gpICA: A Novel Nonlinear ICA Algorithm Using Geometric Linearization

NASA Astrophysics Data System (ADS)

Nguyen, Thang Viet; Patra, Jagdish Chandra; Emmanuel, Sabu

2006-12-01

A new geometric approach for nonlinear independent component analysis (ICA) is presented in this paper. Nonlinear environment is modeled by the popular post nonlinear (PNL) scheme. To eliminate the nonlinearity in the observed signals, a novel linearizing method named as geometric post nonlinear ICA (gpICA) is introduced. Thereafter, a basic linear ICA is applied on these linearized signals to estimate the unknown sources. The proposed method is motivated by the fact that in a multidimensional space, a nonlinear mixture is represented by a nonlinear surface while a linear mixture is represented by a plane, a special form of the surface. Therefore, by geometrically transforming the surface representing a nonlinear mixture into a plane, the mixture can be linearized. Through simulations on different data sets, superior performance of gpICA algorithm has been shown with respect to other algorithms.

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.

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.

Identifying reliable independent components via split-half comparisons

PubMed Central

Groppe, David M.; Makeig, Scott; Kutas, Marta

2011-01-01

Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which the estimated ICs are reliable. An IC may not be reliable if ICA was trained on insufficient data, if ICA training was stopped prematurely or at a local minimum (for some algorithms), or if multiple global minima were present. Consequently, evidence of ICA reliability is critical for the credibility of ICA results. In this paper, we present a new algorithm for assessing the reliability of ICs based on applying ICA separately to split-halves of a data set. This algorithm improves upon existing methods in that it considers both IC scalp topographies and activations, uses a probabilistically interpretable threshold for accepting ICs as reliable, and requires applying ICA only three times per data set. As evidence of the method’s validity, we show that the method can perform comparably to more time intensive bootstrap resampling and depends in a reasonable manner on the amount of training data. Finally, using the method we illustrate the importance of checking the reliability of ICs by demonstrating that IC reliability is dramatically increased by removing the mean EEG at each channel for each epoch of data rather than the mean EEG in a prestimulus baseline. PMID:19162199

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

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).

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.

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.

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.

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

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.

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.

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

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.

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.

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.

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.

Frequency-domain-independent vector analysis for mode-division multiplexed transmission

NASA Astrophysics Data System (ADS)

Liu, Yunhe; Hu, Guijun; Li, Jiao

2018-04-01

In this paper, we propose a demultiplexing method based on frequency-domain independent vector analysis (FD-IVA) algorithm for mode-division multiplexing (MDM) system. FD-IVA extends frequency-domain independent component analysis (FD-ICA) from unitary variable to multivariate variables, and provides an efficient method to eliminate the permutation ambiguity. In order to verify the performance of FD-IVA algorithm, a 6 ×6 MDM system is simulated. The simulation results show that the FD-IVA algorithm has basically the same bit-error-rate(BER) performance with the FD-ICA algorithm and frequency-domain least mean squares (FD-LMS) algorithm. Meanwhile, the convergence speed of FD-IVA algorithm is the same as that of FD-ICA. However, compared with the FD-ICA and the FD-LMS, the FD-IVA has an obviously lower computational complexity.

A two-step super-Gaussian independent component analysis approach for fMRI data.

PubMed

Ge, Ruiyang; Yao, Li; Zhang, Hang; Long, Zhiying

2015-09-01

Independent component analysis (ICA) has been widely applied to functional magnetic resonance imaging (fMRI) data analysis. Although ICA assumes that the sources underlying data are statistically independent, it usually ignores sources' additional properties, such as sparsity. In this study, we propose a two-step super-GaussianICA (2SGICA) method that incorporates the sparse prior of the sources into the ICA model. 2SGICA uses the super-Gaussian ICA (SGICA) algorithm that is based on a simplified Lewicki-Sejnowski's model to obtain the initial source estimate in the first step. Using a kernel estimator technique, the source density is acquired and fitted to the Laplacian function based on the initial source estimates. The fitted Laplacian prior is used for each source at the second SGICA step. Moreover, the automatic target generation process for initial value generation is used in 2SGICA to guarantee the stability of the algorithm. An adaptive step size selection criterion is also implemented in the proposed algorithm. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of 2SGICA and made a performance comparison between InfomaxICA, FastICA, mean field ICA (MFICA) with Laplacian prior, sparse online dictionary learning (ODL), SGICA and 2SGICA. Both simulated and real fMRI experiments showed that the 2SGICA was most robust to noises, and had the best spatial detection power and the time course estimation among the six methods. Copyright © 2015. Published by Elsevier Inc.

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

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

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.

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.

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.

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.

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

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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

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.

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

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

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

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.

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.

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.

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

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 novel approach for dimension reduction of microarray.

PubMed

Aziz, Rabia; Verma, C K; Srivastava, Namita

2017-12-01

This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA+ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA+ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA+ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA+ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA+ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA+ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

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%).

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.

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 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.

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.

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.

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

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.

A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems

NASA Astrophysics Data System (ADS)

Abtahi, Amir-Reza; Bijari, Afsane

2017-03-01

In this paper, a hybrid meta-heuristic algorithm, based on imperialistic competition algorithm (ICA), harmony search (HS), and simulated annealing (SA) is presented. The body of the proposed hybrid algorithm is based on ICA. The proposed hybrid algorithm inherits the advantages of the process of harmony creation in HS algorithm to improve the exploitation phase of the ICA algorithm. In addition, the proposed hybrid algorithm uses SA to make a balance between exploration and exploitation phases. The proposed hybrid algorithm is compared with several meta-heuristic methods, including genetic algorithm (GA), HS, and ICA on several well-known benchmark instances. The comprehensive experiments and statistical analysis on standard benchmark functions certify the superiority of the proposed method over the other algorithms. The efficacy of the proposed hybrid algorithm is promising and can be used in several real-life engineering and management problems.

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.

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

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.

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.

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

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.

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/.

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.

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.

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.

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.

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 comparison of PCA/ICA for data preprocessing in remote sensing imagery classification

NASA Astrophysics Data System (ADS)

He, Hui; Yu, Xianchuan

2005-10-01

In this paper a performance comparison of a variety of data preprocessing algorithms in remote sensing image classification is presented. These selected algorithms are principal component analysis (PCA) and three different independent component analyses, ICA (Fast-ICA (Aapo Hyvarinen, 1999), Kernel-ICA (KCCA and KGV (Bach & Jordan, 2002), EFFICA (Aiyou Chen & Peter Bickel, 2003). These algorithms were applied to a remote sensing imagery (1600×1197), obtained from Shunyi, Beijing. For classification, a MLC method is used for the raw and preprocessed data. The results show that classification with the preprocessed data have more confident results than that with raw data and among the preprocessing algorithms, ICA algorithms improve on PCA and EFFICA performs better than the others. The convergence of these ICA algorithms (for data points more than a million) are also studied, the result shows EFFICA converges much faster than the others. Furthermore, because EFFICA is a one-step maximum likelihood estimate (MLE) which reaches asymptotic Fisher efficiency (EFFICA), it computers quite small so that its demand of memory come down greatly, which settled the "out of memory" problem occurred in the other algorithms.

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

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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).

A new statistical PCA-ICA algorithm for location of R-peaks in ECG.

PubMed

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

2008-09-16

The success of ICA to separate the independent components from the mixture depends on the properties of the electrocardiogram (ECG) recordings. This paper discusses some of the conditions of independent component analysis (ICA) that could affect the reliability of the separation and evaluation of issues related to the properties of the signals and number of sources. Principal component analysis (PCA) scatter plots are plotted to indicate the diagnostic features in the presence and absence of base-line wander in interpreting the ECG signals. In this analysis, a newly developed statistical algorithm by authors, based on the use of combined PCA-ICA for two correlated channels of 12-channel ECG data is proposed. ICA technique has been successfully implemented in identifying and removal of noise and artifacts from ECG signals. Cleaned ECG signals are obtained using statistical measures like kurtosis and variance of variance after ICA processing. This analysis also paper deals with the detection of QRS complexes in electrocardiograms using combined PCA-ICA algorithm. The efficacy of the combined PCA-ICA algorithm lies in the fact that the location of the R-peaks is bounded from above and below by the location of the cross-over points, hence none of the peaks are ignored or missed.

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 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

[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.

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.

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.

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 component analysis algorithm FPGA design to perform real-time blind source separation

NASA Astrophysics Data System (ADS)

Meyer-Baese, Uwe; Odom, Crispin; Botella, Guillermo; Meyer-Baese, Anke

2015-05-01

The conditions that arise in the Cocktail Party Problem prevail across many fields creating a need for of Blind Source Separation. The need for BSS has become prevalent in several fields of work. These fields include array processing, communications, medical signal processing, and speech processing, wireless communication, audio, acoustics and biomedical engineering. The concept of the cocktail party problem and BSS led to the development of Independent Component Analysis (ICA) algorithms. ICA proves useful for applications needing real time signal processing. The goal of this research was to perform an extensive study on ability and efficiency of Independent Component Analysis algorithms to perform blind source separation on mixed signals in software and implementation in hardware with a Field Programmable Gate Array (FPGA). The Algebraic ICA (A-ICA), Fast ICA, and Equivariant Adaptive Separation via Independence (EASI) ICA were examined and compared. The best algorithm required the least complexity and fewest resources while effectively separating mixed sources. The best algorithm was the EASI algorithm. The EASI ICA was implemented on hardware with Field Programmable Gate Arrays (FPGA) to perform and analyze its performance in real time.

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.

A three-way parallel ICA approach to analyze links among genetics, brain structure and brain function.

PubMed

Vergara, Victor M; Ulloa, Alvaro; Calhoun, Vince D; Boutte, David; Chen, Jiayu; Liu, Jingyu

2014-09-01

Multi-modal data analysis techniques, such as the Parallel Independent Component Analysis (pICA), are essential in neuroscience, medical imaging and genetic studies. The pICA algorithm allows the simultaneous decomposition of up to two data modalities achieving better performance than separate ICA decompositions and enabling the discovery of links between modalities. However, advances in data acquisition techniques facilitate the collection of more than two data modalities from each subject. Examples of commonly measured modalities include genetic information, structural magnetic resonance imaging (MRI) and functional MRI. In order to take full advantage of the available data, this work extends the pICA approach to incorporate three modalities in one comprehensive analysis. Simulations demonstrate the three-way pICA performance in identifying pairwise links between modalities and estimating independent components which more closely resemble the true sources than components found by pICA or separate ICA analyses. In addition, the three-way pICA algorithm is applied to real experimental data obtained from a study that investigate genetic effects on alcohol dependence. Considered data modalities include functional MRI (contrast images during alcohol exposure paradigm), gray matter concentration images from structural MRI and genetic single nucleotide polymorphism (SNP). The three-way pICA approach identified links between a SNP component (pointing to brain function and mental disorder associated genes, including BDNF, GRIN2B and NRG1), a functional component related to increased activation in the precuneus area, and a gray matter component comprising part of the default mode network and the caudate. Although such findings need further verification, the simulation and in-vivo results validate the three-way pICA algorithm presented here as a useful tool in biomedical data fusion applications. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

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.

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.

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.

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.

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.

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.

[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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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 Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

PubMed Central

Wang, Huaqing; Li, Ruitong; Tang, Gang; Yuan, Hongfang; Zhao, Qingliang; Cao, Xi

2014-01-01

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. PMID:25289644

Low cost and efficient kurtosis-based deflationary ICA method: application to MRS sources separation problem.

PubMed

Saleh, M; Karfoul, A; Kachenoura, A; Senhadji, L; Albera, L

2016-08-01

Improving the execution time and the numerical complexity of the well-known kurtosis-based maximization method, the RobustICA, is investigated in this paper. A Newton-based scheme is proposed and compared to the conventional RobustICA method. A new implementation using the nonlinear Conjugate Gradient one is investigated also. Regarding the Newton approach, an exact computation of the Hessian of the considered cost function is provided. The proposed approaches and the considered implementations inherit the global plane search of the initial RobustICA method for which a better convergence speed for a given direction is still guaranteed. Numerical results on Magnetic Resonance Spectroscopy (MRS) source separation show the efficiency of the proposed approaches notably the quasi-Newton one using the BFGS method.

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.

[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.

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.

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.

Functional connectivity analysis of the neural bases of emotion regulation: A comparison of independent component method with density-based k-means clustering method.

PubMed

Zou, Ling; Guo, Qian; Xu, Yi; Yang, Biao; Jiao, Zhuqing; Xiang, Jianbo

2016-04-29

Functional magnetic resonance imaging (fMRI) is an important tool in neuroscience for assessing connectivity and interactions between distant areas of the brain. To find and characterize the coherent patterns of brain activity as a means of identifying brain systems for the cognitive reappraisal of the emotion task, both density-based k-means clustering and independent component analysis (ICA) methods can be applied to characterize the interactions between brain regions involved in cognitive reappraisal of emotion. Our results reveal that compared with the ICA method, the density-based k-means clustering method provides a higher sensitivity of polymerization. In addition, it is more sensitive to those relatively weak functional connection regions. Thus, the study concludes that in the process of receiving emotional stimuli, the relatively obvious activation areas are mainly distributed in the frontal lobe, cingulum and near the hypothalamus. Furthermore, density-based k-means clustering method creates a more reliable method for follow-up studies of brain functional connectivity.

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.

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

NASA Astrophysics Data System (ADS)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

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

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.

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.

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.

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.

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.

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.

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.

A BLIND METHOD TO DETREND INSTRUMENTAL SYSTEMATICS IN EXOPLANETARY LIGHT CURVES

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

Morello, G., E-mail: giuseppe.morello.11@ucl.ac.uk

2015-07-20

The study of the atmospheres of transiting exoplanets requires a photometric precision, and repeatability, of one part in ∼10{sup 4}. This is beyond the original calibration plans of current observatories, hence the necessity to disentangle the instrumental systematics from the astrophysical signals in raw data sets. Most methods used in the literature are based on an approximate instrument model. The choice of parameters of the model and their functional forms can sometimes be subjective, causing controversies in the literature. Recently, Morello et al. (2014, 2015) have developed a non-parametric detrending method that gave coherent and repeatable results when applied tomore » Spitzer/IRAC data sets that were debated in the literature. Said method is based on independent component analysis (ICA) of individual pixel time-series, hereafter “pixel-ICA”. The main purpose of this paper is to investigate the limits and advantages of pixel-ICA on a series of simulated data sets with different instrument properties, and a range of jitter timescales and shapes, non-stationarity, sudden change points, etc. The performances of pixel-ICA are compared against the ones of other methods, in particular polynomial centroid division, and pixel-level decorrelation method. We find that in simulated cases pixel-ICA performs as well or better than other methods, and it also guarantees a higher degree of objectivity, because of its purely statistical foundation with no prior information on the instrument systematics. The results of this paper, together with previous analyses of Spitzer/IRAC data sets, suggest that photometric precision and repeatability of one part in 10{sup 4} can be achieved with current infrared space instruments.« less

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.

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

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

PubMed Central

PONTIFEX, MATTHEW B.; GWIZDALA, KATHRYN L.; PARKS, ANDREW C.; BILLINGER, MARTIN; BRUNNER, CLEMENS

2017-01-01

Despite the growing use of independent component analysis (ICA) algorithms for isolating and removing eyeblink-related activity from EEG data, we have limited understanding of how variability associated with ICA uncertainty may be influencing the reconstructed EEG signal after removing the eyeblink artifact components. To characterize the magnitude of this ICA uncertainty and to understand the extent to which it may influence findings within ERP and EEG investigations, ICA decompositions of EEG data from 32 college-aged young adults were repeated 30 times for three popular ICA algorithms. Following each decomposition, eyeblink components were identified and removed. The remaining components were back-projected, and the resulting clean EEG data were further used to analyze ERPs. Findings revealed that ICA uncertainty results in variation in P3 amplitude as well as variation across all EEG sampling points, but differs across ICA algorithms as a function of the spatial location of the EEG channel. This investigation highlights the potential of ICA uncertainty to introduce additional sources of variance when the data are back-projected without artifact components. Careful selection of ICA algorithms and parameters can reduce the extent to which ICA uncertainty may introduce an additional source of variance within ERP/EEG studies. PMID:28026876

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.

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

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.

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.

Fully automated registration of first-pass myocardial perfusion MRI using independent component analysis.

PubMed

Milles, J; van der Geest, R J; Jerosch-Herold, M; Reiber, J H C; Lelieveldt, B P F

2007-01-01

This paper presents a novel method for registration of cardiac perfusion MRI. The presented method successfully corrects for breathing motion without any manual interaction using Independent Component Analysis to extract physiologically relevant features together with their time-intensity behavior. A time-varying reference image mimicking intensity changes in the data of interest is computed based on the results of ICA, and used to compute the displacement caused by breathing for each frame. Qualitative and quantitative validation of the method is carried out using 46 clinical quality, short-axis, perfusion MR datasets comprising 100 images each. Validation experiments showed a reduction of the average LV motion from 1.26+/-0.87 to 0.64+/-0.46 pixels. Time-intensity curves are also improved after registration with an average error reduced from 2.65+/-7.89% to 0.87+/-3.88% between registered data and manual gold standard. We conclude that this fully automatic ICA-based method shows an excellent accuracy, robustness and computation speed, adequate for use in a clinical environment.

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.

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

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.

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.

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 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.

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.

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

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.

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.

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

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.

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.

One-year test-retest reliability of intrinsic connectivity network fMRI in older adults

PubMed Central

Guo, Cong C.; Kurth, Florian; Zhou, Juan; Mayer, Emeran A.; Eickhoff, Simon B; Kramer, Joel H.; Seeley, William W.

2014-01-01

“Resting-state” or task-free fMRI can assess intrinsic connectivity network (ICN) integrity in health and disease, suggesting a potential for use of these methods as disease-monitoring biomarkers. Numerous analytical options are available, including model-driven ROI-based correlation analysis and model-free, independent component analysis (ICA). High test-retest reliability will be a necessary feature of a successful ICN biomarker, yet available reliability data remains limited. Here, we examined ICN fMRI test-retest reliability in 24 healthy older subjects scanned roughly one year apart. We focused on the salience network, a disease-relevant ICN not previously subjected to reliability analysis. Most ICN analytical methods proved reliable (intraclass coefficients > 0.4) and could be further improved by wavelet analysis. Seed-based ROI correlation analysis showed high map-wise reliability, whereas graph theoretical measures and temporal concatenation group ICA produced the most reliable individual unit-wise outcomes. Including global signal regression in ROI-based correlation analyses reduced reliability. Our study provides a direct comparison between the most commonly used ICN fMRI methods and potential guidelines for measuring intrinsic connectivity in aging control and patient populations over time. PMID:22446491

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.

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.

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.

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.

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.

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

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.

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

Fully automated motion correction in first-pass myocardial perfusion MR image sequences.

PubMed

Milles, Julien; van der Geest, Rob J; Jerosch-Herold, Michael; Reiber, Johan H C; Lelieveldt, Boudewijn P F

2008-11-01

This paper presents a novel method for registration of cardiac perfusion magnetic resonance imaging (MRI). The presented method is capable of automatically registering perfusion data, using independent component analysis (ICA) to extract physiologically relevant features together with their time-intensity behavior. A time-varying reference image mimicking intensity changes in the data of interest is computed based on the results of that ICA. This reference image is used in a two-pass registration framework. Qualitative and quantitative validation of the method is carried out using 46 clinical quality, short-axis, perfusion MR datasets comprising 100 images each. Despite varying image quality and motion patterns in the evaluation set, validation of the method showed a reduction of the average right ventricle (LV) motion from 1.26+/-0.87 to 0.64+/-0.46 pixels. Time-intensity curves are also improved after registration with an average error reduced from 2.65+/-7.89% to 0.87+/-3.88% between registered data and manual gold standard. Comparison of clinically relevant parameters computed using registered data and the manual gold standard show a good agreement. Additional tests with a simulated free-breathing protocol showed robustness against considerable deviations from a standard breathing protocol. We conclude that this fully automatic ICA-based method shows an accuracy, a robustness and a computation speed adequate for use in a clinical environment.

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

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.

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.

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.

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

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.

ComDim for explorative multi-block data analysis of Cantal-type cheeses: Effects of salts, gentle heating and ripening.

PubMed

Loudiyi, M; Rutledge, D N; Aït-Kaddour, A

2018-10-30

Common Dimension (ComDim) chemometrics method for multi-block data analysis was employed to evaluate the impact of different added salts and ripening times on physicochemical, color, dynamic low amplitude oscillatory rheology, texture profile, and molecular structure (fluorescence and MIR spectroscopies) of five Cantal-type cheeses. Firstly, Independent Components Analysis (ICA) was applied separately on fluorescence and MIR spectra in order to extract the relevant signal source and the associated proportions related to molecular structure characteristics. ComDim was then applied on the 31 data tables corresponding to the proportion of ICA signals obtained for spectral methods and the global analysis of cheeses by the other techniques. The ComDim results indicated that generally cheeses made with 50% NaCl or with 75:25% NaCl/KCl exhibit the equivalent characteristics in structural, textural, meltability and color properties. The proposed methodology demonstrates the applicability of ComDim for the characterization of samples when different techniques describe the same samples. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

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.

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information.

PubMed

Zhang, Chi; Tong, Li; Zeng, Ying; Jiang, Jingfang; Bu, Haibing; Yan, Bin; Li, Jianxin

2015-01-01

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

PubMed Central

Zhang, Chi; Tong, Li; Zeng, Ying; Jiang, Jingfang; Bu, Haibing; Li, Jianxin

2015-01-01

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition. PMID:26380294

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.

Endoscopic endonasal control of the paraclival internal carotid artery by Fogarty balloon catheter inflation: an anatomical study.

PubMed

Ruggeri, Andrea; Enseñat, Joaquim; Prats-Galino, Alberto; Lopez-Rueda, Antonio; Berenguer, Joan; Cappelletti, Martina; De Notaris, Matteo; d'Avella, Elena

2017-03-01

OBJECTIVE Neurosurgical management of many vascular and neoplastic lesions necessitates control of the internal carotid artery (ICA). The aim of this study was to investigate the feasibility of achieving control of the ICA through the endoscopic endonasal approach by temporary occlusion with a Fogarty balloon catheter. METHODS Ten endoscopic endonasal paraseptal approaches were performed on cadaveric specimens. A Fogarty balloon catheter was inserted through a sellar bony opening and pushed laterally and posteriorly extraarterially along the paraclival carotid artery. The balloon was then inflated, thus achieving temporary occlusion of the vessel. The position of the catheter was confirmed with CT scans, and occlusion of the ICA was demonstrated with angiography. The technique was performed in 2 surgical cases of pituitary macroadenoma with cavernous sinus invasion. RESULTS Positioning the Fogarty balloon catheter at the level of the paraclival ICA was achieved in all cadaveric dissections and surgical cases through a minimally invasive, quick, and safe approach. Inflation of the Fogarty balloon caused interruption of blood flow in 100% of cases. CONCLUSIONS Temporary occlusion of the paraclival ICA performed through the endoscopic endonasal route with the aid of a Fogarty balloon catheter may be another maneuver for dealing with intraoperative ICA control. Further clinical studies are required to prove the efficacy of this method.

A SVM-based quantitative fMRI method for resting-state functional network detection.

PubMed

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components.

PubMed

Dharmaprani, Dhani; Nguyen, Hoang K; Lewis, Trent W; DeLosAngeles, Dylan; Willoughby, John O; Pope, Kenneth J

2016-08-01

Independent Component Analysis (ICA) is a powerful statistical tool capable of separating multivariate scalp electrical signals into their additive independent or source components, specifically EEG or electroencephalogram and artifacts. Although ICA is a widely accepted EEG signal processing technique, classification of the recovered independent components (ICs) is still flawed, as current practice still requires subjective human decisions. Here we build on the results from Fitzgibbon et al. [1] to compare three measures and three ICA algorithms. Using EEG data acquired during neuromuscular paralysis, we tested the ability of the measures (spectral slope, peripherality and spatial smoothness) and algorithms (FastICA, Infomax and JADE) to identify components containing EMG. Spatial smoothness showed differentiation between paralysis and pre-paralysis ICs comparable to spectral slope, whereas peripherality showed less differentiation. A combination of the measures showed better differentiation than any measure alone. Furthermore, FastICA provided the best discrimination between muscle-free and muscle-contaminated recordings in the shortest time, suggesting it may be the most suited to EEG applications of the considered algorithms. Spatial smoothness results suggest that a significant number of ICs are mixed, i.e. contain signals from more than one biological source, and so the development of an ICA algorithm that is optimised to produce ICs that are easily classifiable is warranted.

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.

Spatial independent component analysis of functional MRI time-series: to what extent do results depend on the algorithm used?

PubMed

Esposito, Fabrizio; Formisano, Elia; Seifritz, Erich; Goebel, Rainer; Morrone, Renato; Tedeschi, Gioacchino; Di Salle, Francesco

2002-07-01

Independent component analysis (ICA) has been successfully employed to decompose functional MRI (fMRI) time-series into sets of activation maps and associated time-courses. Several ICA algorithms have been proposed in the neural network literature. Applied to fMRI, these algorithms might lead to different spatial or temporal readouts of brain activation. We compared the two ICA algorithms that have been used so far for spatial ICA (sICA) of fMRI time-series: the Infomax (Bell and Sejnowski [1995]: Neural Comput 7:1004-1034) and the Fixed-Point (Hyvärinen [1999]: Adv Neural Inf Proc Syst 10:273-279) algorithms. We evaluated the Infomax- and Fixed Point-based sICA decompositions of simulated motor, and real motor and visual activation fMRI time-series using an ensemble of measures. Log-likelihood (McKeown et al. [1998]: Hum Brain Mapp 6:160-188) was used as a measure of how significantly the estimated independent sources fit the statistical structure of the data; receiver operating characteristics (ROC) and linear correlation analyses were used to evaluate the algorithms' accuracy of estimating the spatial layout and the temporal dynamics of simulated and real activations; cluster sizing calculations and an estimation of a residual gaussian noise term within the components were used to examine the anatomic structure of ICA components and for the assessment of noise reduction capabilities. Whereas both algorithms produced highly accurate results, the Fixed-Point outperformed the Infomax in terms of spatial and temporal accuracy as long as inferential statistics were employed as benchmarks. Conversely, the Infomax sICA was superior in terms of global estimation of the ICA model and noise reduction capabilities. Because of its adaptive nature, the Infomax approach appears to be better suited to investigate activation phenomena that are not predictable or adequately modelled by inferential techniques. Copyright 2002 Wiley-Liss, Inc.

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.

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.

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

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

[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.

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.

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.

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.

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.

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.

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

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).

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)

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

Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2018-02-01

Conventional bias correction is usually applied on a grid-by-grid basis, meaning that the resulting corrections cannot address biases in the spatial distribution of climate variables. To solve this problem, a two-step bias correction method is proposed here to correct time series at multiple locations conjointly. The first step transforms the data to a set of statistically independent univariate time series, using a technique known as independent component analysis (ICA). The mutually independent signals can then be bias corrected as univariate time series and back-transformed to improve the representation of spatial dependence in the data. The spatially corrected data are then bias corrected at the grid scale in the second step. The method has been applied to two CMIP5 General Circulation Model simulations for six different climate regions of Australia for two climate variables—temperature and precipitation. The results demonstrate that the ICA-based technique leads to considerable improvements in temperature simulations with more modest improvements in precipitation. Overall, the method results in current climate simulations that have greater equivalency in space and time with observational data.

The calcium current of Helix neuron

PubMed Central

1978-01-01

Calcium current, Ica, was studied in isolated nerve cell bodies of Helix aspersa after suppression of Na+ and K+ currents. The suction pipette method described in the preceding paper was used. Ica rises to a peak value and then subsides exponentially and has a null potential of 150 mV or more and a relationship with [Ca2+]o that is hyperbolic over a small range of [Ca2+]o's. When [Ca2+]i is increased, Ica is reduced disproportionately, but the effect is not hyperbolic. Ica is blocked by extracellular Ni2+, La3+, Cd2+, and Co2+ and is greater when Ba2+ and Sr2+ carry the current. Saturation and blockage are described by a Langmuir adsorption relationship similar to that found in Balanus. Thus, the calcium conductance probably contains a site which binds the ions referred to. The site also appears to be voltage-dependent. Activation and inactivation of Ica are described by first order kinetics, and there is evidence that the processes are coupled. For example, inactivation is delayed slightly in its onset and tau inactivation depends upon the method of study. However, the currents are described equally well by either a noncoupled Hodgkin-Huxley mh scheme or a coupled reaction. Facilitation of Ica by prepulses was not observed. For times up to 50 ms, currents even at small depolarizations were accounted for by suitable adjustment of the activation and inactivation rate constants. PMID:660160

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.

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

How to validate similarity in linear transform models of event-related potentials between experimental conditions?

PubMed

Cong, Fengyu; Lin, Qiu-Hua; Astikainen, Piia; Ristaniemi, Tapani

2014-10-30

It is well-known that data of event-related potentials (ERPs) conform to the linear transform model (LTM). For group-level ERP data processing using principal/independent component analysis (PCA/ICA), ERP data of different experimental conditions and different participants are often concatenated. It is theoretically assumed that different experimental conditions and different participants possess the same LTM. However, how to validate the assumption has been seldom reported in terms of signal processing methods. When ICA decomposition is globally optimized for ERP data of one stimulus, we gain the ratio between two coefficients mapping a source in brain to two points along the scalp. Based on such a ratio, we defined a relative mapping coefficient (RMC). If RMCs between two conditions for an ERP are not significantly different in practice, mapping coefficients of this ERP between the two conditions are statistically identical. We examined whether the same LTM of ERP data could be applied for two different stimulus types of fearful and happy facial expressions. They were used in an ignore oddball paradigm in adult human participants. We found no significant difference in LTMs (based on ICASSO) of N170 responses to the fearful and the happy faces in terms of RMCs of N170. We found no methods for straightforward comparison. The proposed RMC in light of ICA decomposition is an effective approach for validating the similarity of LTMs of ERPs between experimental conditions. This is very fundamental to apply group-level PCA/ICA to process ERP data. Copyright © 2014 Elsevier B.V. All rights reserved.

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

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.

Reconstruction of fetal vector electrocardiogram from maternal abdominal signals under fetus body rotations.

PubMed

Nabeshima, Yuji; Kimura, Yoshitaka; Ito, Takuro; Ohwada, Kazunari; Karashima, Akihiro; Katayama, Norihiro; Nakao, Mitsuyuki

2013-01-01

Fetal electrocardiogram (fECG) and its vector form (fVECG) could provide significant clinical information concerning physiological conditions of a fetus. So far various independent component analysis (ICA)-based methods for extracting fECG from maternal abdominal signals have been proposed. Because full extraction of component waves such as P, Q, R, S, and T, is difficult to be realized under noisy and nonstationary situations, the fVECG is further hard to be reconstructed, where different projections of the fetal heart vector are required. In order to reconstruct fVECG, we proposed a novel method for synthesizing different projections of the heart vector, making good use of the fetus movement. This method consists of ICA, estimation of rotation angles of fetus, and synthesis of projections of the heart vector. Through applications to the synthetic and actual data, our method is shown to precisely estimate rotation angle of the fetus and to successfully reconstruct the fVECG.

A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders

PubMed Central

Du, Yuhui; Pearlson, Godfrey D; Liu, Jingyu; Sui, Jing; Yu, Qingbao; He, Hao; Castro, Eduardo; Calhoun, Vince D.

2015-01-01

Schizophrenia (SZ), bipolar disorder (BP) and schizoaffective disorder (SAD) share some common symptoms, and there is a debate about whether SAD is an independent category. To the best of our knowledge, no study has been done to differentiate these three disorders or to investigate the distinction of SAD as an independent category using fMRI data. The present study is aimed to explore biomarkers from resting-state fMRI networks for differentiating these disorders and investigate the relationship among these disorders based on fMRI networks with an emphasis on SAD. Firstly, a novel group ICA method, group information guided independent component analysis (GIG-ICA), was applied to extract subject-specific brain networks from fMRI data of 20 healthy controls (HC), 20 SZ patients, 20 BP patients, 20 patients suffering SAD with manic episodes (SADM), and 13 patients suffering SAD with depressive episodes exclusively (SADD). Then, five-level one-way analysis of covariance and multiclass support vector machine recursive feature elimination were employed to identify discriminative regions from the networks. Subsequently, the t-distributed stochastic neighbor embedding (t-SNE) projection and the hierarchical clustering methods were implemented to investigate the relationship among those groups. Finally, to evaluate the generalization ability, 16 new subjects were classified based on the found regions and the trained model using original 93 subjects. Results show that the discriminative regions mainly include frontal, parietal, precuneus, cingulate, supplementary motor, cerebellar, insula and supramarginal cortices, which performed well in distinguishing different groups. SADM and SADD were the most similar to each other, although SADD had greater similarity to SZ compared to other groups, which indicates SAD may be an independent category. BP was closer to HC compared with other psychotic disorders. In summary, resting-state fMRI brain networks extracted via GIG-ICA provide a promising potential to differentiate SZ, BP, and SAD. PMID:26216278

EVALUATION OF THE I-STAT PORTABLE CLINICAL ANALYZER FOR MEASUREMENT OF IONIZED CALCIUM AND SELECTED BLOOD CHEMISTRY VALUES IN ASIAN ELEPHANTS (ELEPHAS MAXIMUS).

PubMed

Tarbert, Danielle K; Behling-Kelly, Erica; Priest, Heather; Childs-Sanford, Sara

2017-06-01

Thei-STAT® portable clinical analyzer (PCA) provides patient-side results for hematologic, biochemical, and blood gas values when immediate results are desired. This analyzer is commonly used in nondomestic animals; however, validation of this method in comparison with traditional benchtop methods should be performed for each species. In this study, the i-STAT PCA was compared with the Radiometer ABL 800 Flex benchtop analyzer using 24 heparinized whole blood samples obtained from healthy E. maximus . In addition, the effect of sample storage was evaluated on the i-STAT PCA. Analytes evaluated were hydrogen ion concentration (pH), glucose, potassium (K + ), sodium (Na + ), bicarbonate (HCO 3 - ), total carbon dioxide (TCO 2 ), partial pressure of carbon dioxide (PCO 2 ), and ionized calcium (iCa 2+ ). Statistical analysis using correlation coefficients, Passing-Bablok regression analysis, and Bland-Altman plots found good agreement between results from samples run immediately after phlebotomy and 4 hr postsampling on the i-STAT PCA with the exception of K + , which is known to change with sample storage. Comparison of the results from the two analyzers at 4 hr postsampling found very strong or strong correlation in all values except K + , with statistically significant bias in all values except glucose and PCO 2 . Despite bias, mean differences assessed via Bland-Altman plots were clinically acceptable for all analytes excluding K + . Within the reference range for iCa 2+ , the iCa 2+ values obtained by the i-STAT PCA and Radiometer ABL 800 Flex were close in value, however in light of the constant and proportionate biases detected, overestimation at higher values and underestimation at lower values of iCa 2+ by the i-STAT PCA would be of potential concern. This study supports the use of the i-STAT PCA for the evaluation of these analytes, with the exception of K + , in the Asian elephant.

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.

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.

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.

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.

Using Independent Components Analysis to diminish the response of groundwater in borehole strainmeter

NASA Astrophysics Data System (ADS)

Chen, Chih-Yen; Hu, Jyr-Ching

2017-04-01

With designed feather, borehole strainmeter can not only record minor signals of tectonic movements, but also broad environmental signs such as barometry, rainfall and groundwater. Among these external factor, groundwater will influence the observation of borehole strainmeter mostly. According to essential observation, groundwater will cause much bigger response than the target tectonic strain change. We use co-sited piezometer to record pore pressure of groundwater in the rock formation in order to obtain the relationship of stain change and pore pressure. But there still exist some puzzle that can not be solved. First, due to instrument limitation, we could not set the pore pressure transducer in the same aquifer as strainmeter did. In this case, the response due to pore pressure change might be not fully correct. Furthermore, through pore-pressure transducers were set in most observatory, problem of electricity and connectivity will cause the record lack and lost. Therefore, it is necessary to find out a better and more stable method to diminish the groundwater response of strainmeter data.Strain transducer with different orientation can observe the groundwater response in different scale. If we can extract out groundwater signal from each independent strain transducer and estimate its original source. That will significantly rise signal strength and lower noise level. The case belongs some kind of blind-signal-separation (BSS) problem. The procedure of BSS extract or rebuild signal that can't be observed directly in many mixed sources and Independent-Component-Analysis (ICA) is one method adopted broadly. ICA is an analysis to find out parts which have statistics independence and non-Gaussian factor in complex signals. We use FastICA developed by to figure out the groundwater response strain in original strain data, and try to diminish it to rise the signal strength. We preceded strain data previously, then using ICA to separate data into serval independent components. Among them, we found one is highly correlated to groundwater result. It has not only good correlation in long-term trend, but also in short-term fluctuations. It can minimize the groundwater response in borehole strainmeter data effectively.

Blind colour separation of H&E stained histological images by linearly transforming the colour space.

PubMed

Celis, R; Romo, D; Romero, E

2015-12-01

Blind source separation methods aim to split information into the original sources. In histology, each dye component attempts to specifically characterize different microscopic structures. In the case of the hematoxylin-eosin stain, universally used for routine examination, quantitative analysis may often require the inspection of different morphological signatures related mainly to nuclei patterns, but also to stroma distribution. Stain separation is usually a preprocessing operation that is transversal to different applications. This paper presents a novel colour separation method that finds the hematoxylin and eosin clusters by projecting the whole (r,g,b) space to a folded surface connecting the distributions of a series of [(r-b),g] planes that divide the cloud of H&E tones. The proposed method produces density maps closer to those obtained with the colour mixing matrices set by an expert, when comparing with the density maps obtained using nonnegative matrix factorization (NMF), independent component analysis (ICA) and a state-of-the-art method. The method has outperformed three baseline methods, NMF, Macenko and ICA, in about 8%, 12% and 52% for the eosin component, whereas this was about 4%, 8% and 26% for the hematoxylin component. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

Investigation of biofilm forming ability in Staphylococci causing bovine mastitis using phenotypic and genotypic assays.

PubMed

Darwish, Samah F; Asfour, Hanaa A E

2013-01-01

A total of 40 S. aureus and 68 coagulase negative Staphylococcus (CNS) isolates from bovine subclinical mastitis were investigated for their ability to form biofilm as one of the most important virulence factors.Using Congo Red Agar (CRA) method, 32.5%, 35%, and 32.5% of S. aureus strains were strong, intermediate, and negative biofilm producers, while in CNS the percentages were 29.5%, 42.6%, and 27.9%, respectively. By microtiter plate (MTP) method, 52.5%, 27.5%, and 20% of S. aureus isolates were strong, moderate, and weak biofilm producers, while in CNS the percentages were 44%, 30.9%, and 19.2%, respectively. Indian ink staining was used to detect the EPS layer of biofilm producers. All isolates were screened for presence of biofilm related genes, eno, icaA, icaD, and bap. In S. aureus isolates, the positive rates of eno, icaA, icaD, and bap genes were 75%, 15%, 62.5%, and 2.5% while in CNS were 92.6%, 5.9%, 47.1%, and 4.4%, respectively. The eno gene had the highest rate while the bap gene had the lowest rate. Presence of icaA and icaD genes was not always correlated with biofilm production. This study demonstrated high prevalence of Staphylococcus biofilm producers among bovine mastitis in Egypt. Therefore, attention must be paid toward implementation of new ways for effective treatment of such infections.

Investigation of Biofilm Forming Ability in Staphylococci Causing Bovine Mastitis Using Phenotypic and Genotypic Assays

PubMed Central

Darwish, Samah F.; Asfour, Hanaa A. E.

2013-01-01

A total of 40 S. aureus and 68 coagulase negative Staphylococcus (CNS) isolates from bovine subclinical mastitis were investigated for their ability to form biofilm as one of the most important virulence factors.Using Congo Red Agar (CRA) method, 32.5%, 35%, and 32.5% of S. aureus strains were strong, intermediate, and negative biofilm producers, while in CNS the percentages were 29.5%, 42.6%, and 27.9%, respectively. By microtiter plate (MTP) method, 52.5%, 27.5%, and 20% of S. aureus isolates were strong, moderate, and weak biofilm producers, while in CNS the percentages were 44%, 30.9%, and 19.2%, respectively. Indian ink staining was used to detect the EPS layer of biofilm producers. All isolates were screened for presence of biofilm related genes, eno, icaA, icaD, and bap. In S. aureus isolates, the positive rates of eno, icaA, icaD, and bap genes were 75%, 15%, 62.5%, and 2.5% while in CNS were 92.6%, 5.9%, 47.1%, and 4.4%, respectively. The eno gene had the highest rate while the bap gene had the lowest rate. Presence of icaA and icaD genes was not always correlated with biofilm production. This study demonstrated high prevalence of Staphylococcus biofilm producers among bovine mastitis in Egypt. Therefore, attention must be paid toward implementation of new ways for effective treatment of such infections. PMID:24298212

Robust artifactual independent component classification for BCI practitioners.

PubMed

Winkler, Irene; Brandl, Stephanie; Horn, Franziska; Waldburger, Eric; Allefeld, Carsten; Tangermann, Michael

2014-06-01

EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain-computer interfaces (BCIs). Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control. Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.

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 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.

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.

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.

The perception of coherent and non-coherent auditory objects: a signature in gamma frequency band.

PubMed

Knief, A; Schulte, M; Bertran, O; Pantev, C

2000-07-01

The pertinence of gamma band activity in magnetoencephalographic and electroencephalographic recordings for the performance of a gestalt recognition process is a question at issue. We investigated the functional relevance of gamma band activity for the perception of auditory objects. An auditory experiment was performed as an analog to the Kanizsa experiment in the visual modality, comprising four different coherent and non-coherent stimuli. For the first time functional differences of evoked gamma band activity due to the perception of these stimuli were demonstrated by various methods (localization of sources, wavelet analysis and independent component analysis, ICA). Responses to coherent stimuli were found to have more features in common compared to non-coherent stimuli (e.g. closer located sources and smaller number of ICA components). The results point to the existence of a pitch processor in the auditory pathway.

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.

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.

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.

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

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.

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

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

Efficiency test of filtering methods for the removal of transcranial magnetic stimulation artifacts on human electroencephalography with artificially transcranial magnetic stimulation-corrupted signals

NASA Astrophysics Data System (ADS)

Zilber, Nicolas A.; Katayama, Yoshinori; Iramina, Keiji; Erich, Wintermantel

2010-05-01

A new approach is proposed to test the efficiency of methods, such as the Kalman filter and the independent component analysis (ICA), when applied to remove the artifacts induced by transcranial magnetic stimulation (TMS) from electroencephalography (EEG). By using EEG recordings corrupted by TMS induction, the shape of the artifacts is approximately described with a model based on an equivalent circuit simulation. These modeled artifacts are subsequently added to other EEG signals—this time not influenced by TMS. The resulting signals prove of interest since we also know their form without the pseudo-TMS artifacts. Therefore, they enable us to use a fit test to compare the signals we obtain after removing the artifacts with the original signals. This efficiency test turned out very useful in comparing the methods between them, as well as in determining the parameters of the filtering that give satisfactory results with the automatic ICA.

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.

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.

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.

Pulsating Tonsil Due to Medial Displacement of the Internal Carotid Artery.

PubMed

Alsini, Albaraa Y; Ibrahim, Alsheikhi

2017-05-06

BACKGROUND The internal carotid artery (ICA) is about 2.5 cm away from the tonsils. It has no branches in the cervical portion. ICA anomalies of the neck zone may result in a massive arterial bleeding during pharynx and neck surgery. Due to these anomalies, the surgeon must be aware of this risk during tonsillectomy, adenoidectomy, and pharyngeal operations.  CASE REPORT A 23-year-old woman who was discovered to have an acute S curling-type anomaly of the ICA in contact with the lateral border of the right tonsil during a work-up for a tonsillectomy. This anomaly was incidentally discovered via computed tomography (CT) with contrast. In re-evaluating the course of treatment, we found a severe S-shape kink on the right side, bringing it close to the right tonsil by approximately 2 mm, and putting it at severe risk of injury during a simple tonsillectomy, possibly exposing the patient to serious bleeding. Partial tonsillectomy was performed for this patient with the aim to preserve and not expose the internal carotid artery. Pulsation of right tonsil was recorded. The patient made an uneventful postoperative recovery. CONCLUSIONS Undetected ICA anomaly variation can lead to fatal bleeding during a simple procedure, like tonsillectomy. We recommend vigilance during tonsillectomy if one is using a hot dissection method versus a cold dissection method, which may allow for detection of a perioperative ICA anomaly. Tonsillectomy performed by a junior resident should be under direct supervision, particularly if the hot dissection method is used.

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.

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.

SU-E-T-84: Comparison of Three Different Systems for Patient-Specific Quality Assurance: Cranial Stereotactic Radiosurgery Using VMAT with Multiple Non Coplanar Arcs

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

Fusella, M; Fiandra, C; Giglioli, F

2014-06-01

Purpose: Patient-specific quality assurance in volumetric modulated arc therapy (VMAT) brain stereotactic radiosurgery raises specific issues on dosimetric procedures, mainly represented by the small radiation fields associated with the lack of lateral electronic equilibrium, the need of small detectors and the high dose delivered. The purpose of the study is to compare three different dosimeters for pre-treatment QA. Methods: Nineteen patients (affected by neurinomas, brain metastases, and by meningiomas) were treated with VMAT plans computed on a Monte Carlo based TPS. Gafchromic films inside a slab phantom (GF), 3-D cylindrical phantom with two orthogonal diodes array (DA), and 3-D cylindricalmore » phantom with a single rotating ionization chambers array (ICA), have been evaluated. The dosimeters are, respectively, characterized by a spatial resolution of: 0.4 (in our method), 5 and 2.5 mm. For GF we used a double channel method for calibration and reading protocol; for DA and ICA we used the 3-D dose distributions reconstructed by the two software sold with the dosimeters. With the need of a common system for analyze different measuring approaches, we used an in-house software that analyze a single coronal plane in the middle of the phantoms and Gamma values (2% / 2 mm and 3% / 3 mm) were computed for all patients and dosimeters. Results: The percentage of points with gamma values less than one was: 95.7% for GF, 96.8% for DA and 95% for ICA, using 3%/3mm criteria, and 90.1% for GF, 92.4% for DA and 92% for ICA, using 2% / 2mm gamma criteria. Tstudent test p-values obtained by comparing the three datasets were not statistically significant for both gamma criteria. Conclusion: Gamma index analysis is not affected by different spatial resolution of the three dosimeters.« less

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

Selection of independent components based on cortical mapping of electromagnetic activity

NASA Astrophysics Data System (ADS)

Chan, Hui-Ling; Chen, Yong-Sheng; Chen, Li-Fen

2012-10-01

Independent component analysis (ICA) has been widely used to attenuate interference caused by noise components from the electromagnetic recordings of brain activity. However, the scalp topographies and associated temporal waveforms provided by ICA may be insufficient to distinguish functional components from artifactual ones. In this work, we proposed two component selection methods, both of which first estimate the cortical distribution of the brain activity for each component, and then determine the functional components based on the parcellation of brain activity mapped onto the cortical surface. Among all independent components, the first method can identify the dominant components, which have strong activity in the selected dominant brain regions, whereas the second method can identify those inter-regional associating components, which have similar component spectra between a pair of regions. For a targeted region, its component spectrum enumerates the amplitudes of its parceled brain activity across all components. The selected functional components can be remixed to reconstruct the focused electromagnetic signals for further analysis, such as source estimation. Moreover, the inter-regional associating components can be used to estimate the functional brain network. The accuracy of the cortical activation estimation was evaluated on the data from simulation studies, whereas the usefulness and feasibility of the component selection methods were demonstrated on the magnetoencephalography data recorded from a gender discrimination study.

A Just-in-Time Learning based Monitoring and Classification Method for Hyper/Hypocalcemia Diagnosis.

PubMed

Peng, Xin; Tang, Yang; He, Wangli; Du, Wenli; Qian, Feng

2017-01-20

This study focuses on the classification and pathological status monitoring of hyper/hypo-calcemia in the calcium regulatory system. By utilizing the Independent Component Analysis (ICA) mixture model, samples from healthy patients are collected, diagnosed, and subsequently classified according to their underlying behaviors, characteristics, and mechanisms. Then, a Just-in-Time Learning (JITL) has been employed in order to estimate the diseased status dynamically. In terms of JITL, for the purpose of the construction of an appropriate similarity index to identify relevant datasets, a novel similarity index based on the ICA mixture model is proposed in this paper to improve online model quality. The validity and effectiveness of the proposed approach have been demonstrated by applying it to the calcium regulatory system under various hypocalcemic and hypercalcemic diseased conditions.

A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

PubMed

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network

PubMed Central

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J.

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. PMID:25849483

Integration of multivariate empirical mode decomposition and independent component analysis for fetal ECG separation from abdominal signals.

PubMed

Thanaraj, Palani; Roshini, Mable; Balasubramanian, Parvathavarthini

2016-11-14

The fetal electrocardiogram (FECG) signals are essential to monitor the health condition of the baby. Fetal heart rate (FHR) is commonly used for diagnosing certain abnormalities in the formation of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG) signals are obtained by placing surface electrodes in the abdomen region of the pregnant woman. AbECG signals are often not suitable for the direct analysis of fetal heart activity. Moreover, the strength and magnitude of the FECG signals are low compared to the maternal electrocardiogram (MECG) signals. The MECG signals are often superimposed with the FECG signals that make the monitoring of FECG signals a difficult task. Primary goal of the paper is to separate the fetal electrocardiogram (FECG) signals from the unwanted maternal electrocardiogram (MECG) signals. A multivariate signal processing procedure is proposed here that combines the Multivariate Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). The proposed method is evaluated with clinical abdominal signals taken from three pregnant women (N= 3) recorded during the 38-41 weeks of the gestation period. The number of fetal R-wave detected (NEFQRS), the number of unwanted maternal peaks (NMQRS), the number of undetected fetal R-wave (NUFQRS) and the FHR detection accuracy quantifies the performance of our method. Clinical investigation with three test subjects shows an overall detection accuracy of 92.8%. Comparative analysis with benchmark signal processing method such as ICA suggests the noteworthy performance of our method.

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

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.

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

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.

Estimating intracranial volume using intracranial area in healthy children and those with childhood status epilepticus

PubMed Central

Piper, Rory J; Yoong, Michael M; Pujar, Suresh; Chin, Richard F

2014-01-01

Background Correcting volumetric measurements of brain structures for intracranial volume (ICV) is important in comparing volumes across subjects with different ICV. The aim of this study was to investigate whether intracranial area (ICA) reliably predicts actual ICV in a healthy pediatric cohort and in children with convulsive status epilepticus (CSE). Methods T1-weighted volumetric MRI was performed on 20 healthy children (control group), 10 with CSE with structurally normal MRI (CSE/MR-), and 12 with CSE with structurally abnormal MRI (CSE/MR+). ICA, using a mid-sagittal slice, and the actual ICV were measured. Results A high Spearman correlation was found between the ICA and ICV measurements in the control (r = 0.96; P < 0.0001), CSE/MR− (r = 0.93; P = 0.0003), and CSE/MR+ (r = 0.94; P < 0.0001) groups. On comparison of predicted and actual ICV, there was no significant difference in the CSE/MR− group (P = 0.77). However, the comparison between predicted and actual ICV was significantly different in the CSE/MR+ (P = 0.001) group. Our Bland–Altman plot showed that the ICA method consistently overestimated ICV in children in the CSE/MR+ group, especially in those with small ICV or widespread structural abnormalities. Conclusions After further validation, ICA measurement may be a reliable alternative to measuring actual ICV when correcting volume measurements for ICV, even in children with localized MRI abnormalities. Caution should be applied when the method is used in children with small ICV and those with multilobar brain pathology. PMID:25365798

Integration of ultrasonography and endoscopy into transsphenoidal surgery with a "picture-in-picture" viewing system--technical note.

PubMed

Yamasaki, Toshiki; Moritake, Kouzo; Nagai, Hidemasa; Kimura, Yoriyoshi

2002-06-01

A technique to integrate ultrasonography and endoscopy is described for transsphenoidal surgery to prevent intraoperative internal carotid artery (ICA)-related, life-threatening complications such as aneurysmal formation and carotid-cavernous fistula. The ultrasound unit helps avoid direct injury to the ICA. The technical advantage of this system is the miniature 1-mm diameter microvascular probe, which does not disturb the operative field. An arterial or venous flow source of even an invisible vessel can be detected easily, noninvasively, and reproducibly. Real-time information with a 100% detection rate for the ICA is helpful for predicting localization even in the intracavernous portion, where the ICA is invisible. The endoscope unit can visualize the dead angle areas of the operating microscope by varying the endoscopic gateways and display on a "picture-in-picture" system. The advantage of both devices is the integration with a video processor, so that the real-time information from each unit can be switched intraoperatively onto the display as required. This method is of particular help for removing lesions with intracavernous invasion or encasement of the ICA.

Ultrasonographic Evaluation of Femoral Cartilage Thickness in Patients with Ankylosing Spondylitis

PubMed Central

Batmaz, İ; Kara, M; Tiftik, T; Çapkin, E; Karkucak, M; Serdar, ÖF; Kartal, F; Sarıyıldız, MA; Özçakar, L

2014-01-01

Objective: To evaluate femoral cartilage thickness in patients with ankylosing spondylitis (AS) by using ultrasonography. Methods: Eighty-four patients (55 M, 29 F) with a diagnosis of AS and 84 age-, gender- and body mass index-matched healthy subjects were enrolled. Demographic and clinical characteristics of the patients including disease duration, morning stiffness and medications were recorded. The femoral cartilage thicknesses of both knees were measured with a 7–12 MHz linear probe while subjects' knees were held in maximum flexion. Three mid-point measurements were taken from both knees (lateral femoral condyle (LFC), intercondylar area (ICA) and medial femoral condyle (MFC)). Results: Concerning both ICA (p < 0.001) and left MFC (p = 0.013), cartilage measurements were significantly thicker in AS patients than control subjects. In a subgroup analysis (anti-tumour necrosis factor (TNF) users vs anti-TNF naive) cartilage thickness measurements – bilateral ICA (p = 0.000) and left MFC (p = 0.017) – were found to be greater in AS patients under anti-TNF treatment (n = 65) when compared with those of healthy controls. Conclusion: We imply that AS patients seem to have thicker femoral cartilage, which could be related to anti-TNF treatment. PMID:25429476

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.

An investigation of correlation between left coronary bifurcation angle and hemodynamic changes in coronary stenosis by coronary computed tomography angiography-derived computational fluid dynamics

PubMed Central

Chaichana, Thanapong

2017-01-01

Background To investigate the correlation between left coronary bifurcation angle and coronary stenosis as assessed by coronary computed tomography angiography (CCTA)-generated computational fluid dynamics (CFD) analysis when compared to the CCTA analysis of coronary lumen stenosis and plaque lesion length with invasive coronary angiography (ICA) as the reference method. Methods Thirty patients (22 males, mean age: 59±6.9 years) with calcified plaques at the left coronary artery were included in the study with all patients undergoing CCTA and ICA examinations. CFD simulation was performed to analyze hemodynamic changes to the left coronary artery models in terms of wall shear stress, wall pressure and flow velocity, with findings correlated to the coronary stenosis and degree of bifurcation angle. Calcified plaque length was measured in the left coronary artery with diagnostic value compared to that from coronary lumen and bifurcation angle assessments. Results Of 26 significant stenosis at left anterior descending (LAD) and 13 at left circumflex (LCx) on CCTA, only 14 and 5 of them were confirmed to be >50% stenosis at LAD and LCx respectively on ICA, resulting in sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 100%, 52%, 49% and 100%. The mean plaque length was measured 5.3±3.6 and 4.4±1.9 mm at LAD and LCx, respectively, with diagnostic sensitivity, specificity, PPV and NPV being 92.8%, 46.7%, 61.9% and 87.5% for extensively calcified plaques. The mean bifurcation angle was measured 83.9±13.6º and 83.8±13.3º on CCTA and ICA, respectively, with no significant difference (P=0.98). The corresponding sensitivity, specificity, PPV and NPV were 100%, 78.6%, 84.2% and 100% based on bifurcation angle measurement on CCTA, 100%, 73.3%, 78.9% and 100% based on bifurcation angle measurements on ICA, respectively. Wall shear stress was noted to increase in the LAD and LCx models with significant stenosis and wider angulation (>80º), but demonstrated little or no change in most of the coronary models with no significant stenosis and narrower angulation (<80º). Conclusions This study further clarifies the relationship between left coronary bifurcation angle and significant stenosis, with angulation measurement serving as a more accurate approach than coronary lumen assessment or plaque lesion length for determining significant coronary stenosis. Left coronary bifurcation angle is suggested to be incorporated into coronary artery disease (CAD) assessment when diagnosing significant CAD. PMID:29184766

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

Sea level reconstructions from altimetry and tide gauges using independent component analysis

NASA Astrophysics Data System (ADS)

Brunnabend, Sandra-Esther; Kusche, Jürgen; Forootan, Ehsan

2017-04-01

Many reconstructions of global and regional sea level rise derived from tide gauges and satellite altimetry used the method of empirical orthogonal functions (EOF) to reduce noise, improving the spatial resolution of the reconstructed outputs and investigate the different signals in climate time series. However, the second order EOF method has some limitations, e.g. in the separation of individual physical signals into different modes of sea level variations and in the capability to physically interpret the different modes as they are assumed to be orthogonal. Therefore, we investigate the use of the more advanced statistical signal decomposition technique called independent component analysis (ICA) to reconstruct global and regional sea level change from satellite altimetry and tide gauge records. Our results indicate that the used method has almost no influence on the reconstruction of global mean sea level change (1.6 mm/yr from 1960-2010 and 2.9 mm/yr from 1993-2013). Only different numbers of modes are needed for the reconstruction. Using the ICA method is advantageous for separating independent climate variability signals from regional sea level variations as the mixing problem of the EOF method is strongly reduced. As an example, the modes most dominated by the El Niño-Southern Oscillation (ENSO) signal are compared. Regional sea level changes near Tianjin, China, Los Angeles, USA, and Majuro, Marshall Islands are reconstructed and the contributions from ENSO are identified.

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

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/

Dynamic cardiac PET imaging: extraction of time-activity curves using ICA and a generalized Gaussian distribution model.

PubMed

Mabrouk, Rostom; Dubeau, François; Bentabet, Layachi

2013-01-01

Kinetic modeling of metabolic and physiologic cardiac processes in small animals requires an input function (IF) and a tissue time-activity curves (TACs). In this paper, we present a mathematical method based on independent component analysis (ICA) to extract the IF and the myocardium's TACs directly from dynamic positron emission tomography (PET) images. The method assumes a super-Gaussian distribution model for the blood activity, and a sub-Gaussian distribution model for the tissue activity. Our appreach was applied on 22 PET measurement sets of small animals, which were obtained from the three most frequently used cardiac radiotracers, namely: desoxy-fluoro-glucose ((18)F-FDG), [(13)N]-ammonia, and [(11)C]-acetate. Our study was extended to PET human measurements obtained with the Rubidium-82 ((82) Rb) radiotracer. The resolved mathematical IF values compare favorably to those derived from curves extracted from regions of interest (ROI), suggesting that the procedure presents a reliable alternative to serial blood sampling for small-animal cardiac PET studies.

Enhancement of IVR images by combining an ICA shrinkage filter with a multi-scale filter

NASA Astrophysics Data System (ADS)

Chen, Yen-Wei; Matsuo, Kiyotaka; Han, Xianhua; Shimizu, Atsumoto; Shibata, Koichi; Mishina, Yukio; Mukuta, Yoshihiro

2007-11-01

Interventional Radiology (IVR) is an important technique to visualize and diagnosis the vascular disease. In real medical application, a weak x-ray radiation source is used for imaging in order to reduce the radiation dose, resulting in a low contrast noisy image. It is important to develop a method to smooth out the noise while enhance the vascular structure. In this paper, we propose to combine an ICA Shrinkage filter with a multiscale filter for enhancement of IVR images. The ICA shrinkage filter is used for noise reduction and the multiscale filter is used for enhancement of vascular structure. Experimental results show that the quality of the image can be dramatically improved without any blurring in edge by the proposed method. Simultaneous noise reduction and vessel enhancement have been achieved.

Classification of high-resolution multispectral satellite remote sensing images using extended morphological attribute profiles and independent component analysis

NASA Astrophysics Data System (ADS)

Wu, Yu; Zheng, Lijuan; Xie, Donghai; Zhong, Ruofei

2017-07-01

In this study, the extended morphological attribute profiles (EAPs) and independent component analysis (ICA) were combined for feature extraction of high-resolution multispectral satellite remote sensing images and the regularized least squares (RLS) approach with the radial basis function (RBF) kernel was further applied for the classification. Based on the major two independent components, the geometrical features were extracted using the EAPs method. In this study, three morphological attributes were calculated and extracted for each independent component, including area, standard deviation, and moment of inertia. The extracted geometrical features classified results using RLS approach and the commonly used LIB-SVM library of support vector machines method. The Worldview-3 and Chinese GF-2 multispectral images were tested, and the results showed that the features extracted by EAPs and ICA can effectively improve the accuracy of the high-resolution multispectral image classification, 2% larger than EAPs and principal component analysis (PCA) method, and 6% larger than APs and original high-resolution multispectral data. Moreover, it is also suggested that both the GURLS and LIB-SVM libraries are well suited for the multispectral remote sensing image classification. The GURLS library is easy to be used with automatic parameter selection but its computation time may be larger than the LIB-SVM library. This study would be helpful for the classification application of high-resolution multispectral satellite remote sensing images.

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.

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 (%).

Automatic and Direct Identification of Blink Components from Scalp EEG

PubMed Central

Kong, Wanzeng; Zhou, Zhanpeng; Hu, Sanqing; Zhang, Jianhai; Babiloni, Fabio; Dai, Guojun

2013-01-01

Eye blink is an important and inevitable artifact during scalp electroencephalogram (EEG) recording. The main problem in EEG signal processing is how to identify eye blink components automatically with independent component analysis (ICA). Taking into account the fact that the eye blink as an external source has a higher sum of correlation with frontal EEG channels than all other sources due to both its location and significant amplitude, in this paper, we proposed a method based on correlation index and the feature of power distribution to automatically detect eye blink components. Furthermore, we prove mathematically that the correlation between independent components and scalp EEG channels can be translating directly from the mixing matrix of ICA. This helps to simplify calculations and understand the implications of the correlation. The proposed method doesn't need to select a template or thresholds in advance, and it works without simultaneously recording an electrooculography (EOG) reference. The experimental results demonstrate that the proposed method can automatically recognize eye blink components with a high accuracy on entire datasets from 15 subjects. PMID:23959240

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

An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques

PubMed Central

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

2013-01-01

Extraction of relevant features from multitask functional MRI (fMRI) data in order to identify potential biomarkers for disease, is an attractive goal. In this paper, we introduce a novel feature-based framework, which is sensitive and accurate in detecting group differences (e.g. controls vs. patients) by proposing three key ideas. First, we integrate two goal-directed techniques: coefficient-constrained independent component analysis (CC-ICA) and principal component analysis with reference (PCA-R), both of which improve sensitivity to group differences. Secondly, an automated artifact-removal method is developed for selecting components of interest derived from CC-ICA, with an average accuracy of 91%. Finally, we propose a strategy for optimal feature/component selection, aiming to identify optimal group-discriminative brain networks as well as the tasks within which these circuits are engaged. The group-discriminating performance is evaluated on 15 fMRI feature combinations (5 single features and 10 joint features) collected from 28 healthy control subjects and 25 schizophrenia patients. Results show that a feature from a sensorimotor task and a joint feature from a Sternberg working memory (probe) task and an auditory oddball (target) task are the top two feature combinations distinguishing groups. We identified three optimal features that best separate patients from controls, including brain networks consisting of temporal lobe, default mode and occipital lobe circuits, which when grouped together provide improved capability in classifying group membership. The proposed framework provides a general approach for selecting optimal brain networks which may serve as potential biomarkers of several brain diseases and thus has wide applicability in the neuroimaging research community. PMID:19457398

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.

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.

Evaluation of Freehand B-Mode and Power-Mode 3D Ultrasound for Visualisation and Grading of Internal Carotid Artery Stenosis.

PubMed

Pelz, Johann Otto; Weinreich, Anna; Karlas, Thomas; Saur, Dorothee

2017-01-01

Currently, colour-coded duplex sonography (2D-CDS) is clinical standard for detection and grading of internal carotid artery stenosis (ICAS). However, unlike angiographic imaging modalities, 2D-CDS assesses ICAS by its hemodynamic effects rather than luminal changes. Aim of this study was to evaluate freehand 3D ultrasound (3DUS) for direct visualisation and quantification of ICAS. Thirty-seven patients with 43 ICAS were examined with 2D-CDS as reference standard and with freehand B-mode respectively power-mode 3DUS. Stenotic value of 3D reconstructed ICAS was calculated as distal diameter respectively distal cross-sectional area (CSA) reduction percentage and compared with 2D-CDS. There was a trend but no significant difference in successful 3D reconstruction of ICAS between B-mode and power mode (examiner 1 {Ex1} 81% versus 93%, examiner 2 {Ex2} 84% versus 88%). Inter-rater agreement was best for power-mode 3DUS and assessment of stenotic value as distal CSA reduction percentage (intraclass correlation coefficient {ICC} 0.90) followed by power-mode 3DUS and distal diameter reduction percentage (ICC 0.81). Inter-rater agreement was poor for B-mode 3DUS (ICC, distal CSA reduction 0.36, distal diameter reduction 0.51). Intra-rater agreement for power-mode 3DUS was good for both measuring methods (ICC, distal CSA reduction 0.88 {Ex1} and 0.78 {Ex2}; ICC, distal diameter reduction 0.83 {Ex1} and 0.76 {Ex2}). In comparison to 2D-CDS inter-method agreement was good and clearly better for power-mode 3DUS (ICC, distal diameter reduction percentage: Ex1 0.85, Ex2 0.78; distal CSA reduction percentage: Ex1 0.63, Ex2 0.57) than for B-mode 3DUS (ICC, distal diameter reduction percentage: Ex1 0.40, Ex2 0.52; distal CSA reduction percentage: Ex1 0.15, Ex2 0.51). Non-invasive power-mode 3DUS is superior to B-mode 3DUS for imaging and quantification of ICAS. Thereby, further studies are warranted which should now compare power-mode 3DUS with the angiographic gold standard imaging modalities for quantification of ICAS, i.e. with CTA or CE-MRA.

Tailored flow sequestration treatment using high-flow and low-flow bypass for partially thrombosed giant internal carotid artery aneurysm-a technical case report.

PubMed

Hasegawa, Hirotaka; Inoue, Tomohiro; Tamura, Akira; Saito, Isamu

2016-10-01

Direct clipping of giant partially thrombosed intracranial internal carotid artery (ICA) aneurysms is challenging, especially when important perforating arteries are involved. Proximal occlusion with bypass represents a possible alternative approach. An 80-year-old female presented with worsening visual acuity and severe headache caused by partially thrombosed giant (38 mm in diameter) aneurysms of the right ICA, suggestive of impending rupture. Direct clipping in conjunction with temporary occlusion of the lesion involving the anterior choroidal artery (AChA) was considered too risky. Thus, we sequestrated the ipsilateral ICA flow into a low-flow and a high-flow system using two external carotid artery (ECA)-ICA bypasses and one in situ bypass with cervical ICA ligation. As a result, the low-flow system by the superficial temporal artery-middle cerebral artery (MCA) bypass perfused mainly the proximal MCA lesions and aneurysm, whereas the high-flow system by ECA-radial artery-M2 bypass exclusively supplied the residual distal MCA area. This tailored flow sequestration successfully interrupted intra-aneurysmal flow and accelerated near-complete thrombosis of the aneurysm while preserving the AChA and avoiding any significant neurological deterioration. We conclude that this method is effective for the management of giant partially thrombosed aneurysms of the ICA, especially when direct clipping is difficult.

Icariin Improves Cognitive Impairment after Traumatic Brain Injury by Enhancing Hippocampal Acetylation.

PubMed

Zhang, Zi-Gang; Wang, Xin; Zai, Jin-Hai; Sun, Cai-Hua; Yan, Bing-Chun

2018-05-01

To examine the effect of icariin (ICA) on the cognitive impairment induced by traumatic brain injury (TBI) in mice and the underlying mechanisms related to changes in hippocampal acetylation level. The modifified free-fall method was used to establish the TBI mouse model. Mice with post-TBI cognitive impairment were randomly divided into 3 groups using the randomised block method (n=7): TBI (vehicle-treated), low-dose (75 mg/kg) and high-dose (150 mg/kg) of ICA groups. An additional sham-operated group (vehicle-treated) was employed. The vehicle or ICA was administrated by gavage for 28 consecutive days. The Morris water maze (MWM) test was conducted. Acetylcholine (ACh) content, mRNA and protein levels of choline acetyltransferase (ChAT), and protein levels of acetylated H3 (Ac-H3) and Ac-H4 were detected in the hippocampus. Compared with the sham-operated group, the MWM performance, hippocampal ACh content, mRNA and protein levels of ChAT, and protein levels of Ac-H3 and Ac-H4 were signifificantly decreased in the TBI group (P<0.05). High-dose of ICA signifificantly ameliorated the TBI-induced weak MWM performance, increased hippocampal ACh content, and mRNA and protein levels of ChAT, as well as Ac-H3 protein level compared with the TBI group (P<0.05). ICA improved post-TBI cognitive impairment in mice by enhancing hippocampal acetylation, which improved hippocampal cholinergic function and ultimately improved cognition.

Covered Stent-Graft Treatment of Traumatic Internal Carotid Artery Pseudoaneurysms: A Review

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

Maras, Dimitrios; Lioupis, Christos; Magoufis, George

Objective. To review the literature concerning the management with placement of covered stent-grafts of traumatic pseudoaneurysms of the extracranial internal carotid artery (ICA) resulting from penetrating craniocervical injuries or skull base fractures. Method. We have reviewed, from the Medline database, all the published cases in the English literature since 1990 and we have added a new case. Results. We identified 20 patients with traumatic extracranial ICA pseudoaneurysms due to penetrating craniocervical injuries or skull base fractures who had been treated with covered stent-graft implantation. Many discrepancies have been ascertained regarding the anticoagulation therapy. In 3 patients the ICA was totallymore » occluded in the follow-up period, giving an overall occlusion rate 15%. No serious complication was reported as a result of the endovascular procedure. Conclusion. Preliminary results suggest that placement of stent-grafts is a safe and effective method of treating ICA traumatic pseudoaneurysms resulting from penetrating craniocervical injuries or skull base fractures. The immediate results are satisfactory when the procedure takes place with appropriate anticoagulation therapy. The periprocedural morbidity and mortality and the early patency are also acceptable. A surveillance program with appropriate interventions to manage restenosis may improve the long-term patency.« less

Clean image synthesis and target numerical marching for optical imaging with backscattering light

PubMed Central

Pu, Yang; Wang, Wubao

2011-01-01

Scanning backscattering imaging and independent component analysis (ICA) are used to probe targets hidden in the subsurface of a turbid medium. A new correction procedure is proposed and used to synthesize a “clean” image of a homogeneous host medium numerically from a set of raster-scanned “dirty” backscattering images of the medium with embedded targets. The independent intensity distributions on the surface of the medium corresponding to individual targets are then unmixed using ICA of the difference between the set of dirty images and the clean image. The target positions are localized by a novel analytical method, which marches the target to the surface of the turbid medium until a match with the retrieved independent component is accomplished. The unknown surface property of the turbid medium is automatically accounted for by this method. Employing clean image synthesis and target numerical marching, three-dimensional (3D) localization of objects embedded inside a turbid medium using independent component analysis in a backscattering geometry is demonstrated for the first time, using as an example, imaging a small piece of cancerous prostate tissue embedded in a host consisting of normal prostate tissue. PMID:21483608

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.

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.

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.

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.

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.

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

Evaluation and optimization of sampling errors for the Monte Carlo Independent Column Approximation

NASA Astrophysics Data System (ADS)

Räisänen, Petri; Barker, W. Howard

2004-07-01

The Monte Carlo Independent Column Approximation (McICA) method for computing domain-average broadband radiative fluxes is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. McICA's sampling errors are evaluated here using a global climate model (GCM) dataset and a correlated-k distribution (CKD) radiation scheme. Two approaches to reduce McICA's sampling variance are discussed. The first is to simply restrict all of McICA's samples to cloudy regions. This avoids wasting precious few samples on essentially homogeneous clear skies. Clear-sky fluxes need to be computed separately for this approach, but this is usually done in GCMs for diagnostic purposes anyway. Second, accuracy can be improved by repeated sampling, and averaging those CKD terms with large cloud radiative effects. Although this naturally increases computational costs over the standard CKD model, random errors for fluxes and heating rates are reduced by typically 50% to 60%, for the present radiation code, when the total number of samples is increased by 50%. When both variance reduction techniques are applied simultaneously, globally averaged flux and heating rate random errors are reduced by a factor of #3.

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

Investigation of biofilm formation on contact eye lenses caused by methicillin resistant Staphylococcus aureus.

PubMed

Khalil, M A; Sonbol, F I

2014-01-01

The objective was to investigate the biofilm-forming capacity of methicillin resistant Staphylococcus aureus (MRSA) isolated from eye lenses of infected patients. A total of 32 MRSA isolated from contact lenses of patients with ocular infections were screened for their biofilm-forming capacity using tube method (TM), Congo red agar (CRA), and microtiter plate (MtP) methods. The effect of some stress factor on the biofilm formation was studied. The biofilm-forming related genes, icaA, icaD and 10 microbial surface components that recognize adhesive matrix molecule (MSCRAMM), of the selected MRSA were also detected using polymerase chain reaction. Of 32 MRSA isolates, 34.37%, 59.37%, and 81.25% showed positive results using CRA, TM or MtP, respectively. Biofilm production was found to be reduced in the presence of ethanol or ethylenediaminetetraacetic acid and at extreme pH values. On the other hand, glucose or heparin leads to a concentration dependent increase of biofilm production by the isolates. The selected biofilm producing MRSA isolate was found to harbor the icaA, icaD and up to nine of 10 tested MSCRAMM genes, whereas the selected non biofilm producing MRSA isolate did not carry any of the tested genes. The MtP method was found to be the most effective phenotypic screening method for detection of biofilm formation by MRSA. Furthermore, the molecular approach should be taken into consideration for the rapid and correct diagnosis of virulent bacteria associated with contact eye lenses.

Paraffin section immunocytochemistry and cytosol-based ligand-binding assays for ER and PR detection in breast cancer: the time has come for more objectivity.

PubMed

Goussard, J

1998-10-23

The importance of the receptor level in breast cancer as an indicator of hormone response has been extensively studied for more than 20 years. Besides cytosol-based ligand-binding assays (dextran-coated charcoal assay, DCC), new methods using monoclonal antibodies raised against estrogen and progesterone receptors allow for the detection of receptors both in cytosol extracts (enzyme immunoassay, EIA) and in tissue sections (immunocytochemical assay, ICA). The biochemical assays (DCC and EIA) as well as the immunochemical detection (ICA) have specific qualities and produce original information which is useful for the therapeutic decision. While DCC gives a measure of the receptor level, whatever the real source of synthesis (normal and/or neoplastic tissue), ICA locates the positive cells and their relative proportion in the tumor. Both methods present their own advantages and disadvantages which are summarized in this study.

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

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 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.

Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging.

PubMed

Schouten, Tijn M; Koini, Marisa; Vos, Frank de; Seiler, Stephan; Rooij, Mark de; Lechner, Anita; Schmidt, Reinhold; Heuvel, Martijn van den; Grond, Jeroen van der; Rombouts, Serge A R B

2017-05-15

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0.888, and 0.902. The ICA-clustered measures ranged between an AUC of 0.893, and 0.920. The AUC for the structural connectivity graph was 0.900, while graph measures based upon this graph ranged between an AUC of 0.531, and 0.840. All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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

PubMed

Kassouf, Amine; Maalouly, Jacqueline; Rutledge, Douglas N; Chebib, Hanna; Ducruet, Violette

2014-11-01

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 energy 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. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

Gene expression levels of elastin and fibulin-5 according to differences between carotid plaque regions.

PubMed

Sivrikoz, Emre; Timirci-Kahraman, Özlem; Ergen, Arzu; Zeybek, Ümit; Aksoy, Murat; Yanar, Fatih; İsbir, Turgay; Kurtoğlu, Mehmet

2015-01-01

The purpose of this study was to investigate the gene expression levels of elastin and fibulin-5 according to differences between carotid plaque regions and to correlate it with clinical features of plaque destabilization. The study included 44 endarterectomy specimens available from operated symptomatic carotid artery stenoses. The specimens were separated according to anatomic location: internal carotid artery (ICA), external carotid artery (ECA) and common carotid artery (CCA), and then stored in liquid nitrogen. The amounts of cDNA for elastin and fibulin-5 were determined by Quantitative real-time PCR (Q-RT-PCR). Target gene copy numbers were normalized using hypoxanthine-guanine phosphoribosyltransferase (HPRT1) gene. The delta-delta CT method was applied for relative quantification. Q-RT-PCR data showed that relative fibulin-5 gene expression was increased in ICA plaque regions when compared to CCA regions but not reaching significance (p=0.061). At the same time, no differences were observed in elastin mRNA level between different anatomic plaque regions (p>0.05). Moreover, elastin and fibulin-5 mRNA expression and clinical parameters were compared in ICA plaques versus CCA and ECA regions, respectively. Up-regulation of elastin and fibulin-5 mRNA levels in ICA were strongly correlated with family history of cardiovascular disease when compared to CCA (p<0.05). Up-regulation of fibulin-5 in ICA was significantly associated with diabetes, and elevated triglycerides and very low density lipoprotein (VLDL) when compared to ECA (p<0.05). The clinical significance is the differences between the proximal and distal regions of the lesion, associated with the ICA, CCA and ECA respectively, with increased fibulin-5 in the ICA region. Copyright © 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Contralateral transmaxillary corridor: an augmented endoscopic approach to the petrous apex.

PubMed

Patel, Chirag R; Wang, Eric W; Fernandez-Miranda, Juan C; Gardner, Paul A; Snyderman, Carl H

2017-10-20

OBJECTIVE The endoscopic endonasal approach (EEA) has been shown to be an effective means of accessing lesions of the petrous apex. Lesions that are lateral to the paraclival segment of the internal carotid artery (ICA) require lateralization of the paraclival segment of the ICA or a transpterygoid infrapetrous approach. In this study the authors studied the feasibility of adding a contralateral transmaxillary (CTM) corridor to provide greater access to the petrous apex with decreased need for manipulation of the ICA. METHODS Using image guidance, EEA and CTM extension were performed bilaterally on 5 cadavers. The anterior wall of the sphenoid sinus and rostrum were removed. The angle of the surgical approach from the axis of the petrous segment of the ICA was measured. Five illustrative clinical cases are presented. RESULTS The CTM corridor required a partial medial maxillectomy. When measured from the axis of the petrous ICA, the CTM corridor decreased the angle from 44.8° ± 2.78° to 20.1° ± 4.31°, a decrease of 24.7° ± 2.58°. Drilling through the CTM corridor allowed the drill to reach lateral aspects of the petrous apex that would have required lateralization of the ICA or would not have been accessible via EEA. The CTM corridor allowed us to achieve gross-total resection of the petrous apex region in 5 clinical cases with significant paraclival extension. CONCLUSIONS The CTM corridor is a feasible extension to the standard EEA to the petrous apex that offers a more lateral trajectory with improved access. This approach may reduce the risk and morbidity associated with manipulation of the paraclival ICA.

Modulation of voltage-gated channel currents by harmaline and harmane

PubMed Central

Splettstoesser, Frank; Bonnet, Udo; Wiemann, Martin; Bingmann, Dieter; Büsselberg, Dietrich

2004-01-01

Harmala alkaloids are endogenous substances, which are involved in neurodegenerative disorders such as M. Parkinson, but some of them also have neuroprotective effects in the nervous system. While several sites of action at the cellular level (e.g. benzodiazepine receptors, 5-HT and GABAA receptors) have been identified, there is no report on how harmala alkaloids interact with voltage-gated membrane channels. The aim of this study was to investigate the effects of harmaline and harmane on voltage-activated calcium- (ICa(V)), sodium- (INa(V)) and potassium (IK(V))-channel currents, using the whole-cell patch-clamp method with cultured dorsal root ganglion neurones of 3-week-old rats. Currents were elicited by voltage steps from the holding potential to different command potentials. Harmaline and harmane reduced ICa(V), INa(V) and IK(V) concentration-dependent (10–500 μM) over the voltage range tested. ICa(V) was reduced with an IC50 of 100.6 μM for harmaline and by a significantly lower concentration of 75.8 μM (P<0.001, t-test) for harmane. The Hill coefficient was close to 1. Threshold concentration was around 10 μM for both substances. The steady state of inhibition of ICa(V) by harmaline or harmane was reached within several minutes. The action was not use dependent and at least partly reversible. It was mainly due to a reduction in the sustained calcium channel current (ICa(L+N)), while the transient voltage-gated calcium channel current (ICa(T)) was only partially affected. We conclude that harmaline and harmane are modulators of ICa(V) in vitro. This might be related to their neuroprotective effects. PMID:15644868

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.

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.

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.

Calcium-creatinine ratio in a morning urine sample for the estimation of hypercalciuria associated with non-glomerular hematuria observed in children and adolescents.

PubMed

Quiñones-Vázquez, Susana; Liriano-Ricabal, María Del Rosario; Santana-Porbén, Sergio; Salabarría-González, José Reinaldo

2018-01-01

Hypercalciuria might be revealed during the differential diagnosis of hematuria accompanying renal lithiasis (RL). In spite of this, diagnostic accuracy of calcium urinary excretion might be affected by incomplete 24-hour urine collections. In the present study, the diagnostic utility of calcium/creatinine (ICaCre) index for determining hypercalciuria associated with non-glomerular hematuria (NGH) and RL was assessed. ICaCre (mg/mg) index was calculated from calcium (mmol/l) and creatinine (µmol/l) concentrations in an aliquot from a 24-hour urine collection in 169 children and adolescents with NGH or RL. Calciuria values > 4.0 mg/kg in 24 hours were distributed according to the presence of NGH or RL. Mean ICaCre index was 0.2 ± 0.1 mg/mg. Calciuria values estimated from ICaCre were statistically higher to those from 24-hour urine collection (p < 0.05). The frequency of hypercalciuria was independent from the measurement method (estimated from ICaCre 39.5% vs. 24 h collection 32.1%; p > 0.05). Hypercalciuria distribution was as follows: no NGH + no RL: 59.0%; no NGH + RL: 60.0% (∆ = +1.0%); NGH + no RL: 68.2% (∆ = +9.2%); NGH + RL: 73.3% (∆ = +14.4%). The use of ICaCre index for determining calcium urine excretion might be effective in the study of hypercalciuria associated with NGH and RL. Copyright: © 2018 Permanyer.

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.

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.

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.

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.

Fetal ECG Extraction From Maternal Body Surface Measurement Using Independent Component Analysis

DTIC Science & Technology

2001-10-25

Ibaraki 305-0901, Japan Abstract – A method applying independent component analysis (ICA) to detect the electrocardiogram of a prenatal cattle foetus is...monitoring the health status of an unborn cattle foetus is indispensable in preventing natural abortion and premature birth [3]. One of the applicable...and Y. Honda, “ECG and Heart Rate Detection of Prenatal Cattle Foetus Using Adaptive Digital Filtering,” World Congress on Med. Phys.& Biomed. Eng., Chicago TU-CXH-75, pp. 1-4, 2000.

Denoising the Speaking Brain: Toward a Robust Technique for Correcting Artifact-Contaminated fMRI Data under Severe Motion

PubMed Central

Xu, Yisheng; Tong, Yunxia; Liu, Siyuan; Chow, Ho Ming; AbdulSabur, Nuria Y.; Mattay, Govind S.; Braun, Allen R.

2014-01-01

A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established as follows: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; 3) a reliable method for validating the effectiveness of denoising. Here we perform a thorough investigation of these issues and demonstrate the power of our technique by resolving the problem of severe imaging artifacts associated with continuous overt speech production. As a key methodological feature, a dual-mask sICA method is proposed to isolate a variety of imaging artifacts by directly revealing their extracerebral spatial origins. It also plays an important role for understanding the mechanistic properties of noise components in conjunction with temporal measures of physical or physiological motion. The potentials of a spatially-based machine learning classifier and the general criteria for feature selection have both been examined, in order to maximize the performance and generalizability of automated component classification. The effectiveness of denoising is quantitatively validated by comparing the activation maps of fMRI with those of positron emission tomography acquired under the same task conditions. The general applicability of this technique is further demonstrated by the successful reduction of distance-dependent effect of head motion on resting-state functional connectivity. PMID:25225001

Denoising the speaking brain: toward a robust technique for correcting artifact-contaminated fMRI data under severe motion.

PubMed

Xu, Yisheng; Tong, Yunxia; Liu, Siyuan; Chow, Ho Ming; AbdulSabur, Nuria Y; Mattay, Govind S; Braun, Allen R

2014-12-01

A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established as follows: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; and 3) a reliable method for validating the effectiveness of denoising. Here we perform a thorough investigation of these issues and demonstrate the power of our technique by resolving the problem of severe imaging artifacts associated with continuous overt speech production. As a key methodological feature, a dual-mask sICA method is proposed to isolate a variety of imaging artifacts by directly revealing their extracerebral spatial origins. It also plays an important role for understanding the mechanistic properties of noise components in conjunction with temporal measures of physical or physiological motion. The potentials of a spatially-based machine learning classifier and the general criteria for feature selection have both been examined, in order to maximize the performance and generalizability of automated component classification. The effectiveness of denoising is quantitatively validated by comparing the activation maps of fMRI with those of positron emission tomography acquired under the same task conditions. The general applicability of this technique is further demonstrated by the successful reduction of distance-dependent effect of head motion on resting-state functional connectivity. Copyright © 2014 Elsevier Inc. All rights reserved.

A New Analysis of Resting State Connectivity and Graph Theory Reveals Distinctive Short-Term Modulations due to Whisker Stimulation in Rats.

PubMed

Kreitz, Silke; de Celis Alonso, Benito; Uder, Michael; Hess, Andreas

2018-01-01

Resting state (RS) connectivity has been increasingly studied in healthy and diseased brains in humans and animals. This paper presents a new method to analyze RS data from fMRI that combines multiple seed correlation analysis with graph-theory (MSRA). We characterize and evaluate this new method in relation to two other graph-theoretical methods and ICA. The graph-theoretical methods calculate cross-correlations of regional average time-courses, one using seed regions of the same size (SRCC) and the other using whole brain structure regions (RCCA). We evaluated the reproducibility, power, and capacity of these methods to characterize short-term RS modulation to unilateral physiological whisker stimulation in rats. Graph-theoretical networks found with the MSRA approach were highly reproducible, and their communities showed large overlaps with ICA components. Additionally, MSRA was the only one of all tested methods that had the power to detect significant RS modulations induced by whisker stimulation that are controlled by family-wise error rate (FWE). Compared to the reduced resting state network connectivity during task performance, these modulations implied decreased connectivity strength in the bilateral sensorimotor and entorhinal cortex. Additionally, the contralateral ventromedial thalamus (part of the barrel field related lemniscal pathway) and the hypothalamus showed reduced connectivity. Enhanced connectivity was observed in the amygdala, especially the contralateral basolateral amygdala (involved in emotional learning processes). In conclusion, MSRA is a powerful analytical approach that can reliably detect tiny modulations of RS connectivity. It shows a great promise as a method for studying RS dynamics in healthy and pathological conditions.

A New Analysis of Resting State Connectivity and Graph Theory Reveals Distinctive Short-Term Modulations due to Whisker Stimulation in Rats

PubMed Central

Kreitz, Silke; de Celis Alonso, Benito; Uder, Michael; Hess, Andreas

2018-01-01

Resting state (RS) connectivity has been increasingly studied in healthy and diseased brains in humans and animals. This paper presents a new method to analyze RS data from fMRI that combines multiple seed correlation analysis with graph-theory (MSRA). We characterize and evaluate this new method in relation to two other graph-theoretical methods and ICA. The graph-theoretical methods calculate cross-correlations of regional average time-courses, one using seed regions of the same size (SRCC) and the other using whole brain structure regions (RCCA). We evaluated the reproducibility, power, and capacity of these methods to characterize short-term RS modulation to unilateral physiological whisker stimulation in rats. Graph-theoretical networks found with the MSRA approach were highly reproducible, and their communities showed large overlaps with ICA components. Additionally, MSRA was the only one of all tested methods that had the power to detect significant RS modulations induced by whisker stimulation that are controlled by family-wise error rate (FWE). Compared to the reduced resting state network connectivity during task performance, these modulations implied decreased connectivity strength in the bilateral sensorimotor and entorhinal cortex. Additionally, the contralateral ventromedial thalamus (part of the barrel field related lemniscal pathway) and the hypothalamus showed reduced connectivity. Enhanced connectivity was observed in the amygdala, especially the contralateral basolateral amygdala (involved in emotional learning processes). In conclusion, MSRA is a powerful analytical approach that can reliably detect tiny modulations of RS connectivity. It shows a great promise as a method for studying RS dynamics in healthy and pathological conditions. PMID:29875622

Angioplasty and stenting for patients with symptomatic intracranial atherosclerosis: study protocol of a randomised controlled trial

PubMed Central

Cui, Xiao-Ping; Lin, Min; Mu, Jun-Shan; Ye, Jian-Xin; He, Wen-Qing; Fu, Mao-Lin; Li, Hua; Fang, Jia-Yang; Shen, Feng-Feng; Lin, Hang

2016-01-01

Introduction Whether adding percutaneous transluminal angioplasty and stenting (PTAS) to background medical treatment is effective for decreasing the incidence of stroke or death in patients with symptomatic intracranial atherosclerosis (ICAS) is still controversial. We perform a randomised controlled trial to examine the effectiveness and safety of an improved PTAS procedure for patients with ICAS. Methods and analysis A randomised controlled trial will be conducted in three hospitals in China. Eligible patients with ICAS will be randomly assigned to receive medication treatment (MT) plus PTAS or MT alone. The MT will be initiated immediately after randomisation, while the PTAS will be performed when patients report relief of alarm symptoms defined as sudden weakness or numbness. All patients will be followed up at 30 days, 3 and 12 months after randomisation. The primary end point will be the incidence of stroke or death at 30 days after randomisation. Secondary outcomes will be the incidence of ischaemic stroke in the territory of stenosis arteries, the incidence of in-stent restenosis, the Chinese version of the modified Rankin Scale and the Chinese version of the Stroke-Specific Quality of Life (CSQoL). Ethics and dissemination The study protocol is approved by institutional review boards in participating hospitals (reference number FZ20160003, 180PLA20160101 and 476PLA2016007). The results of this study will be disseminated to patients, physicians and policymakers through publication in a peer-reviewed journal or presentations in conferences. It is anticipated that the results of this study will improve the quality of the current PTAS procedure and guide clinical decision-making for patients with ICAS. Trial registration number NCT02689037 PMID:27852711

Noise reduction in functional near-infrared spectroscopy signals by independent component analysis

NASA Astrophysics Data System (ADS)

Santosa, Hendrik; Jiyoun Hong, Melissa; Kim, Sung-Phil; Hong, Keum-Shik

2013-07-01

Functional near-infrared spectroscopy (fNIRS) is used to detect concentration changes of oxy-hemoglobin and deoxy-hemoglobin in the human brain. The main difficulty entailed in the analysis of fNIRS signals is the fact that the hemodynamic response to a specific neuronal activation is contaminated by physiological and instrument noises, motion artifacts, and other interferences. This paper proposes independent component analysis (ICA) as a means of identifying the original hemodynamic response in the presence of noises. The original hemodynamic response was reconstructed using the primary independent component (IC) and other, less-weighting-coefficient ICs. In order to generate experimental brain stimuli, arithmetic tasks were administered to eight volunteer subjects. The t-value of the reconstructed hemodynamic response was improved by using the ICs found in the measured data. The best t-value out of 16 low-pass-filtered signals was 37, and that of the reconstructed one was 51. Also, the average t-value of the eight subjects' reconstructed signals was 40, whereas that of all of their low-pass-filtered signals was only 20. Overall, the results showed the applicability of the ICA-based method to noise-contamination reduction in brain mapping.

Experimental study of hemodynamics in the circle of willis

PubMed Central

2015-01-01

Background 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. Methods 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. Results 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. Conclusion 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. PMID:25603138

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.

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.

Endovascular revascularization results in IMS III: intracranial ICA and M1 occlusions.

PubMed

Tomsick, Thomas A; Yeatts, Sharon D; Liebeskind, David S; Carrozzella, Janice; Foster, Lydia; Goyal, Mayank; von Kummer, Ruediger; Hill, Michael D; Demchuk, Andrew M; Jovin, Tudor; Yan, Bernard; Zaidat, Osama O; Schonewille, Wouter; Engelter, Stefan; Martin, Renee; Khatri, Pooja; Spilker, Judith; Palesch, Yuko Y; Broderick, Joseph P

2015-11-01

Interventional Management of Stroke III did not show that combining IV recombinant tissue plasminogen activator (rt-PA) with endovascular therapies (EVTs) is better than IV rt-PA alone. To report efficacy and safety results for EVT of intracranial internal carotid artery (ICA) and middle cerebral artery trunk (M1) occlusion. Five revascularization methods for persistent occlusions after IV rt-PA treatment were evaluated for prespecified primary and secondary endpoints, after accounting for differences in key baselines variables using propensity scores. Revascularization was scored using the arterial occlusive lesion (AOL) and the modified Thrombolysis in Cerebral Ischemia (mTICI) scores. EVT of 200 subjects with intracranial ICA or M1 occlusion resulted in 81.5% AOL 2-3 recanalization, in addition to 76% mTICI 2-3 and 42.5% mTICI 2b-3 reperfusion. Adverse events included symptomatic intracranial hemorrhage (SICH) (8.0%), vessel perforations (1.5%), and new emboli (14.9%). EVT techniques used were standard microcatheter n=51; EKOS n=14; Merci n=77; Penumbra n=39; Solitaire n=4; multiple n=15. Good clinical outcome was associated with both TICI 2-3 and TICI 2b-3 reperfusion. Neither modified Rankin scale (mRS) 0-2 (28.5%), nor 90-day mortality (28.5%), nor asymptomatic ICH (36.0%) differed among revascularization methods after propensity score adjustment for subjects with intracranial ICA or M1 occlusion. Good clinical outcome was associated with good reperfusion for ICA and M1 occlusion. No significant differences in efficacy or safety among revascularization methods were demonstrated after adjustment. Lack of high-quality reperfusion, adverse events, and prolonged time to treatment contributed to lower-than-expected mRS 0-2 outcomes and study futility compared with IV rt-PA. NCT00359424. 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.

Identification of statistically independent climatic pattern in GRACE and hydrological model data over West-Africa

NASA Astrophysics Data System (ADS)

Kusche, J.; Forootan, E.; Eicker, A.; Hoffmann-Dobrev, H.

2012-04-01

West-African countries have been exposed to changes in rainfall patterns over the last decades, including a significant negative trend. This causes adverse effects on water resources, for instance reduced freshwater availability, and changes in the frequency, duration and magnitude of droughts and floods. Extracting the main patterns of water storage change in West Africa from remote sensing and linking them to climate variability, is therefore an essential step to understand the hydrological aspects of the region. In this study, the higher order statistical method of Independent Component Analysis (ICA) is employed to extract statistically independent water storage patterns from monthly Gravity Recovery And Climate Experiment (GRACE), from the WaterGAP Global Hydrology Model (WGHM) and from Tropical Rainfall Measuring Mission (TRMM) products over West Africa, for the period 2002-2012. Then, to reveal the influences of climatic teleconnections on the individual patterns, these results were correlated to the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) indices. To study the predictability of water storage changes, advanced statistical methods were applied on the main independent Sea Surface Temperature (SST) patterns over the Atlantic and Indian Oceans for the period 2002-2012 and the ICA results. Our results show a water storage decrease over the coastal regions of West Africa (including Sierra Leone, Liberia, Togo and Nigeria), associated with rainfall decrease. The comparison between GRACE estimations and WGHM results indicates some inconsistencies that underline the importance of forcing data for hydrological modeling of West Africa. Keywords: West Africa; GRACE-derived water storage; ICA; ENSO; IOD

High Spatial Inhomogeneity in the Intima-Media Thickness of the Common Carotid Artery is Associated with a Larger Degree of Stenosis in the Internal Carotid Artery: The PARISK Study.

PubMed

Steinbuch, Jeire; van Dijk, Anouk C; Schreuder, Floris H B M; Truijman, Martine T B; de Rotte, Alexandra A J; Nederkoorn, Paul J; van der Lugt, Aad; Hermeling, Evelien; Hoeks, Arnold P G; Mess, Werner H

2017-10-01

Purpose  Inhomogeneity of arterial wall thickness may be indicative of distal plaques. This study investigates the intra-subject association between relative spatial intima-media thickness (IMT) inhomogeneity of the common carotid artery (CCA) and the degree of stenosis of plaques in the internal carotid artery (ICA). Materials and Methods  We included 240 patients with a recent ischemic stroke or transient ischemic attack and mild-to-moderate stenosis in the ipsilateral ICA. IMT inhomogeneity was extracted from B-mode ultrasound recordings. The degree of ICA stenosis was assessed on CT angiography according to the European Carotid Surgery Trial method. Patients were divided into groups with a low (≤ 2 %) and a high (> 2 %) IMT inhomogeneity scaled with respect to the local end-diastolic diameter. Results  182 patients had suitable CT and ultrasound measurements. Relative CCA-IMT inhomogeneity was similar for the symptomatic and asymptomatic side (difference: 0.02 %, p = 0.85). High relative IMT inhomogeneity was associated with a larger IMT (difference: 235 µm, p < 0.001) and larger degree of ICA stenosis (difference: 5 %, p = 0.023) which remained significant (p = 0.016) after adjustment for common risk factors. Conclusion  Regardless of common risk factors, high relative CCA-IMT inhomogeneity is associated with a greater degree of ICA stenosis and is therefore indicative of atherosclerotic disease. The predictive value of CCA-IMT inhomogeneity for plaque progression and recurrence of cerebrovascular symptoms will be determined in the follow-up phase of PARISK. © Georg Thieme Verlag KG Stuttgart · New York.

New method for obliterative treatment of an anterior wall aneurysm in the internal carotid artery: encircling silicone sheet clip procedure--technical case report.

PubMed

Kurokawa, Y; Wanibuchi, M; Ishiguro, M; Inaba, K

2001-08-01

Aneurysms on the anterior surface of the internal carotid artery (ICA) have been shown to be somewhat different from ordinary berry aneurysms because they are rather small, grow rapidly in a short time, and easily lead to rupture, especially during surgery. The most difficult problem is that this type of aneurysm cannot be eliminated easily by an ordinary clipping procedure without causing apparent arterial stenosis or occlusion. A 52-year-old man experienced a subarachnoid hemorrhage because of a ruptured aneurysm located on the anterior surface of the ICA. The tiny aneurysmal body, which was covered with a layer of brain tissue, was successfully exposed. The ICA seemed to be atherosclerotic, and the aneurysmal portion was solitary and had a reddish color. A large silicone sheet encircling clip (Vascwrap; Mizuho Ikakogyo Co., Ltd., Tokyo, Japan) was selected for this patient. The proximal margin of the silicone sheet was incised with a V-shaped cut, and the middle part of the sheet, which covered the diagonal part of the ICA, was trimmed to make it shorter. The blade of the fenestrated clip was applied to obliterate the aneurysm and was attached to the normal arterial wall together with this modified Vascwrap sheet to create a small space between the normal arterial wall and the surrounding Vascwrap sheet. Then tiny pieces of Teflon fiber (E.I. duPont de Nemours and Co., Wilmington, DE) was inserted from both margins, and the whole Vascwrap sheet was sealed with fibrin glue to ensure good adhesion. This method seemed adequate in treating this difficult aneurysm without causing postoperative regrowth or occlusion of the patient's ICA.

Prevention of Retrograde Blood Flow Into Large or Giant Internal Carotid Artery Aneurysms by Endovascular Coil Embolization with High-Flow Bypass: Surgical Technique and Long-Term Results.

PubMed

Nakajima, Norio; Nagahiro, Shinji; Satomi, Junichiro; Tada, Yoshiteru; Nakajima, Kohei; Sogabe, Shu; Hanaoka, Mami; Matsubara, Shunji; Uno, Masaaki; Satoh, Koichi

2015-06-01

Recanalization has been reported in large or giant aneurysms of the internal carotid artery (ICA) addressed by high-flow bypass and endovascular treatment. Aneurysmal recanalization may be attributable to retrograde blood flow into the aneurysm through the ICA branches, such as the ophthalmic artery or the meningohypophyseal trunk, or through the surgically created bypass. We modified the endovascular treatment of aneurysms to prevent retrograde flow and evaluated the long-term efficacy of our method. We used a hybrid operative/endovascular technique to treat 5 patients with large or giant aneurysms arising from the C2-C4 segment of the ICA who presented with visual symptoms due to the mass effect of the aneurysm. To prevent retrograde flow into the aneurysm our modified endovascular treatment involves coil embolization of the aneurysmal orifice and the ICA, including the origin of the ophthalmic artery and meningohypophyseal trunk, and placement of a high-flow bypass using a radial artery graft. During the 5- to 12-year follow-up period, 4 aneurysms disappeared, and the other decreased in size. There were no subarachnoid hemorrhages. All bypass grafts remained patent. Visual preservation was achieved in 2 patients; 1 patient manifested visual improvement. Although 2 patients experienced transient neurological deficits we encountered no permanent complications in this series. The final modified Rankin scale of the 5 patients was 0 or 1. Prevention of retrograde flow into the aneurysm by coil embolization with high-flow bypass is a safe and effective method. It prevents the recanalization of large or giant ICA aneurysms. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

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

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

An ICA-based method for the segmentation of pigmented skin lesions in macroscopic images.

PubMed

Cavalcanti, Pablo G; Scharcanski, Jacob; Di Persia, Leandro E; Milone, Diego H

2011-01-01

Segmentation is an important step in computer-aided diagnostic systems for pigmented skin lesions, since that a good definition of the lesion area and its boundary at the image is very important to distinguish benign from malignant cases. In this paper a new skin lesion segmentation method is proposed. This method uses Independent Component Analysis to locate skin lesions in the image, and this location information is further refined by a Level-set segmentation method. Our method was evaluated in 141 images and achieved an average segmentation error of 16.55%, lower than the results for comparable state-of-the-art methods proposed in literature.

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 fMRI analysis methods for heterogeneous BOLD responses in block design studies.

PubMed

Liu, Jia; Duffy, Ben A; Bernal-Casas, David; Fang, Zhongnan; Lee, Jin Hyung

2017-02-15

A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods' performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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.

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.

Molecular & phenotypic characterization of Staphylococcus epidermidis in implant related infections

PubMed Central

Prasad, Sujata; Nayak, N.; Satpathy, G.; Nag, H.L.; Venkatesh, P.; Ramakrishnan, S.; Ghose, Supriyo; Nag, T.C.

2012-01-01

Background & objectives: The discrimination between the Staphylococcus epidermidis colonizing the deep seated indwelling devices and those which are mere commensals has always been a challenge for the clinical microbiologist. This study was aimed to characterize the S. epidermidis isolates obtained from device related infection for their phenotypic and molecular markers of virulence and to see whether these markers can be used to differentiate the pathogenic S. epidermidis from the commensals. Methods: Fifty five S. epidermidis isolates from various device related infections such as endophthalmitis following intra-ocular lens (IOL) implantation, intravascular (IV) catheter related sepsis and orthopaedic implant infections, were studied for slime production, biotyping, antibiotic sensitivity; and mec A and ica positivity by the recommended procedures. Results: Twenty three (41.8%) isolates were multi-drug resistant, 26 (65.2%) were slime producers, 30 (54.5%) were adherent, 23 (41.8%) possessed the intercellular adhesin (ica) gene, and 28 (50.9%) harboured the mec A gene. Biotypes I and III were the commonest, most members of which were multi- drug resistant. Twenty two (73.3%) of the 30 adherent bacteria were slime producers as opposed to only 4 (16%) of the 25 non-adherent bacteria (P<0.001). A vast majority i.e. 21 (91.3%) of the 23 ica positive organisms were adherent to artificial surfaces in contrast to only 9 (28.1%) of the 32 non-ica positive organisms (P<0.001). Twenty (86.9%) of the 23 ica positive bacteria were slime producers, as opposed to only 6 (18.7%) of the 32 ica negative bacteria (P<0.001). Of the 23 multi-drug resistant isolates, 19 (82.6%) carried the mec A gene. Interpretation & conclusions: The present findings showed that ica AB and mec A were the two important virulence markers of S. epidermidis in implant infections and slime was responsible for the sessile mode of attachment on the devices. PMID:23041744

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

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

Automated selection of brain regions for real-time fMRI brain-computer interfaces

NASA Astrophysics Data System (ADS)

Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

2017-02-01

Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.

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.

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.

Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies

PubMed Central

Bernal-Casas, David; Fang, Zhongnan; Lee, Jin Hyung

2017-01-01

A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods’ performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data. PMID:27993672

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.

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.

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.

Independet Component Analyses of Ground-based Exoplanetary Transits

NASA Astrophysics Data System (ADS)

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

2016-10-01

Most observations of exoplanetary atmospheres are conducted when a "Hot Jupiter" exoplanet transits in front of its host star. These Jovian-sized planets have small orbital periods, on the order of days, and therefore a short transit time, making them more ameanable to observations. Measurements of Hot Jupiter transits must achieve a 10-4 level of accuracy in the flux to determine the spectral modulations of the exoplanetary atmosphere. In order to accomplish this level of precision, we need to extract systematic errors, and, for ground-based measurements, the effects of Earth's atmosphere, from the signal due to the exoplanet, which is several orders of magnitudes smaller. Currently, the effects of the terrestrial atmosphere and the some of the time-dependent systematic errors are treated by dividing the host star by a reference star at each wavelength and time step of the transit. More recently, Independent Component Analyses (ICA) have been used to remove systematic effects from the raw data of space-based observations (Waldmann 2014,2012; Morello et al.,2015,2016). ICA is a statistical method born from the ideas of the blind-source separation studies, which can be used to de-trend several independent source signals of a data set (Hyvarinen and Oja, 2000). One strength of this method is that it requires no additional prior knowledge of the system. Here, we present a study of the application of ICA to ground-based transit observations of extrasolar planets, which are affected by Earth's atmosphere. We analyze photometric data of two extrasolar planets, WASP-1b and GJ3470b, recorded by the 61" Kuiper Telescope at Stewart Observatory using the Harris B and U filters. The presentation will compare the light curve depths and their dispersions as derived from the ICA analysis to those derived by analyses that ratio of the host star to nearby reference stars.References: Waldmann, I.P. 2012 ApJ, 747, 12, Waldamann, I. P. 2014 ApJ, 780, 23; Morello G. 2015 ApJ, 806; Morello et al. 2016 ApJ, 820, 86; Hyvarinen, A., and Oja, E. 2000 IEEE Transactions on Neural Networks, 13, 411.

An immunochromatographic assay for rapid and direct detection of 3-amino-5-morpholino-2-oxazolidone (AMOZ) in meat and feed samples.

PubMed

Li, Shuqun; Song, Juan; Yang, Hong; Cao, Biyun; Chang, Huafang; Deng, Anping

2014-03-15

Furaltadone (FTD) is a type of nitrofuran and has been banned in many countries as a veterinary drug in food-producing animals owing to its potential carcinogenicity and mutagenicity. FTD is unstable in vivo, rapidly metabolizing to 3-amino-5-methylmorpholino-2-oxazolidinone (AMOZ); thus AMOZ can be used as an indicator for illegal usage of FTD. Usually, for the determination of nitrofurans, the analyte is often a derivative of the metabolite rather than the metabolite itself. In this study, based on the monoclonal antibody (mAb) against AMOZ, a competitive immunochromatographic assay (ICA) using a colloidal gold-mAb probe for rapid and direct detection of AMOZ without a derivatization step in meat and feed samples was developed. The intensity of red color in the test line is inversely related to the analyte concentration and the visual detection limit was found to be 10 ng mL⁻¹. The performance of this assay was simple and convenient because the tedious and time-consuming derivatization step was avoided. The ICA detection was completed within 10 min. The ICA strips could be used for 7 weeks at room temperature without significant loss of activity. The AMOZ spiked samples were detected by ICA and confirmed by enzyme-linked immunosorbent assay. The results of the two methods were in good agreement. The proposed ICA provides a feasible tool for simple, sensitive, rapid, convenient and semi-quantitative detection of AMOZ in meat and feed samples on site. To our knowledge, this is the first report of the ICA for direct detection of AMOZ. © 2013 Society of Chemical Industry.

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.

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.

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.

Assessment and clinical course of hypocalcemia in critical illness

PubMed Central

2013-01-01

Introduction Hypocalcemia is common in critically ill patients. However, its clinical course during the early days of admission and the role of calcium supplementation remain uncertain, and the assessment of calcium status is inconsistent. We aimed to establish the course of hypocalcemia during the early days of critical illness in relation to mortality and to assess the impact of calcium supplementation on calcium normalization and mortality. Methods Data were collected on 1,038 admissions to the critical care units of a tertiary care hospital. One gram of calcium gluconate was administered intravenously once daily to patients with adjusted calcium (AdjCa) <2.2 mmol/L. Demographic and outcome data were compared in normocalcemic (ionized calcium, iCa, 1.1-1.3 mmol/L) and mildly and severely hypocalcemic patients (iCa 0.9-1.1 mmol/L and <0.9 mmol/L, respectively). The change in iCa concentrations was monitored during the first four days of admission and comparisons between groups were made using Repeated Measures ANOVA. Comparisons of normalization and outcome were made between hypocalcemic patients who did and did not receive calcium replacement according to the local protocol. The suitability of AdjCa to predict low iCa was determined by analyzing sensitivity, specificity and receiver operating characteristic (ROC) curves. Multivariate logistic regression was performed to determine associations of other electrolyte derangements with hypocalcemia. Results 55.2% of patients were hypocalcemic on admission; 6.2% severely so. Severely hypocalcemic patients required critical care for longer (P = 0.001) compared to normocalcemic or mildly hypocalcemic patients, but there was no difference in mortality between groups (P = 0.48). iCa levels normalized within four days in most, with no difference in normalization between those who died and survived (P = 0.35). Severely hypocalcemic patients who failed to normalize their iCa by day 4 had double the mortality (38% vs. 19%, P = 0.15). Neither iCa normalization nor survival were superior in hypocalcemic patients receiving supplementation on admission. AdjCa <2.2 mmol/L had a sensitivity of 78.2% and specificity of 63.3% for predicting iCa <1.1 mmol/L. Low magnesium, sodium and albumin were independently associated with hypocalcemia on admission. Conclusions Hypocalcemia usually normalizes within the first four days after admission to ICU and failure to normalize in severely hypocalcemic patients may be associated with increased mortality. Calcium replacement appears not to improve normalization or mortality. AdjCa is not a good surrogate of iCa in an ICU setting. PMID:23734769

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.

Analysis of the Los Angeles Basin ground subsidence with InSAR data by independent component analysis approach

NASA Astrophysics Data System (ADS)

Xu, B.

2017-12-01

Interferometric Synthetic Aperture Radar (InSAR) has the advantages of high spatial resolution which enable measure line of sight (LOS) surface displacements with nearly complete spatial continuity and a satellite's perspective that permits large areas view of Earth's surface quickly and efficiently. However, using InSAR to observe long wavelength and small magnitude deformation signals is still significantly limited by various unmodeled errors sources i.e. atmospheric delays, orbit induced errors, Digital Elevation Model (DEM) errors. Independent component analysis (ICA) is a probabilistic method for separating linear mixed signals generated by different underlying physical processes.The signal sources which form the interferograms are statistically independent both in space and in time, thus, they can be separated by ICA approach.The seismic behavior in the Los Angeles Basin is active and the basin has experienced numerous moderate to large earthquakes since the early Pliocene. Hence, understanding the seismotectonic deformation in the Los Angeles Basin is important for analyzing seismic behavior. Compare with the tectonic deformations, nontectonic deformations due to groundwater and oil extraction may be mainly responsible for the surface deformation in the Los Angeles basin. Using the small baseline subset (SBAS) InSAR method, we extracted the surface deformation time series in the Los Angeles basin with a time span of 7 years (September 27, 2003-September 25,2010). Then, we successfully separate the atmospheric noise from InSAR time series and detect different processes caused by different mechanisms.

Finding Imaging Patterns of Structural Covariance via Non-Negative Matrix Factorization

PubMed Central

Sotiras, Aristeidis; Resnick, Susan M.; Davatzikos, Christos

2015-01-01

In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA. PMID:25497684

Automatic identification of resting state networks: an extended version of multiple template-matching

NASA Astrophysics Data System (ADS)

Guaje, Javier; Molina, Juan; Rudas, Jorge; Demertzi, Athena; Heine, Lizette; Tshibanda, Luaba; Soddu, Andrea; Laureys, Steven; Gómez, Francisco

2015-12-01

Functional magnetic resonance imaging in resting state (fMRI-RS) constitutes an informative protocol to investigate several pathological and pharmacological conditions. A common approach to study this data source is through the analysis of changes in the so called resting state networks (RSNs). These networks correspond to well-defined functional entities that have been associated to different low and high brain order functions. RSNs may be characterized by using Independent Component Analysis (ICA). ICA provides a decomposition of the fMRI-RS signal into sources of brain activity, but it lacks of information about the nature of the signal, i.e., if the source is artifactual or not. Recently, a multiple template-matching (MTM) approach was proposed to automatically recognize RSNs in a set of Independent Components (ICs). This method provides valuable information to assess subjects at individual level. Nevertheless, it lacks of a mechanism to quantify how much certainty there is about the existence/absence of each network. This information may be important for the assessment of patients with severely damaged brains, in which RSNs may be greatly affected as a result of the pathological condition. In this work we propose a set of changes to the original MTM that improves the RSNs recognition task and also extends the functionality of the method. The key points of this improvement is a standardization strategy and a modification of method's constraints that adds flexibility to the approach. Additionally, we also introduce an analysis to the trustworthiness measurement of each RSN obtained by using template-matching approach. This analysis consists of a thresholding strategy applied over the computed Goodness-of-Fit (GOF) between the set of templates and the ICs. The proposed method was validated on 2 two independent studies (Baltimore, 23 healthy subjects and Liege, 27 healthy subjects) with different configurations of MTM. Results suggest that the method will provide complementary information for characterization of RSNs at individual level.

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

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.

Independent component analysis of DTI data reveals white matter covariances in Alzheimer's disease

NASA Astrophysics Data System (ADS)

Ouyang, Xin; Sun, Xiaoyu; Guo, Ting; Sun, Qiaoyue; Chen, Kewei; Yao, Li; Wu, Xia; Guo, Xiaojuan

2014-03-01

Alzheimer's disease (AD) is a progressive neurodegenerative disease with the clinical symptom of the continuous deterioration of cognitive and memory functions. Multiple diffusion tensor imaging (DTI) indices such as fractional anisotropy (FA) and mean diffusivity (MD) can successfully explain the white matter damages in AD patients. However, most studies focused on the univariate measures (voxel-based analysis) to examine the differences between AD patients and normal controls (NCs). In this investigation, we applied a multivariate independent component analysis (ICA) to investigate the white matter covariances based on FA measurement from DTI data in 35 AD patients and 45 NCs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We found that six independent components (ICs) showed significant FA reductions in white matter covariances in AD compared with NC, including the genu and splenium of corpus callosum (IC-1 and IC-2), middle temporal gyral of temporal lobe (IC-3), sub-gyral of frontal lobe (IC-4 and IC-5) and sub-gyral of parietal lobe (IC-6). Our findings revealed covariant white matter loss in AD patients and suggest that the unsupervised data-driven ICA method is effective to explore the changes of FA in AD. This study assists us in understanding the mechanism of white matter covariant reductions in the development of AD.

Application of Independent Component Analysis for the Data Mining of Simultaneous EEG-fMRI: Preliminary Experience on Sleep Onset

PubMed Central

Lee, Jong-Hwan; Oh, Sungsuk; Jolesz, Ferenc A.; Park, Hyunwook; Yoo, Seung-Schik

2010-01-01

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with the ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta- and alpha-rhythms that are sleep onset related EEG signatures along with the subsequent neural circuitries from a sleep deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable. PMID:19922343

Application of independent component analysis for the data mining of simultaneous Eeg-fMRI: preliminary experience on sleep onset.

PubMed

Lee, Jong-Hwan; Oh, Sungsuk; Jolesz, Ferenc A; Park, Hyunwook; Yoo, Seung-Schik

2009-01-01

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable.

Quantification of localized vertebral deformities using a sparse wavelet-based shape model.

PubMed

Zewail, R; Elsafi, A; Durdle, N

2008-01-01

Medical experts often examine hundreds of spine x-ray images to determine existence of various pathologies. Common pathologies of interest are anterior osteophites, disc space narrowing, and wedging. By careful inspection of the outline shapes of the vertebral bodies, experts are able to identify and assess vertebral abnormalities with respect to the pathology under investigation. In this paper, we present a novel method for quantification of vertebral deformation using a sparse shape model. Using wavelets and Independent component analysis (ICA), we construct a sparse shape model that benefits from the approximation power of wavelets and the capability of ICA to capture higher order statistics in wavelet space. The new model is able to capture localized pathology-related shape deformations, hence it allows for quantification of vertebral shape variations. We investigate the capability of the model to predict localized pathology related deformations. Next, using support-vector machines, we demonstrate the diagnostic capabilities of the method through the discrimination of anterior osteophites in lumbar vertebrae. Experiments were conducted using a set of 150 contours from digital x-ray images of lumbar spine. Each vertebra is labeled as normal or abnormal. Results reported in this work focus on anterior osteophites as the pathology of interest.

Assessment of myocardial metabolic rate of glucose by means of Bayesian ICA and Markov Chain Monte Carlo methods in small animal PET imaging

NASA Astrophysics Data System (ADS)

Berradja, Khadidja; Boughanmi, Nabil

2016-09-01

In dynamic cardiac PET FDG studies the assessment of myocardial metabolic rate of glucose (MMRG) requires the knowledge of the blood input function (IF). IF can be obtained by manual or automatic blood sampling and cross calibrated with PET. These procedures are cumbersome, invasive and generate uncertainties. The IF is contaminated by spillover of radioactivity from the adjacent myocardium and this could cause important error in the estimated MMRG. In this study, we show that the IF can be extracted from the images in a rat heart study with 18F-fluorodeoxyglucose (18F-FDG) by means of Independent Component Analysis (ICA) based on Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling method (BICA). Images of the heart from rats were acquired with the Sherbrooke small animal PET scanner. A region of interest (ROI) was drawn around the rat image and decomposed into blood and tissue using BICA. The Statistical study showed that there is a significant difference (p < 0.05) between MMRG obtained with IF extracted by BICA with respect to IF extracted from measured images corrupted with spillover.

Resting State fMRI in the moving fetus: a robust framework for motion, bias field and spin history correction.

PubMed

Ferrazzi, Giulio; Kuklisova Murgasova, Maria; Arichi, Tomoki; Malamateniou, Christina; Fox, Matthew J; Makropoulos, Antonios; Allsop, Joanna; Rutherford, Mary; Malik, Shaihan; Aljabar, Paul; Hajnal, Joseph V

2014-11-01

There is growing interest in exploring fetal functional brain development, particularly with Resting State fMRI. However, during a typical fMRI acquisition, the womb moves due to maternal respiration and the fetus may perform large-scale and unpredictable movements. Conventional fMRI processing pipelines, which assume that brain movements are infrequent or at least small, are not suitable. Previous published studies have tackled this problem by adopting conventional methods and discarding as much as 40% or more of the acquired data. In this work, we developed and tested a processing framework for fetal Resting State fMRI, capable of correcting gross motion. The method comprises bias field and spin history corrections in the scanner frame of reference, combined with slice to volume registration and scattered data interpolation to place all data into a consistent anatomical space. The aim is to recover an ordered set of samples suitable for further analysis using standard tools such as Group Independent Component Analysis (Group ICA). We have tested the approach using simulations and in vivo data acquired at 1.5 T. After full motion correction, Group ICA performed on a population of 8 fetuses extracted 20 networks, 6 of which were identified as matching those previously observed in preterm babies. Copyright © 2014 Elsevier Inc. All rights reserved.

Cervical Rotatory Manipulation Decreases Uniaxial Tensile Properties of Rabbit Atherosclerotic Internal Carotid Artery

PubMed Central

Qi, Ji; Zhang, Lei; Chen, Chao; Mondal, Shubhro; Ping, Kaike; Chen, Yili

2017-01-01

Objective. To investigate the effects of one of the Chinese massage therapies, cervical rotatory manipulation (CRM), on uniaxial tensile properties of rabbit atherosclerotic internal carotid artery (ICA). Methods. 40 male purebred New Zealand white rabbits were randomly divided into CRM-Model group, Non-CRM-Model group, CRM-Normal group, and Non-CRM-Normal group. After modeling (atherosclerotic model) and intervention (CRM or Non-CRM), uniaxial tensile tests were performed on the ICAs to assess the differences in tensile mechanical properties between the four groups. Results. Both CRM and modeling were the main effects affecting physiological elastic modulus (PEM) of ICA. PEM in CRM-Model group was 1.81 times as much as Non-CRM-Model group, while the value in CRM-Model group was 1.34 times as much as CRM-Normal group. Maximum elastic modulus in CRM-Model group was 1.80 times as much as CRM-Normal group. Max strains in CRM-Model group and Non-CRM-Model group were 30.98% and 28.71% lower than CRM-Normal group and Non-CRM-Normal group, respectively. However, whether treated with CRM or not, the uniaxial tensile properties of healthy ICAs were not statistically different. Conclusion. CRM may decrease the uniaxial tensile properties of rabbit arteriosclerotic ICA, but with no effect on normal group. The study will aid in the meaningful explanation of the controversy about the harmfulness of CRM and the suitable population of CRM. PMID:28303160

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.

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

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.

Quantitative evaluation of deep and shallow tissue layers' contribution to fNIRS signal using multi-distance optodes and independent component analysis.

PubMed

Funane, Tsukasa; Atsumori, Hirokazu; Katura, Takusige; Obata, Akiko N; Sato, Hiroki; Tanikawa, Yukari; Okada, Eiji; Kiguchi, Masashi

2014-01-15

To quantify the effect of absorption changes in the deep tissue (cerebral) and shallow tissue (scalp, skin) layers on functional near-infrared spectroscopy (fNIRS) signals, a method using multi-distance (MD) optodes and independent component analysis (ICA), referred to as the MD-ICA method, is proposed. In previous studies, when the signal from the shallow tissue layer (shallow signal) needs to be eliminated, it was often assumed that the shallow signal had no correlation with the signal from the deep tissue layer (deep signal). In this study, no relationship between the waveforms of deep and shallow signals is assumed, and instead, it is assumed that both signals are linear combinations of multiple signal sources, which allows the inclusion of a "shared component" (such as systemic signals) that is contained in both layers. The method also assumes that the partial optical path length of the shallow layer does not change, whereas that of the deep layer linearly increases along with the increase of the source-detector (S-D) distance. Deep- and shallow-layer contribution ratios of each independent component (IC) are calculated using the dependence of the weight of each IC on the S-D distance. Reconstruction of deep- and shallow-layer signals are performed by the sum of ICs weighted by the deep and shallow contribution ratio. Experimental validation of the principle of this technique was conducted using a dynamic phantom with two absorbing layers. Results showed that our method is effective for evaluating deep-layer contributions even if there are high correlations between deep and shallow signals. Next, we applied the method to fNIRS signals obtained on a human head with 5-, 15-, and 30-mm S-D distances during a verbal fluency task, a verbal working memory task (prefrontal area), a finger tapping task (motor area), and a tetrametric visual checker-board task (occipital area) and then estimated the deep-layer contribution ratio. To evaluate the signal separation performance of our method, we used the correlation coefficients of a laser-Doppler flowmetry (LDF) signal and a nearest 5-mm S-D distance channel signal with the shallow signal. We demonstrated that the shallow signals have a higher temporal correlation with the LDF signals and with the 5-mm S-D distance channel than the deep signals. These results show the MD-ICA method can discriminate between deep and shallow signals. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

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.

Simplified ultrasound protocol for the exclusion of clinically significant carotid artery stenosis.

PubMed

Högberg, Dominika; Dellagrammaticas, Demosthenes; Kragsterman, Björn; Björck, Martin; Wanhainen, Anders

2016-08-01

To evaluate a simplified ultrasound protocol for the exclusion of clinically significant carotid artery stenosis for screening purposes. A total of 9,493 carotid arteries in 4,748 persons underwent carotid ultrasound examination. Most subjects were 65-year-old men attending screening for abdominal aortic aneurysm. The presence of a stenosis on B-mode and/or a mosaic pattern in post-stenotic areas on colour Doppler and maximum peak systolic velocity (PSV) in the internal carotid artery (ICA) were recorded. A carotid stenosis was defined as The North American Symptomatic Carotid Endarterectomy Trial (NASCET) >20% and a significant stenosis as NASCET >50%. The kappa (κ) statistic was used to assess agreement between methods. Sensitivity, specificity, positive predictive (PPV), and negative predictive (NPV) values were calculated for the greyscale/mosaic method compared to conventional assessment by means of PSV measurement. An ICA stenosis was found in 121 (1.3%) arteries; 82 (0.9%) were graded 20%-49%, 16 (0.2%) were 50%-69%, and 23 (0.2%) were 70%-99%. Eighteen (0.2%) arteries were occluded. Overall, the greyscale/mosaic protocol showed a moderate agreement with ICA PSV measurements for the detection of carotid artery stenosis, κ = 0.455. The sensitivity, specificity, PPV, and NPV for detection of >20% ICA stenosis were 91% (95% CI 0.84-0.95), 97% (0.97-0.98), 31% (0.26-0.36), and 97% (0.97-0.97), respectively. The corresponding figures for >50% stenosis were 90% (0.83-0.95), 97% (0.97-0.98), 11% (0.08-0.15), and 100% (0.99-1.00). Compared with PSV measurements, the simplified greyscale/mosaic protocol had a high negative predictive value for detection of >50% carotid stenosis, suggesting that it may be suitable as a screening method to exclude significant disease.

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.

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.

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

Follow-Up of 58 Traumatic Carotid-Cavernous Fistulas after Endovascular Detachable-Balloon Embolization at a Single Center

PubMed Central

Xu, Xiao-Quan; Liu, Sheng; Zu, Qing-Quan; Zhao, Lin-Bo; Xia, Jin-Guo; Zhou, Chun-Gao; Zhou, Wei-Zhong

2013-01-01

Background and Purpose This study evaluated the clinical value of detachable-balloon embolization for traumatic carotid-cavernous fistula (TCCF), focusing on the frequency, risk factors, and retreatment of recurrence. Methods Fifty-eight patients with TCCF underwent transarterial detachable-balloon embolization between October 2004 and March 2011. The clinical follow-up was performed every 3 months until up to 3 years postprocedure. Each patient was placed in either the recurrence group or the nonrecurrence group according to whether a recurrence developed after the first procedure. The relevant factors including gender, fistula location, interval between trauma and the interventional procedure, blood flow in the carotid-cavernous fistula, number of balloons, and whether the internal carotid artery (ICA) was sacrificed were evaluated. Results All 58 TCCFs were successfully treated with transarterial balloon embolization, including 7 patients with ICA sacrifice. Recurrent fistulas occurred in seven patients during the follow-up period. Univariate analysis indicated that the interval between trauma and the interventional procedure (p=0.006) might be the main factor related to the recurrence of TCCF. The second treatments involved ICA sacrifice in two patients, fistula embolization with balloons in four patients, and placement of a covered stent in one patient. Conclusions Detachable balloons can still serve as the first-line treatment for TCCFs and recurrent TCCFs despite having a nonnegligible recurrence rate. Shortening the interval between trauma and the interventional procedure may reduce the risk of recurrence. PMID:23626645

A method for independent component graph analysis of resting-state fMRI.

PubMed

Ribeiro de Paula, Demetrius; Ziegler, Erik; Abeyasinghe, Pubuditha M; Das, Tushar K; Cavaliere, Carlo; Aiello, Marco; Heine, Lizette; di Perri, Carol; Demertzi, Athena; Noirhomme, Quentin; Charland-Verville, Vanessa; Vanhaudenhuyse, Audrey; Stender, Johan; Gomez, Francisco; Tshibanda, Jean-Flory L; Laureys, Steven; Owen, Adrian M; Soddu, Andrea

2017-03-01

Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data. Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory. First, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network. Network graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness. This novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings

PubMed Central

Tamburro, Gabriella; Fiedler, Patrique; Stone, David; Haueisen, Jens

2018-01-01

Background EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts. Methods Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR. Results The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs). Discussion Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings. PMID:29492336

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.

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.

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.

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.

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

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.

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

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.

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings.

PubMed

Tamburro, Gabriella; Fiedler, Patrique; Stone, David; Haueisen, Jens; Comani, Silvia

2018-01-01

EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts. Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity ( p ). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR. The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs). Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings.

A method for simultaneous linear optics and coupling correction for storage rings with turn-by-turn beam position monitor data

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

Yang, Xi; Huang, Xiaobiao

2016-05-13

Here, we propose a method to simultaneously correct linear optics errors and linear coupling for storage rings using turn-by-turn (TbT) beam position monitor (BPM) data. The independent component analysis (ICA) method is used to isolate the betatron normal modes from the measured TbT BPM data. The betatron amplitudes and phase advances of the projections of the normal modes on the horizontal and vertical planes are then extracted, which, combined with dispersion measurement, are used to fit the lattice model. The fitting results are used for lattice correction. Finally, the method has been successfully demonstrated on the NSLS-II storage ring.

A method for simultaneous linear optics and coupling correction for storage rings with turn-by-turn beam position monitor data

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

Yang, Xi; Huang, Xiaobiao

2016-08-01

We propose a method to simultaneously correct linear optics errors and linear coupling for storage rings using turn-by-turn (TbT) beam position monitor (BPM) data. The independent component analysis (ICA) method is used to isolate the betatron normal modes from the measured TbT BPM data. The betatron amplitudes and phase advances of the projections of the normal modes on the horizontal and vertical planes are then extracted, which, combined with dispersion measurement, are used to fit the lattice model. Furthermore, the fitting results are used for lattice correction. Our method has been successfully demonstrated on the NSLS-II storage ring.

A method for simultaneous linear optics and coupling correction for storage rings with turn-by-turn beam position monitor data

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

Yang, Xi; Huang, Xiaobiao

2016-08-01

We propose a method to simultaneously correct linear optics errors and linear coupling for storage rings using turn-by-turn (TbT) beam position monitor (BPM) data. The independent component analysis (ICA) method is used to isolate the betatron normal modes from the measured TbT BPM data. The betatron amplitudes and phase advances of the projections of the normal modes on the horizontal and vertical planes are then extracted, which, combined with dispersion measurement, are used to fit the lattice model. The fitting results are used for lattice correction. The method has been successfully demonstrated on the NSLS-II storage ring.

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.

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.

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.

Development of a rapid immunochromatographic assay to detect contamination of raw oysters with enteropathogenic Vibrio parahaemolyticus.

PubMed

Sakata, Junko; Yonekita, Taro; Kawatsu, Kentaro

2018-01-02

Thermostable direct hemolysin (TDH) and TDH-related hemolysin (TRH) are major virulence factors of enteropathogenic Vibrio parahaemolyticus. TDH and TRH are bacterial exotoxins, and their presence in culture medium serves as a specific marker for detecting this significant pathogen. Here, we developed and evaluated an immunochromatographic assay (TDH/TRH-ICA) to simultaneously or individually detect TDH and TRH. The TDH/TRH-ICA detected TDH in all broth cultures of 47 V. parahaemolyticus strains carrying tdh. The genes encoding TRH are classified as variants trh1 and trh2, and TRH was detected in all broth cultures of 25 V. parahaemolyticus strains carrying trh1 and certain proportion (5/31) of broth cultures of V. parahaemolyticus strains carrying trh2. In contrast, TDH and TRH were not detected in broth cultures of 12 non-enteropathogenic V. parahaemolyticus strains without tdh and trh. It was difficult to detect TRH2 using the TDH/TRH-ICA. However, TRH2 may not serve as a suitable marker for detecting enteropathogenic V. parahaemolyticus, and evidence indicates that TRH2 may not contribute to enteropathogenesis. Further, a screening method using a combination of TDH/TRH-ICA and SPP medium supplemented with 1.5% NaCl (modified-SPP medium) detected oyster samples artificially spiked with 1.1-22 colony-forming units of enteropathogenic V. parahaemolyticus per 25g of oysters within approximately 8.5h, including the enrichment culture. The assay may serve as a method that facilitates the rapid and easy detection of raw oysters contaminated with enteropathogenic V. parahaemolyticus. Copyright © 2017. Published by Elsevier B.V.

Evaluation of I/Ca ratios in benthic foraminifera from the Peruvian oxygen minimum zone as proxy for redox conditions in the ambient water masses

NASA Astrophysics Data System (ADS)

Glock, N.; Liebetrau, V.; Eisenhauer, A.

2014-12-01

Tropical oxygen minimum zones (OMZs) are most important areas of oxygen depletion in today´s oceans and nutrient cycling in these regions has a large socio-economic impact because they account for about 17% of the global commercial fish catches(1). Possibly increasing magnitude and area of oxygen depletion in these regions, might endanger rich pelagic fish habitats in the future threatening the global marine food supply. By the use of a quantitative redox proxy in OMZs, reconstruction of the temporal variation in OMZ extension eventually providing information about past and future changes in oxygenation and the anthropogenic role in the recent trend of expanding OMZs(2). Recent work has shown that iodine/calcium (I/Ca) ratios in marine carbonates are a promising proxy for ambient oxygen concentration(3). Our study explores the correlation of I/Ca ratios in four benthic foraminiferal species (three calcitic, one aragonitic) from the Peruvian OMZ to bottom water oxygen concentrations ([O2]BW) and evaluates foraminiferal I/Ca ratios as a possible redox proxy for the ambient water masses. Our results show that all species have a positive trend in the I/Ca ratios as a function of [O2]BW. Only for the aragonitic species Hoeglundina elegans this trend is not significant. The highest significance has been found for Uvigerina striata (I/Ca = 0.032(±0.004).[O2]BW + 0.29(±0.03), R² = 0.61, F = 75, P < 0.0001). Although I/Ca ratios in benthic foraminifera appear to be a robust redox proxy there are some methodical issues which have to be considered. These "pitfalls" include: (i) the volatility of iodine in acidic solutions, (ii) a species dependency of the I/Ca-[O2]BW relationship which is either related to a strong vital effect or toa species dependency on the calcification depth within sediment, and (iii) the inter-test variability of I/Ca between different specimens from the same species and habitat. (1): FAO FishStat: Fisheries and aquaculture software. In: FAO Fisheries and Aquaculture Department[online]. Rome. Updated 28 Nov. 2013. (2): Stramma et al.: Expanding Oxygen-Minimum Zones in the Tropical Oceans, Science, 320, 655-658, 2008. (3): Lu et al.: Iodine to calcium ratios in marine carbonate as a paleo-redox proxy during oceanic anoxic events, Geology, 38, 1107-1110, 2010.

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.

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

Translation of EEG Spatial Filters from Resting to Motor Imagery Using Independent Component Analysis

PubMed Central

Wang, Yijun; Wang, Yu-Te; Jung, Tzyy-Ping

2012-01-01

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA) was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%), which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%). The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters. PMID:22666377

Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization.

PubMed

Sotiras, Aristeidis; Resnick, Susan M; Davatzikos, Christos

2015-03-01

In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

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.

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.

Independent component analysis (ICA) and self-organizing map (SOM) approach to multidetection system for network intruders

NASA Astrophysics Data System (ADS)

Abdi, Abdi M.; Szu, Harold H.

2003-04-01

With the growing rate of interconnection among computer systems, network security is becoming a real challenge. Intrusion Detection System (IDS) is designed to protect the availability, confidentiality and integrity of critical network information systems. Today"s approach to network intrusion detection involves the use of rule-based expert systems to identify an indication of known attack or anomalies. However, these techniques are less successful in identifying today"s attacks. Hackers are perpetually inventing new and previously unanticipated techniques to compromise information infrastructure. This paper proposes a dynamic way of detecting network intruders on time serious data. The proposed approach consists of a two-step process. Firstly, obtaining an efficient multi-user detection method, employing the recently introduced complexity minimization approach as a generalization of a standard ICA. Secondly, we identified unsupervised learning neural network architecture based on Kohonen"s Self-Organizing Map for potential functional clustering. These two steps working together adaptively will provide a pseudo-real time novelty detection attribute to supplement the current intrusion detection statistical methodology.

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.

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…

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

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

NOTE: Entropy-based automated classification of independent components separated from fMCG

NASA Astrophysics Data System (ADS)

Comani, S.; Srinivasan, V.; Alleva, G.; Romani, G. L.

2007-03-01

Fetal magnetocardiography (fMCG) is a noninvasive technique suitable for the prenatal diagnosis of the fetal heart function. Reliable fetal cardiac signals can be reconstructed from multi-channel fMCG recordings by means of independent component analysis (ICA). However, the identification of the separated components is usually accomplished by visual inspection. This paper discusses a novel automated system based on entropy estimators, namely approximate entropy (ApEn) and sample entropy (SampEn), for the classification of independent components (ICs). The system was validated on 40 fMCG datasets of normal fetuses with the gestational age ranging from 22 to 37 weeks. Both ApEn and SampEn were able to measure the stability and predictability of the physiological signals separated with ICA, and the entropy values of the three categories were significantly different at p <0.01. The system performances were compared with those of a method based on the analysis of the time and frequency content of the components. The outcomes of this study showed a superior performance of the entropy-based system, in particular for early gestation, with an overall ICs detection rate of 98.75% and 97.92% for ApEn and SampEn respectively, as against a value of 94.50% obtained with the time-frequency-based system.

Separating spectral mixtures in hyperspectral image data using independent component analysis: validation with oral cancer tissue sections

NASA Astrophysics Data System (ADS)

Duann, Jeng-Ren; Jan, Chia-Ing; Ou-Yang, Mang; Lin, Chia-Yi; Mo, Jen-Feng; Lin, Yung-Jiun; Tsai, Ming-Hsui; Chiou, Jin-Chern

2013-12-01

Recently, hyperspectral imaging (HSI) systems, which can provide 100 or more wavelengths of emission autofluorescence measures, have been used to delineate more complete spectral patterns associated with certain molecules relevant to cancerization. Such a spectral fingerprint may reliably correspond to a certain type of molecule and thus can be treated as a biomarker for the presence of that molecule. However, the outcomes of HSI systems can be a complex mixture of characteristic spectra of a variety of molecules as well as optical interferences due to reflection, scattering, and refraction. As a result, the mixed nature of raw HSI data might obscure the extraction of consistent spectral fingerprints. Here we present the extraction of the characteristic spectra associated with keratinized tissues from the HSI data of tissue sections from 30 oral cancer patients (31 tissue samples in total), excited at two different wavelength ranges (330 to 385 and 470 to 490 nm), using independent and principal component analysis (ICA and PCA) methods. The results showed that for both excitation wavelength ranges, ICA was able to resolve much more reliable spectral fingerprints associated with the keratinized tissues for all the oral cancer tissue sections with significantly higher mean correlation coefficients as compared to PCA (p<0.001).

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.

An information-theoretic approach for the evaluation of surrogate endpoints based on causal inference.

PubMed

Alonso, Ariel; Van der Elst, Wim; Molenberghs, Geert; Buyse, Marc; Burzykowski, Tomasz

2016-09-01

In this work a new metric of surrogacy, the so-called individual causal association (ICA), is introduced using information-theoretic concepts and a causal inference model for a binary surrogate and true endpoint. The ICA has a simple and appealing interpretation in terms of uncertainty reduction and, in some scenarios, it seems to provide a more coherent assessment of the validity of a surrogate than existing measures. The identifiability issues are tackled using a two-step procedure. In the first step, the region of the parametric space of the distribution of the potential outcomes, compatible with the data at hand, is geometrically characterized. Further, in a second step, a Monte Carlo approach is proposed to study the behavior of the ICA on the previous region. The method is illustrated using data from the Collaborative Initial Glaucoma Treatment Study. A newly developed and user-friendly R package Surrogate is provided to carry out the evaluation exercise. © 2016, The International Biometric Society.

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.

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.

Stent Retriever Thrombectomy in Different Thrombus Locations of Anterior Cerebral Circulation

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

Protto, Sara; Sillanpää, Niko, E-mail: niko.sillanpaa@pshp.fi; Pienimäki, Juha-Pekka

BackgroundMechanical thrombectomy (MT) is a safe and efficient treatment for acute ischemic stroke in patients with proximal anterior occlusion and large penumbra. We evaluated the technical and clinical success of MT in relation to the location of the occlusion (internal carotid artery, M1 and M2 segments of the middle cerebral artery).MethodsWe prospectively reviewed 130 patients of whom 105 met the inclusion criteria. Baseline clinical, procedural and imaging variables, technical outcome (TICI, thrombolysis in cerebral infarction), 24 h imaging outcome and three-month clinical outcome (mRS, modified Rankin Scale) were recorded. Differences between the groups were studied with statistical tests according to themore » type of the variable.ResultsThere were 37, 46 and 22 patients in the internal carotid artery (ICA), M1 and M2 groups, respectively. TICI 2b or 3 was achieved in 92 cases (88 %) with a non-significant trend towards a better recanalization outcome in the ICA and M1 groups. Overall, 57 of the 105 patients (55 %) experienced favorable clinical outcome (mRS ≤ 2) with no significant differences between the groups. Excellent outcome (mRS ≤ 1) was seen in 40 patients (39 %) and there proportionally more patients with excellent outcome in the ICA and M1 groups (ICA: 44 %, M1: 41 %, M2: 23 % of patients, p = 0.22).ConclusionsThere were no statistically significant differences in the technical or clinical outcomes between the different sites of occlusion (ICA, M1 or M2). There was a non-significant trend towards achieving excellent clinical outcome (3-month mRS ≤ 1) more often and better recanalization results in the two more proximal locations.« less

Intraoperative trans-fontanellar cerebral ultrasonography in infants during cardiac surgery under cardiopulmonary bypass: an observational study.

PubMed

Park, Yong-Hee; Song, In-Kyung; Lee, Ji-Hyun; Kim, Hee-Soo; Kim, Chong-Sung; Kim, Jin-Tae

2017-02-01

This study was performed to evaluate the feasibility of intraoperative point-of-care trans-fontanellar cerebral ultrasonography (TFCU) to obtain blood flow velocity (BFV) reference values at the internal carotid arteries (ICAs) and peri-callosal part of the anterior cerebral artery (pACA) during pediatric cardiac surgery under cardiopulmonary bypass (CPB). TFCU was performed at three time points (after induction of anesthesia, during CPB, after CPB) in 35 infants. BFV was measured at both ICAs and pACA through the anterior fontanelle with an ultrasound sector probe. We divided patients into Group S (<5 kg, n = 16) and Group L (≥5 kg, n = 19) for comparisons according to weight. We also analyzed BFV in low cerebral regional oxygen saturation (rSO 2 ) data. All measurements of the BFV at both the ICAs and the pACA were possible. BFVs at the ICAs were lower in Group S than in Group L at all three time points. BFVs at the pACA were similar in both groups except higher value in Group L after CPB. When the rSO 2 was <50, most BFVs (14 of 15 measurements) were lower than the median BFV value during CPB. However, a low rSO 2 did not always reflect low BFV before and after CPB. Point-of-care TFCU can determine BFV at the ICAs and pACA during pediatric cardiac surgery. BFV differs according to the patient's size and CPB application. TFCU can be a practical cerebral blood flow monitoring method when rSO 2 changes without any specific reason in infants.ClinicalTrials.gov NCT01996020.

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.

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

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%.

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.

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.

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.

Carotid artery stenting for vulnerable plaques on MR angiography and ultrasonography: utility of dual protection and blood aspiration method.

PubMed

Sakamoto, Shigeyuki; Kiura, Yoshihiro; Okazaki, Takahito; Shinagawa, Katsuhiro; Ishii, Daizo; Ichinose, Nobuhiko; Kurisu, Kaoru

2016-10-01

Carotid artery stenting (CAS) for internal carotid artery (ICA) stenosis with vulnerable plaques is controversial. We analyzed the effect of a dual protection and blood aspiration method during CAS in patients with vulnerable plaques. A total of 111 patients with ICA stenosis underwent CAS using the dual protection (simultaneous flow reversal and distal filter) and blood aspiration method. In 103 of 111 patients, preoperative carotid plaque was estimated by both 3 T MRI and ultrasonography (US). ICA plaques with a high intensity signal on time-of-flight-MR angiography (TOF-MRA) and/or mobile component on US were defined as vulnerable plaques. We assessed major adverse events (MAE) (ie, major stroke, myocardial infarction, and death) and hyperintense spots on diffusion-weighted images (DWI) after CAS. We then evaluated the visible debris captured by dual protection and blood aspiration during CAS. The preoperative ICA plaque on TOF-MRA and US was judged to be vulnerable in 48.5% (50/103 patients). The success rate of the CAS procedure was 100% with no MAE within 30 days. DWI showed small hyperintense spots in 18% (9/50 patients) and 18.9% (10/53 patients) in the vulnerable and non-vulnerable plaque groups, respectively. Visible debris was captured in 68% (34/50 patients) and 45.3% (24/53 patients) in the vulnerable and non-vulnerable plaque groups, respectively (p=0.0286). The combination of dual protection and blood aspiration could provide effective distal embolic protection although vulnerable plaques on TOF-MRA and US had a high incidence of debris during CAS. Thus, CAS using dual protection and blood aspiration is safe in patients with vulnerable plaques. 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/

Analysis of isocyanates in indoor dust.

PubMed

Bekki, Kanae; Uchiyama, Shigehisa; Kunugita, Naoki

2018-05-04

Isocyanates are harmful semi-volatile organic compounds that are emitted from various consumer products like polyurethane foam-based mattresses. Although it is a concern that isocyanates might accumulate in indoor dust, causing infants and toddlers, in particular, to be exposed to them, little information is available on the levels of isocyanates in the indoor environment. In this study, we investigated the suitability of an analytical method for determining the presence and level of isocyanates in the indoor dust. The method we developed displayed acceptable linearity, accuracy, and precision in the analysis of eleven different isocyanates. By using this analytical method, we could detect five isocyanates (ICA, MIC, EIC, PIC, and PHI) and quantify three isocyanates (MIC, EIC, and PHI) in indoor dust collected in different houses. This study is the first to focus on the pollution of indoor dust by isocyanates, and the tested method is suitable for the estimation of the level of isocyanate exposure. Graphical abstract ᅟ.

Optimizing methods for linking cinematic features to fMRI data.

PubMed

Kauttonen, Janne; Hlushchuk, Yevhen; Tikka, Pia

2015-04-15

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film 'At Land' by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is - in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice - in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features. Copyright © 2015. Published by Elsevier Inc.

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.

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.

[Efficacy of fusion image for the preoperative assessment of anatomical variation of the anterior choroidal artery].

PubMed

Aoki, Yasuko; Endo, Hidenori; Niizuma, Kuniyasu; Inoue, Takashi; Shimizu, Hiroaki; Tominaga, Teiji

2013-12-01

We report two cases with internal carotid artery(ICA)aneurysms, in which fusion image effectively indicated the anatomical variations of the anterior choroidal artery (AchoA). Fusion image was obtained using fusion application software (Integrated Registration, Advantage Workstation VS4, GE Healthcare). When the artery passed through the choroidal fissure, it was diagnosed as AchoA. Case 1 had an aneurysm at the left ICA. Left internal carotid angiography (ICAG) showed that an artery arising from the aneurysmal neck supplied the medial occipital lobe. Fusion image showed that this artery had a branch passing through the choroidal fissure, which was diagnosed as hyperplastic AchoA. Case 2 had an aneurysm at the supraclinoid segment of the right ICA. AchoA or posterior communicating artery (PcomA) were not detected by the right ICAG. Fusion image obtained from 3D vertebral angiography (VAG) and MRI showed that the right AchoA arose from the right PcomA. Fusion image obtained from the right ICAG and the left VAG suggested that the aneurysm was located on the ICA where the PcomA regressed. Fusion image is an effective tool for assessing anatomical variations of AchoA. The present method is simple and quick for obtaining a fusion image that can be used in a real-time clinical setting.

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

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.

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.

Adaptive noise canceling of electrocardiogram artifacts in single channel electroencephalogram.

PubMed

Cho, Sung Pil; Song, Mi Hye; Park, Young Cheol; Choi, Ho Seon; Lee, Kyoung Joung

2007-01-01

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.

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.

Principal and independent component analysis of concomitant functional near infrared spectroscopy and magnetic resonance imaging data

NASA Astrophysics Data System (ADS)

Schelkanova, Irina; Toronov, Vladislav

2011-07-01

Although near infrared spectroscopy (NIRS) is now widely used both in emerging clinical techniques and in cognitive neuroscience, the development of the apparatuses and signal processing methods for these applications is still a hot research topic. The main unresolved problem in functional NIRS is the separation of functional signals from the contaminations by systemic and local physiological fluctuations. This problem was approached by using various signal processing methods, including blind signal separation techniques. In particular, principal component analysis (PCA) and independent component analysis (ICA) were applied to the data acquired at the same wavelength and at multiple sites on the human or animal heads during functional activation. These signal processing procedures resulted in a number of principal or independent components that could be attributed to functional activity but their physiological meaning remained unknown. On the other hand, the best physiological specificity is provided by broadband NIRS. Also, a comparison with functional magnetic resonance imaging (fMRI) allows determining the spatial origin of fNIRS signals. In this study we applied PCA and ICA to broadband NIRS data to distill the components correlating with the breath hold activation paradigm and compared them with the simultaneously acquired fMRI signals. Breath holding was used because it generates blood carbon dioxide (CO2) which increases the blood-oxygen-level-dependent (BOLD) signal as CO2 acts as a cerebral vasodilator. Vasodilation causes increased cerebral blood flow which washes deoxyhaemoglobin out of the cerebral capillary bed thus increasing both the cerebral blood volume and oxygenation. Although the original signals were quite diverse, we found very few different components which corresponded to fMRI signals at different locations in the brain and to different physiological chromophores.

Modulation by EEG features of BOLD responses to interictal epileptiform discharges

PubMed Central

LeVan, Pierre; Tyvaert, Louise; Gotman, Jean

2013-01-01

Introduction EEG-fMRI of interictal epileptiform discharges (IEDs) usually assumes a fixed hemodynamic response function (HRF). This study investigates HRF variability with respect to IED amplitude fluctuations using independent component analysis (ICA), with the goal of improving the specificity of EEG-fMRI analyses. Methods We selected EEG-fMRI data from 10 focal epilepsy patients with a good quality EEG. IED amplitudes were calculated in an average reference montage. The fMRI data were decomposed by ICA and a deconvolution method identified IED-related components by detecting time courses with a significant HRF time-locked to the IEDs (F-test, p<0.05). Individual HRF amplitudes were then calculated for each IED. Components with a significant HRF/IED amplitude correlation (Spearman test, p< 0.05) were compared to the presumed epileptogenic focus and to results of a general linear model (GLM) analysis. Results In 7 patients, at least one IED-related component was concordant with the focus, but many IED-related components were at distant locations. When considering only components with a significant HRF/IED amplitude correlation, distant components could be discarded, significantly increasing the relative proportion of activated voxels in the focus (p=0.02). In the 3 patients without concordant IED-related components, no HRF/IED amplitude correlations were detected inside the brain. Integrating IED-related amplitudes in the GLM significantly improved fMRI signal modeling in the epileptogenic focus in 4 patients (p< 0.05). Conclusion Activations in the epileptogenic focus appear to show significant correlations between HRF and IED amplitudes, unlike distant responses. These correlations could be integrated in the analysis to increase the specificity of EEG-fMRI studies in epilepsy. PMID:20026222

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.

Sparse dictionary learning for resting-state fMRI analysis

NASA Astrophysics Data System (ADS)

Lee, Kangjoo; Han, Paul Kyu; Ye, Jong Chul

2011-09-01

Recently, there has been increased interest in the usage of neuroimaging techniques to investigate what happens in the brain at rest. Functional imaging studies have revealed that the default-mode network activity is disrupted in Alzheimer's disease (AD). However, there is no consensus, as yet, on the choice of analysis method for the application of resting-state analysis for disease classification. This paper proposes a novel compressed sensing based resting-state fMRI analysis tool called Sparse-SPM. As the brain's functional systems has shown to have features of complex networks according to graph theoretical analysis, we apply a graph model to represent a sparse combination of information flows in complex network perspectives. In particular, a new concept of spatially adaptive design matrix has been proposed by implementing sparse dictionary learning based on sparsity. The proposed approach shows better performance compared to other conventional methods, such as independent component analysis (ICA) and seed-based approach, in classifying the AD patients from normal using resting-state analysis.