of sources (varying parametrically and non-parametrically across representations of the data), using computer simulations. Neural sources were simulated with spatiotemporal and noise attributes derived from experimental data. The best performance, maximizing both cross-modal data fusion and the separation of brain sources, occurred with a moderate number of representations of the ERP/fMRI data (10–30), as in a mixed block/event related experimental design. Importantly, the type of relationship between instantiations of the ERP/fMRI activity, whether linear, non-linear or uncoupled, did not in itself impact jICA performance, and was accurately recovered in the common profiles (i.e., mixing coefficients). Thus, jICA provides an unbiased way to characterize the relationship between ERP and fMRI activity across brain regions, in individual data, rendering it potentially useful for characterizing pathological conditions in which neurovascular coupling is adversely affected. PMID:29410611

Dealing with ocular artifacts on lateralized ERPs in studies of visual-spatial attention and memory: ICA correction versus epoch rejection.

PubMed

Drisdelle, Brandi Lee; Aubin, Sébrina; Jolicoeur, Pierre

2017-01-01

The objective of the present study was to assess the robustness and reliability of independent component analysis (ICA) as a method for ocular artifact correction in electrophysiological studies of visual-spatial attention and memory. The N2pc and sustained posterior contralateral negativity (SPCN), electrophysiological markers of visual-spatial attention and memory, respectively, are lateralized posterior ERPs typically observed following the presentation of lateral stimuli (targets and distractors) along with instructions to maintain fixation on the center of the visual search for the entire trial. Traditionally, trials in which subjects may have displaced their gaze are rejected based on a cutoff threshold, minimizing electrophysiological contamination by saccades. Given the loss of data resulting from rejection, we examined ocular correction by comparing results using standard fixation instructions against a condition where subjects were instructed to shift their gaze toward possible targets. Both conditions were analyzed using a rejection threshold and ICA correction for saccade activity management. Results demonstrate that ICA conserves data that would have otherwise been removed and leaves the underlying neural activity intact, as demonstrated by experimental manipulations previously shown to modulate the N2pc and the SPCN. Not only does ICA salvage and not distort data, but also large eye movements had only subtle effects. Overall, the findings provide convincing evidence for ICA correction for not only special cases (e.g., subjects did not follow fixation instruction) but also as a candidate for standard ocular artifact management in electrophysiological studies interested in visual-spatial attention and memory. © 2016 Society for Psychophysiological Research.

Extending the Kerberos Protocol for Distributed Data as a Service

DTIC Science & Technology

2012-09-20

exported as a UIMA [11] PEAR file for deployment to IBM Content Analytics (ICA). A UIMA PEAR file is a deployable text analytics “pipeline” (analogous...to a web application packaged in a WAR file). ICA is a text analysis and search application that supports UIMA . The key entities targeted by NLP rules...workbench. [Online]. Available: https: //www.ibm.com/developerworks/community/alphaworks/lrw/ [11] Apache UIMA . [Online]. Available: http

Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kassouf, Amine, E-mail: amine.kassouf@agroparistech.fr; INRA, UMR1145 Ingénierie Procédés Aliments, 1 Avenue des Olympiades, 91300 Massy; AgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16 rue Claude Bernard, 75005 Paris

2014-11-15

Highlights: • An innovative technique, MIR-ICA, was applied to plastic packaging separation. • This study was carried out on PE, PP, PS, PET and PLA plastic packaging materials. • ICA was applied to discriminate plastics and 100% separation rates were obtained. • Analyses performed on two spectrometers proved the reproducibility of the method. • MIR-ICA is a simple and fast technique allowing plastic identification/classification. - Abstract: Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energymore » recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of the proposed approach was also tested using two spectrometers with considerable differences in their sensitivities. Discrimination rates were not affected proving that the developed approach could be extrapolated to different spectrometers. MIR combined with ICA is a promising tool for plastic waste separation that can help improve performance in this field; however further technological improvements and developments are required before it can be applied at an industrial level given that all tests presented here were performed under laboratory conditions.« less

Burden of Intracranial Atherosclerosis Is Associated With Long-Term Vascular Outcome in Patients With Ischemic Stroke.

PubMed

Kim, Byung-Su; Chung, Pil-Wook; Park, Kwang-Yeol; Won, Hong-Hee; Bang, Oh Young; Chung, Chin-Sang; Lee, Kwang Ho; Kim, Gyeong-Moon

2017-10-01

Ischemic stroke patients often have intracranial atherosclerosis (ICAS), despite heterogeneity in the cause of stroke. We tested the hypothesis that ICAS burden can independently reflect the risk of long-term vascular outcome. This was a retrospective cohort study analyzing data from a prospective stroke registry enrolling consecutive patients with acute ischemic stroke or transient ischemic attack. A total of 1081 patients were categorized into no ICAS, single ICAS, and advanced ICAS (ICAS ≥2 different intracranial arteries) groups. Primary and secondary end points were time to occurrence of recurrent ischemic stroke and composite vascular outcome, respectively. Study end points by ICAS burden were compared using Cox proportional hazards models in overall and propensity-matched patients. ICAS was present in 405 patients (37.3%). During a median 5-year follow-up, recurrent stroke and composite vascular outcome occurred in 6.8% and 16.8% of patients, respectively. As the number of ICAS increased, the risk for study end points increased after adjustment of potential covariates (hazard ratio per 1 increase in ICAS, 1.19; 95% confidence interval, 1.01-1.42 for recurrent ischemic stroke and hazard ratio, 1.18; 95% confidence interval, 1.05-1.33 for composite vascular outcome). The hazard ratios (95% confidence interval) for recurrent stroke and composite vascular outcome in patients with advanced ICAS compared with those without ICAS were 1.56 (0.88-2.74) and 1.72 (1.17-2.53), respectively, in the overall patients. The corresponding values in the propensity-matched patients were 1.28 (0.71-2.30) and 1.95 (1.27-2.99), respectively. ICAS burden was independently associated with the risk of subsequent composite vascular outcome in patients with ischemic stroke. These findings suggest that ICAS burden can reflect the risk of long-term vascular outcome. © 2017 American Heart Association, Inc.

Comparison of two methods for composite score generation in dry eye syndrome.

PubMed

See, Craig; Bilonick, Richard A; Feuer, William; Galor, Anat

2013-09-19

To compare two methods of composite score generation in dry eye syndrome (DES). Male patients seen in the Miami Veterans Affairs eye clinic with normal eyelid, corneal, and conjunctival anatomy were recruited to participate in the study. Patients filled out the Dry Eye Questionnaire 5 (DEQ5) and underwent measurement of tear film parameters. DES severity scores were generated by independent component analysis (ICA) and latent class analysis (LCA). A total of 247 men were included in the study. Mean age was 69 years (SD 9). Using ICA analysis, osmolarity was found to carry the largest weight, followed by eyelid vascularity and meibomian orifice plugging. Conjunctival injection and tear breakup time (TBUT) carried the lowest weights. Using LCA analysis, TBUT was found to be best at discriminating healthy from diseased eyes, followed closely by Schirmer's test. DEQ5, eyelid vascularity, and conjunctival injection were the poorest at discrimination. The adjusted correlation coefficient between the two generated composite scores was 0.63, indicating that the shared variance was less than 40%. Both ICA and LCA produced composite scores for dry eye severity, with weak to moderate agreement; however, agreement for the relative importance of single diagnostic tests was poor between the two methods.

Hypocalcemia Following Resuscitation from Cardiac Arrest Revisited

PubMed Central

Youngquist, Scott T.; Heyming, Theodore; Rosborough, John P.; Niemann, James T.

2009-01-01

Objective Hypocalcemia associated with cardiac arrest has been reported. However, mechanistic hypotheses for the decrease in ionized calcium (iCa) vary and its importance unknown. The objective of this study was to assess the relationships of iCa, pH, base excess (BE), and lactate in two porcine cardiac arrest models, and to determine the effect of exogenous calcium administration on postresuscitation hemodynamics. Methods Swine were instrumented and VF was induced either electrically (EVF, n=65) or spontaneously, ischemically induced (IVF) with balloon occlusion of the LAD (n=37). Animals were resuscitated after 7 minutes of VF. BE, iCa, and pH, were determined prearrest and at 15, 30, 60, 90, 120 min after ROSC. Lactate was also measured in 26 animals in the EVF group. Twelve EVF animals were randomized to receive 1 gm of CaCl2 infused over 20 min after ROSC or normal saline. Results iCa, BE, and pH declined significantly over the 60 min following ROSC, regardless of VF type, with the lowest levels observed at the nadir of left ventricular stroke work post resuscitation. Lactate was strongly correlated with BE (r = −0.89, p<0.0001) and iCa (r= −0.40, p < 0.0001). In a multivariate generalized linear mixed model, iCa was 0.005 mg/dL higher for every one unit increase in BE (95% CI 0.003–0.007, p<0.0001), while controlling for type of induced VF. While there was a univariate correlation between iCa and BE, when BE was included in the regression analysis with lactate, only lactate showed a statistically significant relationship with iCa (p=0.02). Postresuscitation CaCl2 infusion improved post-ROSC hemodynamics when compared to saline infusion (LV stroke work control 8 ± 5 gm-m vs 23 ± 4, p = 0.014, at 30 min) with no significant difference in tau between groups. Conclusions Ionized hypocalcemia occurs following ROSC. CaCl2 improves post-ROSC hemodynamics suggesting that hypocalcemia may play a role in early post-resuscitation myocardial dysfunction. PMID:19913975

The Novel KV7.2/KV7.3 Channel Opener ICA-069673 Reveals Subtype-Specific Functional Roles in Guinea Pig Detrusor Smooth Muscle Excitability and Contractility

PubMed Central

Provence, Aaron; Malysz, John

2015-01-01

The physiologic roles of voltage-gated KV7 channel subtypes (KV7.1–KV7.5) in detrusor smooth muscle (DSM) are poorly understood. Here, we sought to elucidate the functional roles of KV7.2/KV7.3 channels in guinea pig DSM excitability and contractility using the novel KV7.2/KV7.3 channel activator ICA-069673 [N-(2-chloro-5-pyrimidinyl)-3,4-difluorobenzamide]. We employed a multilevel experimental approach using Western blot analysis, immunocytochemistry, isometric DSM tension recordings, fluorescence Ca2+ imaging, and perforated whole-cell patch-clamp electrophysiology. Western blot experiments revealed the protein expression of KV7.2 and KV7.3 channel subunits in DSM tissue. In isolated DSM cells, immunocytochemistry with confocal microscopy further confirmed protein expression for KV7.2 and KV7.3 channel subunits, where they localize within the vicinity of the cell membrane. ICA-069673 inhibited spontaneous phasic, pharmacologically induced, and nerve-evoked contractions in DSM isolated strips in a concentration-dependent manner. The inhibitory effects of ICA-069673 on DSM spontaneous phasic and tonic contractions were abolished in the presence of the KV7 channel inhibitor XE991 [10,10-bis(4-pyridinylmethyl)-9(10H)-anthracenone dihydrochloride]. Under conditions of elevated extracellular K+ (60 mM), the effects of ICA-069673 on DSM tonic contractions were significantly attenuated. ICA-069673 decreased the global intracellular Ca2+ concentration in DSM cells, an effect blocked by the L-type Ca2+ channel inhibitor nifedipine. ICA-069673 hyperpolarized the membrane potential and inhibited spontaneous action potentials of isolated DSM cells, effects that were blocked in the presence of XE991. In conclusion, using the novel KV7.2/KV7.3 channel activator ICA-069673, this study provides strong evidence for a critical role for the KV7.2- and KV7.3-containing channels in DSM function at both cellular and tissue levels. PMID:26087697

The Novel KV7.2/KV7.3 Channel Opener ICA-069673 Reveals Subtype-Specific Functional Roles in Guinea Pig Detrusor Smooth Muscle Excitability and Contractility.

PubMed

Provence, Aaron; Malysz, John; Petkov, Georgi V

2015-09-01

The physiologic roles of voltage-gated KV7 channel subtypes (KV7.1-KV7.5) in detrusor smooth muscle (DSM) are poorly understood. Here, we sought to elucidate the functional roles of KV7.2/KV7.3 channels in guinea pig DSM excitability and contractility using the novel KV7.2/KV7.3 channel activator ICA-069673 [N-(2-chloro-5-pyrimidinyl)-3,4-difluorobenzamide]. We employed a multilevel experimental approach using Western blot analysis, immunocytochemistry, isometric DSM tension recordings, fluorescence Ca(2+) imaging, and perforated whole-cell patch-clamp electrophysiology. Western blot experiments revealed the protein expression of KV7.2 and KV7.3 channel subunits in DSM tissue. In isolated DSM cells, immunocytochemistry with confocal microscopy further confirmed protein expression for KV7.2 and KV7.3 channel subunits, where they localize within the vicinity of the cell membrane. ICA-069673 inhibited spontaneous phasic, pharmacologically induced, and nerve-evoked contractions in DSM isolated strips in a concentration-dependent manner. The inhibitory effects of ICA-069673 on DSM spontaneous phasic and tonic contractions were abolished in the presence of the KV7 channel inhibitor XE991 [10,10-bis(4-pyridinylmethyl)-9(10H)-anthracenone dihydrochloride]. Under conditions of elevated extracellular K(+) (60 mM), the effects of ICA-069673 on DSM tonic contractions were significantly attenuated. ICA-069673 decreased the global intracellular Ca(2+) concentration in DSM cells, an effect blocked by the L-type Ca(2+) channel inhibitor nifedipine. ICA-069673 hyperpolarized the membrane potential and inhibited spontaneous action potentials of isolated DSM cells, effects that were blocked in the presence of XE991. In conclusion, using the novel KV7.2/KV7.3 channel activator ICA-069673, this study provides strong evidence for a critical role for the KV7.2- and KV7.3-containing channels in DSM function at both cellular and tissue levels. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

Analysis and visualization of single-trial event-related potentials

NASA Technical Reports Server (NTRS)

Jung, T. P.; Makeig, S.; Westerfield, M.; Townsend, J.; Courchesne, E.; Sejnowski, T. J.

2001-01-01

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.

Structural, vibrational spectroscopic and quantum chemical studies on indole-3-carboxaldehyde

NASA Astrophysics Data System (ADS)

Premkumar, R.; Asath, R. Mohamed; Mathavan, T.; Benial, A. Milton Franklin

2017-05-01

The potential energy surface (PES) scan was performed for indole-3-carboxaldehyde (ICA) and the most stable optimized conformer was predicted using DFT/B3LYP method with 6-31G basis set. The vibrational frequencies of ICA were theoretically calculated by the DFT/B3LYP method with cc-pVTZ basis set using Gaussian 09 program. The vibrational spectra were experimentally recorded by Fourier transform-infrared (FT-IR) and Fourier transform-Raman spectrometer (FT-Raman). The computed vibrational frequencies were scaled by scaling factors to yield a good agreement with observed vibrational frequencies. The theoretically calculated and experimentally observed vibrational frequencies were assigned on the basis of potential energy distribution (PED) calculation using VEDA 4.0 program. The molecular interaction, stability and intramolecular charge transfer of ICA were studied using frontier molecular orbitals (FMOs) analysis and Mulliken atomic charge distribution shows the distribution of the atomic charges. The presence of intramolecular charge transfer was studied using natural bond orbital (NBO) analysis.

Using independent component analysis for electrical impedance tomography

NASA Astrophysics Data System (ADS)

Yan, Peimin; Mo, Yulong

2004-05-01

Independent component analysis (ICA) is a way to resolve signals into independent components based on the statistical characteristics of the signals. It is a method for factoring probability densities of measured signals into a set of densities that are as statistically independent as possible under the assumptions of a linear model. Electrical impedance tomography (EIT) is used to detect variations of the electric conductivity of the human body. Because there are variations of the conductivity distributions inside the body, EIT presents multi-channel data. In order to get all information contained in different location of tissue it is necessary to image the individual conductivity distribution. In this paper we consider to apply ICA to EIT on the signal subspace (individual conductivity distribution). Using ICA the signal subspace will then be decomposed into statistically independent components. The individual conductivity distribution can be reconstructed by the sensitivity theorem in this paper. Compute simulations show that the full information contained in the multi-conductivity distribution will be obtained by this method.

ICA-Based Imagined Conceptual Words Classification on EEG Signals.

PubMed

Imani, Ehsan; Pourmohammad, Ali; Bagheri, Mahsa; Mobasheri, Vida

2017-01-01

Independent component analysis (ICA) has been used for detecting and removing the eye artifacts conventionally. However, in this research, it was used not only for detecting the eye artifacts, but also for detecting the brain-produced signals of two conceptual danger and information category words. In this cross-sectional research, electroencephalography (EEG) signals were recorded using Micromed and 19-channel helmet devices in unipolar mode, wherein Cz electrode was selected as the reference electrode. In the first part of this research, the statistical community test case included four men and four women, who were 25-30 years old. In the designed task, three groups of traffic signs were considered, in which two groups referred to the concept of danger, and the third one referred to the concept of information. In the second part, the three volunteers, two men and one woman, who had the best results, were chosen from among eight participants. In the second designed task, direction arrows (up, down, left, and right) were used. For the 2/8 volunteers in the rest times, very high-power alpha waves were observed from the back of the head; however, in the thinking times, they were different. According to this result, alpha waves for changing the task from thinking to rest condition took at least 3 s for the two volunteers, and it was at most 5 s until they went to the absolute rest condition. For the 7/8 volunteers, the danger and information signals were well classified; these differences for the 5/8 volunteers were observed in the right hemisphere, and, for the other three volunteers, the differences were observed in the left hemisphere. For the second task, simulations showed that the best classification accuracies resulted when the time window was 2.5 s. In addition, it also showed that the features of the autoregressive (AR)-15 model coefficients were the best choices for extracting the features. For all the states of neural network except hardlim discriminator function, the classification accuracies were almost the same and not very different. Linear discriminant analysis (LDA) in comparison with the neural network yielded higher classification accuracies. ICA is a suitable algorithm for recognizing of the word's concept and its place in the brain. Achieved results from this experiment were the same compared with the results from other methods such as functional magnetic resonance imaging and methods based on the brain signals (EEG) in the vowel imagination and covert speech. Herein, the highest classification accuracy was obtained by extracting the target signal from the output of the ICA and extracting the features of coefficients AR model with time interval of 2.5 s. Finally, LDA resulted in the highest classification accuracy more than 60%.

Intramural location and size of arterial calcification are associated with stenosis at carotid bifurcation.

PubMed

Yamada, Shigeki; Oshima, Marie; Watanabe, Yoshihiko; Ogata, Hideki; Hashimoto, Kenji; Miyake, Hidenori

2014-06-01

The purpose of this study was to investigate the association between internal carotid artery (ICA) stenosis and intramural location and size of calcification at the ICA origins and the origins of the cervical arteries proximal to the ICA. A total of 1139 ICAs were evaluated stenosis and calcification on the multi-detector row CT angiography. The intramural location was categorized into none, outside and inside location. The calcification size was evaluated on the 4-point grading scale. The multivariate analyses were adjusted for age, serum creatinine level, hypertension, hyperlipidemia, diabetes mellitus, smoking and alcohol habits. Outside calcification at the ICA origins showed the highest multivariate odds ratio (OR) for the presence of ICA stenosis (30.0) and severe calcification (a semicircle or more of calcification at the arterial cross-sectional surfaces) did the second (14.3). In the subgroups of >70% ICA stenosis, the multivariate OR of outside location increased to 44.8 and that of severe calcification also increased to 32.7. Four of 5 calcified carotid plaque specimens extracted by carotid endarterectomy were histologically confirmed to be calcified burdens located outside the internal elastic lamia which were defined as arterial medial calcification. ICA stenosis was strongly associated with severe calcification located mainly outside the carotid plaque. Outside calcification at the ICA origins should be evaluated separately from inside calcification, as a marker for the ICA stenosis. Additionally, we found that calcification at the origins of the cervical arteries proximal to the ICA was significantly associated with the ICA stenosis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Computational Fluid Dynamics Modeling of Symptomatic Intracranial Atherosclerosis May Predict Risk of Stroke Recurrence

PubMed Central

Leng, Xinyi; Scalzo, Fabien; Ip, Hing Lung; Johnson, Mark; Fong, Albert K.; Fan, Florence S. Y.; Chen, Xiangyan; Soo, Yannie O. Y.; Miao, Zhongrong; Liu, Liping; Feldmann, Edward; Leung, Thomas W. H.; Liebeskind, David S.; Wong, Ka Sing

2014-01-01

Background Patients with symptomatic intracranial atherosclerosis (ICAS) of ≥70% luminal stenosis are at high risk of stroke recurrence. We aimed to evaluate the relationships between hemodynamics of ICAS revealed by computational fluid dynamics (CFD) models and risk of stroke recurrence in this patient subset. Methods Patients with a symptomatic ICAS lesion of 70–99% luminal stenosis were screened and enrolled in this study. CFD models were reconstructed based on baseline computed tomographic angiography (CTA) source images, to reveal hemodynamics of the qualifying symptomatic ICAS lesions. Change of pressures across a lesion was represented by the ratio of post- and pre-stenotic pressures. Change of shear strain rates (SSR) across a lesion was represented by the ratio of SSRs at the stenotic throat and proximal normal vessel segment, similar for the change of flow velocities. Patients were followed up for 1 year. Results Overall, 32 patients (median age 65; 59.4% males) were recruited. The median pressure, SSR and velocity ratios for the ICAS lesions were 0.40 (−2.46–0.79), 4.5 (2.2–20.6), and 7.4 (5.2–12.5), respectively. SSR ratio (hazard ratio [HR] 1.027; 95% confidence interval [CI], 1.004–1.051; P = 0.023) and velocity ratio (HR 1.029; 95% CI, 1.002–1.056; P = 0.035) were significantly related to recurrent territorial ischemic stroke within 1 year by univariate Cox regression, respectively with the c-statistics of 0.776 (95% CI, 0.594–0.903; P = 0.014) and 0.776 (95% CI, 0.594–0.903; P = 0.002) in receiver operating characteristic analysis. Conclusions Hemodynamics of ICAS on CFD models reconstructed from routinely obtained CTA images may predict subsequent stroke recurrence in patients with a symptomatic ICAS lesion of 70–99% luminal stenosis. PMID:24818753

FastICA peel-off for ECG interference removal from surface EMG.

PubMed

Chen, Maoqi; Zhang, Xu; Chen, Xiang; Zhu, Mingxing; Li, Guanglin; Zhou, Ping

2016-06-13

Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart. A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surface EMG signals. Although demonstrating spatial variability in waveform shape, the ECG interference in different channels shares the same firing instants. Utilizing the firing information estimated from FastICA, ECG interference can be separated from surface EMG by a "peel off" processing. The performance of the method was quantified with synthetic signals by combining a series of experimentally recorded "clean" surface EMG and "pure" ECG interference. It was demonstrated that the new method can remove ECG interference efficiently with little distortion to surface EMG amplitude and frequency. The proposed method was also validated using experimental surface EMG signals contaminated by ECG interference. The proposed FastICA peel-off method can be used as a new and practical solution to eliminating ECG interference from multichannel EMG recordings.

Preanalytical variables in measurement of free (ionized) calcium in lithium heparin-containing blood collection tubes.

PubMed

Haverstick, Doris M; Brill, Louis B; Scott, Mitchell G; Bruns, David E

2009-05-01

Measurements of free (ionized) calcium (iCa) are increasingly requested in patient care locations where immediate analysis is unavailable. Evacuated blood collection tubes containing lithium heparin and gel separator material are widely used in clinical laboratories, but little information is available on the effects of these tubes or of delay prior to analysis on the concentration or stability of iCa. We collected blood from volunteers into lithium-heparin tubes (PST, Vacutainer PST, BD Pre-Analytic Systems) of multiple lots and into electrolyte-balanced heparin syringes (Portex Dry Heparin, Smiths Medical). iCa was measured (Siemens 1265 blood gas analyzers) immediately and, in PST, at 0-7 h with or without transportation of the tubes from remote sites. The mean difference of free calcium results in the PST tubes and electrolyte-balanced syringes was -0.08 (95% confidence interval -0.17 to 0.012) mmol/l, and the SD of the residuals (Sy, x) of the regression was 0.03 mmol/l. There was no detectable lot-to-lot variation in results. Free calcium was stable in tubes at room temperature and at 4 degrees C for at least 7 h with or without transportation. iCa measured in the examined blood collection tubes is stable and unaffected by lot-to-lot variation of tubes, but results are slightly lower than with special blood gas syringes.

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

PubMed

Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

2018-05-01

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

Bone mass improved effect of icariin for postmenopausal osteoporosis in ovariectomy-induced rats: a meta-analysis and systematic review.

PubMed

Xu, Jin-Hai; Yao, Min; Ye, Jie; Wang, Guo-Dong; Wang, Jing; Cui, Xue-Jun; Mo, Wen

2016-10-01

Ovariectomy (OVX)-induced rats are the most frequently used animal model to research postmenopausal osteoporosis. Our objective was to summarize and critically assess the bone mass improved effect of icariin (ICA) for treatment of postmenopausal osteoporosis in an OVX-induced rat model. The PUBMED, EMBASE, and Chinese databases were searched from their inception date to February 2015. Two reviewers independently selected animal studies that evaluated the bone mass improved effect of ICA compared with control in OVX-induced rats. Extracted data were analyzed by RevMan statistical software, and the methodological quality of each study was assessed. Seven studies with adequate randomization were included in the systematic review. Overall, ICA seemed to significantly improve bone mass as assessed using the bone mineral density (seven studies, n = 169; weighted mean difference, 0.02; 95% CI, 0.01-0.02, I = 77%, P < 0.00001) using a random-effects model. There is no significant difference between ICA and estrogen (E) (six studies, n = 128; weighted mean difference, 0.00; 95% CI, -0.00 to 0.01, I = 54%, P = 0.01). Bone mass improved effect of ICA for postmenopausal osteoporosis was observed in OVX-induced rats. Assessment of the methodological quality of studies involving OVX-induced animal models is required, and good methodological quality should be valued in systematic reviews of animal studies.

Time-course of neurodegeneration and memory impairment following the 4-vessel occlusion/internal carotid artery model of chronic cerebral hypoperfusion in middle-aged rats.

PubMed

Pereira, Francine Martins; Ferreira, Emilene Dias Fiuza; de Oliveira, Rúbia Maria Weffort; Milani, Humberto

2012-04-15

The present work extends previous studies with the aim of developing the 4-vessel occlusion/internal carotid artery (4-VO/ICA) model of chronic cerebral hypoperfusion. The permanent occlusion of the vertebral arteries (VAs) and internal carotid arteries (ICAs) followed the sequence VA→ICA→ICA. The interstage interval (ISI, →), chronicity of 4-VO/ICA, and age of the animals may determine the success of the model with regard to neurohistological and behavioral outcomes. Using middle-aged rats, the present study evaluated (i) how brain damage evolves as the ISI is reduced and duration (i.e., "chronicity") of 4-VO/ICA is prolonged and (ii) how the duration of 4-VO/ICA affects retrograde memory performance. Male Wistar rats (12-15 months of age) were subjected to 4-VO/ICA with an ISI of 7, 5, 4, or 3 days, and hippocampal and cortical damage was examined 7, 30, and 90 days later. Using an ISI of 4 days, retrograde memory performance was assessed in the aversive radial maze after 4-VO/ICA with a duration of 7, 30, and 90 days. The severity of brain neurodegeneration and rate of mortality progressively increased as the ISI length decreased from 7 to 3 days, an effect that was not significantly altered by the chronicity of 4-VO/ICA. Permanent 4-VO/ICA effectively caused retrograde amnesia, an effect that worsened as the chronicity of 4-VO/ICA was prolonged. The findings confirm and expand the notion that permanent, 3-stage 4-VO/ICA effectively produces extensive neurodegeneration and persistent learning/memory impairment in middle-aged rats and that the ISI length, more than the chronicity of 4-VO/ICA, determines the final results. Copyright © 2012 Elsevier B.V. All rights reserved.

Unusual angiographic changes in an internal carotid artery pseudoaneurysm after infection in the deep neck space.

PubMed

Hayashi, Yasuhiko; Shima, Hiroshi; Miyashita, Katsuyoshi; Kinoshita, Masashi; Nakada, Mitsutoshi; Kida, Shinya; Hamada, Jun-ichiro

2008-05-01

A 48-year-old man presented with a pseudoaneurysm at the cervical portion of the left internal carotid artery (ICA) secondary to infection in the deep neck space. Magnetic resonance (MR) imaging demonstrated enhancement of the wall of the ICA and a pseudoaneurysm, considered to be sequelae of infection spread. ICA occlusion occurred on the next day resulting in sudden onset of right hemiparesis and motor aphasia. The ICA pseudoaneurysm shrank gradually and his neurological deficits improved with conservative therapy. One month later, he presented with aneurysm regrowth. The common carotid artery was occluded with Guglielmi detachable coils to block arterial flow into the pseudoaneurysm. There were no neurological complications. Marked enhancement of the ICA wall on computed tomography and MR imaging may indicate the possibility of vascular complications such as rupture, pseudoaneurysm development, or ICA occlusion, and consequent neurological deficits. ICA occlusion caused by spread of infection in the deep neck space may cause accelerated coagulopathy due to ICA wall inflammation.

Voltage-clamp analysis of membrane currents and excitation-contraction coupling in a crustacean muscle.

PubMed

Weiss, T; Erxleben, C; Rathmayer, W

2001-01-01

A single fibre preparation from the extensor muscle of a marine isopod crustacean is described which allows the analysis of membrane currents and simultaneously recorded contractions under two-electrode voltage-clamp conditions. We show that there are three main depolarisation-gated currents, two are outward and carried by K+, the third is an inward Ca2+ current, I(Ca). Normally, the K+ currents which can be isolated by using K+ channel blockers, mask I(Ca). I(Ca) activates at potentials more positive than -40 mV, is maximal around 0 mV, and shows strong inactivation at higher depolarisation. Inactivation depends on current rather than voltage. Ba2+, Sr2+ and Mg2+ can substitute for Ca2+. Ba2+ currents are about 80% larger than Ca2+ currents and inactivate little. The properties of I(Ca) characterise it as a high threshold L-type current. The outward current consists primarily of a fast, transient A current, I(K(A)) and a maintained, delayed rectifier current, I(K(V)). In some fibres, a small Ca2+-dependent K+ current is also present. I(K(A)) activates fast at depolarisation above -45 mV, shows pronounced inactivation and is almost completely inactivated at holding potentials more positive than -40 mV. I(K(A)) is half-maximally blocked by 70 microM 4-aminopyridine (4-AP), and 70 mM tetraethylammonium (TEA). I(K(V)) activates more slowly, at about -30 mV, and shows no inactivation. It is half-maximally blocked by 2 mM TEA but rather insensitive to 4-AP. Physiologically, the two K+ currents prevent all-or-nothing action potentials and determine the graded amplitude of active electrical responses and associated contractions. Tension development depends on and is correlated with depolarisation-induced Ca2+ influx mediated by I(Ca). The voltage dependence of peak tension corresponds directly to the voltage dependence of the integrated I(Ca). The threshold potential for contraction is at about -38 mV. Peak tension increases with increasing voltage steps, reaches maximum at around 0 mV, and declines with further depolarisation.

Signal extraction and wave field separation in tunnel seismic prediction by independent component analysis

NASA Astrophysics Data System (ADS)

Yue, Y.; Jiang, T.; Zhou, Q.

2017-12-01

In order to ensure the rationality and the safety of tunnel excavation, the advanced geological prediction has been become an indispensable step in tunneling. However, the extraction of signal and the separation of P and S waves directly influence the accuracy of geological prediction. Generally, the raw data collected in TSP system is low quality because of the numerous disturb factors in tunnel projects, such as the power interference and machine vibration interference. It's difficult for traditional method (band-pass filtering) to remove interference effectively as well as bring little loss to signal. The power interference, machine vibration interference and the signal are original variables and x, y, z component as observation signals, each component of the representation is a linear combination of the original variables, which satisfy applicable conditions of independent component analysis (ICA). We perform finite-difference simulations of elastic wave propagation to synthetic a tunnel seismic reflection record. The method of ICA was adopted to process the three-component data, and the results show that extract the estimates of signal and the signals are highly correlated (the coefficient correlation is up to more than 0.93). In addition, the estimates of interference that separated from ICA and the interference signals are also highly correlated, and the coefficient correlation is up to more than 0.99. Thus, simulation results showed that the ICA is an ideal method for extracting high quality data from mixed signals. For the separation of P and S waves, the conventional separation techniques are based on physical characteristics of wave propagation, which require knowledge of the near-surface P and S waves velocities and density. Whereas the ICA approach is entirely based on statistical differences between P and S waves, and the statistical technique does not require a priori information. The concrete results of the wave field separation will be presented in the meeting. In summary, we can safely draw the conclusion that ICA can not only extract high quality data from the mixed signals, but also can separate P and S waves effectively.

Layered Manufacturing of Dental Ceramics: Fracture Mechanics, Microstructure, and Elemental Composition of Lithography-Sintered Ceramic.

PubMed

Uçar, Yurdanur; Aysan Meriç, İpek; Ekren, Orhun

2018-02-11

To compare the fracture mechanics, microstructure, and elemental composition of lithography-based ceramic manufacturing with pressing and CAD/CAM. Disc-shaped specimens (16 mm diameter, 1.2 mm thick) were used for mechanical testing (n = 10/group). Biaxial flexural strength of three groups (In-Ceram alumina [ICA], lithography-based alumina, ZirkonZahn) were determined using the "piston on 3-ball" technique as suggested in test Standard ISO-6872. Vickers hardness test was performed. Fracture toughness was calculated using fractography. Results were statistically analyzed using Kruskal-Wallis test followed by Dunnett T3 (α = 0.05). Weibull analysis was conducted. Polished and fracture surface characterization was made using scanning electron microscope (SEM). Energy dispersive spectroscopy (EDS) was used for elemental analysis. Biaxial flexural strength of ICA, LCM alumina (LCMA), and ZirkonZahn were 147 ± 43 MPa, 490 ± 44 MPa, and 709 ± 94 MPa, respectively, and were statistically different (P ≤ 0.05). The Vickers hardness number of ICA was 850 ± 41, whereas hardness values for LCMA and ZirkonZahn were 1581 ± 144 and 1249 ± 57, respectively, and were statistically different (P ≤ 0.05). A statistically significant difference was found between fracture toughness of ICA (2 ± 0.4 MPa⋅m 1/2 ), LCMA (6.5 ± 1.5 MPa⋅m 1/2 ), and ZirkonZahn (7.7 ± 1 MPa⋅m 1/2 ) (P ≤ 0.05). Weibull modulus was highest for LCMA (m = 11.43) followed by ZirkonZahn (m = 8.16) and ICA (m = 5.21). Unlike LCMA and ZirkonZahn groups, a homogeneous microstructure was not observed for ICA. EDS results supported the SEM images. Within the limitations of this in vitro study, it can be concluded that LCM seems to be a promising technique for final ceramic object manufacturing in dental applications. Both the manufacturing method and the material used should be improved. © 2018 by the American College of Prosthodontists.

Association between direct measurement of active serum calcium and risk of type 2 diabetes mellitus: A prospective study.

PubMed

Zaccardi, F; Webb, D R; Carter, P; Pitocco, D; Khunti, K; Davies, M J; Kurl, S; Laukkanen, J A

2015-06-01

Previous prospective studies showing a positive association between serum calcium and incidence of type 2 diabetes mellitus (T2DM) have relied on total calcium or an indirect estimate of active, ionized calcium (iCa). We aimed to assess this relationship using a direct measurement of iCa. iCa and cardiometabolic risk factors were measured in a population-based sample of 2350 men without a known history of T2DM at baseline. Associations between iCa levels and incident cases of T2DM (self-reported, ascertained with a glucose tolerance test, or determined by record linkage to national registers) were estimated using Cox regression analyses adjusted for potential confounders. At baseline, mean (standard deviation) age was 53 (5) years and mean iCa 1.18 (0.05) mmol/L. During a median follow-up of 23.1 years, 140 new cases of T2DM were recorded. In a multivariable analysis adjusted for age, body mass index, systolic blood pressure, serum HDL-cholesterol, and family history of T2DM, there was no association comparing second (hazard ratio 0.84; 95% confidence interval 0.59-1.18), third (0.77; 0.52-1.14), or fourth (0.98; 0.69-1.39) vs first quartile of iCa (p for trend 0.538); further adjustment for C-reactive protein, physical activity level, and triglycerides did not change the estimates (p for trend 0.389). In this study, we did not find evidence of an association between direct measurement of active calcium and risk of T2DM. Further studies are needed to confirm our findings and define the relationship between factors influencing indirect calcium estimation and incident T2DM. Copyright © 2015 Elsevier B.V. All rights reserved.

Expanding the endoscopic transpterygoid corridor to the petroclival region: anatomical study and volumetric comparative analysis.

PubMed

Freeman, Jacob L; Sampath, Raghuram; Quattlebaum, Steven Craig; Casey, Michael A; Folzenlogen, Zach A; Ramakrishnan, Vijay R; Youssef, A Samy

2017-07-21

OBJECTIVE The endoscopic endonasal transmaxillary transpterygoid (TMTP) approach has been the gateway for lateral skull base exposure. Removal of the cartilaginous eustachian tube (ET) and lateral mobilization of the internal carotid artery (ICA) are technically demanding adjunctive steps that are used to access the petroclival region. The gained expansion of the deep working corridor provided by these maneuvers has yet to be quantified. METHODS The TMTP approach with cartilaginous ET removal and ICA mobilization was performed in 5 adult cadaveric heads (10 sides). Accessible portions of the petrous apex were drilled during the following 3 stages: 1) before ET removal, 2) after ET removal but before ICA mobilization, and 3) after ET removal and ICA repositioning. Resection volumes were calculated using 3D reconstructions generated from thin-slice CT scans obtained before and after each step of the dissection. RESULTS The average petrous temporal bone resection volumes at each stage were 0.21 cm 3 , 0.71 cm 3 , and 1.32 cm 3 (p < 0.05, paired t-test). Without ET removal, inferior and superior access to the petrous apex was limited. Furthermore, without ICA mobilization, drilling was confined to the inferior two-thirds of the petrous apex. After mobilization, the resection was extended superiorly through the upper extent of the petrous apex. CONCLUSIONS The transpterygoid corridor to the petroclival region is maximally expanded by the resection of the cartilaginous ET and mobilization of the paraclival ICA. These added maneuvers expanded the deep window almost 6 times and provided more lateral access to the petroclival region with a maximum volume of 1.5 cm 3 . This may result in the ability to resect small-to-moderate sized intradural petroclival lesions up to that volume. Larger lesions may better be approached through an open transcranial approach.

[Determination of soluble solids content in Nanfeng Mandarin by Vis/NIR spectroscopy and UVE-ICA-LS-SVM].

PubMed

Sun, Tong; Xu, Wen-Li; Hu, Tian; Liu, Mu-Hua

2013-12-01

The objective of the present research was to assess soluble solids content (SSC) of Nanfeng mandarin by visible/near infrared (Vis/NIR) spectroscopy combined with new variable selection method, simplify prediction model and improve the performance of prediction model for SSC of Nanfeng mandarin. A total of 300 Nanfeng mandarin samples were used, the numbers of Nanfeng mandarin samples in calibration, validation and prediction sets were 150, 75 and 75, respectively. Vis/NIR spectra of Nanfeng mandarin samples were acquired by a QualitySpec spectrometer in the wavelength range of 350-1000 nm. Uninformative variables elimination (UVE) was used to eliminate wavelength variables that had few information of SSC, then independent component analysis (ICA) was used to extract independent components (ICs) from spectra that eliminated uninformative wavelength variables. At last, least squares support vector machine (LS-SVM) was used to develop calibration models for SSC of Nanfeng mandarin using extracted ICs, and 75 prediction samples that had not been used for model development were used to evaluate the performance of SSC model of Nanfeng mandarin. The results indicate t hat Vis/NIR spectroscopy combinedwith UVE-ICA-LS-SVM is suitable for assessing SSC o f Nanfeng mandarin, and t he precision o f prediction ishigh. UVE--ICA is an effective method to eliminate uninformative wavelength variables, extract important spectral information, simplify prediction model and improve the performance of prediction model. The SSC model developed by UVE-ICA-LS-SVM is superior to that developed by PLS, PCA-LS-SVM or ICA-LS-SVM, and the coefficient of determination and root mean square error in calibration, validation and prediction sets were 0.978, 0.230%, 0.965, 0.301% and 0.967, 0.292%, respectively.

Hybrid systems analysis of the control of burst duration by low-voltage-activated calcium current in leech heart interneurons.

PubMed

Olypher, Andrey; Cymbalyuk, Gennady; Calabrese, Ronald L

2006-12-01

The leech heartbeat CPG is paced by the alternating bursting of pairs of mutually inhibitory heart interneurons that form elemental half-center oscillators. We explore the control of burst duration in heart interneurons using a hybrid system, where a living, pharmacologically isolated, heart interneuron is connected with artificial synapses to a model heart interneuron running in real-time, by focusing on a low-voltage-activated (LVA) calcium current I(CaS). The transition from silence to bursting in this half-center oscillator occurs when the spike frequency of the bursting interneuron declines to a critical level, f(Final), at which the inhibited interneuron escapes owing to a build-up of the hyperpolarization-activated cation current, I(h). We varied I(CaS) inactivation time constant either in the living heart interneuron or in the model heart interneuron. In both cases, varying I(CaS) inactivation time constant did not affect f(Final) of either interneuron, but in the varied interneuron, the time constant of decline of spike frequency during bursts to f(Final) and thus the burst duration varied directly and nearly linearly with I(CaS) inactivation time constant. Bursts of the opposite, nonvaried interneuron did not change. We show also that control of burst duration by I(CaS) inactivation does not require synaptic interaction by reconstituting autonomous bursting in synaptically isolated living interneurons with injected I(CaS). Therefore inactivation of LVA calcium current is critically important for setting burst duration and thus period in a heart interneuron half-center oscillator and is potentially a general intrinsic mechanism for regulating burst duration in neurons.

Attitude towards one's illness vs. attitude towards a surgical operation, displayed by patients diagnosed with asymptomatic abdominal aortic aneurysm and asymptomatic internal carotid artery stenosis.

PubMed

Stanisić, M; Rzepa, T

2012-08-01

Two most frequent asymptomatic diseases qualifying for vascular surgery are abdominal aortic aneurysm (AAA) and internal carotid artery stenosis (ICAS). Emotions experienced by the patient activate processes of dealing with the cognitive dissonance of asymptomatic disease. The aim of this paper was to compare the reasons involved in decision making on surgery in two asymptomatic vascular pathologies. Fifty patients were divided into two groups: the ICAS group-27 (CAS or CEA) and the AAA group-23 (EVAR or open surgical operation (OSR). Specific questionnaire regarding: 1) self-image; 2) attitude to one's illness; 3) reasons for decision on surgery was applied for the study. The χ² test was used to for the analysis. The AAA patients reacted emotionally (88.2%) comparing to ICAS patients reacting "rationally" (59.3%) (α=0.05). In AAA patients attitude towards themselves had worsened (α=0.001) AAA patients were less likely to seek support in decision on surgery (α=0.01). ICAS patients are internally motivated (78.7%), whereas AAA patients are externally motivated (63.9%) (α=0.001). Reasons underlying the decision on surgery, were predominantly rational (55.8%). In the process of decision-making on surgery by asymptomatic patients, evolutionary transformation takes place - the emotional attitude to one's illness leads to rationally evaluated decision. Regardless of the causes the process of making a decision on surgical operation tended to run more smoothly in ICAS patients. The ICAS patients tended to display a rational attitude to their illness. AAA patients displayed a distinctly emotional attitude towards their illness.

Phenotypic and genotypic characterization of biofilm formation among Staphylococcus aureus isolates from clinical specimens, an Atomic Force Microscopic (AFM) study.

PubMed

Bazari, Pelin Aslani Menareh; Honarmand Jahromy, Sahar; Zare Karizi, Shohreh

2017-09-01

Staphylococcus aureus is a major cause of nosocomial infections. Biofilm formation is an important factor for bacterial pathogenesis. Its mechanisms are complex and include of many genes depends on expression of icaADBC operon involved in the synthesis of a polysaccharide intercellular adhesion. The aim of study was to investigate biofilm forming ability of Staphylococcus aureus strains by phenotypic and genotypic methods. Also Atomic Force microscope (AFM) was used to visualize biofilm formation. 140 Isolates were collected from clinical specimens of patients in Milad Hospital, Tehran and diagnosed by biochemical tests. The ability of strains to produce slime was evaluated by CRA method. For diagnosing of bacterial EPS, Indian ink staining were used and finally biofilm surface of 3 isolates observed by AFM. The prevalence of icaA and icaD genes was determined by PCR. By CRA method 15% of samples considered as positive slime producers, 44.28% as intermediate and 40.71% indicative as negative slime producers. 118 staphylococcus aureus strains showed a distinct halo transparent zone but 22 strains showed no halo zone. AFM analysis of Slime positive isolates showed a distinct and complete biofilm formation. In slime negative strain, there was not observed biofilm. The prevalence of icaA, icaD genes was 44.2% and 10% of the isolates had both genes simultaneously. There is a relationship between exopolysaccharide layer and biofilm formation of Staphylococcus aureus isolates. The presence of icaAD genes among isolates is not associated with in vitro formation of biofilm. AFM is a useful tool for observation of bacterial biofilm formation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Independent component analysis-based algorithm for automatic identification of Raman spectra applied to artistic pigments and pigment mixtures.

PubMed

González-Vidal, Juan José; Pérez-Pueyo, Rosanna; Soneira, María José; Ruiz-Moreno, Sergio

2015-03-01

A new method has been developed to automatically identify Raman spectra, whether they correspond to single- or multicomponent spectra. The method requires no user input or judgment. There are thus no parameters to be tweaked. Furthermore, it provides a reliability factor on the resulting identification, with the aim of becoming a useful support tool for the analyst in the decision-making process. The method relies on the multivariate techniques of principal component analysis (PCA) and independent component analysis (ICA), and on some metrics. It has been developed for the application of automated spectral analysis, where the analyzed spectrum is provided by a spectrometer that has no previous knowledge of the analyzed sample, meaning that the number of components in the sample is unknown. We describe the details of this method and demonstrate its efficiency by identifying both simulated spectra and real spectra. The method has been applied to artistic pigment identification. The reliable and consistent results that were obtained make the methodology a helpful tool suitable for the identification of pigments in artwork or in paint in general.

Fast multi-scale feature fusion for ECG heartbeat classification

NASA Astrophysics Data System (ADS)

Ai, Danni; Yang, Jian; Wang, Zeyu; Fan, Jingfan; Ai, Changbin; Wang, Yongtian

2015-12-01

Electrocardiogram (ECG) is conducted to monitor the electrical activity of the heart by presenting small amplitude and duration signals; as a result, hidden information present in ECG data is difficult to determine. However, this concealed information can be used to detect abnormalities. In our study, a fast feature-fusion method of ECG heartbeat classification based on multi-linear subspace learning is proposed. The method consists of four stages. First, baseline and high frequencies are removed to segment heartbeat. Second, as an extension of wavelets, wavelet-packet decomposition is conducted to extract features. With wavelet-packet decomposition, good time and frequency resolutions can be provided simultaneously. Third, decomposed confidences are arranged as a two-way tensor, in which feature fusion is directly implemented with generalized N dimensional ICA (GND-ICA). In this method, co-relationship among different data information is considered, and disadvantages of dimensionality are prevented; this method can also be used to reduce computing compared with linear subspace-learning methods (PCA). Finally, support vector machine (SVM) is considered as a classifier in heartbeat classification. In this study, ECG records are obtained from the MIT-BIT arrhythmia database. Four main heartbeat classes are used to examine the proposed algorithm. Based on the results of five measurements, sensitivity, positive predictivity, accuracy, average accuracy, and t-test, our conclusion is that a GND-ICA-based strategy can be used to provide enhanced ECG heartbeat classification. Furthermore, large redundant features are eliminated, and classification time is reduced.

A Novel Anti-Spoofing Solution for Iris Recognition Toward Cosmetic Contact Lens Attack Using Spectral ICA Analysis.

PubMed

Hsieh, Sheng-Hsun; Li, Yung-Hui; Wang, Wei; Tien, Chung-Hao

2018-03-06

In this study, we maneuvered a dual-band spectral imaging system to capture an iridal image from a cosmetic-contact-lens-wearing subject. By using the independent component analysis to separate individual spectral primitives, we successfully distinguished the natural iris texture from the cosmetic contact lens (CCL) pattern, and restored the genuine iris patterns from the CCL-polluted image. Based on a database containing 200 test image pairs from 20 CCL-wearing subjects as the proof of concept, the recognition accuracy (False Rejection Rate: FRR) was improved from FRR = 10.52% to FRR = 0.57% with the proposed ICA anti-spoofing scheme.

Improving MEG source localizations: an automated method for complete artifact removal based on independent component analysis.

PubMed

Mantini, D; Franciotti, R; Romani, G L; Pizzella, V

2008-03-01

The major limitation for the acquisition of high-quality magnetoencephalography (MEG) recordings is the presence of disturbances of physiological and technical origins: eye movements, cardiac signals, muscular contractions, and environmental noise are serious problems for MEG signal analysis. In the last years, multi-channel MEG systems have undergone rapid technological developments in terms of noise reduction, and many processing methods have been proposed for artifact rejection. Independent component analysis (ICA) has already shown to be an effective and generally applicable technique for concurrently removing artifacts and noise from the MEG recordings. However, no standardized automated system based on ICA has become available so far, because of the intrinsic difficulty in the reliable categorization of the source signals obtained with this technique. In this work, approximate entropy (ApEn), a measure of data regularity, is successfully used for the classification of the signals produced by ICA, allowing for an automated artifact rejection. The proposed method has been tested using MEG data sets collected during somatosensory, auditory and visual stimulation. It was demonstrated to be effective in attenuating both biological artifacts and environmental noise, in order to reconstruct clear signals that can be used for improving brain source localizations.

Reduction hybrid artifacts of EMG-EOG in electroencephalography evoked by prefrontal transcranial magnetic stimulation

NASA Astrophysics Data System (ADS)

Bai, Yang; Wan, Xiaohong; Zeng, Ke; Ni, Yinmei; Qiu, Lirong; Li, Xiaoli

2016-12-01

Objective. When prefrontal-transcranial magnetic stimulation (p-TMS) performed, it may evoke hybrid artifact mixed with muscle activity and blink activity in EEG recordings. Reducing this kind of hybrid artifact challenges the traditional preprocessing methods. We aim to explore method for the p-TMS evoked hybrid artifact removal. Approach. We propose a novel method used as independent component analysis (ICA) post processing to reduce the p-TMS evoked hybrid artifact. Ensemble empirical mode decomposition (EEMD) was used to decompose signal into multi-components, then the components were separated with artifact reduced by blind source separation (BSS) method. Three standard BSS methods, ICA, independent vector analysis, and canonical correlation analysis (CCA) were tested. Main results. Synthetic results showed that EEMD-CCA outperformed others as ICA post processing step in hybrid artifacts reduction. Its superiority was clearer when signal to noise ratio (SNR) was lower. In application to real experiment, SNR can be significantly increased and the p-TMS evoked potential could be recovered from hybrid artifact contaminated signal. Our proposed method can effectively reduce the p-TMS evoked hybrid artifacts. Significance. Our proposed method may facilitate future prefrontal TMS-EEG researches.

Detection of explosives on the surface of banknotes by Raman hyperspectral imaging and independent component analysis.

PubMed

Almeida, Mariana R; Correa, Deleon N; Zacca, Jorge J; Logrado, Lucio Paulo Lima; Poppi, Ronei J

2015-02-20

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm(-2). Copyright © 2014 Elsevier B.V. All rights reserved.

Methods of Collection of Biological Information for Fatigue Evaluation during Visual Display Terminals (VDT) Operation

NASA Astrophysics Data System (ADS)

Hachiya, Yuriko; Ogai, Harutoshi; Okazaki, Hiroko; Fujisaki, Takeshi; Uchida, Kazuhiko; Oda, Susumu; Wada, Futoshi; Mori, Koji

A method for the analysis of fatigue parameters has been rarely researched in VDT operation. Up to now, fatigue was evaluated by changing of biological information. If signals regarding fatigue are detected, fatigue can be measured. The purpose of this study proposed experiment and analysis method to extract parameters related to fatigue from the biological information during VDT operation using the Independent Component Analysis (ICA). An experiment had 11 subjects. As for the experiment were light loaded VDT operation and heavy loaded VDT operation. A measurement item were amount of work, a mistake number, subjective symptom, surface skin temperature (forehead and apex nasi), heart rate, skin blood flow of forearm and respiratory rate. In the heavy loaded operation group, mistake number and subjective symptom score were increased to compare with the other. And Two-factor ANOVA was used for analysis. The result of mistake number was confirmed that heavy loaded. After the moving averages of waveshape were calculated, it was made to extract independent components by using the ICA. The results of the ICA suggest that the independent components increase according to accumulation of fatigue. Thus, the independent components would be a possible parameter of fatigue. However, further experiments should continue in order to obtain the conclusive finding of our research.

Polysaccharide intercellular adhesin in biofilm: structural and regulatory aspects

PubMed Central

Arciola, Carla Renata; Campoccia, Davide; Ravaioli, Stefano; Montanaro, Lucio

2015-01-01

Staphylococcus aureus and Staphylococcus epidermidis are the leading etiologic agents of implant-related infections. Biofilm formation is the main pathogenetic mechanism leading to the chronicity and irreducibility of infections. The extracellular polymeric substances of staphylococcal biofilms are the polysaccharide intercellular adhesin (PIA), extracellular-DNA, proteins, and amyloid fibrils. PIA is a poly-β(1-6)-N-acetylglucosamine (PNAG), partially deacetylated, positively charged, whose synthesis is mediated by the icaADBC locus. DNA sequences homologous to ica locus are present in many coagulase-negative staphylococcal species, among which S. lugdunensis, however, produces a biofilm prevalently consisting of proteins. The product of icaA is an N-acetylglucosaminyltransferase that synthetizes PIA oligomers from UDP-N-acetylglucosamine. The product of icaD gives optimal efficiency to IcaA. The product of icaC is involved in the externalization of the nascent polysaccharide. The product of icaB is an N-deacetylase responsible for the partial deacetylation of PIA. The expression of ica locus is affected by environmental conditions. In S. aureus and S. epidermidis ica-independent alternative mechanisms of biofilm production have been described. S. epidermidis and S. aureus undergo to a phase variation for the biofilm production that has been ascribed, in turn, to the transposition of an insertion sequence in the icaC gene or to the expansion/contraction of a tandem repeat naturally harbored within icaC. A role is played by the quorum sensing system, which negatively regulates biofilm formation, favoring the dispersal phase that disseminates bacteria to new infection sites. Interfering with the QS system is a much debated strategy to combat biofilm-related infections. In the search of vaccines against staphylococcal infections deacetylated PNAG retained on the surface of S. aureus favors opsonophagocytosis and is a potential candidate for immune-protection. PMID:25713785

The Effect of Spatial and Temporal Resolution of Cine Phase Contrast MRI on Wall Shear Stress and Oscillatory Shear Index Assessment

PubMed Central

Gijsen, Frank J.; Marquering, Henk; van Ooij, Pim; vanBavel, Ed; Wentzel, Jolanda J.; Nederveen, Aart J.

2016-01-01

Introduction Wall shear stress (WSS) and oscillatory shear index (OSI) are associated with atherosclerotic disease. Both parameters are derived from blood velocities, which can be measured with phase-contrast MRI (PC-MRI). Limitations in spatiotemporal resolution of PC-MRI are known to affect these measurements. Our aim was to investigate the effect of spatiotemporal resolution using a carotid artery phantom. Methods A carotid artery phantom was connected to a flow set-up supplying pulsatile flow. MRI measurement planes were placed at the common carotid artery (CCA) and internal carotid artery (ICA). Two-dimensional PC-MRI measurements were performed with thirty different spatiotemporal resolution settings. The MRI flow measurement was validated with ultrasound probe measurements. Mean flow, peak flow, flow waveform, WSS and OSI were compared for these spatiotemporal resolutions using regression analysis. The slopes of the regression lines were reported in %/mm and %/100ms. The distribution of low and high WSS and OSI was compared between different spatiotemporal resolutions. Results The mean PC-MRI CCA flow (2.5±0.2mL/s) agreed with the ultrasound probe measurements (2.7±0.02mL/s). Mean flow (mL/s) depended only on spatial resolution (CCA:-13%/mm, ICA:-49%/mm). Peak flow (mL/s) depended on both spatial (CCA:-13%/mm, ICA:-17%/mm) and temporal resolution (CCA:-19%/100ms, ICA:-24%/100ms). Mean WSS (Pa) was in inverse relationship only with spatial resolution (CCA:-19%/mm, ICA:-33%/mm). OSI was dependent on spatial resolution for CCA (-26%/mm) and temporal resolution for ICA (-16%/100ms). The regions of low and high WSS and OSI matched for most of the spatiotemporal resolutions (CCA:30/30, ICA:28/30 cases for WSS; CCA:23/30, ICA:29/30 cases for OSI). Conclusion We show that both mean flow and mean WSS are independent of temporal resolution. Peak flow and OSI are dependent on both spatial and temporal resolution. However, the magnitude of mean and peak flow, WSS and OSI, and the spatial distribution of OSI and WSS did not exhibit a strong dependency on spatiotemporal resolution. PMID:27669568

Rotenone-stimulated superoxide release from mitochondrial complex I acutely augments L-type Ca2+ current in A7r5 aortic smooth muscle cells

PubMed Central

Dhagia, Vidhi; Lakhkar, Anand; Patel, Dhara; Wolin, Michael S.; Gupte, Sachin A.

2016-01-01

Voltage-gated L-type Ca2+ current (ICa,L) induces contraction of arterial smooth muscle cells (ASMCs), and ICa,L is increased by H2O2 in ASMCs. Superoxide released from the mitochondrial respiratory chain (MRC) is dismutated to H2O2. We studied whether superoxide per se acutely modulates ICa,L in ASMCs using cultured A7r5 cells derived from rat aorta. Rotenone is a toxin that inhibits complex I of the MRC and increases mitochondrial superoxide release. The superoxide content of mitochondria was estimated using mitochondrial-specific MitoSOX and HPLC methods, and was shown to be increased by a brief exposure to 10 μM rotenone. ICa,L was recorded with 5 mM BAPTA in the pipette solution. Rotenone administration (10 nM to 10 μM) resulted in a greater ICa,L increase in a dose-dependent manner to a maximum of 22.1% at 10 μM for 1 min, which gradually decreased to 9% after 5 min. The rotenone-induced ICa,L increase was associated with a shift in the current-voltage relationship (I-V) to a hyperpolarizing direction. DTT administration resulted in a 17.9% increase in ICa,L without a negative shift in I–V, and rotenone produced an additional increase with a shift. H2O2 (0.3 mM) inhibited ICa,L by 13%, and additional rotenone induced an increase with a negative shift. Sustained treatment with Tempol (4-hydroxy tempo) led to a significant ICa,L increase but it inhibited the rotenone-induced increase. Staurosporine, a broad-spectrum protein kinase inhibitor, partially inhibited ICa,L and completely suppressed the rotenone-induced increase. Superoxide released from mitochondria affected protein kinases and resulted in stronger ICa,L preceding its dismutation to H2O2. The removal of nitric oxide is a likely mechanism for the increase in ICa,L. PMID:26873970

Role of acidosis-induced increases in calcium on PTH secretion in acute metabolic and respiratory acidosis in the dog.

PubMed

López, Ignacio; Aguilera-Tejero, Escolástico; Estepa, José Carlos; Rodríguez, Mariano; Felsenfeld, Arnold J

2004-05-01

Recently, we showed that both acute metabolic acidosis and respiratory acidosis stimulate parathyroid hormone (PTH) secretion in the dog. To evaluate the specific effect of acidosis, ionized calcium (iCa) was clamped at a normal value. Because iCa values normally increase during acute acidosis, we now have studied the PTH response to acute metabolic and respiratory acidosis in dogs in which the iCa concentration was allowed to increase (nonclamped) compared with dogs with a normal iCa concentration (clamped). Five groups of dogs were studied: control, metabolic (clamped and nonclamped), and respiratory (clamped and nonclamped) acidosis. Metabolic (HCl infusion) and respiratory (hypoventilation) acidosis was progressively induced during 60 min. In the two clamped groups, iCa was maintained at a normal value with an EDTA infusion. Both metabolic and respiratory acidosis increased (P < 0.05) iCa values in nonclamped groups. In metabolic acidosis, the increase in iCa was progressive and greater (P < 0.05) than in respiratory acidosis, in which iCa increased by 0.04 mM and then remained constant despite further pH reductions. The increase in PTH values was greater (P < 0.05) in clamped than in nonclamped groups (metabolic and respiratory acidosis). In the nonclamped metabolic acidosis group, PTH values first increased and then decreased from peak values when iCa increased by > 0.1 mM. In the nonclamped respiratory acidosis group, PTH values exceeded (P < 0.05) baseline values only after iCa values stopped increasing at a pH of 7.30. For the same increase in iCa in the nonclamped groups, PTH values increased more in metabolic acidosis. In conclusion, 1) both metabolic acidosis and respiratory acidosis stimulate PTH secretion; 2) the physiological increase in the iCa concentration during the induction of metabolic and respiratory acidosis reduces the magnitude of the PTH increase; 3) in metabolic acidosis, the increase in the iCa concentration can be of sufficient magnitude to reverse the increase in PTH values; and 4) for the same degree of acidosis-induced hypercalcemia, the increase in PTH values is greater in metabolic than in respiratory acidosis.

Stealthy false data injection attacks using matrix recovery and independent component analysis in smart grid

NASA Astrophysics Data System (ADS)

JiWei, Tian; BuHong, Wang; FuTe, Shang; Shuaiqi, Liu

2017-05-01

Exact state estimation is vital important to maintain common operations of smart grids. Existing researches demonstrate that state estimation output could be compromised by malicious attacks. However, to construct the attack vectors, a usual presumption in most works is that the attacker has perfect information regarding the topology and so on even such information is difficult to acquire in practice. Recent research shows that Independent Component Analysis (ICA) can be used for inferring topology information which can be used to originate undetectable attacks and even to alter the price of electricity for the profits of attackers. However, we found that the above ICA-based blind attack tactics is merely feasible in the environment with Gaussian noises. If there are outliers (device malfunction and communication errors), the Bad Data Detector will easily detect the attack. Hence, we propose a robust ICA based blind attack strategy that one can use matrix recovery to circumvent the outlier problem and construct stealthy attack vectors. The proposed attack strategies are tested with IEEE representative 14-bus system. Simulations verify the feasibility of the proposed method.

Extracranial internal carotid artery dissection caused by compression from a giant osteophyte due to atlantoaxial osteoarthritis: case report.

PubMed

Ikedo, Taichi; Nakamura, Kazuhito; Sano, Noritaka; Nagata, Manabu; Okada, Yumiko; Kawakami, Taichiro; Murata, Takaho

2017-10-01

Deformed osseous structures have been reported as rare causes of extracranial internal carotid artery (ICA) dissection, including the styloid process and the hyoid bone. Here, the authors describe the first known case of symptomatic ICA dissection caused by a giant osteophyte due to atlantoaxial osteoarthritis. The left ICA was fixed at the skull base and at the ICA portion compressed by the osteophyte, and it was highly stretched and injured between the two portions during neck rotation. The patient was successfully treated with ligation of the affected ICA following balloon test occlusion. Atlantoaxial osteoarthritis should be considered in the differential diagnosis of ICA dissection in patients with a severely deformed cervical spine.

Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study.

PubMed

Yu, Qingbao; Du, Yuhui; Chen, Jiayu; He, Hao; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D

2017-11-01

A key challenge in building a brain graph using fMRI data is how to define the nodes. Spatial brain components estimated by independent components analysis (ICA) and regions of interest (ROIs) determined by brain atlas are two popular methods to define nodes in brain graphs. It is difficult to evaluate which method is better in real fMRI data. Here we perform a simulation study and evaluate the accuracies of a few graph metrics in graphs with nodes of ICA components, ROIs, or modified ROIs in four simulation scenarios. Graph measures with ICA nodes are more accurate than graphs with ROI nodes in all cases. Graph measures with modified ROI nodes are modulated by artifacts. The correlations of graph metrics across subjects between graphs with ICA nodes and ground truth are higher than the correlations between graphs with ROI nodes and ground truth in scenarios with large overlapped spatial sources. Moreover, moving the location of ROIs would largely decrease the correlations in all scenarios. Evaluating graphs with different nodes is promising in simulated data rather than real data because different scenarios can be simulated and measures of different graphs can be compared with a known ground truth. Since ROIs defined using brain atlas may not correspond well to real functional boundaries, overall findings of this work suggest that it is more appropriate to define nodes using data-driven ICA than ROI approaches in real fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

Human parietofrontal networks related to action observation detected at rest.

PubMed

Molinari, Elisa; Baraldi, Patrizia; Campanella, Martina; Duzzi, Davide; Nocetti, Luca; Pagnoni, Giuseppe; Porro, Carlo A

2013-01-01

Recent data show a broad correspondence between human resting-state and task-related brain networks. We performed a functional magnetic resonance imaging (fMRI) study to compare, in the same subjects, the spatial independent component analysis (ICA) maps obtained at rest and during the observation of either reaching/grasping hand actions or matching static pictures. Two parietofrontal networks were identified by ICA from action observation task data. One network, specific to reaching/grasping observation, included portions of the anterior intraparietal cortex and of the dorsal and ventral lateral premotor cortices. A second network included more posterior portions of the parietal lobe, the dorsomedial frontal cortex, and more anterior and ventral parts, respectively, of the dorsal and ventral premotor cortices, extending toward Broca's area; this network was more generally related to the observation of hand action and static pictures. A good spatial correspondence was found between the 2 observation-related ICA maps and 2 ICA maps identified from resting-state data. The anatomical connectivity among the identified clusters was tested in the same volunteers, using persistent angular structure-MRI and deterministic tractography. These findings extend available knowledge of human parietofrontal circuits and further support the hypothesis of a persistent coherence within functionally relevant networks during rest.

A natural basis for efficient brain-actuated control

NASA Technical Reports Server (NTRS)

Makeig, S.; Enghoff, S.; Jung, T. P.; Sejnowski, T. J.

2000-01-01

The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few 'locked-in' subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central mu-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to be a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central mu activities. We demonstrate using data from a visual selective attention task that ICA-derived mu-components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels. ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain-actuated control in motor-limited and locked-in subjects.

Independent components analysis coupled with 3D-front-face fluorescence spectroscopy to study the interaction between plastic food packaging and olive oil.

PubMed

Kassouf, Amine; El Rakwe, Maria; Chebib, Hanna; Ducruet, Violette; Rutledge, Douglas N; Maalouly, Jacqueline

2014-08-11

Olive oil is one of the most valued sources of fats in the Mediterranean diet. Its storage was generally done using glass or metallic packaging materials. Nowadays, plastic packaging has gained worldwide spread for the storage of olive oil. However, plastics are not inert and interaction phenomena may occur between packaging materials and olive oil. In this study, extra virgin olive oil samples were submitted to accelerated interaction conditions, in contact with polypropylene (PP) and polylactide (PLA) plastic packaging materials. 3D-front-face fluorescence spectroscopy, being a simple, fast and non destructive analytical technique, was used to study this interaction. Independent components analysis (ICA) was used to analyze raw 3D-front-face fluorescence spectra of olive oil. ICA was able to highlight a probable effect of a migration of substances with antioxidant activity. The signals extracted by ICA corresponded to natural olive oil fluorophores (tocopherols and polyphenols) as well as newly formed ones which were tentatively identified as fluorescent oxidation products. Based on the extracted fluorescent signals, olive oil in contact with plastics had slower aging rates in comparison with reference oils. Peroxide and free acidity values validated the results obtained by ICA, related to olive oil oxidation rates. Sorbed olive oil in plastic was also quantified given that this sorption could induce a swelling of the polymer thus promoting migration. Copyright © 2014 Elsevier B.V. All rights reserved.

Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing.

PubMed

Starck, Tuomo; Nikkinen, Juha; Rahko, Jukka; Remes, Jukka; Hurtig, Tuula; Haapsamo, Helena; Jussila, Katja; Kuusikko-Gauffin, Sanna; Mattila, Marja-Leena; Jansson-Verkasalo, Eira; Pauls, David L; Ebeling, Hanna; Moilanen, Irma; Tervonen, Osmo; Kiviniemi, Vesa J

2013-01-01

In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered.

Serum ionized calcium in dogs with chronic renal failure and metabolic acidosis.

PubMed

Kogika, Marcia M; Lustoza, Marcio D; Notomi, Marcia K; Wirthl, Vera A B F; Mirandola, Regina M S; Hagiwara, Mitika K

2006-12-01

Chronic renal failure (CRF) is a common disease in dogs, and many metabolic disorders can be observed, including metabolic acidosis and calcium and phosphorus disturbances. Acidosis may change the ionized calcium (i-Ca) fraction, usually increasing its concentration. In this study we evaluated the influence of acidosis on the serum concentration of i-Ca in dogs with CRF and metabolic acidosis. Dogs were studied in 2 groups: group I (control group = 40 clinically normal dogs) and group II (25 dogs with CRF and metabolic acidosis). Serum i-Ca was measured by an ion-selective electrode method; other biochemical analytes were measured using routine methods. The i-Ca concentration was significantly lower in dogs in group II than in group I; 56% of the dogs in group II were hypocalcemic. Hypocalcemia was observed in only 8% of dogs in group II when based on total calcium (t-Ca) concentration. No correlation between pH and i-Ca concentration was observed. A slight but significant correlation was detected between i-Ca and serum phosphorus concentration (r = -.284; P = .022), as well as between serum t-Ca and i-Ca concentration (r = .497; P < .0001). The i-Ca concentration in dogs with CRF and metabolic acidosis varied widely from that of t-Ca, showing the importance of determining the biologically active form of calcium. Metabolic acidosis did not influence the increase in i-Ca concentration, so other factors besides acidosis in CRF might alter the i-Ca fraction, such as hyperphosphatemia and other compounds that may form complexes with calcium.

External carotid artery stenosis after internal and common carotid stenting.

PubMed

Siracuse, Jeffrey J; Epelboym, Irene; Li, Boyangzi; Hoque, Rahima; Catz, Diana; Morrissey, Nicholas J

2015-04-01

The external carotid artery (ECA) can be an important collateral for cerebral perfusion in the presence of severe internal carotid artery (ICA) disease. ICA stenting that covers the ECA origin may put the ECA at increased risk of stenosis. Our objective was to determine the rate of ECA stenosis secondary to ICA stenting, determine predictive factors, and describe any subsequent associated symptoms. We retrospectively reviewed clinical data on all ICA stents crossing the origin of the ECA placed by vascular surgeons at our institution. We analyzed patient demographics, comorbidities, stent type and sizes, as well as medication profile to determine predictors of ECA stenosis. Between 2005 and 2013, there were 72 (out of 119 total ICA stenting) patients (mean age 71, 68% male) who underwent placement of ICA stents that also crossed the origin of the ECA. Six patients (8.3%) had a significantly increased ECA stenosis postprocedure. There were no occlusions. All patients with ECA stenosis maintained patency of their ICA stent and were asymptomatic. Age, gender, comorbidities, stent type and size, and medication profile were not associated with ECA stenosis after stenting. ECA stenosis after ICA stenting covering the ECA origin is uncommon and not clinically significant in patients with patent ICA stents. The clinical significance of concurrent ECA and ICA stenosis after stenting is unclear as it is not captured here. The potential for ECA stenosis should not deter stenting across the ECA origin if necessary. Patient and stent factors are not predictive of ECA stenosis. Copyright © 2015 Elsevier Inc. All rights reserved.

Calcium current in isolated neonatal rat ventricular myocytes.

PubMed Central

Cohen, N M; Lederer, W J

1987-01-01

1. Calcium currents (ICa) from neonatal rat ventricular heart muscle cells grown in primary culture were examined using the 'whole-cell' voltage-clamp technique (Hamill, Marty, Neher, Sakmann & Sigworth, 1981). Examination of ICa was limited to one calcium channel type, 'L' type (Nilius, Hess, Lansman & Tsien, 1985), by appropriate voltage protocols. 2. We measured transient and steady-state components of ICa, and could generally describe ICa in terms of the steady-state activation (d infinity) and inactivation (f infinity) parameters. 3. We observed that the reduction of ICa by the calcium channel antagonist D600 can be explained by both a shift of d infinity to more positive potentials as well as a slight reduction of ICa conductance. D600 did not significantly alter either the rate of inactivation of ICa or the voltage dependence of f infinity. 4. The calcium channel modulator BAY K8644 shifted both d infinity and f infinity to more negative potentials. Additionally, BAY K8644 increased the rate of inactivation at potentials between +5 and +55 mV. Furthermore, BAY K8644 also increased ICa conductance, a change consistent with a promotion of 'mode 2' calcium channel activity (Hess, Lansman & Tsien, 1984). 5. We conclude that, as predicted by d infinity and f infinity, there is a significant steady-state component of ICa ('window current') at plateau potentials in neonatal rat heart cells. Modulation of the steady-state and transient components of ICa by various agents can be attributed both to specific alterations in d infinity and f infinity and to more complicated alterations in the mode of calcium channel activity. PMID:2451004

Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies vs. usual care in patients with suspected coronary artery disease: the prospective longitudinal trial of FFR(CT): outcome and resource impacts study.

PubMed

Douglas, Pamela S; Pontone, Gianluca; Hlatky, Mark A; Patel, Manesh R; Norgaard, Bjarne L; Byrne, Robert A; Curzen, Nick; Purcell, Ian; Gutberlet, Matthias; Rioufol, Gilles; Hink, Ulrich; Schuchlenz, Herwig Walter; Feuchtner, Gudrun; Gilard, Martine; Andreini, Daniele; Jensen, Jesper M; Hadamitzky, Martin; Chiswell, Karen; Cyr, Derek; Wilk, Alan; Wang, Furong; Rogers, Campbell; De Bruyne, Bernard

2015-12-14

In symptomatic patients with suspected coronary artery disease (CAD), computed tomographic angiography (CTA) improves patient selection for invasive coronary angiography (ICA) compared with functional testing. The impact of measuring fractional flow reserve by CTA (FFRCT) is unknown. At 11 sites, 584 patients with new onset chest pain were prospectively assigned to receive either usual testing (n = 287) or CTA/FFR(CT) (n = 297). Test interpretation and care decisions were made by the clinical care team. The primary endpoint was the percentage of those with planned ICA in whom no significant obstructive CAD (no stenosis ≥50% by core laboratory quantitative analysis or invasive FFR < 0.80) was found at ICA within 90 days. Secondary endpoints including death, myocardial infarction, and unplanned revascularization were independently and blindly adjudicated. Subjects averaged 61 ± 11 years of age, 40% were female, and the mean pre-test probability of obstructive CAD was 49 ± 17%. Among those with intended ICA (FFR(CT)-guided = 193; usual care = 187), no obstructive CAD was found at ICA in 24 (12%) in the CTA/FFR(CT) arm and 137 (73%) in the usual care arm (risk difference 61%, 95% confidence interval 53-69, P< 0.0001), with similar mean cumulative radiation exposure (9.9 vs. 9.4 mSv, P = 0.20). Invasive coronary angiography was cancelled in 61% after receiving CTA/FFR(CT) results. Among those with intended non-invasive testing, the rates of finding no obstructive CAD at ICA were 13% (CTA/FFR(CT)) and 6% (usual care; P = 0.95). Clinical event rates within 90 days were low in usual care and CTA/FFR(CT) arms. Computed tomographic angiography/fractional flow reserve by CTA was a feasible and safe alternative to ICA and was associated with a significantly lower rate of invasive angiography showing no obstructive CAD. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Cardiology.

Stabilizing contact resistance of isotropically conductive adhesives on various metal surfaces by incorporating sacrificial anode materials

NASA Astrophysics Data System (ADS)

Moon, Kyoung-Sik; Liong, Silvia; Li, Haiying; Wong, C. P.

2004-11-01

The contact resistance stability of isotropically conductive adhesives (ICAs) on non-noble metal surfaces under the 85°C/85% relative humidity (RH) aging test was investigated. Previously, we demonstrated that galvanic corrosion has been shown as the main mechanism of the unstable contact resistance of ICAs on non-noble metal surfaces. A sacrificial anode was introduced into the ICA joint for cathodic protection. Zinc, chromium, and magnesium were employed in the ICA formulations as sacrificial anode materials that have much lower electrode-potential values than the metal pad surface, such as tin or tin-based alloys. The effect of particle sizes and loading levels of sacrificial anode materials were studied. Chromium was not as effective in suppressing corrosion as magnesium or zinc because of its strong tendency to self-passivate. The corrosion potential of ICAs was reduced by half with the addition of zinc and magnesium into the ICA formulation. The addition of zinc and magnesium was very effective in controlling galvanic corrosion that takes place in the ICA joints, resulting in stabilized contact resistance of ICAs on Sn, SnPb, and SnAgCu surfaces during the 85°C/85% RH aging test.

Evidence of stimulus correlated empathy modes--Group ICA of fMRI data.

PubMed

Vemuri, Kavita; Surampudi, Bapi Raju

2015-03-01

The analysis of the cognitive processes in response to a narrative as presented in a movie provides an insight into momentary reaction to a single depicted action like a facial expression and an aggregate processing of the entire sequence of events. In this study we report results from fMRI data analyzed by group independent component analysis (ICA) method from a free viewing experiment using a diverse set of movie clips--an animation, a Hollywood and an Indian Hindi movie. The fMRI data were collected from 15 college students as they viewed 5-8 min clips from three movies. The movie clips were rated for depiction of emotional expressions, the emotion as per the narrative and the viewer's own empathy response. The neural correlates attributed to cognitive, motor and emotional empathy were the focus of the study. The methodology of using long duration stimuli in free viewing mode combined with ICA analysis has the potential to tease out spatially distributed but temporally coherent brain activity as demonstrated in this study. The independent components obtained from group ICA method isolated spatial maps with activations which can be safely ascribed to stimulus processing. We found that the activity in the areas attributable to cognitive and motor empathy was comparable for all the three stimuli while certain critical areas for emotional empathy were not noticed for the animation movie. These findings lead to interesting questions on possible differential emotion response in viewer(s) for computer generated actors compared to actors in live-action movies and the role of narrative and exposure to different genre of movies on racial, ethnic and cultural differences in empathy response. Copyright © 2015 Elsevier Inc. All rights reserved.

Alcohol Dose Effects on Brain Circuits During Simulated Driving: An fMRI Study

PubMed Central

Meda, Shashwath A.; Calhoun, Vince D.; Astur, Robert S.; Turner, Beth M.; Ruopp, Kathryn; Pearlson, Godfrey D.

2009-01-01

Driving while intoxicated remains a major public health hazard. Driving is a complex task involving simultaneous recruitment of multiple cognitive functions. The investigators studied the neural substrates of driving and their response to different blood alcohol concentrations (BACs), using functional magnetic resonance imaging (fMRI) and a virtual reality driving simulator. We used independent component analysis (ICA) to isolate spatially independent and temporally correlated driving-related brain circuits in 40 healthy, adult moderate social drinkers. Each subject received three individualized, separate single-blind doses of beverage alcohol to produce BACs of 0.05% (moderate), 0.10% (high), or 0% (placebo). 3 T fMRI scanning and continuous behavioral measurement occurred during simulated driving. Brain function was assessed and compared using both ICA and a conventional general linear model (GLM) analysis. ICA results replicated and significantly extended our previous 1.5T study (Calhoun et al. [2004a]: Neuropsychopharmacology 29:2097–2017). GLM analysis revealed significant dose-related functional differences, complementing ICA data. Driving behaviors including opposite white line crossings and mean speed independently demonstrated significant dose-dependent changes. Behavior-based factors also predicted a frontal-basal-temporal circuit to be functionally impaired with alcohol dosage across baseline scaled, good versus poorly performing drivers. We report neural correlates of driving behavior and found dose-related spatio-temporal disruptions in critical driving-associated regions including the superior, middle and orbito frontal gyri, anterior cingulate, primary/supplementary motor areas, basal ganglia, and cerebellum. Overall, results suggest that alcohol (especially at high doses) causes significant impairment of both driving behavior and brain functionality related to motor planning and control, goal directedness, error monitoring, and memory. PMID:18571794

A network analysis of ¹⁵O-H₂O PET reveals deep brain stimulation effects on brain network of Parkinson's disease.

PubMed

Park, Hae-Jeong; Park, Bumhee; Kim, Hae Yu; Oh, Maeng-Keun; Kim, Joong Il; Yoon, Misun; Lee, Jong Doo; Chang, Jin Woo

2015-05-01

As Parkinson's disease (PD) can be considered a network abnormality, the effects of deep brain stimulation (DBS) need to be investigated in the aspect of networks. This study aimed to examine how DBS of the bilateral subthalamic nucleus (STN) affects the motor networks of patients with idiopathic PD during motor performance and to show the feasibility of the network analysis using cross-sectional positron emission tomography (PET) images in DBS studies. We obtained [¹⁵O]H₂O PET images from ten patients with PD during a sequential finger-to-thumb opposition task and during the resting state, with DBS-On and DBS-Off at STN. To identify the alteration of motor networks in PD and their changes due to STN-DBS, we applied independent component analysis (ICA) to all the cross-sectional PET images. We analysed the strength of each component according to DBS effects, task effects and interaction effects. ICA blindly decomposed components of functionally associated distributed clusters, which were comparable to the results of univariate statistical parametric mapping. ICA further revealed that STN-DBS modifies usage-strengths of components corresponding to the basal ganglia-thalamo-cortical circuits in PD patients by increasing the hypoactive basal ganglia and by suppressing the hyperactive cortical motor areas, ventrolateral thalamus and cerebellum. Our results suggest that STN-DBS may affect not only the abnormal local activity, but also alter brain networks in patients with PD. This study also demonstrated the usefulness of ICA for cross-sectional PET data to reveal network modifications due to DBS, which was not observable using the subtraction method.

Clipping in Awake Surgery as End-Stage in a Complex Internal Carotid Artery Aneurysm After Failure of Multimodal Endovascular and Extracranial-Intracranial Bypass Treatment.

PubMed

Cannizzaro, Delia; Peschillo, Simone; Mancarella, Cristina; La Pira, Biagia; Rastelli, Emanuela; Passacantilli, Emiliano; Santoro, Antonio

2017-06-01

Intracranial carotid artery aneurysm can be treated via microsurgical or endovascular techniques. The optimal planning is the result of the careful patient selection through clinical, anatomic, and angiographic analysis. We present a case of ruptured internal carotid artery (ICA) aneurysm that became a complex aneurysm after failure of multi-endovascular and surgery treatment. We describe complete trapping in awake craniotomy after failure of coiling, stenting, and bypassing. ICA aneurysms could become complex aneurysms following multi-treatment failure. Endovascular approaches to treat ICA aneurysms include coiling, stenting, flow diverter stenting, and stenting-assisted coiling technique. The role of surgery remains relevant. To avoid severe neurologic deficits, recurrence, and the need of retreatment, a multidisciplinary discussion with experienced endovascular and vascular neurosurgeons is mandatory in such complex cases. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging

PubMed Central

Griffanti, Ludovica; Salimi-Khorshidi, Gholamreza; Beckmann, Christian F.; Auerbach, Edward J.; Douaud, Gwenaëlle; Sexton, Claire E.; Zsoldos, Enikő; Ebmeier, Klaus P; Filippini, Nicola; Mackay, Clare E.; Moeller, Steen; Xu, Junqian; Yacoub, Essa; Baselli, Giuseppe; Ugurbil, Kamil; Miller, Karla L.; Smith, Stephen M.

2014-01-01

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB’s ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures were assessed using timeseries (amplitude and spectra), network matrix and spatial map analyses. For timeseries and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses. PMID:24657355

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.

PubMed

Griffanti, Ludovica; Salimi-Khorshidi, Gholamreza; Beckmann, Christian F; Auerbach, Edward J; Douaud, Gwenaëlle; Sexton, Claire E; Zsoldos, Enikő; Ebmeier, Klaus P; Filippini, Nicola; Mackay, Clare E; Moeller, Steen; Xu, Junqian; Yacoub, Essa; Baselli, Giuseppe; Ugurbil, Kamil; Miller, Karla L; Smith, Stephen M

2014-07-15

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses. Copyright © 2014 Elsevier Inc. All rights reserved.

Electrophysiological effects of calcitonin gene-related peptide in bull-frog and guinea-pig atrial myocytes.

PubMed Central

Ono, K; Giles, W R

1991-01-01

1. Electrophysiological effects of calcitonin gene-related peptide (CGRP) on action potentials and corresponding transmembrane currents in single myocytes from bull-frog and guinea-pig atria were studied using a whole-cell voltage-clamp method. 2. CGRP at relatively low concentrations increased the height of the action potential plateau in a dose-dependent manner in both bull-frog and guinea-pig myocytes. In addition, in bull-frog cells CGRP accelerated the early phase of repolarization, thus shortening the overall duration of the action potential. In contrast, in guinea-pig myocytes CGRP prolonged the action potential duration at all concentrations that were studied. 3. Voltage-clamp measurements demonstrated that CGRP increased transmembrane calcium current (ICa) in guinea-pig myocytes without a significant change in its voltage dependence. The ED50 value for this effect on ICa was 1.28 +/- 0.55 X 10(-8) M (n = 4). The time course of the inactivation of ICa was not affected by CGRP. 4. CGRP increased the delayed rectifier K+ current (IK) at relatively low concentrations in bull-frog atria, whereas relatively high concentrations were needed to increase IK in guinea-pig myocytes. This effect was observed even after complete inhibition of ICa. 5. CGRP had no significant effect on the inwardly rectifying background K+ current, IK1, even at very high concentrations. 6. Comparison of the time course of ICa augmentation in bull-frog and guinea-pig myocytes revealed an important difference in the effect of CGRP in these two types of cells. CGRP at maximal concentrations increased ICa transiently in bull-frog myocytes, whereas this response was sustained in guinea-pig myocytes. Isoprenaline (Iso) induced sustained increase in ICa in both species. When ICa was fully activated by Iso, CGRP at high concentrations strongly inhibited ICa in the bull-frog, whereas it had little effect on ICa in guinea-pig myocytes. 7. Intracellular application of GTP gamma S (guanosine 5'-O-(3-thiotriphosphate) 10(-4) M) greatly potentiated the CGRP effect on ICa; in contrast, GDP beta S (guanosine 5'-O-(2-thiodiphosphate), 2 x 10(-3) M) partially inhibited the CGRP-induced augmentation of ICa. Taken together, these results indicate that the stimulation of ICa by CGRP is mediated by a GTP-binding protein. 8. The observed dose-dependent changes in ICa and IK in bull-frog and guinea-pig myocytes can explain the different patterns of CGRP-induced changes in action potential shape in these two myocyte preparations. PMID:1905755

A Novel Anti-Spoofing Solution for Iris Recognition Toward Cosmetic Contact Lens Attack Using Spectral ICA Analysis

PubMed Central

Hsieh, Sheng-Hsun; Wang, Wei; Tien, Chung-Hao

2018-01-01

In this study, we maneuvered a dual-band spectral imaging system to capture an iridal image from a cosmetic-contact-lens-wearing subject. By using the independent component analysis to separate individual spectral primitives, we successfully distinguished the natural iris texture from the cosmetic contact lens (CCL) pattern, and restored the genuine iris patterns from the CCL-polluted image. Based on a database containing 200 test image pairs from 20 CCL-wearing subjects as the proof of concept, the recognition accuracy (False Rejection Rate: FRR) was improved from FRR = 10.52% to FRR = 0.57% with the proposed ICA anti-spoofing scheme. PMID:29509692

mGluR5 stimulating Homer–PIKE formation initiates icariin induced cardiomyogenesis of mouse embryonic stem cells by activating reactive oxygen species

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Limin; Huang, Yujie; Zhang, Yingying

2013-06-10

Icariin (ICA) has been reported to facilitate cardiac differentiation of mouse embryonic stem (ES) cells; however, the mechanism by which ICA induced cardiomyogenesis has not been fully elucidated yet. Here, an underlying signaling network including metabotropic glutamate receptor 5 (mGluR5), Homer, phosphatidylinositol 3-Kinase Enhancer (PIKE), phosphatidylinositol 3-Kinase (PI3K), reactive oxygen species (ROS) and nuclear factor-kappaB (NF-κB) was investigated in ICA induced cardiomyogenesis. Our results showed that the co-expression of mGluR5 together with α-actinin or Troponin T in embryoid bodies (EBs) treated with ICA was elevated to 10.86% and 9.62%, compared with the case in the control (4.04% and 3.45%, respectively).more » Exposure of EBs to ICA for 2 h remarkably increased the dimeric form of mGluR5, which was inhibited by small interfering RNA targeting mGluR5 (si-mGluR5). Moreover, the extracellular glutamate concentration in ICA treatment medium was elevated to 28.9±3.5 μM. Furthermore, the activation of mGluR5 by ICA triggered the formation of Homer–PIKE complex and activated PI3K, stimulating ROS generation and NF-κB nuclear translocation. Knockdown of mGluR5 or inhibition of PI3K by LY294002 blocked ICA induced cardiomyogenesis via repressing mGluR5 pathway, reducing ROS and NF-κB activation. These results revealed that the inducible mechanisms of ICA were related to activate mGluR5 pathway. -- Highlights: • ICA increased mGluR5 expression in cardiac differentiation of ES cells. • ICA enhanced the glutamate level and the receptor mGluR5 dimerization, stimulating the formation of Homer–PIKE complex. • Knockdown of mGluR5 or inhibition of PI3K by LY294002 inhibited ICA induced ROS generation and NF-κB nuclear translocation.« less

Extracting Independent Local Oscillatory Geophysical Signals by Geodetic Tropospheric Delay

NASA Technical Reports Server (NTRS)

Botai, O. J.; Combrinck, L.; Sivakumar, V.; Schuh, H.; Bohm, J.

2010-01-01

Zenith Tropospheric Delay (ZTD) due to water vapor derived from space geodetic techniques and numerical weather prediction simulated-reanalysis data exhibits non-linear and non-stationary properties akin to those in the crucial geophysical signals of interest to the research community. These time series, once decomposed into additive (and stochastic) components, have information about the long term global change (the trend) and other interpretable (quasi-) periodic components such as seasonal cycles and noise. Such stochastic component(s) could be a function that exhibits at most one extremum within a data span or a monotonic function within a certain temporal span. In this contribution, we examine the use of the combined Ensemble Empirical Mode Decomposition (EEMD) and Independent Component Analysis (ICA): the EEMD-ICA algorithm to extract the independent local oscillatory stochastic components in the tropospheric delay derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) over six geodetic sites (HartRAO, Hobart26, Wettzell, Gilcreek, Westford, and Tsukub32). The proposed methodology allows independent geophysical processes to be extracted and assessed. Analysis of the quality index of the Independent Components (ICs) derived for each cluster of local oscillatory components (also called the Intrinsic Mode Functions (IMFs)) for all the geodetic stations considered in the study demonstrate that they are strongly site dependent. Such strong dependency seems to suggest that the localized geophysical signals embedded in the ZTD over the geodetic sites are not correlated. Further, from the viewpoint of non-linear dynamical systems, four geophysical signals the Quasi-Biennial Oscillation (QBO) index derived from the NCEP/NCAR reanalysis, the Southern Oscillation Index (SOI) anomaly from NCEP, the SIDC monthly Sun Spot Number (SSN), and the Length of Day (LoD) are linked to the extracted signal components from ZTD. Results from the synchronization analysis show that ZTD and the geophysical signals exhibit (albeit subtle) site dependent phase synchronization index.

Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis.

PubMed

Goldbaum, Michael H; Jang, Gil-Jin; Bowd, Chris; Hao, Jiucang; Zangwill, Linda M; Liebmann, Jeffrey; Girkin, Christopher; Jung, Tzyy-Ping; Weinreb, Robert N; Sample, Pamela A

2009-12-01

To determine if the patterns uncovered with variational Bayesian-independent component analysis-mixture model (VIM) applied to a large set of normal and glaucomatous fields obtained with the Swedish Interactive Thresholding Algorithm (SITA) are distinct, recognizable, and useful for modeling the severity of the field loss. SITA fields were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Inc, Dublin, California) on 1,146 normal eyes and 939 glaucoma eyes from subjects followed by the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. VIM modifies independent component analysis (ICA) to develop separate sets of ICA axes in the cluster of normal fields and the 2 clusters of abnormal fields. Of 360 models, the model with the best separation of normal and glaucomatous fields was chosen for creating the maximally independent axes. Grayscale displays of fields generated by VIM on each axis were compared. SITA fields most closely associated with each axis and displayed in grayscale were evaluated for consistency of pattern at all severities. The best VIM model had 3 clusters. Cluster 1 (1,193) was mostly normal (1,089, 95% specificity) and had 2 axes. Cluster 2 (596) contained mildly abnormal fields (513) and 2 axes; cluster 3 (323) held mostly moderately to severely abnormal fields (322) and 5 axes. Sensitivity for clusters 2 and 3 combined was 88.9%. The VIM-generated field patterns differed from each other and resembled glaucomatous defects (eg, nasal step, arcuate, temporal wedge). SITA fields assigned to an axis resembled each other and the VIM-generated patterns for that axis. Pattern severity increased in the positive direction of each axis by expansion or deepening of the axis pattern. VIM worked well on SITA fields, separating them into distinctly different yet recognizable patterns of glaucomatous field defects. The axis and pattern properties make VIM a good candidate as a preliminary process for detecting progression.

Graphical modeling of gene expression in monocytes suggests molecular mechanisms explaining increased atherosclerosis in smokers.

PubMed

Verdugo, Ricardo A; Zeller, Tanja; Rotival, Maxime; Wild, Philipp S; Münzel, Thomas; Lackner, Karl J; Weidmann, Henri; Ninio, Ewa; Trégouët, David-Alexandre; Cambien, François; Blankenberg, Stefan; Tiret, Laurence

2013-01-01

Smoking is a risk factor for atherosclerosis with reported widespread effects on gene expression in circulating blood cells. We hypothesized that a molecular signature mediating the relation between smoking and atherosclerosis may be found in the transcriptome of circulating monocytes. Genome-wide expression profiles and counts of atherosclerotic plaques in carotid arteries were collected in 248 smokers and 688 non-smokers from the general population. Patterns of co-expressed genes were identified by Independent Component Analysis (ICA) and network structure of the pattern-specific gene modules was inferred by the PC-algorithm. A likelihood-based causality test was implemented to select patterns that fit models containing a path "smoking→gene expression→plaques". Robustness of the causal inference was assessed by bootstrapping. At a FDR ≤0.10, 3,368 genes were associated to smoking or plaques, of which 93% were associated to smoking only. SASH1 showed the strongest association to smoking and PPARG the strongest association to plaques. Twenty-nine gene patterns were identified by ICA. Modules containing SASH1 and PPARG did not show evidence for the "smoking→gene expression→plaques" causality model. Conversely, three modules had good support for causal effects and exhibited a network topology consistent with gene expression mediating the relation between smoking and plaques. The network with the strongest support for causal effects was connected to plaques through SLC39A8, a gene with known association to HDL-cholesterol and cellular uptake of cadmium from tobacco, while smoking was directly connected to GAS6, a gene reported to have anti-inflammatory effects in atherosclerosis and to be up-regulated in the placenta of women smoking during pregnancy. Our analysis of the transcriptome of monocytes recovered genes relevant for association to smoking and atherosclerosis, and connected genes that before, were only studied in separate contexts. Inspection of correlation structure revealed candidates that would be missed by expression-phenotype association analysis alone.

Graphical Modeling of Gene Expression in Monocytes Suggests Molecular Mechanisms Explaining Increased Atherosclerosis in Smokers

PubMed Central

Verdugo, Ricardo A.; Zeller, Tanja; Rotival, Maxime; Wild, Philipp S.; Münzel, Thomas; Lackner, Karl J.; Weidmann, Henri; Ninio, Ewa; Trégouët, David-Alexandre; Cambien, François; Blankenberg, Stefan; Tiret, Laurence

2013-01-01

Smoking is a risk factor for atherosclerosis with reported widespread effects on gene expression in circulating blood cells. We hypothesized that a molecular signature mediating the relation between smoking and atherosclerosis may be found in the transcriptome of circulating monocytes. Genome-wide expression profiles and counts of atherosclerotic plaques in carotid arteries were collected in 248 smokers and 688 non-smokers from the general population. Patterns of co-expressed genes were identified by Independent Component Analysis (ICA) and network structure of the pattern-specific gene modules was inferred by the PC-algorithm. A likelihood-based causality test was implemented to select patterns that fit models containing a path “smoking→gene expression→plaques”. Robustness of the causal inference was assessed by bootstrapping. At a FDR ≤0.10, 3,368 genes were associated to smoking or plaques, of which 93% were associated to smoking only. SASH1 showed the strongest association to smoking and PPARG the strongest association to plaques. Twenty-nine gene patterns were identified by ICA. Modules containing SASH1 and PPARG did not show evidence for the “smoking→gene expression→plaques” causality model. Conversely, three modules had good support for causal effects and exhibited a network topology consistent with gene expression mediating the relation between smoking and plaques. The network with the strongest support for causal effects was connected to plaques through SLC39A8, a gene with known association to HDL-cholesterol and cellular uptake of cadmium from tobacco, while smoking was directly connected to GAS6, a gene reported to have anti-inflammatory effects in atherosclerosis and to be up-regulated in the placenta of women smoking during pregnancy. Our analysis of the transcriptome of monocytes recovered genes relevant for association to smoking and atherosclerosis, and connected genes that before, were only studied in separate contexts. Inspection of correlation structure revealed candidates that would be missed by expression-phenotype association analysis alone. PMID:23372645

Enabling computer decisions based on EEG input.

PubMed

Culpepper, Benjamin J; Keller, Robert M

2003-12-01

Multilayer neural networks were successfully trained to classify segments of 12-channel electroencephalogram (EEG) data into one of five classes corresponding to five cognitive tasks performed by a subject. Independent component analysis (ICA) was used to segregate obvious artifact EEG components from other sources, and a frequency-band representation was used to represent the sources computed by ICA. Examples of results include an 85% accuracy rate on differentiation between two tasks, using a segment of EEG only 0.05 s long and a 95% accuracy rate using a 0.5-s-long segment.

Enabling computer decisions based on EEG input

NASA Technical Reports Server (NTRS)

Culpepper, Benjamin J.; Keller, Robert M.

2003-01-01

Multilayer neural networks were successfully trained to classify segments of 12-channel electroencephalogram (EEG) data into one of five classes corresponding to five cognitive tasks performed by a subject. Independent component analysis (ICA) was used to segregate obvious artifact EEG components from other sources, and a frequency-band representation was used to represent the sources computed by ICA. Examples of results include an 85% accuracy rate on differentiation between two tasks, using a segment of EEG only 0.05 s long and a 95% accuracy rate using a 0.5-s-long segment.

Biomarkers, Natural Course and Prognosis.

PubMed

Arenillas, Juan F; López-Cancio, Elena; Wong, Ka Sing

2016-01-01

Increasing our knowledge about intracranial atherosclerosis (ICAS) natural history and prognostic factors is essential to improve its preventive therapy and thus reduce the dramatic clinical consequences caused by this entity. ICAS is characterized by a chronic and progressive course until it becomes symptomatic, mostly through complication of an unstable intracranial atherosclerotic plaque. Population-based studies in healthy subjects have shown that the prevalence of asymptomatic ICAS is higher in Asian than in Caucasian populations. In both settings, asymptomatic ICAS is associated with classical vascular risk factors and with the metabolic syndrome, and it is burdened with an increasing risk of having incident stroke and cognitive impairment. When it reaches its symptomatic stage, ICAS is a dynamic and aggressive condition, and affected patients are at high risk of having recurrent stroke and other major vascular events. The Stenting versus Aggressive Medical Therapy for Intracranial Arterial Stenosis (SAMMPRIS) trial has recently shown a robust impact of intensive medical therapy reducing the risk of clinical recurrence of symptomatic ICAS. However, even under best medical therapy and degree of risk factor control, symptomatic ICAS-related recurrence risk continues to be the highest among all stroke etiologic subtypes. The second part of the chapter reviews the current understanding of prognostic factors that may help discriminate the high-risk ICAS patients, divided into local factors (vulnerable ICAS plaque) and systemic factors (vulnerable ICAS patient). Regarding research on local factors, high-resolution magnetic resonance imaging (HRMRI) is an emerging technique that allows in vivo evaluation of intracranial arterial wall, which is displacing our research focus from intracranial stenosis degree towards intracranial atherosclerotic plaque composition and activity. Characterization of the vulnerable ICAS patient may be improved with biomarker research. The latest contributions in this field help support the hypothesis that inflammation determines the risk of progression and complication of this disease, as it occurs in atherosclerosis affecting extracranial arterial beds. © 2016 S. Karger AG, Basel.

Geometrical features assessment of liver's tumor with application of artificial neural network evolved by imperialist competitive algorithm.

PubMed

Keshavarz, M; Mojra, A

2015-05-01

Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach. Copyright © 2015 John Wiley & Sons, Ltd.

Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation.

PubMed

Moosmann, Matthias; Eichele, Tom; Nordby, Helge; Hugdahl, Kenneth; Calhoun, Vince D

2008-03-01

An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.

Two Ca current components of the receptor current in the electroreceptors of the marine catfish Plotosus.

PubMed

Sugawara, Y

1989-02-01

In the isolated sensory epithelium of the Plotosus electroreceptor, the receptor current has been dissected into inward Ca current, ICa, and superimposed outward transient of Ca-gated K current, IK(Ca). In control saline (170 mM/liter Na), with IK(Ca) abolished by K blockers, ICa declined in two successive exponential phases with voltage-dependent time constants. Double-pulse experiments revealed that the test ICa was partially depressed by prepulses, maximally near voltage levels for the control ICa maximum, which suggests current-dependent inactivation. In low Na saline (80 mM/liter), ICa declined in a single phase with time constants similar to those of the slower phase in control saline. The test ICa was then unaffected by prepulses. The implied presence of two Ca current components, the fast and slow ICa's, were further examined. In control saline, the PSP externally recorded from the afferent nerve showed a fast peak and a slow tonic phase. The double-pulse experiments revealed that IK(Ca) and the peak PSP were similarly depressed, i.e., secondarily to inactivation of the peak current. The steady inward current, however, was unaffected by prolonged prepulses that were stepped to 0 mV, the in situ DC level. Therefore, the fast ICa seems to initiate IK(Ca) and phasic release of transmitter, which serves for phasic receptor responses. The slow ICa may provide persistent active current, which has been shown to maintain tonic receptor operation.

A scoping review of interdisciplinary collaboration in addictions education and training.

PubMed

Broyles, Lauren M; Conley, James W; Harding, John D; Gordon, Adam J

2013-01-01

Developing a workforce of multidisciplinary healthcare professionals equipped with the knowledge and skills to collaboratively address the public health crisis of alcohol and other drug (AOD) use is critical for effectively identifying, preventing, and managing AOD conditions and their sequelae. Despite general enthusiasm for interdisciplinary education and training, little is known overall about the nature and outcomes of interdisciplinary collaboration in addictions education and training. We conducted a five-stage scoping review of the literature to provide an eight domain overview of the state of interdisciplinary collaboration in addictions education (ICAE). In our final review of 30 articles, we identified a lack of conceptual and terminological clarity around ICAE but a wide range of learners and professional collaborators in ICAE initiatives, which focused on a variety of AOD topics and used a constellation of didactic, interactive, and service-learning teaching strategies and formats. Although we found limited substantive educational or practice-oriented outcomes available for ICAE initiatives, learner and faculty feedback reflected high enthusiasm for ICAE and widespread perceptions of benefit for improved clinical care. Facilitators and barriers to the implementation of ICAE initiatives occurred at the level of the individual and the institution and ranged from pragmatic to conceptual. Emerging trends in ICAE initiatives included increased application of learning and implementation theory and extension of ICAE into research training. We conclude with recommendations to support ICAE as a new paradigm for addictions education for all health professionals.

Thrombospondin-4 divergently regulates voltage-gated Ca2+ channel subtypes in sensory neurons after nerve injury.

PubMed

Pan, Bin; Guo, Yuan; Wu, Hsiang-En; Park, John; Trinh, Van Nancy; Luo, Z David; Hogan, Quinn H

2016-09-01

Loss of high-voltage-activated (HVA) calcium current (ICa) and gain of low-voltage-activated (LVA) ICa after painful peripheral nerve injury cause elevated excitability in sensory neurons. Nerve injury is also accompanied by increased expression of the extracellular matrix glycoprotein thrombospondin-4 (TSP4), and interruption of TSP4 function can reverse or prevent behavioral hypersensitivity after injury. We therefore investigated TSP4 regulation of ICa in dorsal root ganglion (DRG) neurons. During depolarization adequate to activate HVA ICa, TSP4 decreases both N- and L-type ICa and the associated intracellular calcium transient. In contrast, TSP4 increases ICa and the intracellular calcium signal after low-voltage depolarization, which we confirmed is due to ICa through T-type channels. These effects are blocked by gabapentin, which ameliorates neuropathic pain by targeting the α2δ1 calcium subunit. Injury-induced changes of HVA and LVA ICa are attenuated in TSP4 knockout mice. In the neuropathic pain model of spinal nerve ligation, TSP4 application did not further regulate ICa of injured DRG neurons. Taken together, these findings suggest that elevated TSP4 after peripheral nerve injury may contribute to hypersensitivity of peripheral sensory systems by decreasing HVA and increasing LVA in DRG neurons by targeting the α2δ1 calcium subunit. Controlling TSP4 overexpression in peripheral sensory neurons may be a target for analgesic drug development for neuropathic pain.

Biofilm and toxin profile: A phenotypic and genotypic characterization of coagulase-negative staphylococci isolated from human bloodstream infections.

PubMed

Pedroso, S H S P; Sandes, S H C; Luiz, K C M; Dias, R S; Filho, R A T; Serufo, J C; Farias, L M; Carvalho, M A R; Bomfim, M R Q; Santos, S G

2016-11-01

Coagulase-negative staphylococci (CNS) represent one of the most prevalent microorganisms in nosocomial infections worldwide, nevertheless little is known about their pathogenicity features. Thus, our aim was to characterize virulence aspects of CNS isolated from patients with bloodstream infections assisted in hospitals of Belo Horizonte, MG, Brazil. Strains were identified using bioMérieuxVitek ® and for biofilm production evaluation, Congo Red Agar (CRA) and polystyrene plates were used. PCR was applied to detect icaA, icaB, icaC, atlE, sea, sec, sed, tsst-1 and agr. For statistical analyses were used hierarchical cluster, chi-square test and correspondence. 59 strains were analyzed, being S. haemolyticus the most prevalent. On CRA, 96.5% were biofilm producer, whereas on polystyrene plate, 100% showed adhesion at different times evaluated. Regarding genotypic analyses, 15.2%, 38.9%, 8.4%, 49.1%, 76.2%, 23.7%, 1.6%, 30.5% and 38.9% were positive for icaA, icaB, icaC, atlE, sea, sec, sed, tsst-1 and agr, respectively. Six clusters were formed and frequency distributions of agr, atlE, icaA, icaB, sea, sec, tsst-1 differed (P < 0.001). In conclusion, all strains were biofilm producer, with high prevalence of atlE, and had potential of toxin production, with high prevalence of sea. According to the group-analyses, icaB showed relationship with the strong adherence in samples. Copyright © 2016 Elsevier Ltd. All rights reserved.

Ultrasound guided transarterial coil placement in the internal and external carotid artery in horses.

PubMed

Muñoz, Juan; Iglesias, Manuel; Chao, Eduardo Lloret; Bussy, Christian

2015-04-01

To assess ultrasound guided transarterial coil placement (UGTACP) for occlusion of the internal carotid artery (ICA) and external carotid artery (ECA) in horses. Cadaveric and in vivo study. Cadaveric horses (n = 10), healthy horses (3), and 1 clinical case. Cadaveric and in vivo (healthy horses): UGTACP was performed in the caudal part of the ICA and ECA. Coil placement in the rostral part of the ICA was performed blindly and controlled by conventional radiography. No coils were placed in the rostral part of the ECA. UGTACP of the ICA was in a horse with guttural pouch mycosis of the left guttural pouch. Accurate ultrasound-guided catheterization of the ICA and ECA was performed in all specimens. Ultrasound-guided coil placement was successfully performed in all cases except 1. No complications occurred in the in vivo study. The clinical case fully recovered and returned to its intended use. Based on our study, UGTACP of the ICA and ECA caudal part is a feasible alternative to fluoroscopy. An advantage of this technique is the accuracy with which you can catheterize both ICA and ECA and the ability to identify unusual branching at the origin of the ICA. Regarding the rostral part of the ICA, angiographic catheter guidance in this region is probably more precise using fluoroscopy as it is performed blindly. In a clinical situation, combination of US and fluoroscopy guidance can result in reduction of radiation exposure time. © Copyright 2014 by The American College of Veterinary Surgeons.

The ARTICO study: identification of patients at high risk of vascular recurrence after a first non-cardioembolic stroke.

PubMed

Serena, Joaquín; Segura, Tomás; Roquer, Jaume; García-Gil, María; Castillo, José

2015-03-11

About 20% of patients with a first ischaemic stroke will experience a new vascular event within the first year. The atherosclerotic burden, an indicator of the extension of atherosclerosis in a patient, has been associated with the risk of new cardiovascular events in the general population. However, no predictive models reliably identify groups at a high risk of recurrence. The ARTICO study prospectively analysed the predictive value for the risk of recurrence of specific atherosclerotic markers. The multicentre ARTICO study included 620 consecutive independent patients older than 60 years suffering from a first non-cardioembolic stroke. We analysed classical stroke risk factors; duplex study of supraaortic trunk including intima-media thickness (IMT) measurement; quantification of internal carotid (ICA) stenosis; number, morphology and surface characteristics of carotid plaques; ankle brachial index (ABI); and the presence of microalbuminuria. Patients were followed up at 6 and 12 months after inclusion. The primary end-point was death or major cardiovascular events. Any vascular event or death at 12 months occurred in 78 (13.8%) patients. In 40 (7.1%) of these the vascular event was a stroke recurrence. Weight, history of diabetes mellitus, history of symptomatic PAD, ABI <0.9 and significant ICA stenosis (>50%) were associated with a higher risk of vascular events on follow-up in the bivariate analysis. In the final Cox regression analysis, body mass index (BMI), systolic blood pressure, history of diabetes mellitus, symptomatic PAD (HR, 2.76; 95% CI, 1.10-6.95; p=0.03), and particularly patients with both ICA stenosis >50% and PAD (HR 4.52; 95% CI, 2.14-9.53; p<0.001) were independently associated with an increased risk of vascular events. Neither isolated ICA stenosis >50% nor isolated abnormal ABI remained associated with an increased risk of recurrence in comparison with the whole population. Symptomatic PAD identifies a high risk group of vascular recurrence after a first non-cardioembolic stroke. The associated increased risk was particularly high in patients with both ICA stenosis and either symptomatic or asymptomatic PAD. Neither asymptomatic PAD alone nor isolated ICA stenosis >50% were associated with an increased risk of recurrence in this particularly high-risk group of non-cardioembolic stroke.

Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve for Therapeutic Decision Making.

PubMed

Tesche, Christian; Vliegenthart, Rozemarijn; Duguay, Taylor M; De Cecco, Carlo N; Albrecht, Moritz H; De Santis, Domenico; Langenbach, Marcel C; Varga-Szemes, Akos; Jacobs, Brian E; Jochheim, David; Baquet, Moritz; Bayer, Richard R; Litwin, Sheldon E; Hoffmann, Ellen; Steinberg, Daniel H; Schoepf, U Joseph

2017-12-15

This study investigated the performance of coronary computed tomography angiography (cCTA) with cCTA-derived fractional flow reserve (CT-FFR) compared with invasive coronary angiography (ICA) with fractional flow reserve (FFR) for therapeutic decision making in patients with suspected coronary artery disease (CAD). Seventy-four patients (62 ± 11 years, 62% men) with at least 1 coronary stenosis of ≥50% on clinically indicated dual-source cCTA, who had subsequently undergone ICA with FFR measurement, were retrospectively evaluated. CT-FFR values were computed using an on-site machine-learning algorithm to assess the functional significance of CAD. The therapeutic strategy (optimal medical therapy alone vs revascularization) and the appropriate revascularization procedure (percutaneous coronary intervention vs coronary artery bypass grafting) were selected using cCTA-CT-FFR. Thirty-six patients (49%) had a functionally significant CAD based on ICA-FFR. cCTA-CT-FFR correctly identified a functionally significant CAD and the need of revascularization in 35 of 36 patients (97%). When revascularization was deemed indicated, the same revascularization procedure (32 percutaneous coronary interventions and 3 coronary artery bypass grafting) was chosen in 35 of 35 patients (100%). Overall, identical management strategies were selected in 73 of the 74 patients (99%). cCTA-CT-FFR shows excellent performance to identify patients with and without the need for revascularization and to select the appropriate revascularization strategy. cCTA-CT-FFR as a noninvasive "one-stop shop" has the potential to change diagnostic workflows and to directly inform therapeutic decision making in patients with suspected CAD. Copyright © 2017 Elsevier Inc. All rights reserved.

Staphylococcus epidermidis and Staphylococcus haemolyticus: detection of biofilm genes and biofilm formation in blood culture isolates from patients in a Brazilian teaching hospital.

PubMed

Pinheiro, Luiza; Brito, Carla Ivo; Oliveira, Adilson de; Pereira, Valéria Cataneli; Cunha, Maria de Lourdes Ribeiro de Souza da

2016-09-01

Infections with coagulase-negative staphylococci are often related to biofilm formation. This study aimed to detect biofilm formation and biofilm-associated genes in blood culture isolates of Staphylococcus epidermidis and S. haemolyticus. Half (50.6%) of the 85 S. epidermidis isolates carried the icaAD genes and 15.3% the bhp gene, while these numbers were 42.9% and 0 for S. haemolyticus, respectively. According to the plate test, 30 S. epidermidis isolates were biofilm producers and 40% of them were strongly adherent, while only one (6%) of the 17 S. haemolyticus biofilm-producing isolates exhibited a strongly adherent biofilm. The concomitant presence of icaA and icaD was significantly associated with the plate and tube test results (P ≤ 0.0004). The higher frequency of icaA in S. epidermidis and of icaD in S. haemolyticus is correlated with the higher biofilm-producing capacity of the former since, in contrast to IcaD, IcaA activity is sufficient to produce small amounts of polysaccharide. Although this study emphasizes the importance of icaAD and bhp for biofilm formation in S. epidermidis, other mechanisms seem to be involved in S. haemolyticus. Copyright © 2016 Elsevier Inc. All rights reserved.

Taguchi Based Performance and Reliability Improvement of an Ion Chamber Amplifier for Enhanced Nuclear Reactor Safety

NASA Astrophysics Data System (ADS)

Kulkarni, R. D.; Agarwal, Vivek

2008-08-01

An ion chamber amplifier (ICA) is used as a safety device for neutronic power (flux) measurement in regulation and protection systems of nuclear reactors. Therefore, performance reliability of an ICA is an important issue. Appropriate quality engineering is essential to achieve a robust design and performance of the ICA circuit. It is observed that the low input bias current operational amplifiers used in the input stage of the ICA circuit are the most critical devices for proper functioning of the ICA. They are very sensitive to the gamma radiation present in their close vicinity. Therefore, the response of the ICA deteriorates with exposure to gamma radiation resulting in a decrease in the overall reliability, unless desired performance is ensured under all conditions. This paper presents a performance enhancement scheme for an ICA operated in the nuclear environment. The Taguchi method, which is a proven technique for reliability enhancement, has been used in this work. It is demonstrated that if a statistical, optimal design approach, like the Taguchi method is used, the cost of high quality and reliability may be brought down drastically. The complete methodology and statistical calculations involved are presented, as are the experimental and simulation results to arrive at a robust design of the ICA.

New exact solutions of the Tzitzéica-type equations in non-linear optics using the expa function method

NASA Astrophysics Data System (ADS)

Hosseini, K.; Ayati, Z.; Ansari, R.

2018-04-01

One specific class of non-linear evolution equations, known as the Tzitzéica-type equations, has received great attention from a group of researchers involved in non-linear science. In this article, new exact solutions of the Tzitzéica-type equations arising in non-linear optics, including the Tzitzéica, Dodd-Bullough-Mikhailov and Tzitzéica-Dodd-Bullough equations, are obtained using the expa function method. The integration technique actually suggests a useful and reliable method to extract new exact solutions of a wide range of non-linear evolution equations.

Predictors of antegrade flow at internal carotid artery during carotid artery stenting with proximal protection.

PubMed

Harada, Kei; Kakumoto, Kosuke; Oshikata, Shogo; Fukuyama, Kozo

2018-06-01

Carotid artery stenting (CAS) with proximal occlusion effectively prevent distal cerebral embolism by flow arrest at internal carotid artery (ICA); however, the method can expose antegrade flow at ICA due to incomplete flow arrest. The aim of this study was to identify predictors of antegrade flow during CAS with proximal protection. We retrospectively analyzed clinical and angiographic data among 143 lesions treated with CAS with proximal protection by occluding the common carotid artery (CCA) and external carotid artery (ECA). Flow arrest or antegrade flow at ICA was confirmed by contrast injection during proximal protection. Antegrade flow at ICA was observed in 12 lesions (8.4%). Compared with lesions in which flow arrest of ICA was achieved, the diameter of the superior thyroid artery (STA) was significantly larger (2.4 ± 0.34 vs. 1.4 ± 0.68 mm, p < 0.001), and the rate of ECA branches other than the STA located 0-10 mm above the bifurcation was significantly higher (50 vs. 8.4%, p < 0.001). Results of multivariate analysis revealed that a diameter of the STA ≥ 2.3 mm (OR 44, 95% CI 8.1-237; p < 0.001) and ECA branches other than the STA located 0-10 mm above the bifurcation (OR 6.0, 95% CI 1.1-32; p = 0.036) were independent predictors of antegrade flow. Distal filter protection should be combined with proximal protection for the lesions with antegrade flow to prevent distal migration of the carotid debris.

Specific Immunoassays for Placental Alkaline Phosphatase As a Tumor Marker

PubMed Central

Stinghen, Sérvio T.; Moura, Juliana F.; Zancanella, Patrícia; Rodrigues, Giovanna A.; Pianovski, Mara A.; Lalli, Enzo; Arnold, Dodie L.; Minozzo, João C.; Callefe, Luis G.; Ribeiro, Raul C.; Figueiredo, Bonald C.

2006-01-01

Human placental (hPLAP) and germ cell (PLAP-like) alkaline phosphatases are polymorphic and heat-stable enzymes. This study was designed to develop specific immunoassays for quantifying hPLAP and PLAP-like enzyme activity (EA) in sera of cancer patients, pregnant women, or smokers. Polyclonal sheep anti-hPLAP antibodies were purified by affinity chromatography with whole hPLAP protein (ICA-PLAP assay) or a synthetic peptide (aa 57–71) of hPLAP (ICA-PEP assay); the working range was 0.1–11 U/L and cutoff value was 0.2 U/L EA for nonsmokers. The intra- and interassay coefficients of variation were 3.7%–6.5% (ICA-PLAP assay) and 9.0%–9.9% (ICA-PEP assay). An insignificant cross-reactivity was noted for high levels of unheated intestinal alkaline phosphatase in ICA-PEP assay. A positive correlation between the regression of tumor size and EA was noted in a child with embryonal carcinoma. It can be concluded that ICA-PEP assay is more specific than ICA-PLAP, which is still useful to detect other PLAP/PLAP-like phenotypes. PMID:17489017

New Families of Skewed Higher-Order Kernel Estimators to Solve the BSS/ICA Problem for Multimodal Sources Mixtures.

PubMed

Jabbar, Ahmed Najah

2018-04-13

This letter suggests two new types of asymmetrical higher-order kernels (HOK) that are generated using the orthogonal polynomials Laguerre (positive or right skew) and Bessel (negative or left skew). These skewed HOK are implemented in the blind source separation/independent component analysis (BSS/ICA) algorithm. The tests for these proposed HOK are accomplished using three scenarios to simulate a real environment using actual sound sources, an environment of mixtures of multimodal fast-changing probability density function (pdf) sources that represent a challenge to the symmetrical HOK, and an environment of an adverse case (near gaussian). The separation is performed by minimizing the mutual information (MI) among the mixed sources. The performance of the skewed kernels is compared to the performance of the standard kernels such as Epanechnikov, bisquare, trisquare, and gaussian and the performance of the symmetrical HOK generated using the polynomials Chebyshev1, Chebyshev2, Gegenbauer, Jacobi, and Legendre to the tenth order. The gaussian HOK are generated using the Hermite polynomial and the Wand and Schucany procedure. The comparison among the 96 kernels is based on the average intersymbol interference ratio (AISIR) and the time needed to complete the separation. In terms of AISIR, the skewed kernels' performance is better than that of the standard kernels and rivals most of the symmetrical kernels' performance. The importance of these new skewed HOK is manifested in the environment of the multimodal pdf mixtures. In such an environment, the skewed HOK come in first place compared with the symmetrical HOK. These new families can substitute for symmetrical HOKs in such applications.

Extracting factors for interest rate scenarios

NASA Astrophysics Data System (ADS)

Molgedey, L.; Galic, E.

2001-04-01

Factor based interest rate models are widely used for risk managing purposes, for option pricing and for identifying and capturing yield curve anomalies. The movements of a term structure of interest rates are commonly assumed to be driven by a small number of orthogonal factors such as SHIFT, TWIST and BUTTERFLY (BOW). These factors are usually obtained by a Principal Component Analysis (PCA) of historical bond prices (interest rates). Although PCA diagonalizes the covariance matrix of either the interest rates or the interest rate changes, it does not use both covariance matrices simultaneously. Furthermore higher linear and nonlinear correlations are neglected. These correlations as well as the mean reverting properties of the interest rates become crucial, if one is interested in a longer time horizon (infrequent hedging or trading). We will show that Independent Component Analysis (ICA) is a more appropriate tool than PCA, since ICA uses the covariance matrix of the interest rates as well as the covariance matrix of the interest rate changes simultaneously. Additionally higher linear and nonlinear correlations may be easily incorporated. The resulting factors are uncorrelated for various time delays, approximately independent but nonorthogonal. This is in contrast to the factors obtained from the PCA, which are orthogonal and uncorrelated for identical times only. Although factors from the ICA are nonorthogonal, it is sufficient to consider only a few factors in order to explain most of the variation in the original data. Finally we will present examples that ICA based hedges outperforms PCA based hedges specifically if the portfolio is sensitive to structural changes of the yield curve.

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

PubMed

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

Functional Connectivity Parcellation of the Human Thalamus by Independent Component Analysis.

PubMed

Zhang, Sheng; Li, Chiang-Shan R

2017-11-01

As a key structure to relay and integrate information, the thalamus supports multiple cognitive and affective functions through the connectivity between its subnuclei and cortical and subcortical regions. Although extant studies have largely described thalamic regional functions in anatomical terms, evidence accumulates to suggest a more complex picture of subareal activities and connectivities of the thalamus. In this study, we aimed to parcellate the thalamus and examine whole-brain connectivity of its functional clusters. With resting state functional magnetic resonance imaging data from 96 adults, we used independent component analysis (ICA) to parcellate the thalamus into 10 components. On the basis of the independence assumption, ICA helps to identify how subclusters overlap spatially. Whole brain functional connectivity of each subdivision was computed for independent component's time course (ICtc), which is a unique time series to represent an IC. For comparison, we computed seed-region-based functional connectivity using the averaged time course across all voxels within a thalamic subdivision. The results showed that, at p < 10 -6 , corrected, 49% of voxels on average overlapped among subdivisions. Compared with seed-region analysis, ICtc analysis revealed patterns of connectivity that were more distinguished between thalamic clusters. ICtc analysis demonstrated thalamic connectivity to the primary motor cortex, which has eluded the analysis as well as previous studies based on averaged time series, and clarified thalamic connectivity to the hippocampus, caudate nucleus, and precuneus. The new findings elucidate functional organization of the thalamus and suggest that ICA clustering in combination with ICtc rather than seed-region analysis better distinguishes whole-brain connectivities among functional clusters of a brain region.

Delayed Visual Loss and Its Surgical Rescue Following Extracranial-Intracranial Arterial Bypass and Native Internal Carotid Artery Sacrifice.

PubMed

Li, Lai-Fung; Leung, Gilberto Ka-Kit; Lui, Wai-Man

2017-02-01

High-flow extracranial-intracranial (EC-IC) bypass followed by sacrifice of the native internal carotid artery (ICA) is a recognized treatment option for giant ICA aneurysm and skull base tumor involving the ICA. Distal clipping at the supraclinoid portion of the ICA is technically straightforward, but it can potentially compromise ophthalmic artery (OA) perfusion. Because of the extensive EC-IC anastomoses with the OA, visual symptoms are fortunately uncommon. We report a patient who developed complete blindness after distal trapping of the supraclinoid ICA; it was reversed after emergency clip removal. Our patient is a 47-year-old man with recurrent nasopharyngeal carcinoma in close proximity to the left petrosal ICA. The first stage of the procedure involved an EC-IC bypass using radial artery graft, followed by a second stage with combined craniofacial excision. Trapping of the native ICA was achieved using a permanent aneurysm clip placed at the supraclinoid ICA distal to the origin of the OA. He complained of a new onset of complete left eye visual loss approximately 6 hours after the distal aneurysm clip was applied. He was immediately sent to the operating theatre for the removal of the supraclinoid aneurysm clip. On the next day, his vision improved and left pupil became reactive again. OA flow following ICA trapping is complicated and precarious. Delayed onset of visual loss is possible. Prompt action by direct exploration and clip removal is needed and can be effective in reversing blindness. Copyright © 2016 Elsevier Inc. All rights reserved.

Brain Functional Connectivity in Small Cell Lung Cancer Population after Chemotherapy Treatment: an ICA fMRI Study

NASA Astrophysics Data System (ADS)

Bromis, K.; Kakkos, I.; Gkiatis, K.; Karanasiou, I. S.; Matsopoulos, G. K.

2017-11-01

Previous neurocognitive assessments in Small Cell Lung Cancer (SCLC) population, highlight the presence of neurocognitive impairments (mainly in attention processing and executive functioning) in this type of cancer. The majority of these studies, associate these deficits with the Prophylactic Cranial Irradiation (PCI) that patients undergo in order to avoid brain metastasis. However, there is not much evidence exploring cognitive impairments induced by chemotherapy in SCLC patients. For this reason, we aimed to investigate the underlying processes that may potentially affect cognition by examining brain functional connectivity in nineteen SCLC patients after chemotherapy treatment, while additionally including fourteen healthy participants as control group. Independent Component Analysis (ICA) is a functional connectivity measure aiming to unravel the temporal correlation between brain regions, which are called brain networks. We focused on two brain networks related to the aforementioned cognitive functions, the Default Mode Network (DMN) and the Task-Positive Network (TPN). Permutation tests were performed between the two groups to assess the differences and control for familywise errors in the statistical parametric maps. ICA analysis showed functional connectivity disruptions within both of the investigated networks. These results, propose a detrimental effect of chemotherapy on brain functioning in the SCLC population.

Dual-energy x-ray image decomposition by independent component analysis

NASA Astrophysics Data System (ADS)

Jiang, Yifeng; Jiang, Dazong; Zhang, Feng; Zhang, Dengfu; Lin, Gang

2001-09-01

The spatial distributions of bone and soft tissue in human body are separated by independent component analysis (ICA) of dual-energy x-ray images. It is because of the dual energy imaging modelí-s conformity to the ICA model that we can apply this method: (1) the absorption in body is mainly caused by photoelectric absorption and Compton scattering; (2) they take place simultaneously but are mutually independent; and (3) for monochromatic x-ray sources the total attenuation is achieved by linear combination of these two absorption. Compared with the conventional method, the proposed one needs no priori information about the accurate x-ray energy magnitude for imaging, while the results of the separation agree well with the conventional one.

Icariin combined with human umbilical cord mesenchymal stem cells significantly improve the impaired kidney function in chronic renal failure.

PubMed

Li, Wen; Wang, Li; Chu, Xiaoqian; Cui, Huantian; Bian, Yuhong

2017-04-01

At present, the main therapy for chronic renal failure (CRF) is dialysis and renal transplantation, but neither obtains satisfactory results. Human umbilical cord mesenchymal stem cells (huMSCs) are isolated from the fetal umbilical cord which has a high self-renewal and multi-directional differentiation potential. Icariin (ICA), a kidney-tonifying Chinese Medicine can enhance the multipotency of huMSCs. Therefore, this work seeks to employ the use of ICA-treated huMSCs for the treatment of chronic renal failure. Blood urea nitrogen and creatinine (Cr) analyses showed amelioration of functional parameters in ICA-treated huMSCs for the treatment of CRF rats at 3, 7, and 14 days after transplantation. ICA-treated huMSCs can obviously increase the number of cells in injured renal tissues at 3, 7, and 14 days after transplantation by optical molecular imaging system. Hematoxylin-eosin staining demonstrated that ICA-treated huMSCs reduced the levels of fibrosis in CRF rats at 14 days after transplantation. Superoxide dismutase and Malondialdehyde analyses showed that ICA-treated huMSCs reduced the oxidative damage in CRF rats. Moreover, transplantation with ICA-treated huMSCs decreased inflammatory responses, promoted the expression of growth factors, and protected injured renal tissues. Taken together, our findings suggest that ICA-treated huMSCs could improve the kidney function in CRF rats.

The ICA Communication Audit: Rationale and Development.

ERIC Educational Resources Information Center

Goldhaber, Gerald M.

After reviewing previous research on communication in organizations, the Organizational Communication Division of the International Communication Association (ICA) decided, in 1971, to develop its own measurement system, the ICA Communication Audit. Rigorous pilot-testing, refinement, standardization, and application would allow the construction…

Dictionary Learning Algorithms for Sparse Representation

PubMed Central

Kreutz-Delgado, Kenneth; Murray, Joseph F.; Rao, Bhaskar D.; Engan, Kjersti; Lee, Te-Won; Sejnowski, Terrence J.

2010-01-01

Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial “25 words or less”), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations. Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an over-complete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error). PMID:12590811

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data.

PubMed

Wang, Shuaiqun; Aorigele; Kong, Wei; Zeng, Weiming; Hong, Xiaomin

2016-01-01

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes.

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data

PubMed Central

Aorigele; Zeng, Weiming; Hong, Xiaomin

2016-01-01

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes. PMID:27579323

Comparison of Biofilm Formation Capacities of Two Clinical Isolates of Staphylococcus Epidermidis with and without icaA and icaD Genes on Intraocular Lenses.

PubMed

Kıvanç, Sertaç Argun; Kıvanç, Merih; Kılıç, Volkan; Güllülü, Gülay; Özmen, Ahmet Tuncer

2017-04-01

To compare biofilm formations of two Staphylococcus epidermidis (S. epidermidis) isolates with known biofilm formation capacities on four different intraocular lenses (IOL) that have not been studied before. Two isolates obtained from ocular surfaces and identified in previous studies and stored at -86 °C in 15% glycerol in the microbiology laboratory of the Anadolu University Department of Biology were purified and used in the study. The isolates were S. epidermidis KA 15.8 (ICA+), a known biofilm producer isolate positive for icaA, icaD and bap genes, and S. epidermidis KA 14.5 (ICA-), known as a non-biofilm producer isolate negative for icaA, icaD and bap genes. The biofilm formation capacities of the 2 isolates on 4 different IOLs were compared. Two of the IOLs were acrylic (UD613 [IOL A], Turkey; SA60AT [IOL B], USA), and the other two were polymethyl methacrylate (PMMA) (B60130C [IOL C], India; B55125C [IOL D], India). Bacterial enumeration and optical density measurements were done from biofilms that formed on the IOLs. Biofilms were imaged using scanning electron microscopy. Mean bacterial counts on the IOLs were 7.1±0.4 log 10 CFU/mL with the ICA+ isolate, and 6.7±0.8 log 10 CFU/mL with the ICA- isolate; there were no statistically significant differences. Biofilm formation was lower with acrylic lenses than PMMA lenses with both isolates (p=0.009 and p=0.013). The highest biofilm production was obtained on IOL C (PMMA) (p<0.001) and the lowest was obtained on IOL A (hydrophilic acrylic) (p<0.001). Bacterial counts after biofilm formation were lower on acrylic lenses, especially hydrophilic acrylic with hydrophobic properties. Further animal and in vivo studies are required to support the findings of this study.

Transmembrane Na+ and Ca2+ electrochemical gradients in cardiac muscle and their relationship to force development

PubMed Central

1982-01-01

Na+- and CA2+-sensitive microelectrodes were used to measure intracellular Na+ and Ca2+ activities (alpha iCa) of sheep ventricular muscle and Purkinje strands to study the interrelationship between Na+ and Ca2+ electrochemical gradients (delta muNa and delta muCa) under various conditions. In ventricular muscle, alpha iNa was 6.4 +/- 1.2 mM and alpha iCa was 87 +/- 20 nM ([Ca/+] = 272 nM). A graded decrease of external Na+ activity (alpha oNa) resulted in decrease of alpha iNa, and increase of alpha iCa. There was increase of twitch tension in low- alpha oNa solutions, and occasional increase of resting tension in 40% alpha oNa. Increase of external Ca2+ (alpha oCa) resulted in increase of alpha iCa and decrease of alpha iNa. Decrease of alpha oCa resulted in decrease of alpha iCa and increase of alpha iNa. The apparent resting Na-Ca energy ratio (delta muCa/delta muNa) was between 2.43 and 2.63. When the membrane potential (Vm) was depolarized by 50 mM K+ in ventricular muscle, Vm depolarized by 50 mV, alpha iNa decreased, and alpha iCa increased, with the development of a contracture. The apparent energy coupling ratio did not change with depolarization. 5 x 10(-6) M ouabain induced a large increase in alpha iNa ad alpha iCa, accompanied by an increase in twitch and resting tension. Under the conditions we have studied, delta muNa and delta muCa appeared to be coupled and n was nearly constant at 2.5, as would be expected if the Na-Ca exchange system was able to set the steady level of alpha iCa. Tension threshold was about 230 nM alpha iCa. The magnitude of twitch tension was directly related to alpha iCa. PMID:6292328

Comparison of Biofilm Formation Capacities of Two Clinical Isolates of Staphylococcus Epidermidis with and without icaA and icaD Genes on Intraocular Lenses

PubMed Central

Kıvanç, Sertaç Argun; Kıvanç, Merih; Kılıç, Volkan; Güllülü, Gülay; Özmen, Ahmet Tuncer

2017-01-01

Objectives: To compare biofilm formations of two Staphylococcus epidermidis (S. epidermidis) isolates with known biofilm formation capacities on four different intraocular lenses (IOL) that have not been studied before. Materials and Methods: Two isolates obtained from ocular surfaces and identified in previous studies and stored at -86 °C in 15% glycerol in the microbiology laboratory of the Anadolu University Department of Biology were purified and used in the study. The isolates were S. epidermidis KA 15.8 (ICA+), a known biofilm producer isolate positive for icaA, icaD and bap genes, and S. epidermidis KA 14.5 (ICA-), known as a non-biofilm producer isolate negative for icaA, icaD and bap genes. The biofilm formation capacities of the 2 isolates on 4 different IOLs were compared. Two of the IOLs were acrylic (UD613 [IOL A], Turkey; SA60AT [IOL B], USA), and the other two were polymethyl methacrylate (PMMA) (B60130C [IOL C], India; B55125C [IOL D], India). Bacterial enumeration and optical density measurements were done from biofilms that formed on the IOLs. Biofilms were imaged using scanning electron microscopy. Results: Mean bacterial counts on the IOLs were 7.1±0.4 log10 CFU/mL with the ICA+ isolate, and 6.7±0.8 log10 CFU/mL with the ICA- isolate; there were no statistically significant differences. Biofilm formation was lower with acrylic lenses than PMMA lenses with both isolates (p=0.009 and p=0.013). The highest biofilm production was obtained on IOL C (PMMA) (p<0.001) and the lowest was obtained on IOL A (hydrophilic acrylic) (p<0.001). Conclusion: Bacterial counts after biofilm formation were lower on acrylic lenses, especially hydrophilic acrylic with hydrophobic properties. Further animal and in vivo studies are required to support the findings of this study. PMID:28405479

Computational modeling of inhibition of voltage-gated Ca channels: identification of different effects on uterine and cardiac action potentials.

PubMed

Tong, Wing-Chiu; Ghouri, Iffath; Taggart, Michael J

2014-01-01

The uterus and heart share the important physiological feature whereby contractile activation of the muscle tissue is regulated by the generation of periodic, spontaneous electrical action potentials (APs). Preterm birth arising from premature uterine contractions is a major complication of pregnancy and there remains a need to pursue avenues of research that facilitate the use of drugs, tocolytics, to limit these inappropriate contractions without deleterious actions on cardiac electrical excitation. A novel approach is to make use of mathematical models of uterine and cardiac APs, which incorporate many ionic currents contributing to the AP forms, and test the cell-specific responses to interventions. We have used three such models-of uterine smooth muscle cells (USMC), cardiac sinoatrial node cells (SAN), and ventricular cells-to investigate the relative effects of reducing two important voltage-gated Ca currents-the L-type (ICaL) and T-type (ICaT) Ca currents. Reduction of ICaL (10%) alone, or ICaT (40%) alone, blunted USMC APs with little effect on ventricular APs and only mild effects on SAN activity. Larger reductions in either current further attenuated the USMC APs but with also greater effects on SAN APs. Encouragingly, a combination of ICaL and ICaT reduction did blunt USMC APs as intended with little detriment to APs of either cardiac cell type. Subsequent overlapping maps of ICaL and ICaT inhibition profiles from each model revealed a range of combined reductions of ICaL and ICaT over which an appreciable diminution of USMC APs could be achieved with no deleterious action on cardiac SAN or ventricular APs. This novel approach illustrates the potential for computational biology to inform us of possible uterine and cardiac cell-specific mechanisms. Incorporating such computational approaches in future studies directed at designing new, or repurposing existing, tocolytics will be beneficial for establishing a desired uterine specificity of action.

Computational modeling of inhibition of voltage-gated Ca channels: identification of different effects on uterine and cardiac action potentials

PubMed Central

Tong, Wing-Chiu; Ghouri, Iffath; Taggart, Michael J.

2014-01-01

The uterus and heart share the important physiological feature whereby contractile activation of the muscle tissue is regulated by the generation of periodic, spontaneous electrical action potentials (APs). Preterm birth arising from premature uterine contractions is a major complication of pregnancy and there remains a need to pursue avenues of research that facilitate the use of drugs, tocolytics, to limit these inappropriate contractions without deleterious actions on cardiac electrical excitation. A novel approach is to make use of mathematical models of uterine and cardiac APs, which incorporate many ionic currents contributing to the AP forms, and test the cell-specific responses to interventions. We have used three such models—of uterine smooth muscle cells (USMC), cardiac sinoatrial node cells (SAN), and ventricular cells—to investigate the relative effects of reducing two important voltage-gated Ca currents—the L-type (ICaL) and T-type (ICaT) Ca currents. Reduction of ICaL (10%) alone, or ICaT (40%) alone, blunted USMC APs with little effect on ventricular APs and only mild effects on SAN activity. Larger reductions in either current further attenuated the USMC APs but with also greater effects on SAN APs. Encouragingly, a combination of ICaL and ICaT reduction did blunt USMC APs as intended with little detriment to APs of either cardiac cell type. Subsequent overlapping maps of ICaL and ICaT inhibition profiles from each model revealed a range of combined reductions of ICaL and ICaT over which an appreciable diminution of USMC APs could be achieved with no deleterious action on cardiac SAN or ventricular APs. This novel approach illustrates the potential for computational biology to inform us of possible uterine and cardiac cell-specific mechanisms. Incorporating such computational approaches in future studies directed at designing new, or repurposing existing, tocolytics will be beneficial for establishing a desired uterine specificity of action. PMID:25360118

Slime-producing Staphylococcus epidermidis and S. aureus in acute bacterial conjunctivitis in soft contact lens wearers.

PubMed

Catalanotti, Piergiorgio; Lanza, Michele; Del Prete, Antonio; Lucido, Maria; Catania, Maria Rosaria; Gallè, Francesca; Boggia, Daniela; Perfetto, Brunella; Rossano, Fabio

2005-10-01

In recent years, an increase in ocular pathologies related to soft contact lens has been observed. The most common infectious agents were Staphylococcus spp. Some strains produce an extracellular polysaccharidic slime that can cause severe infections. Polysaccharide synthesis is under genetic control and involves a specific intercellular adhesion (ica) locus, in particular, icaA and icaD genes. Conjunctival swabs from 97 patients with presumably bacterial bilateral conjunctivitis, wearers of soft contact lenses were examined. We determined the ability of staphylococci to produce slime, relating it to the presence of icaA and icaD genes. We also investigated the antibiotic susceptibility and Pulsed Field Gel Electrophoresis (PFGE) patterns of the clinical isolates. We found that 74.1% of the S. epidermidis strains and 61.1% of the S. aureus strains isolated were slime producers and showed icaA and icaD genes. Both S. epidermidis and S. aureus slime-producing strains exhibited more surface hydrophobicity than non-producing slime strains. The PFGE patterns overlapped in S. epidermidis strains with high hydrophobicity. The similar PFGE patterns were not related to biofilm production. We found scarce matching among the Staphylococcus spp. studied, slime production, surface hydrophobicity and antibiotic susceptibility.

Glucose & sodium chloride induced biofilm production & ica operon in clinical isolates of staphylococci.

PubMed

Agarwal, Astha; Jain, Amita

2013-01-01

All colonizing and invasive staphylococcal isolates may not produce biofilm but may turn biofilm producers in certain situations due to change in environmental factors. This study was done to test the hypothesis that non biofilm producing clinical staphylococci isolates turn biofilm producers in presence of sodium chloride (isotonic) and high concentration of glucose, irrespective of presence or absence of ica operon. Clinical isolates of 100 invasive, 50 colonizing and 50 commensal staphylococci were tested for biofilm production by microtiter plate method in different culture media (trypticase soy broth alone or supplemented with 0.9% NaCl/ 5 or 10% glucose). All isolates were tested for the presence of ica ADBC genes by PCR. Biofilm production significantly increased in the presence of glucose and saline, most, when both glucose and saline were used together. All the ica positive staphylococcal isolates and some ica negative isolates turned biofilm producer in at least one of the tested culture conditions. Those remained biofilm negative in different culture conditions were all ica negative. The present results showed that the use of glucose or NaCl or combination of both enhanced biofilm producing capacity of staphylococcal isolates irrespective of presence or absence of ica operon.

Evolution reversed: the ability to bind iron restored to the N-lobe of the murine inhibitor of carbonic anhydrase by strategic mutagenesis.

PubMed

Mason, Anne B; Judson, Gregory L; Bravo, Maria Cristina; Edelstein, Andrew; Byrne, Shaina L; James, Nicholas G; Roush, Eric D; Fierke, Carol A; Bobst, Cedric E; Kaltashov, Igor A; Daughtery, Margaret A

2008-09-16

The murine inhibitor of carbonic anhydrase (mICA) is a member of the superfamily related to the bilobal iron transport protein transferrin (TF), which binds a ferric ion within a cleft in each lobe. Although the gene encoding ICA in humans is classified as a pseudogene, an apparently functional ICA gene has been annotated in mice, rats, cows, pigs, and dogs. All ICAs lack one (or more) of the amino acid ligands in each lobe essential for high-affinity coordination of iron and the requisite synergistic anion, carbonate. The reason why ICA family members have lost the ability to bind iron is potentially related to acquiring a new function(s), one of which is inhibition of certain carbonic anhydrase (CA) isoforms. A recombinant mutant of the mICA (W124R/S188Y) was created with the goal of restoring the ligands required for both anion (Arg124) and iron (Tyr188) binding in the N-lobe. Absorption and fluorescence spectra definitively show that the mutant binds ferric iron in the N-lobe. Electrospray ionization mass spectrometry confirms the presence of both ferric iron and carbonate. At the putative endosomal pH of 5.6, iron is released by two slow processes indicative of high-affinity coordination. Induction of specific iron binding implies that (1) the structure of mICA resembles those of other TF family members and (2) the N-lobe can adopt a conformation in which the cleft closes when iron binds. Because the conformational change in the N-lobe indicated by metal binding does not impact the inhibitory activity of mICA, inhibition of CA was tentatively assigned to the C-lobe. Proof of this assignment is provided by limited trypsin proteolysis of porcine ICA.

Comparison of pre-filter and post-filter ionised calcium monitoring in continuous veno-venous hemodiafiltration (CVVHD-F) with citrate anti-coagulation.

PubMed

Brain, Matthew J; Roodenburg, Owen S; McNeil, John

2017-01-01

It is widespread practice during citrate anticoagulated renal replacement therapy to monitor circuit ionised calcium (iCa2+) to evaluate the effectiveness of anticoagulation. Whether the optimal site to sample the blood path is before or after the haemofilter is a common question. Using a prospectively collected observational dataset from intensive care patients receiving pre-dilution continuous veno-venous haemodiafiltration (CVVHD-F) with integrated citrate anticoagulation we compared paired samples of pre and post filter iCa2+ where the target range was 0.3-0.5 mmol.L-1 as well as concurrently collected arterial iCa2+. Two nested mixed methods linear models were fitted to the data describing post vs pre filter iCa2+, and the relationship of pre, post and arterial samples. An 11 bed general intensive care unit. 450 grouped samples from 152 time periods in seven patients on CRRT with citrate anticoagulation. The relationship of post to pre-filter iCa2+ was not 1:1 with post = 0.082 + 0.751 x pre-filter iCa2+ (95% CI intercept: 0.015-0.152, slope 0.558-0.942). Variation was greatest between patients rather than between circuits within the same patient or citrate dose. Compared to arterial iCa2+ there was no significant difference between pre and post-filter sampling sites (F-value 0.047, p = 0.827). These results demonstrate that there is minimal difference between pre and post filter samples for iCa2+ monitoring of circuit anticoagulation in citrate patients relative to the arterial iCa2+ in CVVHD-F however compared to pre-filter sampling, post filter sampling has a flatter response and greater variation.

Compensatory patterns of collateral flow in stroke patients with unilateral and bilateral carotid stenosis.

PubMed

Fang, Hui; Song, Bo; Cheng, Bo; Wong, Ka Sing; Xu, Yu Ming; Ho, Stella Sin Yee; Chen, Xiang Yan

2016-03-18

Collateral pathways are important in maintaining adequate cerebral blood flow in patients with carotid stenosis. We aimed to evaluate the hemodynamic patterns in relation to carotid stenosis in acute stroke patients. Consecutive 586 stroke patients in a hospital based cohort were included in the present study. Carotid duplex was performed to identify patients with absolute minimal diameter reductions of 50% or greater in their internal carotid arteries (ICAs). Color velocity imaging quantification ultrasound (CVIQ) was used to measure extracranial arterial blood flow volume (BFV) in bilateral common carotid arteries (CCAs) and bilateral vertebral arteries (VAs). The absolute values of BFV and the ratios were compared between patients with and without ICA stenosis. Among 586 acute ischemic stroke patients (mean age: 67.5 ± 12.4y), ICA stenosis was detected in 112 patients (19.1%), including unilateral ICA stenosis in 81 patients (13.8%) and bilateral ICA stenosis in 31 patients (5.3%). Among patients with unilateral ICA stenosis, the BFV in contralateral CCA was significantly higher than that in ipsilateral CCA (325.5 ± 99.8 mL/min vs. 242.2 ± 112.2 mL/min, P < 0.001). Among patients with bilateral ICA stenosis, the sum of BFV in bilateral VAs accounted for 22% of the whole cerebral blood flow, which was significantly higher than that in those without ICA stenosis (14.8%, P < 0.001) or with unilateral ICA stenosis (16.9%, P = 0.007). In patients with unilateral carotid stenosis, contralateral carotid blood flow increases to compensate decreased blood flow, while posterior circulation may compensate for the decreased brain perfusion in those with bilateral carotid stenosis.

Computed tomography versus invasive coronary angiography: design and methods of the pragmatic randomised multicentre DISCHARGE trial.

PubMed

Napp, Adriane E; Haase, Robert; Laule, Michael; Schuetz, Georg M; Rief, Matthias; Dreger, Henryk; Feuchtner, Gudrun; Friedrich, Guy; Špaček, Miloslav; Suchánek, Vojtěch; Fuglsang Kofoed, Klaus; Engstroem, Thomas; Schroeder, Stephen; Drosch, Tanja; Gutberlet, Matthias; Woinke, Michael; Maurovich-Horvat, Pál; Merkely, Béla; Donnelly, Patrick; Ball, Peter; Dodd, Jonathan D; Quinn, Martin; Saba, Luca; Porcu, Maurizio; Francone, Marco; Mancone, Massimo; Erglis, Andrejs; Zvaigzne, Ligita; Jankauskas, Antanas; Sakalyte, Gintare; Harań, Tomasz; Ilnicka-Suckiel, Malgorzata; Bettencourt, Nuno; Gama-Ribeiro, Vasco; Condrea, Sebastian; Benedek, Imre; Čemerlić Adjić, Nada; Adjić, Oto; Rodriguez-Palomares, José; Garcia Del Blanco, Bruno; Roditi, Giles; Berry, Colin; Davis, Gershan; Thwaite, Erica; Knuuti, Juhani; Pietilä, Mikko; Kępka, Cezary; Kruk, Mariusz; Vidakovic, Radosav; Neskovic, Aleksandar N; Díez, Ignacio; Lecumberri, Iñigo; Geleijns, Jacob; Kubiak, Christine; Strenge-Hesse, Anke; Do, The-Hoang; Frömel, Felix; Gutiérrez-Ibarluzea, Iñaki; Benguria-Arrate, Gaizka; Keiding, Hans; Katzer, Christoph; Müller-Nordhorn, Jacqueline; Rieckmann, Nina; Walther, Mario; Schlattmann, Peter; Dewey, Marc

2017-07-01

More than 3.5 million invasive coronary angiographies (ICA) are performed in Europe annually. Approximately 2 million of these invasive procedures might be reduced by noninvasive tests because no coronary intervention is performed. Computed tomography (CT) is the most accurate noninvasive test for detection and exclusion of coronary artery disease (CAD). To investigate the comparative effectiveness of CT and ICA, we designed the European pragmatic multicentre DISCHARGE trial funded by the 7th Framework Programme of the European Union (EC-GA 603266). In this trial, patients with a low-to-intermediate pretest probability (10-60 %) of suspected CAD and a clinical indication for ICA because of stable chest pain will be randomised in a 1-to-1 ratio to CT or ICA. CT and ICA findings guide subsequent management decisions by the local heart teams according to current evidence and European guidelines. Major adverse cardiovascular events (MACE) defined as cardiovascular death, myocardial infarction and stroke as a composite endpoint will be the primary outcome measure. Secondary and other outcomes include cost-effectiveness, radiation exposure, health-related quality of life (HRQoL), socioeconomic status, lifestyle, adverse events related to CT/ICA, and gender differences. The DISCHARGE trial will assess the comparative effectiveness of CT and ICA. • Coronary artery disease (CAD) is a major cause of morbidity and mortality. • Invasive coronary angiography (ICA) is the reference standard for detection of CAD. • Noninvasive computed tomography angiography excludes CAD with high sensitivity. • CT may effectively reduce the approximately 2 million negative ICAs in Europe. • DISCHARGE addresses this hypothesis in patients with low-to-intermediate pretest probability for CAD.

Comparison of pre-filter and post-filter ionised calcium monitoring in continuous veno-venous hemodiafiltration (CVVHD-F) with citrate anti-coagulation

PubMed Central

Roodenburg, Owen S.; McNeil, John

2017-01-01

Background It is widespread practice during citrate anticoagulated renal replacement therapy to monitor circuit ionised calcium (iCa2+) to evaluate the effectiveness of anticoagulation. Whether the optimal site to sample the blood path is before or after the haemofilter is a common question. Methods Using a prospectively collected observational dataset from intensive care patients receiving pre-dilution continuous veno-venous haemodiafiltration (CVVHD-F) with integrated citrate anticoagulation we compared paired samples of pre and post filter iCa2+ where the target range was 0.3–0.5 mmol.L-1 as well as concurrently collected arterial iCa2+. Two nested mixed methods linear models were fitted to the data describing post vs pre filter iCa2+, and the relationship of pre, post and arterial samples. Setting An 11 bed general intensive care unit. Participants 450 grouped samples from 152 time periods in seven patients on CRRT with citrate anticoagulation. Results The relationship of post to pre-filter iCa2+ was not 1:1 with post = 0.082 + 0.751 x pre-filter iCa2+ (95% CI intercept: 0.015–0.152, slope 0.558–0.942). Variation was greatest between patients rather than between circuits within the same patient or citrate dose. Compared to arterial iCa2+ there was no significant difference between pre and post-filter sampling sites (F-value 0.047, p = 0.827) Conclusion These results demonstrate that there is minimal difference between pre and post filter samples for iCa2+ monitoring of circuit anticoagulation in citrate patients relative to the arterial iCa2+ in CVVHD-F however compared to pre-filter sampling, post filter sampling has a flatter response and greater variation. PMID:29272278

Electromyogenic Artifacts and Electroencephalographic Inferences Revisited

PubMed Central

McMenamin, Brenton W.; Shackman, Alexander J.; Greischar, Lawrence L.; Davidson, Richard J.

2010-01-01

Recent years have witnessed a renewed interest in using oscillatory brain electrical activity to understand the neural bases of cognition and emotion. Electrical signals originating from pericranial muscles represent a profound threat to the validity of such research. Recently, McMenamin et al (2010) examined whether independent component analysis (ICA) provides a sensitive and specific means of correcting electromyogenic (EMG) artifacts. This report sparked the accompanying commentary (Olbrich, Jödicke, Sander, Himmerich & Hegerl, in press), and here we revisit the question of how EMG can alter inferences drawn from the EEG and what can be done to minimize its pernicious effects. Accordingly, we briefly summarize salient features of the EMG problem and review recent research investigating the utility of ICA for correcting EMG and other artifacts. We then directly address the key concerns articulated by Olbrich and provide a critique of their efforts at validating ICA. We conclude by identifying key areas for future methodological work and offer some practical recommendations for intelligently addressing EMG artifact. PMID:20981275

Ideal Internal Carotid Artery Trapping Technique without Bypass in a Patient with Insufficient Collateral Flow.

PubMed

Chung, Joon Ho; Shin, Yong Sam; Lim, Yong Cheol; Park, Minjung

2009-04-01

Internal carotid artery (ICA) trapping can be used for treating intracranial giant aneurysm, blood blister-like aneurysms and ICA rupture during the surgery. We present a novel ICA trapping technique which can be used with insufficient collaterals flow via anterior communicating artery (AcoA) and posterior communicating artery (PcoA). A patient was admitted with severe headache and the cerebral angiography demonstrated a typical blood blister-like aneurysm at the contralateral side of PcoA. For trapping the aneurysm, the first clip was placed at the ICA just proximal to the aneurysm whereas the distal clip was placed obliquely proximal to the origin of the PcoA to preserve blood flow from the PcoA to the distal ICA. The patient was completely recovered with good collaterals filling to the right ICA territories via AcoA and PcoA. This technique may be an effective treatment option for trapping the aneurysm, especially when the PcoA preservation is mandatory.

Removal of BCG artifacts from EEG recordings inside the MR scanner: a comparison of methodological and validation-related aspects.

PubMed

Vanderperren, Katrien; De Vos, Maarten; Ramautar, Jennifer R; Novitskiy, Nikolay; Mennes, Maarten; Assecondi, Sara; Vanrumste, Bart; Stiers, Peter; Van den Bergh, Bea R H; Wagemans, Johan; Lagae, Lieven; Sunaert, Stefan; Van Huffel, Sabine

2010-04-15

Multimodal approaches are of growing interest in the study of neural processes. To this end much attention has been paid to the integration of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data because of their complementary properties. However, the simultaneous acquisition of both types of data causes serious artifacts in the EEG, with amplitudes that may be much larger than those of EEG signals themselves. The most challenging of these artifacts is the ballistocardiogram (BCG) artifact, caused by pulse-related electrode movements inside the magnetic field. Despite numerous efforts to find a suitable approach to remove this artifact, still a considerable discrepancy exists between current EEG-fMRI studies. This paper attempts to clarify several methodological issues regarding the different approaches with an extensive validation based on event-related potentials (ERPs). More specifically, Optimal Basis Set (OBS) and Independent Component Analysis (ICA) based methods were investigated. Their validation was not only performed with measures known from previous studies on the average ERPs, but most attention was focused on task-related measures, including their use on trial-to-trial information. These more detailed validation criteria enabled us to find a clearer distinction between the most widely used cleaning methods. Both OBS and ICA proved to be able to yield equally good results. However, ICA methods needed more parameter tuning, thereby making OBS more robust and easy to use. Moreover, applying OBS prior to ICA can optimize the data quality even more, but caution is recommended since the effect of the additional ICA step may be strongly subject-dependent. Copyright 2010 Elsevier Inc. All rights reserved.

Ontario multidetector computed tomographic coronary angiography study: field evaluation of diagnostic accuracy.

PubMed

Chow, Benjamin J W; Freeman, Michael R; Bowen, James M; Levin, Leslie; Hopkins, Robert B; Provost, Yves; Tarride, Jean-Eric; Dennie, Carole; Cohen, Eric A; Marcuzzi, Dan; Iwanochko, Robert; Moody, Alan R; Paul, Narinder; Parker, John D; O'Reilly, Daria J; Xie, Feng; Goeree, Ron

2011-06-13

Computed tomographic coronary angiography (CTCA) has gained clinical acceptance for the detection of obstructive coronary artery disease. Although single-center studies have demonstrated excellent accuracy, multicenter studies have yielded variable results. The true diagnostic accuracy of CTCA in the "real world" remains uncertain. We conducted a field evaluation comparing multidetector CTCA with invasive CA (ICA) to understand CTCA's diagnostic accuracy in a real-world setting. A multicenter cohort study of patients awaiting ICA was conducted between September 2006 and June 2009. All patients had either a low or an intermediate pretest probability for coronary artery disease and underwent CTCA and ICA within 10 days. The results of CTCA and ICA were interpreted visually by local expert observers who were blinded to all clinical data and imaging results. Using a patient-based analysis (diameter stenosis ≥50%) of 169 patients, the sensitivity, specificity, positive predictive value, and negative predictive value were 81.3% (95% confidence interval [CI], 71.0%-89.1%), 93.3% (95% CI, 85.9%-97.5%), 91.6% (95% CI, 82.5%-96.8%), and 84.7% (95% CI, 76.0%-91.2%), respectively; the area under receiver operating characteristic curve was 0.873. The diagnostic accuracy varied across centers (P < .001), with a sensitivity, specificity, positive predictive value, and negative predictive value ranging from 50.0% to 93.2%, 92.0% to 100%, 84.6% to 100%, and 42.9% to 94.7%, respectively. Compared with ICA, CTCA appears to have good accuracy; however, there was variability in diagnostic accuracy across centers. Factors affecting institutional variability need to be better understood before CTCA is universally adopted. Additional real-world evaluations are needed to fully understand the impact of CTCA on clinical care. clinicaltrials.gov Identifier: NCT00371891.

Computed Tomographic Distinction of Intimal and Medial Calcification in the Intracranial Internal Carotid Artery.

PubMed

Kockelkoren, Remko; Vos, Annelotte; Van Hecke, Wim; Vink, Aryan; Bleys, Ronald L A W; Verdoorn, Daphne; Mali, Willem P Th M; Hendrikse, Jeroen; Koek, Huiberdina L; de Jong, Pim A; De Vis, Jill B

2017-01-01

Intracranial internal carotid artery (iICA) calcification is associated with stroke and is often seen as a proxy of atherosclerosis of the intima. However, it was recently shown that these calcifications are predominantly located in the tunica media and internal elastic lamina (medial calcification). Intimal and medial calcifications are thought to have a different pathogenesis and clinical consequences and can only be distinguished through ex vivo histological analysis. Therefore, our aim was to develop CT scoring method to distinguish intimal and medial iICA calcification in vivo. First, in both iICAs of 16 cerebral autopsy patients the intimal and/or medial calcification area was histologically assessed (142 slides). Brain CT images of these patients were matched to the corresponding histological slides to develop a CT score that determines intimal or medial calcification dominance. Second, performance of the CT score was assessed in these 16 patients. Third, reproducibility was tested in a separate cohort. First, CT features of the score were circularity (absent, dot(s), <90°, 90-270° or 270-360°), thickness (absent, ≥1.5mm, or <1.5mm), and morphology (indistinguishable, irregular/patchy or continuous). A high sum of features represented medial and a lower sum intimal calcifications. Second, in the 16 patients the concordance between the CT score and the dominant calcification type was reasonable. Third, the score showed good reproducibility (kappa: 0.72 proportion of agreement: 0.82) between the categories intimal, medial or absent/indistinguishable. The developed CT score shows good reproducibility and can differentiate reasonably well between intimal and medial calcification dominance in the iICA, allowing for further (epidemiological) studies on iICA calcification.

Surgical dissection of the internal carotid artery under flow control by proximal vessel clamping reduces embolic infarcts during carotid endarterectomy.

PubMed

Yoshida, Kazumichi; Kurosaki, Yoshitaka; Funaki, Takeshi; Kikuchi, Takayuki; Ishii, Akira; Takahashi, Jun C; Takagi, Yasushi; Yamagata, Sen; Miyamoto, Susumu

2014-01-01

To evaluate the efficacy of flow control of the internal carotid artery (ICA) by the clamping of the common carotid artery, external carotid artery, and superior thyroid artery during surgical ICA dissection to reduce ischemic complications after carotid endarterectomy (CEA). Sixty-seven patients (59 men; age, 70.5 ± 6.2 years) who underwent CEA by the same surgeon were retrospectively studied. Both conventional CEA (n = 29) and flow-control CEA (n = 38) were performed with the patient under general anesthesia and with the use of somatosensory-evoked potential and near-infrared spectroscopy monitoring as a guide for selective shunting. The number of new postoperative infarcts was assessed with preoperative and postoperative diffusion-weighted images (DWIs) obtained within 3 days of surgery. In addition to surgical technique, the effects of the following factors on new infarcts also were examined: age, side of ICA stenosis, high-grade stenosis, symptoms, and application of shunting. New postoperative DWI lesions were observed in 7 of 67 patients (10.4%), and none of them was symptomatic. With respect to operative technique, the incidence rate of DWI spots was significantly lower in the flow-control group (2.6%) than in the conventional group (20.7%), odds ratio: 0.069; 95% confidence interval: 0.006-0.779; P = 0.031). On multiple logistic regression analysis, age, side of ICA stenosis, high-grade stenosis, symptoms, and the use of internal shunting did not have significant effects on new postoperative DWI lesions, whereas technique did have an effect. The proximal flow-control technique for CEA helps avoid embolic complications during surgical ICA dissection. Copyright © 2014 Elsevier Inc. All rights reserved.

Internal Carotid Artery Agenesis with an Intercavernous Anastomosis: A Rare Case.

PubMed

Erdogan, Mucahid; Senadim, Songul; Ince Yasinoglu, K Nur; Selcuk, H Hakan; Atakli, H Dilek

2017-10-01

Agenesis of the internal carotid artery (ICA) is a rare vascular anomaly that was first observed postmortem. Various anastomoses supply the distal vessels at the site of agenesis. Of these anastomoses, an intercavernous anastomosis is very rare. This paper presents a patient with ischemic stroke in whom we discovered left ICA agenesis and an ipsilateral intercavernous anastomosis. A 58-year-old man with a history of myocardial infarction and diabetes mellitus presented with sudden-onset difficulty in speaking, numbness on the left side of the face, and weakness of the left arm and leg. Neurological examination revealed dysarthria, left facial paralysis, left hemiparesis, and bilateral absence of the plantar reflexes. Diffusion-weighted magnetic resonance imaging showed a right middle cerebral artery (MCA) infarction. On cranial and cervical magnetic resonance angiography, the left ICA could not be seen distal to the bifurcation; the left MCA was supplied through an intercavernous anastomosis between the right ICA and the left ICA. Cranial computed tomography (CT) revealed the absence of the left carotid canal. Digital subtraction angiography led to a diagnosis of left ICA agenesis with an intercavernous anastomosis. The patient was discharged on acetylsalicylic acid and warfarin. ICA agenesis with an intercavernous anastomosis is a rare vascular anomaly that should be differentiated from secondary causes of ICA stenosis and occlusions by showing agenesis of the carotid canal on cranial CT. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Icariin Promotes Tendon-Bone Healing during Repair of Rotator Cuff Tears: A Biomechanical and Histological Study.

PubMed

Ye, Chenyi; Zhang, Wei; Wang, Shengdong; Jiang, Shuai; Yu, Yuanbin; Chen, Erman; Xue, Deting; Chen, Jianzhong; He, Rongxin

2016-10-25

To investigate whether the systematic administration of icariin (ICA) promotes tendon-bone healing after rotator cuff reconstruction in vivo, a total of 64 male Sprague Dawley rats were used in a rotator cuff injury model and underwent rotator cuff reconstruction (bone tunnel suture fixation). Rats from the ICA group ( n = 32) were gavage-fed daily with ICA at 0.125 mg/g, while rats in the control group ( n = 32) received saline only. Micro-computed tomography, biomechanical tests, serum ELISA (calcium; Ca, alkaline phosphatase; AP, osteocalcin; OCN) and histological examinations (Safranin O and Fast Green staining, type I, II and III collagen (Col1, Col2, and Col3), CD31, and vascular endothelial growth factor (VEGF)) were analyzed two and four weeks after surgery. In the ICA group, the serum levels of AP and OCN were higher than in the control group. More Col1-, Col2-, CD31-, and VEGF-positive cells, together with a greater degree of osteogenesis, were detected in the ICA group compared with the control group. During mechanical testing, the ICA group showed a significantly higher ultimate failure load than the control group at both two and four weeks. Our results indicate that the systematic administration of ICA could promote angiogenesis and tendon-bone healing after rotator cuff reconstruction, with superior mechanical strength compared with the controls. Treatment for rotator cuff injury using systematically-administered ICA could be a promising strategy.

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering.

PubMed

Yang, Guang; Raschke, Felix; Barrick, Thomas R; Howe, Franklyn A

2015-09-01

To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method. In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis. © 2014 Wiley Periodicals, Inc.

Quantification method for the appearance of melanin pigmentation using independent component analysis

NASA Astrophysics Data System (ADS)

Ojima, Nobutoshi; Okiyama, Natsuko; Okaguchi, Saya; Tsumura, Norimichi; Nakaguchi, Toshiya; Hori, Kimihiko; Miyake, Yoichi

2005-04-01

In the cosmetics industry, skin color is very important because skin color gives a direct impression of the face. In particular, many people suffer from melanin pigmentation such as liver spots and freckles. However, it is very difficult to evaluate melanin pigmentation using conventional colorimetric values because these values contain information on various skin chromophores simultaneously. Therefore, it is necessary to extract information of the chromophore of individual skins independently as density information. The isolation of the melanin component image based on independent component analysis (ICA) from a single skin image was reported in 2003. However, this technique has not developed a quantification method for melanin pigmentation. This paper introduces a quantification method based on the ICA of a skin color image to isolate melanin pigmentation. The image acquisition system we used consists of commercially available equipment such as digital cameras and lighting sources with polarized light. The images taken were analyzed using ICA to extract the melanin component images, and Laplacian of Gaussian (LOG) filter was applied to extract the pigmented area. As a result, for skin images including those showing melanin pigmentation and acne, the method worked well. Finally, the total amount of extracted area had a strong correspondence to the subjective rating values for the appearance of pigmentation. Further analysis is needed to recognize the appearance of pigmentation concerning the size of the pigmented area and its spatial gradation.

Hypertext Interchange Using ICA.

ERIC Educational Resources Information Center

Rada, Roy; And Others

1995-01-01

Discusses extended ICA (Integrated Chameleon Architecture), a public domain toolset for generating text-to-hypertext translators. A system called SGML-MUCH has been developed using E-ICA (Extended Integrated Chameleon Architecture) and is presented as a case study with converters for the hypertext systems MUCH, Guide, Hyperties, and Toolbook.…

Guided Exploration of Genomic Risk for Gray Matter Abnormalities in Schizophrenia Using Parallel Independent Component Analysis with Reference

PubMed Central

Chen, Jiayu; Calhoun, Vince D.; Pearlson, Godfrey D.; Perrone-Bizzozero, Nora; Sui, Jing; Turner, Jessica A.; Bustillo, Juan R; Ehrlich, Stefan; Sponheim, Scott R.; Cañive, José M.; Ho, Beng-Choon; Liu, Jingyu

2013-01-01

One application of imaging genomics is to explore genetic variants associated with brain structure and function, presenting a new means of mapping genetic influences on mental disorders. While there is growing interest in performing genome-wide searches for determinants, it remains challenging to identify genetic factors of small effect size, especially in limited sample sizes. In an attempt to address this issue, we propose to take advantage of a priori knowledge, specifically to extend parallel independent component analysis (pICA) to incorporate a reference (pICA-R), aiming to better reveal relationships between hidden factors of a particular attribute. The new approach was first evaluated on simulated data for its performance under different configurations of effect size and dimensionality. Then pICA-R was applied to a 300-participant (140 schizophrenia (SZ) patients versus 160 healthy controls) dataset consisting of structural magnetic resonance imaging (sMRI) and single nucleotide polymorphism (SNP) data. Guided by a reference SNP set derived from ANK3, a gene implicated by the Psychiatric Genomic Consortium SZ study, pICA-R identified one pair of SNP and sMRI components with a significant loading correlation of 0.27 (p = 1.64×10−6). The sMRI component showed a significant group difference in loading parameters between patients and controls (p = 1.33×10−15), indicating SZ-related reduction in gray matter concentration in prefrontal and temporal regions. The linked SNP component also showed a group difference (p = 0.04) and was predominantly contributed to by 1,030 SNPs. The effect of these top contributing SNPs was verified using association test results of the Psychiatric Genomic Consortium SZ study, where the 1,030 SNPs exhibited significant SZ enrichment compared to the whole genome. In addition, pathway analyses indicated the genetic component majorly relating to neurotransmitter and nervous system signaling pathways. Given the simulation and experiment results, pICA-R may prove a promising multivariate approach for use in imaging genomics to discover reliable genetic risk factors under a scenario of relatively high dimensionality and small effect size. PMID:23727316

A lower baseline glomerular filtration rate predicts high mortality and newly cerebrovascular accidents in acute ischemic stroke patients.

PubMed

Dong, Kai; Huang, Xiaoqin; Zhang, Qian; Yu, Zhipeng; Ding, Jianping; Song, Haiqing

2017-02-01

Chronic kidney disease (CKD) is gradually recognized as an independent risk factor for cardiovascular and cardio-/cerebrovascular disease. This study aimed to examine the association of the estimated glomerular filtration rate (eGFR) and clinical outcomes at 3 months after the onset of ischemic stroke in a hospitalized Chinese population.Totally, 972 patients with acute ischemic stroke were enrolled into this study. Modified of Diet in Renal Disease (MDRD) equations were used to calculate eGFR and define CKD. The site and degree of the stenosis were examined. Patients were followed-up for 3 months. Endpoint events included all-cause death and newly ischemic events. The multivariate logistic model was used to determine the association between renal dysfunction and patients' outcomes.Of all patients, 130 patients (13.4%) had reduced eGFR (<60 mL/min/1.73 m), and 556 patients had a normal eGFR (≥90 mL/min/1.73 m). A total of 694 patients suffered from cerebral artery stenosis, in which 293 patients only had intracranial artery stenosis (ICAS), 110 only with extracranial carotid atherosclerotic stenosis (ECAS), and 301 with both ICAS and ECAS. The patients with eGFR <60 mL/min/1.73m had a higher proportion of death and newly ischemic events compared with those with a relatively normal eGFR. Multivariate analysis revealed that a baseline eGFR <60 mL/min/1.73 m increased the risk of mortality by 3.089-fold and newly ischemic events by 4.067-fold. In further analysis, a reduced eGFR was associated with increased rates of mortality and newly events both in ICAS patients and ECAS patients. However, only an increased risk of newly events was found as the degree of renal function deteriorated in ICAS patients (odds ratio = 8.169, 95% confidence interval = 2.445-14.127).A low baseline eGFR predicted a high mortality and newly ischemic events at 3 months in ischemic stroke patients. A low baseline eGFR was also a strong independent predictor for newly ischemic events in ICAS patients.

Long-term follow-up analysis of microsurgical clip ligation and endovascular coil embolization for dorsal wall blister aneurysms of the internal carotid artery.

PubMed

Brown, Mason A; Guandique, Cristian F; Parish, Jonathan; McMillan, Aubrey C; Lehnert, Stephen; Mansour, Nassir; Tu, Michael; Bohnstedt, Bradley N; Payner, Troy D; Leipzig, Thomas J; DeNardo, Andrew J; Scott, John A; Cohen-Gadol, Aaron A

2017-05-01

Blister aneurysms at non-branching sites of the dorsal internal carotid artery (dICA) are fragile, rare, and often difficult to treat. The purpose of this study is to address the demographics, treatment modalities, and long-term outcome of patients treated for dICA blister aneurysms. A retrospective review of medical records identified all consecutive patients who presented with a blister aneurysm from 2002 to 2011 at our institution. Eighteen patients (M=7, F=11; mean age: 48.4±15.1years; range: 15-65years) harbored a total of 43 aneurysms, 25 of which were dorsal wall blister aneurysms of the ICA. Eleven (61.1%) patients presented with aneurysmal subarachnoid hemorrhage (aSAH), and 10 (55.6%) patients had multiple aneurysms at admission. Twelve patients had 18 aneurysms that were treated microsurgically. Five (41.7%) of these patients had a single recurrence that was retreated with subsequent repeat clip ligation. Six patients had 7 blister aneurysms that were treated with endovascularly. One (16.7%) of these patients had a single recurrence that was retreated with subsequent coil embolization. Postoperative vasospasm occurred in 8 (44.4%) patients, one of whom suffered from a stroke. This is one of the largest single-institution dICA blister aneurysm studies to date. There was no detected significant difference between microsurgical clip ligation and endovascular coil embolization in terms of surgical outcome. These blister aneurysms demonstrate a propensity to be associated with multiple cerebral aneurysms. Strict clinical and angiographic long-term follow-up may be warranted. Blister aneurysms are focal wall defects covered by a thin layer of fibrous tissue and adventitia, lacking the usual collagenous layer. Due to their pathologically thin vessel wall, blister aneurysms are prone to rupture. The management of these rare and fragile aneurysms presents a number of challenges. Here, we address the long-term outcome of patients treated for blister aneurysms at non-branching sites of the dICA. The presented data and analysis is imperative to determine the necessary strict long-term clinical and angiographic follow-up. Copyright © 2017 Elsevier Ltd. All rights reserved.

Innovative high-performance liquid chromatography method development for the screening of 19 antimalarial drugs based on a generic approach, using design of experiments, independent component analysis and design space.

PubMed

Debrus, B; Lebrun, P; Kindenge, J Mbinze; Lecomte, F; Ceccato, A; Caliaro, G; Mbay, J Mavar Tayey; Boulanger, B; Marini, R D; Rozet, E; Hubert, Ph

2011-08-05

An innovative methodology based on design of experiments (DoE), independent component analysis (ICA) and design space (DS) was developed in previous works and was tested out with a mixture of 19 antimalarial drugs. This global LC method development methodology (i.e. DoE-ICA-DS) was used to optimize the separation of 19 antimalarial drugs to obtain a screening method. DoE-ICA-DS methodology is fully compliant with the current trend of quality by design. DoE was used to define the set of experiments to model the retention times at the beginning, the apex and the end of each peak. Furthermore, ICA was used to numerically separate coeluting peaks and estimate their unbiased retention times. Gradient time, temperature and pH were selected as the factors of a full factorial design. These retention times were modelled by stepwise multiple linear regressions. A recently introduced critical quality attribute, namely the separation criterion (S), was also used to assess the quality of separations rather than using the resolution. Furthermore, the resulting mathematical models were also studied from a chromatographic point of view to understand and investigate the chromatographic behaviour of each compound. Good adequacies were found between the mathematical models and the expected chromatographic behaviours predicted by chromatographic theory. Finally, focusing at quality risk management, the DS was computed as the multidimensional subspace where the probability for the separation criterion to lie in acceptance limits was higher than a defined quality level. The DS was computed propagating the prediction error from the modelled responses to the quality criterion using Monte Carlo simulations. DoE-ICA-DS allowed encountering optimal operating conditions to obtain a robust screening method for the 19 considered antimalarial drugs in the framework of the fight against counterfeit medicines. Moreover and only on the basis of the same data set, a dedicated method for the determination of three antimalarial compounds in a pharmaceutical formulation was optimized to demonstrate both the efficiency and flexibility of the methodology proposed in the present study. Copyright © 2011 Elsevier B.V. All rights reserved.

Multivariate genetic determinants of EEG oscillations in schizophrenia and psychotic bipolar disorder from the BSNIP study

PubMed Central

Narayanan, B; Soh, P; Calhoun, V D; Ruaño, G; Kocherla, M; Windemuth, A; Clementz, B A; Tamminga, C A; Sweeney, J A; Keshavan, M S; Pearlson, G D

2015-01-01

Schizophrenia (SZ) and psychotic bipolar disorder (PBP) are disabling psychiatric illnesses with complex and unclear etiologies. Electroencephalogram (EEG) oscillatory abnormalities in SZ and PBP probands are heritable and expressed in their relatives, but the neurobiology and genetic factors mediating these abnormalities in the psychosis dimension of either disorder are less explored. We examined the polygenic architecture of eyes-open resting state EEG frequency activity (intrinsic frequency) from 64 channels in 105 SZ, 145 PBP probands and 56 healthy controls (HCs) from the multisite BSNIP (Bipolar-Schizophrenia Network on Intermediate Phenotypes) study. One million single-nucleotide polymorphisms (SNPs) were derived from DNA. We assessed eight data-driven EEG frequency activity derived from group-independent component analysis (ICA) in conjunction with a reduced subset of 10 422 SNPs through novel multivariate association using parallel ICA (para-ICA). Genes contributing to the association were examined collectively using pathway analysis tools. Para-ICA extracted five frequency and nine SNP components, of which theta and delta activities were significantly correlated with two different gene components, comprising genes participating extensively in brain development, neurogenesis and synaptogenesis. Delta and theta abnormality was present in both SZ and PBP, while theta differed between the two disorders. Theta abnormalities were also mediated by gene clusters involved in glutamic acid pathways, cadherin and synaptic contact-based cell adhesion processes. Our data suggest plausible multifactorial genetic networks, including novel and several previously identified (DISC1) candidate risk genes, mediating low frequency delta and theta abnormalities in psychoses. The gene clusters were enriched for biological properties affecting neural circuitry and involved in brain function and/or development. PMID:26101851

Early detection of coronary artery disease by 64-slice multidetector computed tomography in asymptomatic hypertensive high-risk patients.

PubMed

Gaudio, Carlo; Mirabelli, Francesca; Pelliccia, Francesco; Francone, Marco; Tanzilli, Gaetano; Di Michele, Sara; Leonetti, Stefania; De Vincentis, Giuseppe; Carbone, Iacopo; Mangieri, Enrico; Catalano, Carlo; Passariello, Roberto

2009-07-10

The 64-slice multidetector-row computed tomography (MDCT) is an accurate noninvasive technique for assessing the degree of luminal narrowing in coronary arteries of patients with chronic ischemic disease. Aim of this study was to determine the value of MDCT in comparison to invasive coronary angiography (ICA) for detecting the presence and extent of coronary atherosclerotic plaques in a population of asymptomatic, hypertensive patients considered to be at high risk for cardiovascular events. We studied 67 asymptomatic, hypertensive patients at high-risk (Euro Score >5%). All patients had negative or nondiagnostic findings at exercise stress testing and therefore underwent both MDCT and ICA. In the per-patient analysis, MDCT correctly identified 16/17 (94%) patients with significant coronary artery disease involving at least 1 vessel and 48/50 (96%) normal subjects. In the per-segment analysis, MDCT correctly detected 21/22 (95%) coronary segments with a stenosis >or=50% and 856/868 (98%) normal segments, with a high negative predictivity of normal scans (100%). There was a good concordance between MDCT and ICA, with a high Pearson correlation coefficient between the coronary narrowings with the two techniques (r=0.84, p<0.01). Mean coronary calcium score was higher for the 17 patients with significant coronary artery disease on ICA than in the 50 patients without (422+/-223 HU vs 72+/-21 HU p<0.001). The ROC curves identified 160 as the best calcium volumetric score cut-off value able to identify >or=1 significant coronary stenosis with sensitivity 88% and specificity 85%. MDCT is an excellent noninvasive technique for early identification of significant coronary stenoses in high risk asymptomatic hypertensive patients and might provide unique information for the screening of this broad population.

An Overview on Coronary Heart Disease (A Comparative Evaluation of Turkey and Europe) and Cost-effectiveness of Diagnostic Strategies

PubMed Central

Taşçı, Cengiz; Özçelik, Nihat

2011-01-01

Objective: Coronary heart disease (CHD) is the leading cause of death for men and women in Turkey as it is in Europe and US. The prevalence of the disease is 3.8% in Turkey and 200,000 patients are added to the pool of CHD annually Because of genetic predisposition and high proportions of physical inactivity, smoking habit, and obesity, CHD is encountered in earlier ages in our country So, the economic burden of the disease is expected to be relatively high, but the amount of health expenditure is not always parallel to the prevalence of a disease in the community. This article was written to overview CHD statistics to make a comparison between Turkey and some European countries and to investigate the value of myocardial perfusion scan (MPS) as a gatekeeper in diagnosing CHD before invasive coronary angiography (ICA). The consequences were evaluated for Turkey In diagnosis; noninvasive testing gains importance in connection with the new approaches in treatment strategies, because a direct ICA strategy results in higher rates of revascularization without improvement in clinical outcomes. A "gatekeeper" is needed to select the patients who are not required to undergo angiography. MPS with its proved power in diagnosis and predicting prognosis, provides a cost-effective solution, and is accepted in some extensive analyses as a "gatekeeper" particularly in intermediate and high risk patients and in patients with known CHD. In conclusion, MPS may provide an optimal solution better than the ongoing situation in Turkey as well, when it is approved as a "gatekeeper in an algorithm before ICA. Conflict of interest:None declared. PMID:23487016

An aberrant carotid artery in the temporal bone with fatal complication.

PubMed

Kimura, Yurika; Makino, Nao; Kobayashi, Hitome; Kitamura, Ken

2016-06-01

We report the case of an 84-year-old female presenting with an aberrant ICA with cerebral air embolization caused by Eustachian tube air inflation (ETAI). High pressure of air inflation developed because of an aberrant ICA blocking the tympanic orifice of the Eustachian tube, with release of the high-pressure air into the aberrant ICA. It must be kept in mind that complications may occur not only during transtympanic treatment, but also in any treatment, such as ETAI, in aberrant ICA cases. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Stimulus-related independent component and voxel-wise analysis of human brain activity during free viewing of a feature film.

PubMed

Lahnakoski, Juha M; Salmi, Juha; Jääskeläinen, Iiro P; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments.

Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

PubMed Central

Lahnakoski, Juha M.; Salmi, Juha; Jääskeläinen, Iiro P.; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments. PMID:22496909

Comparing Independent Component Analysis with Principle Component Analysis in Detecting Alterations of Porphyry Copper Deposit (case Study: Ardestan Area, Central Iran)

NASA Astrophysics Data System (ADS)

Mahmoudishadi, S.; Malian, A.; Hosseinali, F.

2017-09-01

The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) has been evaluated for the visible and near-infrared (VNIR) and Shortwave infrared (SWIR) subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6) were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.

Predictive Value of Dynamic Cerebral Autoregulation Assessment in Surgical Management of Patients with High-Grade Carotid Artery Stenosis

PubMed Central

Semenyutin, Vladimir B.; Asaturyan, Gregory A.; Nikiforova, Anna A.; Aliev, Vugar A.; Panuntsev, Grigory K.; Iblyaminov, Vadim B.; Savello, Alexander V.; Patzak, Andreas

2017-01-01

Dynamic cerebral autoregulation (DCA) capacity along with the degree of internal carotid artery (ICA) stenosis and characteristics of the plaque can also play an important role in selection of appropriate treatment strategy. This study aims to classify the patients with severe ICA stenosis according to preoperative state of DCA and to assess its dynamics after surgery. Thirty-five patients with severe ICA stenosis having different clinical type of disease underwent reconstructive surgery. DCA was assessed with transfer function analysis (TFA) by calculating phase shift (PS) between Mayer waves of blood flow velocity (BFV) and blood pressure (BP) before and after operation. In 18 cases, regardless of clinical type, preoperative PS on ipsilateral side was within the normal range and did not change considerably after surgery. In other 17 cases preoperative PS was reliably lower both in patients with symptomatic and asymptomatic stenosis. Surgical reconstruction led to restoration of impaired DCA evidenced by significant increase of PS in postoperative period. Our data suggest that regardless clinical type of disease various state of DCA may be present in patients with severe ICA stenosis. This finding can contribute to establishing the optimal treatment strategy, and first of all for asymptomatic patients. Patients with compromised DCA should be considered as ones with higher risk of stroke and first candidates for reconstructive surgery. PMID:29163214

[Effectiveness of endarterectomy for symptomatic stenosis of the internal carotid artery; more risk factors important than only the severity of the stenosis].

PubMed

Klijn, C J M; Hoefnagels, W A J; Brouwers, P J A M; Luijckx, G J; Moll, F L; Kappelle, L J

2007-12-15

Carotid endarterectomy prevents ischaemic stroke in patients who have suffered either a transient ischaemic attack (TIA) or a non-disabling ischaemic stroke and are also diagnosed with severe stenosis of the internal carotid artery (ICA). In order to prevent the occurrence ofa single stroke, 6 patients with a symptomatic 70 to 99% ICA stenosis will have to be operated upon. A meta-analysis of individual patient data from 3 randomised trials shows that the decision whether to advise endarterectomy to an individual patient should not be based solely on the degree of the ICA stenosis, but also on the time interval between symptoms and surgery, the type and severity of symptoms and the plaque morphology. In general, endarterectomy is more effective in men than in women, it is very effective in the elderly, and it is even more effective when performed within two weeks of the symptoms occurring. A decision scheme has been set up enabling one to predict the absolute risk of an ipsilateral stroke in the next 5 years in individual patients who have symptomatic ICA stenosis. This is based on 5 factors: sex, age, the most severe symptom in the last 6 months (stroke, TIA, or ischaemic retinopathy), the number of weeks since the last incident and the morphological characteristics of the plaque.

Costs and clinical outcomes for non-invasive versus invasive diagnostic approaches to patients with suspected in-stent restenosis.

PubMed

Min, James K; Hasegawa, James T; Machacz, Susanne F; O'Day, Ken

2016-02-01

This study compared costs and clinical outcomes of invasive versus non-invasive diagnostic evaluations for patients with suspected in-stent restenosis (ISR) after percutaneous coronary intervention. We developed a decision model to compare 2 year diagnosis-related costs for patients who presented with suspected ISR and were evaluated by: (1) invasive coronary angiography (ICA); (2) non-invasive stress testing strategy of myocardial perfusion imaging (MPI) with referral to ICA based on MPI; (3) coronary CT angiography-based testing strategy with referral to ICA based on CCTA. Costs were modeled from the payer's perspective using 2014 Medicare rates. 56 % of patients underwent follow-up diagnostic testing over 2 years. Compared to ICA, MPI (98.6 %) and CCTA (98.1 %) exhibited lower rates of correct diagnoses. Non-invasive strategies were associated with reduced referrals to ICA and costs compared to an ICA-based strategy, with diagnostic costs lower for CCTA than MPI. Overall 2-year costs were highest for ICA for both metallic as well as BVS stents ($1656 and $1656, respectively) when compared to MPI ($1444 and $1411) and CCTA. CCTA costs differed based upon stent size and type, and were highest for metallic stents >3.0 mm followed by metallic stents <3.0 mm, BVS < 3.0 mm and BVS > 3.0 mm ($1466 vs. $1242 vs. $855 vs. $490, respectively). MPI for suspected ISR results in lower costs and rates of complications than invasive strategies using ICA while maintaining high diagnostic performance. Depending upon stent size and type, CCTA results in lower costs than MPI.

Compositional diversity of near-, far-side transitory zone around Naonobu, Webb and Sinus Successus craters: Inferences from Chandrayaan-1 Moon Mineralogy Mapper (M3) data

NASA Astrophysics Data System (ADS)

Bharti, Rishikesh; Ramakrishnan, D.; Singh, K. D.

2014-02-01

This study investigated the potential of Moon Mineralogy Mapper (M3) data for studying compositional variation in the near-, far-side transition zone of the lunar surface. For this purpose, the radiance values of the M3 data were corrected for illumination and emission related effects and converted to apparent reflectance. Dimensionality of the calibrated reflectance image cube was reduced using Independent Component Analysis (ICA) and endmembers were extracted by using Pixel Purity Index (PPI) algorithm. The selected endmembers were linearly unmixed and resolved for mineralogy using United States Geological Survey (USGS) library spectra of minerals. These mineralogically resolved endmembers were used to map the compositional variability within, and outside craters using Spectral Angle Mapper (SAM) algorithm. Cross validation for certain litho types was attempted using band ratios like Optical Maturity (OMAT), Color Ratio Composite and Integrated Band Depth ratio (IBD). The identified lithologies for highland and basin areas match well with published works and strongly support depth related magmatic differentiation. Prevalence of pigeonite-basalt, pigeonite-norite and pyroxenite in crater peaks and floors are unique to the investigated area and are attributed to local, lateral compositional variability in magma composition due to pressure, temperature, and rate of cooling.

Simultaneous and Continuous Estimation of Shoulder and Elbow Kinematics from Surface EMG Signals

PubMed Central

Zhang, Qin; Liu, Runfeng; Chen, Wenbin; Xiong, Caihua

2017-01-01

In this paper, we present a simultaneous and continuous kinematics estimation method for multiple DoFs across shoulder and elbow joint. Although simultaneous and continuous kinematics estimation from surface electromyography (EMG) is a feasible way to achieve natural and intuitive human-machine interaction, few works investigated multi-DoF estimation across the significant joints of upper limb, shoulder and elbow joints. This paper evaluates the feasibility to estimate 4-DoF kinematics at shoulder and elbow during coordinated arm movements. Considering the potential applications of this method in exoskeleton, prosthetics and other arm rehabilitation techniques, the estimation performance is presented with different muscle activity decomposition and learning strategies. Principle component analysis (PCA) and independent component analysis (ICA) are respectively employed for EMG mode decomposition with artificial neural network (ANN) for learning the electromechanical association. Four joint angles across shoulder and elbow are simultaneously and continuously estimated from EMG in four coordinated arm movements. By using ICA (PCA) and single ANN, the average estimation accuracy 91.12% (90.23%) is obtained in 70-s intra-cross validation and 87.00% (86.30%) is obtained in 2-min inter-cross validation. This result suggests it is feasible and effective to use ICA (PCA) with single ANN for multi-joint kinematics estimation in variant application conditions. PMID:28611573

Representation of Probability Density Functions from Orbit Determination using the Particle Filter

NASA Technical Reports Server (NTRS)

Mashiku, Alinda K.; Garrison, James; Carpenter, J. Russell

2012-01-01

Statistical orbit determination enables us to obtain estimates of the state and the statistical information of its region of uncertainty. In order to obtain an accurate representation of the probability density function (PDF) that incorporates higher order statistical information, we propose the use of nonlinear estimation methods such as the Particle Filter. The Particle Filter (PF) is capable of providing a PDF representation of the state estimates whose accuracy is dependent on the number of particles or samples used. For this method to be applicable to real case scenarios, we need a way of accurately representing the PDF in a compressed manner with little information loss. Hence we propose using the Independent Component Analysis (ICA) as a non-Gaussian dimensional reduction method that is capable of maintaining higher order statistical information obtained using the PF. Methods such as the Principal Component Analysis (PCA) are based on utilizing up to second order statistics, hence will not suffice in maintaining maximum information content. Both the PCA and the ICA are applied to two scenarios that involve a highly eccentric orbit with a lower apriori uncertainty covariance and a less eccentric orbit with a higher a priori uncertainty covariance, to illustrate the capability of the ICA in relation to the PCA.

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

PubMed Central

Castro, Eduardo; Hjelm, R. Devon; Plis, Sergey M.; Dinh, Laurent; Turner, Jessica A.; Calhoun, Vince D.

2016-01-01

Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns. PMID:26891483

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition.

PubMed

Sengottuvel, S; Khan, Pathan Fayaz; Mariyappa, N; Patel, Rajesh; Saipriya, S; Gireesan, K

2018-06-01

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

Transposition of the paraclival carotid artery: a novel concept of self-retaining vascular retraction during endoscopic endonasal skull base surgery technical report.

PubMed

Freeman, Jacob L; Sampath, Raghuram; Casey, Michael A; Quattlebaum, Steven Craig; Ramakrishnan, Vijay R; Youssef, A Samy

2016-08-01

Fixed retraction of the internal carotid artery (ICA) has previously been described for use during transcranial microscopic surgery. We report the novel use of a self-retaining microvascular retractor for static repositioning and protection of the ICA during expanded endonasal endoscopic approaches to the paramedian skull base. The transmaxillary, transpterygoid approach was performed in five cadaver heads (ten sides). The self-retaining microvascular retractor was used to laterally reposition the pterygopalatine fossa contents during exposure of the pterygoid base/plates and the paraclival ICA to expose the petrous apex. Maximum ICA retraction distance was measured in the x-axis for all ten sides. The average horizontal distance of ICA retraction measured at the mid-paraclival segment for all ten sides was 4.75 mm. In all cases, the carotid artery was repositioned without injury to the vessel or disruption of the surrounding neurovascular structures. Static repositioning of the ICA and other delicate neurovascular structures was effectively performed during endonasal, endoscopic cadaveric surgery of the skull base and has potential merits in live patients.

High resolution iridocorneal angle imaging system by axicon lens assisted gonioscopy.

PubMed

Perinchery, Sandeep Menon; Shinde, Anant; Fu, Chan Yiu; Jeesmond Hong, Xun Jie; Baskaran, Mani; Aung, Tin; Murukeshan, Vadakke Matham

2016-07-29

Direct visualization and assessment of the iridocorneal angle (ICA) region with high resolution is important for the clinical evaluation of glaucoma. However, the current clinical imaging systems for ICA do not provide sufficient structural details due to their poor resolution. The key challenges in achieving high quality ICA imaging are its location in the anterior region of the eye and the occurrence of total internal reflection due to refractive index difference between cornea and air. Here, we report an indirect axicon assisted gonioscopy imaging probe with white light illumination. The illustrated results with this probe shows significantly improved visualization of structures in the ICA including TM region, compared to the current available tools. It could reveal critical details of ICA and expected to aid management by providing information that is complementary to angle photography and gonioscopy.

High resolution iridocorneal angle imaging system by axicon lens assisted gonioscopy

PubMed Central

Perinchery, Sandeep Menon; Shinde, Anant; Fu, Chan Yiu; Jeesmond Hong, Xun Jie; Baskaran, Mani; Aung, Tin; Murukeshan, Vadakke Matham

2016-01-01

Direct visualization and assessment of the iridocorneal angle (ICA) region with high resolution is important for the clinical evaluation of glaucoma. However, the current clinical imaging systems for ICA do not provide sufficient structural details due to their poor resolution. The key challenges in achieving high quality ICA imaging are its location in the anterior region of the eye and the occurrence of total internal reflection due to refractive index difference between cornea and air. Here, we report an indirect axicon assisted gonioscopy imaging probe with white light illumination. The illustrated results with this probe shows significantly improved visualization of structures in the ICA including TM region, compared to the current available tools. It could reveal critical details of ICA and expected to aid management by providing information that is complementary to angle photography and gonioscopy. PMID:27471000

High resolution iridocorneal angle imaging system by axicon lens assisted gonioscopy

NASA Astrophysics Data System (ADS)

Perinchery, Sandeep Menon; Shinde, Anant; Fu, Chan Yiu; Jeesmond Hong, Xun Jie; Baskaran, Mani; Aung, Tin; Murukeshan, Vadakke Matham

2016-07-01

Direct visualization and assessment of the iridocorneal angle (ICA) region with high resolution is important for the clinical evaluation of glaucoma. However, the current clinical imaging systems for ICA do not provide sufficient structural details due to their poor resolution. The key challenges in achieving high quality ICA imaging are its location in the anterior region of the eye and the occurrence of total internal reflection due to refractive index difference between cornea and air. Here, we report an indirect axicon assisted gonioscopy imaging probe with white light illumination. The illustrated results with this probe shows significantly improved visualization of structures in the ICA including TM region, compared to the current available tools. It could reveal critical details of ICA and expected to aid management by providing information that is complementary to angle photography and gonioscopy.

Induction and separation of motion artifacts in EEG data using a mobile phantom head device.

PubMed

Oliveira, Anderson S; Schlink, Bryan R; Hairston, W David; König, Peter; Ferris, Daniel P

2016-06-01

Electroencephalography (EEG) can assess brain activity during whole-body motion in humans but head motion can induce artifacts that obfuscate electrocortical signals. Definitive solutions for removing motion artifact from EEG have yet to be found, so creating methods to assess signal processing routines for removing motion artifact are needed. We present a novel method for investigating the influence of head motion on EEG recordings as well as for assessing the efficacy of signal processing approaches intended to remove motion artifact. We used a phantom head device to mimic electrical properties of the human head with three controlled dipolar sources of electrical activity embedded in the phantom. We induced sinusoidal vertical motions on the phantom head using a custom-built platform and recorded EEG signals with three different acquisition systems while the head was both stationary and in varied motion conditions. Recordings showed up to 80% reductions in signal-to-noise ratio (SNR) and up to 3600% increases in the power spectrum as a function of motion amplitude and frequency. Independent component analysis (ICA) successfully isolated the three dipolar sources across all conditions and systems. There was a high correlation (r > 0.85) and marginal increase in the independent components' (ICs) power spectrum (∼15%) when comparing stationary and motion parameters. The SNR of the IC activation was 400%-700% higher in comparison to the channel data SNR, attenuating the effects of motion on SNR. Our results suggest that the phantom head and motion platform can be used to assess motion artifact removal algorithms and compare different EEG systems for motion artifact sensitivity. In addition, ICA is effective in isolating target electrocortical events and marginally improving SNR in relation to stationary recordings.

Induction and separation of motion artifacts in EEG data using a mobile phantom head device

NASA Astrophysics Data System (ADS)

Oliveira, Anderson S.; Schlink, Bryan R.; Hairston, W. David; König, Peter; Ferris, Daniel P.

2016-06-01

Objective. Electroencephalography (EEG) can assess brain activity during whole-body motion in humans but head motion can induce artifacts that obfuscate electrocortical signals. Definitive solutions for removing motion artifact from EEG have yet to be found, so creating methods to assess signal processing routines for removing motion artifact are needed. We present a novel method for investigating the influence of head motion on EEG recordings as well as for assessing the efficacy of signal processing approaches intended to remove motion artifact. Approach. We used a phantom head device to mimic electrical properties of the human head with three controlled dipolar sources of electrical activity embedded in the phantom. We induced sinusoidal vertical motions on the phantom head using a custom-built platform and recorded EEG signals with three different acquisition systems while the head was both stationary and in varied motion conditions. Main results. Recordings showed up to 80% reductions in signal-to-noise ratio (SNR) and up to 3600% increases in the power spectrum as a function of motion amplitude and frequency. Independent component analysis (ICA) successfully isolated the three dipolar sources across all conditions and systems. There was a high correlation (r > 0.85) and marginal increase in the independent components’ (ICs) power spectrum (˜15%) when comparing stationary and motion parameters. The SNR of the IC activation was 400%-700% higher in comparison to the channel data SNR, attenuating the effects of motion on SNR. Significance. Our results suggest that the phantom head and motion platform can be used to assess motion artifact removal algorithms and compare different EEG systems for motion artifact sensitivity. In addition, ICA is effective in isolating target electrocortical events and marginally improving SNR in relation to stationary recordings.

Accuracy of Carotid Duplex Criteria in Diagnosis of Significant Carotid Stenosis in Asian Patients.

PubMed

Dharmasaroja, Pornpatr A; Uransilp, Nattaphol; Watcharakorn, Arvemas; Piyabhan, Pritsana

2018-03-01

Extracranial carotid stenosis can be diagnosed by velocity criteria of carotid duplex. Whether they are accurately applied to define severity of internal carotid artery (ICA) stenosis in Asian patients needs to be proved. The purpose of this study was to evaluate the accuracy of 2 carotid duplex velocity criteria in defining significant carotid stenosis. Carotid duplex studies and magnetic resonance angiography were reviewed. Criteria 1 was recommended by the Society of Radiologists in Ultrasound; moderate stenosis (50%-69%): peak systolic velocity (PSV) 125-230 cm/s, diastolic velocity (DV) 40-100 cm/s; severe stenosis (>70%): PSV greater than 230 cm/s, DV greater than 100 cm/s. Criteria 2 used PSV greater than 140 cm/s, DV less than 110 cm/s to define moderate stenosis (50%-75%) and PSV greater than 140 cm/s, DV greater than 110 cm/s for severe stenosis (76%-95%). A total of 854 ICA segments were reviewed. There was moderate stenosis in 72 ICAs, severe stenosis in 50 ICAs, and occlusion in 78 ICAs. Criteria 2 had slightly lower sensitivity, whereas higher specificity and accuracy than criteria 1 were observed in detecting moderate stenosis (criteria 1: sensitivity 95%, specificity 83%, accuracy 84%; criteria 2: sensitivity 92%, specificity 92%, and accuracy 92%). However, in detection of severe ICA stenosis, no significant difference in sensitivity, specificity, and accuracy was found (criteria 1: sensitivity 82%, specificity 99.57%, accuracy 98%; criteria 2: sensitivity 86%, specificity 99.68%, and accuracy 99%). In the subgroup of moderate stenosis, the criteria using ICA PSV greater than 140 cm/s had higher specificity and accuracy than the criteria using ICA PSV 125-230 cm/s. However, there was no significant difference in detection of severe stenosis or occlusion of ICA. Copyright © 2018 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Association of Hypercalcemia Before Treatment With Hypocalcemia After Treatment in Dogs With Primary Hyperparathyroidism.

PubMed

Dear, J D; Kass, P H; Della Maggiore, A M; Feldman, E C

2017-03-01

Development of hypocalcemia after treatment of hyperparathyroidism results in increased costs and risk of poorer outcomes. Previous studies have shown conflicting data about predictors of hypocalcemia after these procedures. The objective of this study was to investigate whether ionized calcium (iCa) concentrations before treatment are predictive of hypocalcemia or its clinical signs after surgical removal or heat ablation in dogs with primary hyperparathyroidism. Fifty-four dogs with primary hyperparathyroidism (29 female, 25 male; 49 retrospective, 5 prospective). Dogs were enrolled if they met the inclusion criteria: persistent hypercalcemia (iCa >1.41 mmol/L) due to primary hyperparathyroidism and absence of preemptive calcitriol treatment. All dogs were treated with parathyroidectomy (n = 37) or percutaneous ultrasound-guided heat ablation (n = 17). After treatment, iCa was monitored twice daily until plateau or intervention. There was a moderate correlation between before-treatment hypercalcemia and after-treatment hypocalcemia. The prospective study was terminated due to ethical concerns given findings in the retrospective section. All dogs were placed into groups according to their pretreatment iCa: 1.46-1.61 mmol/L, 1.62-1.71 mmol/L, iCa 1.72-1.81 mmol/L, or >1.81 mmol/L. After treatment, the mean lowest iCa for each group, respectively, was 1.19, 1.18, 1.13, and 1.01 mmol/L. There was a significant association between higher group and proportion of dogs with iCa <1.00 mmol/L (P = .014). This study demonstrates a moderate correlation between iCa concentration before treatment and hypocalcemia after treatment. Dogs with higher initial iCa concentrations should be treated to prevent rapid decline and development of clinical hypocalcemia. Copyright © 2017 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Enhanced, rapid occlusion of carotid and vertebral arteries using the AMPLATZER Vascular Plug II device: the Duke Cerebrovascular Center experience in 8 patients with 22 AMPLATZER Vascular Plug II devices.

PubMed

Mihlon, Frank; Agrawal, Abishek; Nimjee, Shahid M; Ferrell, Andrew; Zomorodi, Ali R; Smith, Tony P; Britz, Gavin W

2015-01-01

Therapeutic embolization of the common carotid artery (CCA), internal carotid artery (ICA), and vertebral artery (VA) is necessary in the treatment of a subset of chronic arteriovenous fistulas (AVFs), hemorrhages, highly vascularized neoplasms before resection, and giant aneurysms. There are currently no reports of the use of the AMPLATZER Vascular Plug II (AVP II) device to occlude the CCA, ICA, or VA. The objective of this article is to present the Duke Cerebrovascular Center experience using the AVP II device in neurointerventional applications. This case series is a retrospective review of all of the cases at Duke University Hospital in which an AVP II device was used in the CCA, ICA, or VA up to September 2012. The AVP II device was often used in conjunction with embolization coils or as multiple AVP II devices deployed in tandem. During 2010-2012, 8 cases meeting criteria were performed. These included 2 chronic VA to internal jugular AVFs, 1 hemorrhagic CCA to internal jugular AVF secondary to invasive head and neck squamous cell carcinoma, 1 ICA hemorrhage secondary to invasive head and neck squamous cell carcinoma, 1 ICA hemorrhage secondary to trauma, 1 ruptured ICA aneurysm, 1 giant petrous ICA aneurysm, and 1 case of cervical vertebral sarcoma requiring preoperative VA embolization. Successful occlusion of the target vessel was achieved in all 8 cases. There was 1 major complication that consisted of a watershed distribution cerebral infarct; however, this was related to emergent occlusion of the ICA in the setting of intracranial hemorrhage and was not a problem intrinsic to the AVP II device. The AVP II device is relatively large, self-expanding vascular occlusion device that safely allows enhanced, rapid take-down of the CCA, ICA, and VA with low risk of distal migration. Copyright © 2015 Elsevier Inc. All rights reserved.

Spontaneous Recanalization After Carotid Artery Dissection: The Case for an Ultrasound-Only Monitoring Strategy

PubMed Central

Lumsden, Sarah; Rosta, Gabor; Bismuth, Jean; Lumsden, Alan B.; Garami, Zsolt

2017-01-01

Dissection of the internal carotid artery (ICA) accounts for 5% to 25% of ischemic strokes in young adults. We report a case of spontaneous recanalization of a traumatic ICA dissection in which carotid duplex (CDU) and transcranial color-coded duplex ultrasound (TCCD) were used. A 47-year-old male presented with intermittent episodes of headache, blurry vision, anisocoria, and loss of taste sensation following a whiplash injury while body surfing. Magnetic resonance angiogram (MRA) of the neck revealed absent flow in the cavernous ICA and a clot at the skull base. Carotid duplex, used to further evaluate flow, demonstrated reverberating color Doppler and spectrum signal. A TCCD showed ICA occlusion and smaller-caliber intracranial ICA. The patient reported for follow-up after 1 month on anticoagulation therapy. Upon his return, CDU and TCCD were normal and the ICA showed normal color and spectrum signals. Computed tomography angiogram confirmed ultrasound findings of a dramatic improvement of ICA patency. Additionally, the patient reported that his headaches had resolved. Extracranial CDU and TCCD are useful for monitoring patient progress in cases of spontaneous recanalization following carotid artery dissection. These inexpensive and noninvasive imaging modalities proved to be critical in the initial and follow-up evaluations of the extracranial and intracranial vascular system, providing a strong alternative to expensive magnetic resonance imaging and invasive angiograms and offering more hemodynamic information than “static” MRA. PMID:29744017

Association of Inter-arm Blood Pressure Difference with Asymptomatic Intracranial and Extracranial Arterial Stenosis in Hypertension Patients.

PubMed

Wang, Yan; Zhang, Jin; Qian, Yuesheng; Tang, Xiaofeng; Ling, Huawei; Chen, Kemin; Li, Yan; Gao, Pingjin; Zhu, Dingliang

2016-07-14

Inter-arm blood pressure (BP) difference has been associated with ischemic stroke. Local atherosclerosis of stroke differ among vulnerable individuals, whereas intracranial arterial stenosis (ICAS) is more frequently affected Asians, and extracranial arterial stenosis (ECAS) is more prevalent among whites. We hereby sought to explore the association of inter-arm BP difference with ICAS and ECAS in stroke-free hypertensive patients in Chinese population. All the 885 subjects were evaluated of ICAS and ECAS through computerized tomographic angiography. Both arm BP was measured simultaneously by Vascular Profiler-1000 device. In the continuous study, ICAS was significantly associated with age, male, average brachial SBP, diabetes, anti-hypertensive treatment and inter-arm DBP difference. ECAS was associated with age, inter-arm SBP and LDL. In the categorical study, subjects with the top quartile of inter-arm DBP difference (≥4 mmHg) showed significantly higher risk of ICAS (OR = 2.109; 95% CI, 1.24-3.587). And the participants with the top quartile of inter-arm SBP difference (≥6 mmHg) showed significantly higher risk of ECAS (OR = 2.288; 95% CI, 1.309-3.998). In conclusion, we reported a diverse association of inter-arm SBP/DBP difference with the ICAS/ECAS. Inter-arm DBP difference might be the early symbol of ICAS in Chinese population, which need further verification in long-term cohort study.

Standardizing Imperata cylindrica--source material for quality allergen preparations.

PubMed

Bijli, Kaiser M; Singh, B P; Sridhara, Susheela; Gaur, S N; Arora, Naveen

2002-02-01

Tropical countries experience wide variations in daytime temperature and relative humidity. This affects the quality of the source material used for allergen extracts. The present study was undertaken to standardize the processing and preservation conditions of Imperata cylindrica grass pollen. I. cylindrica (Ic) inflorescence were freeze-dried, pollens sieved out and stored at -70 degrees C (IcA). Alternatively, the inflorescence were dried at room temperature and then at 37 degrees C, pollens sieved out and stored at 4 degrees C (IcB). The extracts prepared in PBS were analyzed in vivo by skin tests and in vitro by immunochemical methods. Reduced SDS-PAGE revealed 37 protein bands in IcA extract and 23 in IcB extract. IgE immunoblot with a pool of sera from Ic hypersensitive patients showed 30 allergenic bands in IcA and 14 in IcB. Immunoblot using anti-Ic rabbit sera revealed 33 antigenic bands in IcA and 22 in IcB. In both blots, the IcA extract exhibited sharp bands and the IcB extract exhibited diffuse bands. ELISA, ELISA inhibition and skin test procedures showed that IcA extracts had a higher potency than IcB extracts. Extracts prepared from -70 degrees C processed and preserved pollens (IcA) are allergenically more potent and contain a greater number of major and minor allergens than IcB extracts.

Anatomical nuances of the internal carotid artery in relation to the quadrangular space.

PubMed

Dolci, Ricardo L L; Ditzel Filho, Leo F S; Goulart, Carlos R; Upadhyay, Smita; Buohliqah, Lamia; Lazarini, Paulo R; Prevedello, Daniel M; Carrau, Ricardo L

2018-01-01

OBJECTIVE The aim of this study was to evaluate the anatomical variations of the internal carotid artery (ICA) in relation to the quadrangular space (QS) and to propose a classification system based on the results. METHODS A total of 44 human cadaveric specimens were dissected endonasally under direct endoscopic visualization. During the dissection, the anatomical variations of the ICA and their relationship with the QS were noted. RESULTS The space between the paraclival ICAs (i.e., intercarotid space) can be classified as 1 of 3 different shapes (i.e., trapezoid, square, or hourglass) based on the trajectory of the ICAs. The ICA trajectories also directly influence the volumetric area of the QS. Based on its geometry, the QS was classified as one of the following: 1) Type A has the smallest QS area and is associated with a trapezoid intercarotid space, 2) Type B corresponds to the expected QS area (not minimized or enlarged) and is associated with a square intercarotid space, and 3) Type C has the largest QS area and is associated with an hourglass intercarotid space. CONCLUSIONS The different trajectories of the ICAs can modify the area of the QS and may be an essential parameter to consider for preoperative planning and defining the most appropriate corridor to reach Meckel's cave. In addition, ICA trajectories should be considered prior to surgery to avoid injuring the vessels.

Automatic removal of eye-movement and blink artifacts from EEG signals.

PubMed

Gao, Jun Feng; Yang, Yong; Lin, Pan; Wang, Pei; Zheng, Chong Xun

2010-03-01

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.

A comparison of the metabolism of the abortifacient compounds from Ponderosa Pine needles in conditioned versus naive cattle

USDA-ARS?s Scientific Manuscript database

Isocupressic acid (ICA) is the abortifacient compound in ponderosa pine needles, which can cause late term abortions in cattle. However, cattle rapidly metabolize ICA to agathic acid and subsequent metabolites. When pine needles are dosed orally to cattle, no ICA is detected in their serum while a...

Proceedings of the ICA Conference (4th, Washington, D.C., November 11-13, 1970).

ERIC Educational Resources Information Center

ICA Information, 1971

1971-01-01

The proceedings of the Fourth Conference of the Intergovernmental Council for ADP-ICA, are contained in this, the 7th issue of the ICA-Information, the Council's official publication. The proceedings include the edited minutes of the sessions, which covered the following topics: Management information systems for the government, Policy…

Evaluation of cerebral blood flow changes in focal cerebral ischemia rats by using transcranial Doppler ultrasonography.

PubMed

Li, Le; Ke, Zheng; Tong, Kai Yu; Ying, Michael

2010-04-01

Ischemic stroke is typically characterized by the disruption of cerebral blood flow. This study aimed to consecutively evaluate the cerebral blood flow changes in a focal ischemia rat model during the occlusion-reperfusion procedure and along the recovery stage after stroke. In 12 Sprague Dawley (SD) rats, a middle cerebral artery occlusion/reperfusion (MCAo/r) surgery was conducted, which combines a permanent occlusion of the right common carotid artery (CCA), external carotid artery (ECA) and a transient occlusion of the right internal carotid artery (ICA) and middle cerebral artery (MCA) with a monofilament introduced from the proximal ICA towards the distal right ICA then removed after 90 min. Blood flow velocity (BFV) from the concerned arteries were measured using ultrasonography (13-4 MHz) at the basal stage before the surgery, after the reperfusion stage and during the post-stroke status. At reperfusion stage and after, BFV increased significantly in the left ICA and in the basilar artery (BA) (starting from post-24 h, p < 0.05 vs. basal). Moreover, BFV were reversed in the distal right ICA and reflow was recorded in the right MCA. Time-average maximum BFV in the right MCA at reperfusion and post-stroke 24-96 h was decreased significantly (p < 0.05 vs. basal). The reversed flow in the right ICA was enabled by the settlement of the collateral supply through the circle of Willis which consisted in higher BFV in the opposite ICA and in the BA still 24 h, although the proximal right ICA remain occluded. Ultrasound measurement of BFV helps to provide information on the redistribution of the blood flow supply after the onset of stroke. Copyright 2010 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Emergency reconstructive endovascular management of intraoperative complications involving the internal carotid artery from trans-sphenoidal surgery.

PubMed

Griauzde, Julius; Gemmete, Joseph J; Pandey, Aditya S; McKean, Erin L; Sullivan, Stephen E; Chaudhary, Neeraj

2015-01-01

To report our experience with intraoperative complications involving the internal carotid artery (ICA) during trans-sphenoidal surgery and their outcome with reconstructive endovascular management. A retrospective review was conducted of patients with an ICA injury related to trans-sphenoidal surgery from 2000 to 2012. Demographic data, clinical charts, indications for treatment, radiographic images, lesion characteristics, operative notes, endovascular procedure notes and post-procedure hospital course were reviewed. Three men and one woman of mean age of 52 years (range 33-74) were identified. The lesions included two macroadenomas, one meningioma and one chondrosarcoma. Risk factors for ICA rupture included two patients with carotid dehiscence, one with sphenoid septal attachment to the ICA, two with revision surgery, one with prior radiation to the tumor, one with bromocriptine treatment and two with acromegaly. In three patients, covered stent placement achieved hemostasis at the site of injury within the ICA. One patient developed delayed bleeding 6 h after covered stent placement and underwent successful endovascular occlusion of the ICA but died 6 days after the injury. The fourth patient had an intraoperative ICA stroke requiring suction thrombectomy, thrombolysis, stent placement and evacuation of an epidural hematoma. At 1-year follow-up, two patients had a modified Rankin score (mRS) and National Institute of Health Stroke Scale (NIHSS) score of 0; in the patient who had a stroke the mRS score was 1 and the NIHSS score 2. Endovascular management with arterial reconstruction is helpful in the treatment of ICA injuries during trans-sphenoidal surgery. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A novel PKC-ι inhibitor abrogates cell proliferation and induces apoptosis in neuroblastoma.

PubMed

Pillai, Prajit; Desai, Shraddha; Patel, Rekha; Sajan, Mini; Farese, Robert; Ostrov, David; Acevedo-Duncan, Mildred

2011-05-01

Protein Kinase C-iota (PKC-ι), an atypical protein kinase C isoform manifests its potential as an oncogene by targeting various aspects of cancer cells such as growth, invasion and survival. PKC-ι confers resistance to drug-induced apoptosis in cancer cells. The acquisition of drug resistance is a major obstacle to good prognosis in neuroblastoma. The focus of this research was to identify the efficacy of [4-(5-amino-4-carbamoylimidazol-1-yl)-2,3-dihydroxycyclopentyl] methyl dihydrogen phosphate (ICA-1) as a novel PKC-ι inhibitor in neuroblastoma cell proliferation and apoptosis. ICA-1 specifically inhibits the activity of PKC-ι but not that of PKC-zeta (PKC-ζ), the closely related atypical PKC family member. The IC(50) for the kinase activity assay was approximately 0.1μM which is 1000 times less than that of aurothiomalate, a known PKC-ι inhibitor. Cyclin dependent kinase 7 (Cdk7) phosphorylates cyclin dependent kinases (cdks) and promotes cell proliferation. Our data shows that PKC-ι is an in vitro Cdk7 kinase and the phosphorylation of Cdk7 by PKC-ι was potently inhibited by ICA-1. Furthermore, our data shows that neuroblastoma cells proliferate via a PKC-ι/Cdk7/cdk2 cell signaling pathway and ICA-1 mediates its antiproliferative effects by inhibiting this pathway. ICA-1 (0.1μM) inhibited the in vitro proliferation of BE(2)-C neuroblastoma cells by 58% (P=0.01). Additionally, ICA-1 also induced apoptosis in neuroblastoma cells. Interestingly, ICA-1 did not affect the proliferation of normal neuronal cells suggesting its potential as chemotherapeutic with low toxicity. Hence, our results emphasize the potential of ICA-1 as a novel PKC-ι inhibitor and chemotherapeutic agent for neuroblastoma. Published by Elsevier Ltd.

Attenuated response of L-type calcium current to nitric oxide in atrial fibrillation.

PubMed

Rozmaritsa, Nadiia; Christ, Torsten; Van Wagoner, David R; Haase, Hannelore; Stasch, Johannes-Peter; Matschke, Klaus; Ravens, Ursula

2014-03-01

Nitric oxide (NO) synthesized by cardiomyocytes plays an important role in the regulation of cardiac function. Here, we studied the impact of NO signalling on calcium influx in human right atrial myocytes and its relation to atrial fibrillation (AF). Right atrial appendages (RAAs) were obtained from patients in sinus rhythm (SR) and AF. The biotin-switch technique was used to evaluate endogenous S-nitrosylation of the α1C subunit of L-type calcium channels. Comparing SR to AF, S-nitrosylation of Ca(2+) channels was similar. Direct effects of the NO donor S-nitroso-N-acetyl-penicillamine (SNAP) on L-type calcium current (ICa,L) were studied in cardiomyocytes with standard voltage-clamp techniques. In SR, ICa,L increased with SNAP (100 µM) by 48%, n/N = 117/56, P < 0.001. The SNAP effect on ICa,L involved activation of soluble guanylate cyclase and protein kinase A. Specific inhibition of phosphodiesterase (PDE)3 with cilostamide (1 µM) enhanced ICa,L to a similar extent as SNAP. However, when cAMP was elevated by PDE3 inhibition or β-adrenoceptor stimulation, SNAP reduced ICa,L, pointing to cGMP-cAMP cross-regulation. In AF, the stimulatory effect of SNAP on ICa,L was attenuated, while its inhibitory effect on isoprenaline- or cilostamide-stimulated current was preserved. cGMP elevation with SNAP was comparable between the SR and AF group. Moreover, the expression of PDE3 and soluble guanylate cyclase was not reduced in AF. NO exerts dual effects on ICa,L in SR with an increase of basal and inhibition of cAMP-stimulated current, and in AF NO inhibits only stimulated ICa,L. We conclude that in AF, cGMP regulation of PDE2 is preserved, but regulation of PDE3 is lost.

Current understanding of chronic total occlusion of the internal carotid artery

PubMed Central

Xu, Baofeng; Li, Chao; Guo, Yunbao; Xu, Kan; Yang, Yi; Yu, Jinlu

2018-01-01

At present, there is limited understanding of chronic total occlusion (CTO) of the internal carotid artery (ICA). Therefore, the present report collected related cases from PubMed and reviewed the literature. Cerebral vessels may form collateral circulation immediately or gradually following CTO of the ICA. The natural history of CTO of the ICA includes a variety of outcomes, all of which are biased toward a non-benign progressive process and are characterized by insufficient cerebral perfusion, embolus detachment and cognitive dysfunction. The majority of cases of CTO of the ICA require treatment. In early studies, the results of external-ICA bypass were unsatisfactory, while recanalization is now considered the only viable option. The current treatment indications mainly depend on the degree of injury to the cerebrovascular reserve and the extent to which the oxygen extraction fraction is increased. The length, height and duration of ICA occlusion are also relevant, though more frequently, the condition depends on multiple factors. Endovascular interventional recanalization, carotid endarterectomy (CEA) and hybrid surgery may be conducted in a select group of patients. As novel materials are developed, the success rate of simple recanalization may gradually increase; however, hybrid surgery may be more representative of the current trend, as advanced CEA can remove carotid atherosclerosis plaques, thus reducing the technological demands of the subsequent interventional recanalization. There are many complications that may result from recanalization following CTO of the ICA, including hyperperfusion and technical errors; therefore, the operation must be conducted carefully. If the recanalization is successful, it typically results in a stable improvement of patient condition in the long term. However, despite these conclusions, more studies are required in the future to further improve current understanding of CTO of the ICA. PMID:29435269

Investigation of the effect of biologically active threo-Ds-isocitric acid on oxidative stress in Paramecium caudatum.

PubMed

Morgunov, Igor G; Karpukhina, Olga V; Kamzolova, Svetlana V; Samoilenko, Vladimir A; Inozemtsev, Anatoly N

2018-01-02

The effect of biologically active form (threo-Ds-) of isocitric acid (ICA) on oxidative stress was studied using the infusorian Paramecium caudatum stressed by hydrogen peroxide and salts of some heavy metals (Cu, Pb, Zn, and Cd). ICA at concentrations between 0.5 and 10 mM favorably influenced the infusorian cells with oxidative stress induced by the toxicants studied. The maximal antioxidant effect of ICA was observed at its concentration 10 mM irrespective of the toxicant used (either H 2 O 2 or heavy metal ions). ICA was found to be a more active antioxidant than ascorbic acid. Biologically active pharmaceutically pure threo-Ds-ICA was produced through cultivation of the yeast Yarrowia lipolytica and isolated from the culture liquid in the form of crystalline monopotassium salt with a purity of 99.9%.

Traumatic pseudoaneurysm of the intracavernous ICA presenting with massive epistaxis: imaging diagnosis and endovascular treatment.

PubMed

Han, M H; Sung, M W; Chang, K H; Min, Y G; Han, D H; Han, M C

1994-03-01

Traumatic pseudoaneurysm of the intracavernous internal carotid artery (ICA) is a very rare cause of epistaxis but is a life-threatening clinical situation when left untreated. The authors have experienced four cases of traumatic pseudoaneurysm involving the intracavernous ICA. Delayed massive epistaxes developed 1 to 8 months after trauma and initial transient epistaxis in all four patients. Three of the cases were successfully managed by the detachable balloon occlusion (DBO) of the ICA along with the aneurysm openings. In one case, a large pseudoaneurysm destroying a large area of the central skull base with peripheral blood clot was demonstrated on computed tomography, magnetic resonance imaging, and angiography; this patient died due to massive epistaxis before the trial of DBO. Imaging findings of pseudoaneurysms involving the intracavernous ICA in the four cases are described, and the role of endovascular treatment is discussed.

Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density.

PubMed

Lęski, Szymon; Kublik, Ewa; Swiejkowski, Daniel A; Wróbel, Andrzej; Wójcik, Daniel K

2010-12-01

Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4 × 5 × 7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.

Immunochromatographic strip assay for detection of bioactive Ganoderma triterpenoid, ganoderic acid A in Ganoderma lingzhi.

PubMed

Sakamoto, Seiichi; Kikkawa, Nao; Kohno, Toshitaka; Shimizu, Kuniyoshi; Tanaka, Hiroyuki; Morimoto, Satoshi

2016-10-01

Ganoderic acid A (GAA) is one of the major Ganoderma triterpenes produced by medicinal mushroom belonging to the genus Ganoderma (Ganodermataceae). Due to its interesting pharmacological activities, Ganoderma species have been traditionally used in China for the treatment of various diseases. Herein, we developed a colloidal gold-based immunochromatographic strip assay (ICA) for the rapid detection of GAA using highly specific monoclonal antibody against GAA (MAb 12A) conjugated with gold nanoparticles. Using the developed ICA, the detection of GAA can be completed within 15min after dipping the test strip into an analyte solution with the limit of detection (LOD) for GAA of ~500ng/mL. In addition, this system makes it possible to perform a semi-quantitative analysis of GAA in Ganoderma lingzhi, where high reliability was evaluated by enzyme-linked immunosorbent assay (ELISA). The newly developed ICA can potentially be applied to the standardization of Ganoderma using GAA as an index because GAA is major triterpenoid present much in the mushroom. Copyright © 2016. Published by Elsevier B.V.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hauth, Elke A. M.; Drescher, Robert; Forsting, Michael

We present a patient with a symptomatic, high-grade stenosis of the internal carotid artery (ICA) and contraindication for open surgery. Endovascular treatment was attempted, but stent placement was not possible. In view of good collateral flow to the related hemisphere, embolization of the stenosis of the ICA with Guglielmi detachable coils (GDCs) was performed to occlude the vessel. No complications occurred during the procedure or in the 1-year follow-up period. In cases where open surgery or endovascular treatment of a stenosis of the ICA are contraindicated or not possible, therapeutic occlusion of the stenotic ICA could be an alternative treatmentmore » option in patients with good collateral flow.« less

Social justice and intercountry adoptions: the role of the U.S. social work community.

PubMed

Roby, Jini L; Rotabi, Karen; Bunkers, Kelley M

2013-10-01

Using social justice as the conceptual foundation, the authors present the structural barriers to socially just intercountry adoptions (ICAs) that can exploit and oppress vulnerable children and families participating in ICAs. They argue that such practices threaten the integrity of social work practice in that arena and the survival of ICA as a placement option. Government structures, disparity of power between countries and families on both sides, perceptions regarding poverty, cultural incompetence, misconceptions about orphans and orphanages, lack of knowledge about the impact of institution-based care, and the profit motive are driving forces behind the growing shadow of unethical ICAs. The U.S. social work community has a large role and responsibility in addressing these concerns as the United States receives the most children adopted through ICAs of all receiving countries. In addition to the centrality of social justice as a core value of the profession, the responsibility to carry out ethical and socially just ICA has recently increased as a matter of law, under the implementation legislation to the Hague Convention on Intercountry Adoption. While acknowledging that these issues are complex, authors provide suggestions for corrective policy and practice measures.

[Receptor status (ER and PgR) determined with histochemical and biochemical methods in breast carcinoma].

PubMed

Simone, G; Paradiso, A; Cirillo, R; Mangia, A; Rella, G; Wiesel, S; Petroni, S; De Benedictis, G; De Lena, M

1991-01-01

Recently, a method similar to ER.ICA has been proposed for the progesterone receptor (PgR) using two monoclonal antibodies, JZB39 and KD68, specific for human PgR and characterized by a molecular weight of 95 and 120 Kd, respectively. A series of 73 breast cancer patients was studied with regards to ER and PgR using both immunocytochemical (ICA) and biochemical (DCC) assays. Results showed no substantial differences between the two methods when considering common clinical-pathological parameters. Overall agreement between ICA and DCC methods was found: 79% for PgR and 78% for ER. A slight quantitative correlation was also observed between the "score values" of the ICA method and the Fmol content of ER and PgR using the Brave-Pearson test (r = 0.49 for PgR; r = 0.43 for ER). Specificity of PgR.ICA method was 77% for PgR and 72% for ER; sensitivity was 82% and 83%, respectively. The ICA method is a reliable technique to assess PgR presence as well as ER. Further studies are necessary to evaluate the prognostic role of nuclear PgR.

Coiling of a Ruptured Large Internal Carotid Artery Aneurysm via Extracranial-Intracranial Saphenous Vein Bypass Graft Just After Proximal Ligation of the Internal Carotid Artery.

PubMed

Tajima, Hayato; Araki, Yoshio; Izumi, Takashi; Nishihori, Masahiro; Okamoto, Sho; Wakabayashi, Toshihiko

2017-02-01

Combined direct and endovascular surgery has been performed to treat large to giant internal carotid artery (ICA) aneurysms. This report describes successful treatment of a large ICA aneurysm by coiling of the aneurysm via an extracranial-intracranial saphenous vein (SV) graft just after bypass and ICA proximal ligation. A 66-year-old woman presented with a left ICA supraclinoid aneurysm with progressive visual field defect and impaired visual acuity in the left eye. While waiting for scheduled surgery, she experienced a subarachnoid hemorrhage. An extracranial-intracranial high-flow bypass using an SV graft and proximal ligation of the ICA were performed. Coiling of the aneurysm was immediately performed successfully via the SV bypass graft. The patient experienced no new neurologic deficit after this treatment. Follow-up radiologic evaluations using magnetic resonance imaging and magnetic resonance angiography revealed complete aneurysm occlusion. Aneurysm coiling via an extracranial-intracranial SV bypass graft could offer an alternative when an antegrade access route to the ICA is not used because of prior parent artery ligation. Copyright © 2016 Elsevier Inc. All rights reserved.

Intracellular Cs+ activates the PKA pathway, revealing a fast, reversible, Ca2+-dependent inactivation of L-type Ca2+ current.

PubMed

Brette, Fabien; Lacampagne, Alain; Sallé, Laurent; Findlay, Ian; Le Guennec, Jean-Yves

2003-08-01

Inactivation of the L-type Ca2+ current (ICaL) was studied in isolated guinea pig ventricular myocytes with different ionic solutions. Under basal conditions, ICaL of 82% of cells infused with Cs+-based intracellular solutions showed enhanced amplitude with multiphasic decay and diastolic depolarization-induced facilitation. The characteristics of ICaL in this population of cells were not due to contamination by other currents or an artifact. These phenomena were reduced by ryanodine, caffeine, cyclopiazonic acid, the protein kinase A inhibitor H-89, and the cAMP-dependent protein kinase inhibitor. Forskolin and isoproterenol increased ICaL by only approximately 60% in these cells. Cells infused with either N-methyl-d-glucamine or K+-based intracellular solutions did not show multiphasic decay or facilitation under basal conditions. Isoproterenol increased ICaL by approximately 200% in these cells. In conclusion, we show that multiphasic inactivation of ICaL is due to Ca2+-dependent inactivation that is reversible on a time scale of tens of milliseconds. Cs+ seems to activate the cAMP-dependent protein kinase pathway when used as a substitute for K+ in the pipette solution.

Competitive immunochromatographic assay for the detection of the organophosphorus pesticide chlorpyrifos

PubMed Central

Kim, Young Ah; Lee, Eun-Hye; Kim, Kwang-Ok; Lee, Yong Tae; Hammock, Bruce D.; Lee, Hye-Sung

2014-01-01

An immunochromatographic assay (ICA) based on competitive antigen-coated format using colloidal gold as the label was developed for the detection of the organophosphorus insecticide chlorpyrifos. The ICA test strip consisted of a membrane with a detection zone, a sample pad and an absorbent pad. The membrane was separately coated with chlorpyrifos hapten-OVA conjugate (test line) and anti-mouse IgG (control line). Based on the fact that the competition is between the migrating analyte in the sample and the analyte hapten immobilized on the test strip for the binding sites of the antibody-colloidal gold (Ab-CG) conjugate migrating on the test strip, this study suggests that the relative migration speed between the two migrating substances is a critically important factor for the sensitive detection by competitive ICA. This criterion was utilized for the confirmation of appropriateness of a nitrocellulose (NC) membrane for chlorpyrifos ICA. The detection limit of the ICA for chlorpyrifos standard and chlorpyrifos spiked into agricultural samples were 10 and 50 ng mL−1, respectively. The assay time for the ICA test was less than 10 min, suitable for rapid on-site testing of chlorpyrifos. PMID:21504817

Differential Activation of Vascular Smooth Muscle Kv7.4, Kv7.5, and Kv7.4/7.5 Channels by ML213 and ICA-069673

PubMed Central

Brueggemann, Lyubov I.; Haick, Jennifer M.; Cribbs, Leanne L.

2014-01-01

Recent research suggests that smooth muscle cells express Kv7.4 and Kv7.5 voltage-activated potassium channels, which contribute to maintenance of their resting membrane voltage. New pharmacologic activators of Kv7 channels, ML213 (N-mesitybicyclo[2.2.1]heptane-2-carboxamide) and ICA-069673 N-(6-chloropyridin-3-yl)-3,4-difluorobenzamide), have been reported to discriminate among channels formed from different Kv7 subtypes. We compared the effects of ML213 and ICA-069673 on homomeric human Kv7.4, Kv7.5, and heteromeric Kv7.4/7.5 channels exogenously expressed in A7r5 vascular smooth muscle cells. We found that, despite its previous description as a selective activator of Kv7.2 and Kv7.4, ML213 significantly increased the maximum conductance of homomeric Kv7.4 and Kv7.5, as well as heteromeric Kv7.4/7.5 channels, and induced a negative shift of their activation curves. Current deactivation rates decreased in the presence of the ML213 (10 μM) for all three channel combinations. Mutants of Kv7.4 (W242L) and Kv7.5 (W235L), previously found to be insensitive to another Kv7 channel activator, retigabine, were also insensitive to ML213 (10 μM). In contrast to ML213, ICA-069673 robustly activated Kv7.4 channels but was significantly less effective on homomeric Kv7.5 channels. Heteromeric Kv7.4/7.5 channels displayed intermediate responses to ICA-069673. In each case, ICA-069673 induced a negative shift of the activation curves without significantly increasing maximal conductance. Current deactivation rates decreased in the presence of ICA-069673 in a subunit-specific manner. Kv7.4 W242L responded to ICA-069673-like wild-type Kv7.4, but a Kv7.4 F143A mutant was much less sensitive to ICA-069673. Based on these results, ML213 and ICA-069673 likely bind to different sites and are differentially selective among Kv7.4, Kv7.5, and Kv7.4/7.5 channel subtypes. PMID:24944189

Deriving frequency-dependent spatial patterns in MEG-derived resting state sensorimotor network: A novel multiband ICA technique.

PubMed

Nugent, Allison C; Luber, Bruce; Carver, Frederick W; Robinson, Stephen E; Coppola, Richard; Zarate, Carlos A

2017-02-01

Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band-limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass-filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well-characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single-band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. Hum Brain Mapp 38:779-791, 2017. © 2016 Wiley Periodicals, Inc. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

A new approach to spike sorting for multi-neuronal activities recorded with a tetrode--how ICA can be practical.

PubMed

Takahashi, Susumu; Anzai, Yuichiro; Sakurai, Yoshio

2003-07-01

Multi-neuronal recording with a tetrode is a powerful technique to reveal neuronal interactions in local circuits. However, it is difficult to detect precise spike timings among closely neighboring neurons because the spike waveforms of individual neurons overlap on the electrode when more than two neurons fire simultaneously. In addition, the spike waveforms of single neurons, especially in the presence of complex spikes, are often non-stationary. These problems limit the ability of ordinary spike sorting to sort multi-neuronal activities recorded using tetrodes into their single-neuron components. Though sorting with independent component analysis (ICA) can solve these problems, it has one serious limitation that the number of separated neurons must be less than the number of electrodes. Using a combination of ICA and the efficiency of ordinary spike sorting technique (k-means clustering), we developed an automatic procedure to solve the spike-overlapping and the non-stationarity problems with no limitation on the number of separated neurons. The results for the procedure applied to real multi-neuronal data demonstrated that some outliers which may be assigned to distinct clusters if ordinary spike-sorting methods were used can be identified as overlapping spikes, and that there are functional connections between a putative pyramidal neuron and its putative dendrite. These findings suggest that the combination of ICA and k-means clustering can provide insights into the precise nature of functional circuits among neurons, i.e. cell assemblies.

Separation of Doppler radar-based respiratory signatures.

PubMed

Lee, Yee Siong; Pathirana, Pubudu N; Evans, Robin J; Steinfort, Christopher L

2016-08-01

Respiration detection using microwave Doppler radar has attracted significant interest primarily due to its unobtrusive form of measurement. With less preparation in comparison with attaching physical sensors on the body or wearing special clothing, Doppler radar for respiration detection and monitoring is particularly useful for long-term monitoring applications such as sleep studies (i.e. sleep apnoea, SIDS). However, motion artefacts and interference from multiple sources limit the widespread use and the scope of potential applications of this technique. Utilising the recent advances in independent component analysis (ICA) and multiple antenna configuration schemes, this work investigates the feasibility of decomposing respiratory signatures into each subject from the Doppler-based measurements. Experimental results demonstrated that FastICA is capable of separating two distinct respiratory signatures from two subjects adjacent to each other even in the presence of apnoea. In each test scenario, the separated respiratory patterns correlate closely to the reference respiration strap readings. The effectiveness of FastICA in dealing with the mixed Doppler radar respiration signals confirms its applicability in healthcare applications, especially in long-term home-based monitoring as it usually involves at least two people in the same environment (i.e. two people sleeping next to each other). Further, the use of FastICA to separate involuntary movements such as the arm swing from the respiratory signatures of a single subject was explored in a multiple antenna environment. The separated respiratory signal indeed demonstrated a high correlation with the measurements made by a respiratory strap used currently in clinical settings.

Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks.

PubMed

Calhoun, Vince D; Kiehl, Kent A; Pearlson, Godfrey D

2008-07-01

Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called "resting state networks"); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer's disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail. (c) 2008 Wiley-Liss, Inc.

Modulation of Temporally Coherent Brain Networks Estimated Using ICA at Rest and During Cognitive Tasks

PubMed Central

Calhoun, Vince D.; Kiehl, Kent A.; Pearlson, Godfrey D.

2009-01-01

Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called “resting state networks”); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer’s disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail. PMID:18438867

Unilateral agenesis of internal carotid artery associated with congenital combined pituitary hormone deficiency and pituitary stalk interruption without HESX1, LHX4 or OTX2 mutation: a case report.

PubMed

Lamine, Faïza; Kanoun, Faouzi; Chihaoui, Melika; Saveanu, Alexandru; Menif, Emna; Barlier, Anne; Enjalbert, Alain; Brue, Thierry; Slimane, Hédia

2012-12-01

Agenesis of internal carotid artery (ICA) is an unusual finding in subjects with congenital Combined Pituitary hormone deficiency (CPHD) with only nine cases being reported to date but to our best knowledge none of them was genetically investigated. A 10-years old girl presented with severe growth failure (height 103 cm) with substantial bone age delay (3 years). She had no history of perinatal insults or familial CPHD. There was no evidence of mental retardation or craniofacial dysmorphism or ophtalmological abnormalities. She was first diagnosed with GH and TSH deficiency. Cerebral magnetic resonance imaging (MRI) showed hypoplastic anterior pituitary, flat sella turcica, absent pituitary stalk with ectopic posterior pituitary as well as agenesis of the left ICA and the left carotid canal. Genomic analysis of pituitary transcription factor HESX1, LHX4 and OTX2 showed no mutations. Treatment with GH and thyroxine was started. The patient remained free of neurovascular symptoms for 5 years but she presented at the age of 15 years with delayed puberty related to an evolving gonadotropin deficiency. ICA agenesis associated with CPHD is unusual and is often asymptomatic in children. Since the CPHD with pituitary stalk interruption cannot be due to HESX1, LHX4 or OTX2 mutation in our case, other pathogenetic mechanisms may be responsible for CPHD associated with unilateral ICA agenesis.

Association of Inter-arm Blood Pressure Difference with Asymptomatic Intracranial and Extracranial Arterial Stenosis in Hypertension Patients

PubMed Central

Wang, Yan; Zhang, Jin; Qian, Yuesheng; Tang, Xiaofeng; Ling, Huawei; Chen, Kemin; Li, Yan; Gao, Pingjin; Zhu, Dingliang

2016-01-01

Inter-arm blood pressure (BP) difference has been associated with ischemic stroke. Local atherosclerosis of stroke differ among vulnerable individuals, whereas intracranial arterial stenosis (ICAS) is more frequently affected Asians, and extracranial arterial stenosis (ECAS) is more prevalent among whites. We hereby sought to explore the association of inter-arm BP difference with ICAS and ECAS in stroke-free hypertensive patients in Chinese population. All the 885 subjects were evaluated of ICAS and ECAS through computerized tomographic angiography. Both arm BP was measured simultaneously by Vascular Profiler-1000 device. In the continuous study, ICAS was significantly associated with age, male, average brachial SBP, diabetes, anti-hypertensive treatment and inter-arm DBP difference. ECAS was associated with age, inter-arm SBP and LDL. In the categorical study, subjects with the top quartile of inter-arm DBP difference (≥4 mmHg) showed significantly higher risk of ICAS (OR = 2.109; 95% CI, 1.24–3.587). And the participants with the top quartile of inter-arm SBP difference (≥6 mmHg) showed significantly higher risk of ECAS (OR = 2.288; 95% CI, 1.309–3.998). In conclusion, we reported a diverse association of inter-arm SBP/DBP difference with the ICAS/ECAS. Inter-arm DBP difference might be the early symbol of ICAS in Chinese population, which need further verification in long-term cohort study. PMID:27412818

The critical role of the external carotid artery in cerebral perfusion of patients with total occlusion of the internal carotid artery.

PubMed

Dalainas, I; Avgerinos, E D; Daskalopoulos, M E; Papapetrou, A; Papasideris, C P; Katsikas, V; Xiromeritis, K; Moulakakis, K; Gianakopoulos, T; Liapis, C D

2012-02-01

The ipsilateral external carotid artery (ECA) can potentially provide an important collateral pathway for cerebral blood flow in the presence of occlusion or severe stenosis of the internal carotid artery (ICA), recovering up to 15% of the middle cerebral arterial flow. The aim of the study is to elucidate the role of ECA in cerebral flow of patients with total ICA occlusion. Retrospective study of prospectively collected data of 139 patients with total ICA occlusions. The patients were divided to symptomatic and asymptomatic and were categorized in four subgroups according to the stenosis rates: A) ipsilateral ECA<70% and contralateral internal carotid artery stenosis <70%; B) ipsilateral ECA stenosis <70% and contralateral internal carotid artery stenosis ≥70%; C) ipsilateral ECA stenosis ≥70% and contralateral ICA stenosis <70%; D) ipsilateral ECA stenosis ≥70% and contralateral ICA stenosis ≥75%. Fifty eight (41.7%) patients were asymptomatic. The highest rate (48.2%) of asymptomatic patients was in Group A. Among patients with strokes, the highest rate belonged in groups C and D (44.4% and 50% respectively) where ipsilateral ECA stenosis was ≥70% irrespectively of the contralateral ICA patency. Ipsilateral external carotid artery stenosis ≥70% proved to be and independent risk factor for symptom presentation (P=0.013). The study reveals the significant role of ECA patency in cerebral flow in patients with ICA occlusion.

Analyzing Transient Turbuelnce in a Stenosed Carotid Artery by Proper Orthogonal Decomposition

NASA Astrophysics Data System (ADS)

Grinberg, Leopold; Yakhot, Alexander; Karniadakis, George

2009-11-01

High resolution 3D simulation (involving 100M degrees of freedom) were employed to study transient turbulent flow in a carotid arterial bifurcation with a stenosed internal carotid artery (ICA). In the performed simulation an intermittent (in space and time) laminar-turbulent-laminar regime was observed. The simulation reveals the mechanism of the onset of turbulent flow in the stenosed ICA where the narrowing in the artery generates a strong jet flow. Time- and space-window Proper Orthogonal Decomposition (POD) was applied to quantify the different flow regimes in the occluded artery. A simplified version of the POD analysis that utilizes 2D slices only - more appropriate in the clinical setting - was also investigated.

75 FR 45560 - Airworthiness Directives; Rolls-Royce plc (RR) RB211-Trent 800 Series Turbofan Engines

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-03

... limitations section (ALS) of your instructions for continued airworthiness (ICA) to incorporate Task 05-10-01... ALS of your ICA by incorporating any revision of the Rolls-Royce Trent 800 TLM dated prior to the June... ALS of the ICA, any revision of the Rolls-Royce Trent 800 TLM earlier than the June 15, 2009. Other...

Experimental study of hemodynamics in the Circle of Willis.

PubMed

Zhu, Guangyu; Yuan, Qi; Yang, Jian; Yeo, Joon

2015-01-01

The Circle of Willis (CoW) is an important collateral pathway of the cerebral blood flow. An experimental study of the cerebral blood flow (CBF) distribution in different anatomical variations may help to a better understanding of the collateral mechanism of the CoW. An in-vitro test rig was developed to simulate the physiological cerebral blood flow in the CoW. Ten anatomical variations were considered in this study, include a set of different degrees of stenosis in L-ICA and L-ICA occlusion coexist with common anatomical variations. Volume flow rates of efferent arteries and pressure signals at the end of communicating arteries of each case were recorded. Physiological pressure waveforms were applied as inlet boundary condition. In the development of L-ICA stenosis, the total CBF decreases with the increase of stenosis degree. The blood supply of ipsilateral middle cerebral artery (MCA) was affected most by the stenosis of L-ICA. Anterior communicating artery (ACoA) and ipsilateral posterior communicating artery (PCoA) function as important collateral pathways of cerebral collateral circulation when unilateral stenosis occurred. The blood supply of anterior cerebral circulation was compensated by the posterior cerebral circulation through ipsilateral PCoA when L-ICA stenosis degree is greater than 40% and the affected side was compensated immediately by the unaffected side through ACoA. Blood flow of the anterior circulation and the total CBF reached the minimum among all cases studied when L-ICA occlusion coexist with the absence of PCoA. The results demonstrated the flow distribution patterns of the CoW under anatomical variations and clarified the collateral mechanism of the CoW. The flow ACoA is the most sensitive indexes to the morphology change of ipsilateral ICA. The relative independence of the circulation in anterior and posterior sections of the CoW is not broken and the function of ipsilateral PCoA is not activated until a severe stenosis of unilateral ICA occurs. PCoA is the most important collateral pathway of the collateral circulation and the missing of PCoA has the highest risk of stroke when the ipsilateral ICA has severe stenosis. These findings may provide the basis for future therapeutic and diagnosis applications.

Monitoring alert and drowsy states by modeling EEG source nonstationarity

NASA Astrophysics Data System (ADS)

Hsu, Sheng-Hsiou; Jung, Tzyy-Ping

2017-10-01

Objective. As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different brain states and using EEG data to quantitatively assess the state changes. Approach. This study hypothesizes that brain activities under different states, e.g. levels of alertness, can be modeled as distinct compositions of statistically independent sources using independent component analysis (ICA). This study presents a framework to quantitatively assess the EEG source nonstationarity and estimate levels of alertness. The framework was tested against EEG data collected from 10 subjects performing a sustained-attention task in a driving simulator. Main results. Empirical results illustrate that EEG signals under alert versus drowsy states, indexed by reaction speeds to driving challenges, can be characterized by distinct ICA models. By quantifying the goodness-of-fit of each ICA model to the EEG data using the model deviation index (MDI), we found that MDIs were significantly correlated with the reaction speeds (r  =  -0.390 with alertness models and r  =  0.449 with drowsiness models) and the opposite correlations indicated that the two models accounted for sources in the alert and drowsy states, respectively. Based on the observed source nonstationarity, this study also proposes an online framework using a subject-specific ICA model trained with an initial (alert) state to track the level of alertness. For classification of alert against drowsy states, the proposed online framework achieved an averaged area-under-curve of 0.745 and compared favorably with a classic power-based approach. Significance. This ICA-based framework provides a new way to study changes of brain states and can be applied to monitoring cognitive or mental states of human operators in attention-critical settings or in passive brain-computer interfaces.

Dynamic Responses in Brain Networks to Social Feedback: A Dual EEG Acquisition Study in Adolescent Couples

PubMed Central

Kuo, Ching-Chang; Ha, Thao; Ebbert, Ashley M.; Tucker, Don M.; Dishion, Thomas J.

2017-01-01

Adolescence is a sensitive period for the development of romantic relationships. During this period the maturation of frontolimbic networks is particularly important for the capacity to regulate emotional experiences. In previous research, both functional magnetic resonance imaging (fMRI) and dense array electroencephalography (dEEG) measures have suggested that responses in limbic regions are enhanced in adolescents experiencing social rejection. In the present research, we examined social acceptance and rejection from romantic partners as they engaged in a Chatroom Interact Task. Dual 128-channel dEEG systems were used to record neural responses to acceptance and rejection from both adolescent romantic partners and unfamiliar peers (N = 75). We employed a two-step temporal principal component analysis (PCA) and spatial independent component analysis (ICA) approach to statistically identify the neural components related to social feedback. Results revealed that the early (288 ms) discrimination between acceptance and rejection reflected by the P3a component was significant for the romantic partner but not the unfamiliar peer. In contrast, the later (364 ms) P3b component discriminated between acceptance and rejection for both partners and peers. The two-step approach (PCA then ICA) was better able than either PCA or ICA alone in separating these components of the brain's electrical activity that reflected both temporal and spatial phases of the brain's processing of social feedback. PMID:28620292

Intelligence, Surveillance, and Reconnaissance Fusion for Coalition Operations

DTIC Science & Technology

2008-07-01

classification of the targets of interest. The MMI features extracted in this manner have two properties that provide a sound justification for...are generalizations of well- known feature extraction methods such as Principal Components Analysis (PCA) and Independent Component Analysis (ICA...augment (without degrading performance) a large class of generic fusion processes. Ontologies Classifications Feature extraction Feature analysis

Competitive immunochromatographic assay for the detection of thiodiglycol sulfoxide, a degradation product of sulfur mustard.

PubMed

Sathe, Manisha; Srivastava, Shruti; Merwyn, S; Agarwal, G S; Kaushik, M P

2014-10-21

An immunochromatographic assay (ICA) based on the competitive antigen-coated format using colloidal gold as the label was developed for the detection of thiodiglycol sulfoxide (TDGO), an important metabolite and degradation compound of sulphur mustard (SM). The ICA test strip consisted of a membrane with a detection zone, a sample pad and an absorbent pad. The membrane was separately coated with hapten-OVA conjugate (test line) and anti-rabbit mouse IgG (control line). The visual detection limit for TDGO by ICA detection was found to be 10 μg mL(-1). For validation, the ICA results obtained for spiked water samples were in good agreement with those obtained by indirect competitive inhibition enzyme-linked immunosorbent assay (ELISA) for TDGO. The assay time for detection was less than 10 min. The developed ICA has the potential to be a useful on-site screening tool for the retrospective detection of SM in environmental samples.

The biofilm-positive Staphylococcus epidermidis isolates in raw materials, foodstuffs and on contact surfaces in processing plants.

PubMed

Schlegelová, J; Babák, V; Holasová, M; Dendis, M

2008-01-01

Isolates from the "farm to fork" samples (182 isolates from 2779 samples) were examined genotypically (icaAB genes) and phenotypically (in vitro biofilm formation, typical growth on Congo red agar; CRA) with the aim to assess the risk of penetration of virulent strains of Staphylococcus epidermidis into the food chain. The contamination of meat and milk products was significantly higher in comparison with raw materials. Contamination of contact surfaces in the meat-processing plants was significantly lower than that of contact surfaces in the dairy plants. The ica genes (which precondition the biofilm formation) were concurrently detected in 20 isolates that also showed a typical growth on CRA. Two ica operon-negative isolates produced biofilm in vitro but perhaps by an ica-independent mechanism. The surfaces in the dairy plants and the milk products were more frequently contaminated with ica operon-positive strains (2.3 and 1.2 % samples) than the other sample types (0-0.6 % samples).

Asymmetric dilatation of virchow-robin space in unilateral internal carotid artery steno-occlusive disease.

PubMed

Park, Ah Young; Chung, Tae-Sub; Suh, Sang Hyun; Choi, Hyun Seok; Lee, Yun Hee

2011-01-01

The cause of abnormal Virchow-Robin space (VRS) dilatation is still unclear. The purpose of this study was to test the hypothesis that chronic ischemia from the unilateral significant internal carotid artery (ICA) stenosis is related to asymmetric VRS dilatation. We recruited 78 patients with severe unilateral ICA stenosis (>70%) diagnosed by magnetic resonance angiography, computed tomography angiography, or digital subtraction angiography and retrospectively reviewed 3-T brain magnetic resonance images. All VRSs on bilateral cerebral high-convexity areas were scaled into 4 grades. We analyzed the difference of VRS grades between bilateral hemispheres and the correlation between VRS grade and severity of ICA stenosis and the patient's age. The VRS grades on the ipsilateral hemisphere were higher than those on the contralateral and were positively correlated with the degree of ICA stenosis. The bilateral VRS grades and the patients' ages were positively correlated. Our results suggest that severe ICA steno-occlusive disease would be related with abnormal VRS dilatation.