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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

PubMed

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

2015-01-15

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

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

PubMed

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

2018-03-01

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

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

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.

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.

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).

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).

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.

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

PubMed

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

2013-07-01

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

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.

Enhanced automatic artifact detection based on independent component analysis and Renyi's entropy.

PubMed

Mammone, Nadia; Morabito, Francesco Carlo

2008-09-01

Artifacts are disturbances that may occur during signal acquisition and may affect their processing. The aim of this paper is to propose a technique for automatically detecting artifacts from the electroencephalographic (EEG) recordings. In particular, a technique based on both Independent Component Analysis (ICA) to extract artifactual signals and on Renyi's entropy to automatically detect them is presented. This technique is compared to the widely known approach based on ICA and the joint use of kurtosis and Shannon's entropy. The novel processing technique is shown to detect on average 92.6% of the artifactual signals against the average 68.7% of the previous technique on the studied available database. Moreover, Renyi's entropy is shown to be able to detect muscle and very low frequency activity as well as to discriminate them from other kinds of artifacts. In order to achieve an efficient rejection of the artifacts while minimizing the information loss, future efforts will be devoted to the improvement of blind artifact separation from EEG in order to ensure a very efficient isolation of the artifactual activity from any signals deriving from other brain tasks.

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.

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.

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

Signal extraction and wave field separation in tunnel seismic prediction by independent component analysis

NASA Astrophysics Data System (ADS)

Yue, Y.; Jiang, T.; Zhou, Q.

2017-12-01

In order to ensure the rationality and the safety of tunnel excavation, the advanced geological prediction has been become an indispensable step in tunneling. However, the extraction of signal and the separation of P and S waves directly influence the accuracy of geological prediction. Generally, the raw data collected in TSP system is low quality because of the numerous disturb factors in tunnel projects, such as the power interference and machine vibration interference. It's difficult for traditional method (band-pass filtering) to remove interference effectively as well as bring little loss to signal. The power interference, machine vibration interference and the signal are original variables and x, y, z component as observation signals, each component of the representation is a linear combination of the original variables, which satisfy applicable conditions of independent component analysis (ICA). We perform finite-difference simulations of elastic wave propagation to synthetic a tunnel seismic reflection record. The method of ICA was adopted to process the three-component data, and the results show that extract the estimates of signal and the signals are highly correlated (the coefficient correlation is up to more than 0.93). In addition, the estimates of interference that separated from ICA and the interference signals are also highly correlated, and the coefficient correlation is up to more than 0.99. Thus, simulation results showed that the ICA is an ideal method for extracting high quality data from mixed signals. For the separation of P and S waves, the conventional separation techniques are based on physical characteristics of wave propagation, which require knowledge of the near-surface P and S waves velocities and density. Whereas the ICA approach is entirely based on statistical differences between P and S waves, and the statistical technique does not require a priori information. The concrete results of the wave field separation will be presented in the meeting. In summary, we can safely draw the conclusion that ICA can not only extract high quality data from the mixed signals, but also can separate P and S waves effectively.

Layered Manufacturing of Dental Ceramics: Fracture Mechanics, Microstructure, and Elemental Composition of Lithography-Sintered Ceramic.

PubMed

Uçar, Yurdanur; Aysan Meriç, İpek; Ekren, Orhun

2018-02-11

To compare the fracture mechanics, microstructure, and elemental composition of lithography-based ceramic manufacturing with pressing and CAD/CAM. Disc-shaped specimens (16 mm diameter, 1.2 mm thick) were used for mechanical testing (n = 10/group). Biaxial flexural strength of three groups (In-Ceram alumina [ICA], lithography-based alumina, ZirkonZahn) were determined using the "piston on 3-ball" technique as suggested in test Standard ISO-6872. Vickers hardness test was performed. Fracture toughness was calculated using fractography. Results were statistically analyzed using Kruskal-Wallis test followed by Dunnett T3 (α = 0.05). Weibull analysis was conducted. Polished and fracture surface characterization was made using scanning electron microscope (SEM). Energy dispersive spectroscopy (EDS) was used for elemental analysis. Biaxial flexural strength of ICA, LCM alumina (LCMA), and ZirkonZahn were 147 ± 43 MPa, 490 ± 44 MPa, and 709 ± 94 MPa, respectively, and were statistically different (P ≤ 0.05). The Vickers hardness number of ICA was 850 ± 41, whereas hardness values for LCMA and ZirkonZahn were 1581 ± 144 and 1249 ± 57, respectively, and were statistically different (P ≤ 0.05). A statistically significant difference was found between fracture toughness of ICA (2 ± 0.4 MPa⋅m 1/2 ), LCMA (6.5 ± 1.5 MPa⋅m 1/2 ), and ZirkonZahn (7.7 ± 1 MPa⋅m 1/2 ) (P ≤ 0.05). Weibull modulus was highest for LCMA (m = 11.43) followed by ZirkonZahn (m = 8.16) and ICA (m = 5.21). Unlike LCMA and ZirkonZahn groups, a homogeneous microstructure was not observed for ICA. EDS results supported the SEM images. Within the limitations of this in vitro study, it can be concluded that LCM seems to be a promising technique for final ceramic object manufacturing in dental applications. Both the manufacturing method and the material used should be improved. © 2018 by the American College of Prosthodontists.

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.

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

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

PubMed Central

Nickerson, Lisa D.; Smith, Stephen M.; Öngür, Döst; Beckmann, Christian F.

2017-01-01

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or “shape”) as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia. PMID:28348512

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/.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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

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

PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG.

PubMed

Ball, Kenneth; Bigdely-Shamlo, Nima; Mullen, Tim; Robbins, Kay

2016-01-01

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals.

PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Robbins, Kay

2016-01-01

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals. PMID:27340397

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.

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.

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

PubMed Central

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

2013-01-01

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

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

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

NASA Astrophysics Data System (ADS)

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

2009-04-01

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

The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: a meta-analysis focused on post-test disease probability.

PubMed

Knuuti, Juhani; Ballo, Haitham; Juarez-Orozco, Luis Eduardo; Saraste, Antti; Kolh, Philippe; Rutjes, Anne Wilhelmina Saskia; Jüni, Peter; Windecker, Stephan; Bax, Jeroen J; Wijns, William

2018-05-29

To determine the ranges of pre-test probability (PTP) of coronary artery disease (CAD) in which stress electrocardiogram (ECG), stress echocardiography, coronary computed tomography angiography (CCTA), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and cardiac magnetic resonance (CMR) can reclassify patients into a post-test probability that defines (>85%) or excludes (<15%) anatomically (defined by visual evaluation of invasive coronary angiography [ICA]) and functionally (defined by a fractional flow reserve [FFR] ≤0.8) significant CAD. A broad search in electronic databases until August 2017 was performed. Studies on the aforementioned techniques in >100 patients with stable CAD that utilized either ICA or ICA with FFR measurement as reference, were included. Study-level data was pooled using a hierarchical bivariate random-effects model and likelihood ratios were obtained for each technique. The PTP ranges for each technique to rule-in or rule-out significant CAD were defined. A total of 28 664 patients from 132 studies that used ICA as reference and 4131 from 23 studies using FFR, were analysed. Stress ECG can rule-in and rule-out anatomically significant CAD only when PTP is ≥80% (76-83) and ≤19% (15-25), respectively. Coronary computed tomography angiography is able to rule-in anatomic CAD at a PTP ≥58% (45-70) and rule-out at a PTP ≤80% (65-94). The corresponding PTP values for functionally significant CAD were ≥75% (67-83) and ≤57% (40-72) for CCTA, and ≥71% (59-81) and ≤27 (24-31) for ICA, demonstrating poorer performance of anatomic imaging against FFR. In contrast, functional imaging techniques (PET, stress CMR, and SPECT) are able to rule-in functionally significant CAD when PTP is ≥46-59% and rule-out when PTP is ≤34-57%. The various diagnostic modalities have different optimal performance ranges for the detection of anatomically and functionally significant CAD. Stress ECG appears to have very limited diagnostic power. The selection of a diagnostic technique for any given patient to rule-in or rule-out CAD should be based on the optimal PTP range for each test and on the assumed reference standard.

Deriving frequency-dependent spatial patterns in MEG-derived resting state sensorimotor network: A novel multiband ICA technique.

PubMed

Nugent, Allison C; Luber, Bruce; Carver, Frederick W; Robinson, Stephen E; Coppola, Richard; Zarate, Carlos A

2017-02-01

Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band-limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass-filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well-characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single-band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. Hum Brain Mapp 38:779-791, 2017. © 2016 Wiley Periodicals, Inc. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

A new approach to spike sorting for multi-neuronal activities recorded with a tetrode--how ICA can be practical.

PubMed

Takahashi, Susumu; Anzai, Yuichiro; Sakurai, Yoshio

2003-07-01

Multi-neuronal recording with a tetrode is a powerful technique to reveal neuronal interactions in local circuits. However, it is difficult to detect precise spike timings among closely neighboring neurons because the spike waveforms of individual neurons overlap on the electrode when more than two neurons fire simultaneously. In addition, the spike waveforms of single neurons, especially in the presence of complex spikes, are often non-stationary. These problems limit the ability of ordinary spike sorting to sort multi-neuronal activities recorded using tetrodes into their single-neuron components. Though sorting with independent component analysis (ICA) can solve these problems, it has one serious limitation that the number of separated neurons must be less than the number of electrodes. Using a combination of ICA and the efficiency of ordinary spike sorting technique (k-means clustering), we developed an automatic procedure to solve the spike-overlapping and the non-stationarity problems with no limitation on the number of separated neurons. The results for the procedure applied to real multi-neuronal data demonstrated that some outliers which may be assigned to distinct clusters if ordinary spike-sorting methods were used can be identified as overlapping spikes, and that there are functional connections between a putative pyramidal neuron and its putative dendrite. These findings suggest that the combination of ICA and k-means clustering can provide insights into the precise nature of functional circuits among neurons, i.e. cell assemblies.

A stereoscopic system for viewing the temporal evolution of brain activity clusters in response to linguistic stimuli

NASA Astrophysics Data System (ADS)

Forbes, Angus; Villegas, Javier; Almryde, Kyle R.; Plante, Elena

2014-03-01

In this paper, we present a novel application, 3D+Time Brain View, for the stereoscopic visualization of functional Magnetic Resonance Imaging (fMRI) data gathered from participants exposed to unfamiliar spoken languages. An analysis technique based on Independent Component Analysis (ICA) is used to identify statistically significant clusters of brain activity and their changes over time during different testing sessions. That is, our system illustrates the temporal evolution of participants' brain activity as they are introduced to a foreign language through displaying these clusters as they change over time. The raw fMRI data is presented as a stereoscopic pair in an immersive environment utilizing passive stereo rendering. The clusters are presented using a ray casting technique for volume rendering. Our system incorporates the temporal information and the results of the ICA into the stereoscopic 3D rendering, making it easier for domain experts to explore and analyze the data.

A review of multivariate methods in brain imaging data fusion

NASA Astrophysics Data System (ADS)

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

2010-03-01

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

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

PubMed

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

2014-09-01

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

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.

Attenuated response of L-type calcium current to nitric oxide in atrial fibrillation.

PubMed

Rozmaritsa, Nadiia; Christ, Torsten; Van Wagoner, David R; Haase, Hannelore; Stasch, Johannes-Peter; Matschke, Klaus; Ravens, Ursula

2014-03-01

Nitric oxide (NO) synthesized by cardiomyocytes plays an important role in the regulation of cardiac function. Here, we studied the impact of NO signalling on calcium influx in human right atrial myocytes and its relation to atrial fibrillation (AF). Right atrial appendages (RAAs) were obtained from patients in sinus rhythm (SR) and AF. The biotin-switch technique was used to evaluate endogenous S-nitrosylation of the α1C subunit of L-type calcium channels. Comparing SR to AF, S-nitrosylation of Ca(2+) channels was similar. Direct effects of the NO donor S-nitroso-N-acetyl-penicillamine (SNAP) on L-type calcium current (ICa,L) were studied in cardiomyocytes with standard voltage-clamp techniques. In SR, ICa,L increased with SNAP (100 µM) by 48%, n/N = 117/56, P < 0.001. The SNAP effect on ICa,L involved activation of soluble guanylate cyclase and protein kinase A. Specific inhibition of phosphodiesterase (PDE)3 with cilostamide (1 µM) enhanced ICa,L to a similar extent as SNAP. However, when cAMP was elevated by PDE3 inhibition or β-adrenoceptor stimulation, SNAP reduced ICa,L, pointing to cGMP-cAMP cross-regulation. In AF, the stimulatory effect of SNAP on ICa,L was attenuated, while its inhibitory effect on isoprenaline- or cilostamide-stimulated current was preserved. cGMP elevation with SNAP was comparable between the SR and AF group. Moreover, the expression of PDE3 and soluble guanylate cyclase was not reduced in AF. NO exerts dual effects on ICa,L in SR with an increase of basal and inhibition of cAMP-stimulated current, and in AF NO inhibits only stimulated ICa,L. We conclude that in AF, cGMP regulation of PDE2 is preserved, but regulation of PDE3 is lost.

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.

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.

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.

Towards Solving the Mixing Problem in the Decomposition of Geophysical Time Series by Independent Component Analysis

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Chedin, Alain; Hansen, James E. (Technical Monitor)

2000-01-01

The use of the Principal Component Analysis technique for the analysis of geophysical time series has been questioned in particular for its tendency to extract components that mix several physical phenomena even when the signal is just their linear sum. We demonstrate with a data simulation experiment that the Independent Component Analysis, a recently developed technique, is able to solve this problem. This new technique requires the statistical independence of components, a stronger constraint, that uses higher-order statistics, instead of the classical decorrelation a weaker constraint, that uses only second-order statistics. Furthermore, ICA does not require additional a priori information such as the localization constraint used in Rotational Techniques.

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.

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

PubMed

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

2016-05-02

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

Appliance of Independent Component Analysis to System Intrusion Analysis

NASA Astrophysics Data System (ADS)

Ishii, Yoshikazu; Takagi, Tarou; Nakai, Kouji

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

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

PubMed

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

2015-08-15

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

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.

Heuristic Enhancement of Magneto-Optical Images for NDE

NASA Astrophysics Data System (ADS)

Cacciola, Matteo; Megali, Giuseppe; Pellicanò, Diego; Calcagno, Salvatore; Versaci, Mario; Morabito, FrancescoCarlo

2010-12-01

The quality of measurements in nondestructive testing and evaluation plays a key role in assessing the reliability of different inspection techniques. Each different technique, like the magneto-optic imaging here treated, is affected by some special types of noise which are related to the specific device used for their acquisition. Therefore, the design of even more accurate image processing is often required by relevant applications, for instance, in implementing integrated solutions for flaw detection and characterization. The aim of this paper is to propose a preprocessing procedure based on independent component analysis (ICA) to ease the detection of rivets and/or flaws in the specimens under test. A comparison of the proposed approach with some other advanced image processing methodologies used for denoising magneto-optic images (MOIs) is carried out, in order to show advantages and weakness of ICA in improving the accuracy and performance of the rivets/flaw detection.

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

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

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.

Retrograde Suction Decompression with an Inahara Carotid Shunt for Clipping a Large Distal Internal Carotid Artery Aneurysm.

PubMed

Park, Yong Sook; Nam, Taek Kyun

2017-03-01

We describe a technique to clip a large internal carotid artery (ICA) aneurysm via a retrograde suction decompression (RSD). A large aneurysm in the right distal ICA involving the bifurcation region measuring 1.2×1.1×0.7 cm with posterior projection was managed with assisted RSD technique. The anterior choroidal artery emerged from the side wall of the aneurysm. An Inahara shunt was inserted into the ICA with neck dissection, and RSD was applied after completely clipping the aneurysm. RSD with an Inahara carotid shunt is useful for complete visualization of the aneurysm, including its surrounding structures, and for proximal control of the parent vessels, subsequently achieving satisfactory clip placement.

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.

The dream of a one-stop-shop: Meta-analysis on myocardial perfusion CT.

PubMed

Pelgrim, Gert Jan; Dorrius, Monique; Xie, Xueqian; den Dekker, Martijn A M; Schoepf, U Joseph; Henzler, Thomas; Oudkerk, Matthijs; Vliegenthart, Rozemarijn

2015-12-01

To determine the diagnostic performance of computed tomography (CT) perfusion techniques for the detection of functionally relevant coronary artery disease (CAD) in comparison to reference standards, including invasive coronary angiography (ICA), single photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI). PubMed, Web of Knowledge and Embase were searched from January 1, 1998 until July 1, 2014. The search yielded 9475 articles. After duplicate removal, 6041 were screened on title and abstract. The resulting 276 articles were independently analyzed in full-text by two reviewers, and included if the inclusion criteria were met. The articles reporting diagnostic parameters including true positive, true negative, false positive and false negative were subsequently evaluated for the meta-analysis. Results were pooled according to CT perfusion technique, namely snapshot techniques: single-phase rest, single-phase stress, single-phase dual-energy stress and combined coronary CT angiography [rest] and single-phase stress, as well the dynamic technique: dynamic stress CT perfusion. Twenty-two articles were included in the meta-analysis (1507 subjects). Pooled per-patient sensitivity and specificity of single-phase rest CT compared to rest SPECT were 89% (95% confidence interval [CI], 82-94%) and 88% (95% CI, 78-94%), respectively. Vessel-based sensitivity and specificity of single-phase stress CT compared to ICA-based >70% stenosis were 82% (95% CI, 64-92%) and 78% (95% CI, 61-89%). Segment-based sensitivity and specificity of single-phase dual-energy stress CT in comparison to stress MRI were 75% (95% CI, 60-85%) and 95% (95% CI, 80-99%). Segment-based sensitivity and specificity of dynamic stress CT perfusion compared to stress SPECT were 77% (95% CI, 67-85) and 89% (95% CI, 78-95%). For combined coronary CT angiography and single-phase stress CT, vessel-based sensitivity and specificity in comparison to ICA-based >50% stenosis were 84% (95% CI, 67-93%) and 93% (95% CI, 89-96%). This meta-analysis shows considerable variation in techniques and reference standards for CT of myocardial blood supply. While CT seems sensitive and specific for evaluation of hemodynamically relevant CAD, studies so far are limited in size. Standardization of myocardial perfusion CT technique is essential. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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

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.

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.

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.

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

PubMed

Chen, Zikuan; Calhoun, Vince D

2016-03-01

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

Performance comparison of six independent components analysis algorithms for fetal signal extraction from real fMCG data

NASA Astrophysics Data System (ADS)

Hild, Kenneth E.; Alleva, Giovanna; Nagarajan, Srikantan; Comani, Silvia

2007-01-01

In this study we compare the performance of six independent components analysis (ICA) algorithms on 16 real fetal magnetocardiographic (fMCG) datasets for the application of extracting the fetal cardiac signal. We also compare the extraction results for real data with the results previously obtained for synthetic data. The six ICA algorithms are FastICA, CubICA, JADE, Infomax, MRMI-SIG and TDSEP. The results obtained using real fMCG data indicate that the FastICA method consistently outperforms the others in regard to separation quality and that the performance of an ICA method that uses temporal information suffers in the presence of noise. These two results confirm the previous results obtained using synthetic fMCG data. There were also two notable differences between the studies based on real and synthetic data. The differences are that all six ICA algorithms are independent of gestational age and sensor dimensionality for synthetic data, but depend on gestational age and sensor dimensionality for real data. It is possible to explain these differences by assuming that the number of point sources needed to completely explain the data is larger than the dimensionality used in the ICA extraction.

Localization of spontaneous magnetoencephalographic activity of neonates and fetuses using independent component and Hilbert phase analysis.

PubMed

Vairavan, Srinivasan; Eswaran, Hari; Preissl, Hubert; Wilson, James D; Haddad, Naim; Lowery, Curtis L; Govindan, Rathinaswamy B

2010-01-01

The fetal magnetoencephalogram (fMEG) is measured in the presence of large interference from maternal and fetal magnetocardiograms (mMCG and fMCG). These cardiac interferences can be attenuated by orthogonal projection (OP) technique of the corresponding spatial vectors. However, the OP technique redistributes the fMEG signal among the channels and also leaves some cardiac residuals (partially attenuated mMCG and fMCG) due to loss of stationarity in the signal. In this paper, we propose a novel way to extract and localize the neonatal and fetal spontaneous brain activity by using independent component analysis (ICA) technique. In this approach, we perform ICA on a small subset of sensors for 1-min duration. The independent components obtained are further investigated for the presence of discontinuous patterns as identified by the Hilbert phase analysis and are used as decision criteria for localizing the spontaneous brain activity. In order to locate the region of highest spontaneous brain activity content, this analysis is performed on the sensor subsets, which are traversed across the entire sensor space. The region of the spontaneous brain activity as identified by the proposed approach correlated well with the neonatal and fetal head location. In addition, the burst duration and the inter-burst interval computed for the identified discontinuous brain patterns are in agreement with the reported values.

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

PubMed Central

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

2014-01-01

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

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

PubMed

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

2006-06-01

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

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

PubMed

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

2017-01-01

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

Role of diversity in ICA and IVA: theory and applications

NASA Astrophysics Data System (ADS)

Adalı, Tülay

2016-05-01

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

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.

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

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

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

Borek, Dominika; Bromberg, Raquel; Hattne, Johan

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

Independent Component Analysis-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

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

NASA Astrophysics Data System (ADS)

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

2012-11-01

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

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.

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.

The 25th anniversary of the retrograde suction decompression technique (Dallas technique) for the surgical management of paraclinoid aneurysms: historical background, systematic review, and pooled analysis of the literature.

PubMed

Flores, Bruno C; White, Jonathan A; Batjer, H Hunt; Samson, Duke S

2018-05-04

OBJECTIVE Paraclinoid internal carotid artery (ICA) aneurysms frequently require temporary occlusion to facilitate safe clipping. Brisk retrograde flow through the ophthalmic artery and cavernous ICA branches make simple trapping inadequate to soften the aneurysm. The retrograde suction decompression (RSD), or Dallas RSD, technique was described in 1990 in an attempt to overcome some of those treatment limitations. A frequent criticism of the RSD technique is an allegedly high risk of cervical ICA dissection. An endovascular modification was introduced in 1991 (endovascular RSD) but no studies have compared the 2 RSD variations. METHODS The authors performed a systematic review of MEDLINE/PubMed and Web of Science and identified all studies from 1990-2016 in which either Dallas RSD or endovascular RSD was used for treatment of paraclinoid aneurysms. A pooled analysis of the data was completed to identify important demographic and treatment-specific variables. The primary outcome measure was defined as successful aneurysm obliteration. Secondary outcome variables were divided into overall and RSD-specific morbidity and mortality rates. RESULTS Twenty-six RSD studies met the inclusion criteria (525 patients, 78.9% female). The mean patient age was 53.5 years. Most aneurysms were unruptured (56.6%) and giant (49%). The most common presentations were subarachnoid hemorrhage (43.6%) and vision changes (25.3%). The aneurysm obliteration rate was 95%. The mean temporary occlusion time was 12.7 minutes. Transient or permanent morbidity was seen in 19.9% of the patients. The RSD-specific complication rate was low (1.3%). The overall mortality rate was 4.2%, with 2 deaths (0.4%) attributable to the RSD technique itself. Good or fair outcome were reported in 90.7% of the patients. Aneurysm obliteration rates were similar in the 2 subgroups (Dallas RSD 94.3%, endovascular RSD 96.3%, p = 0.33). Despite a higher frequency of complex (giant or ruptured) aneurysms, Dallas RSD was associated with lower RSD-related morbidity (0.6% vs 2.9%, p = 0.03), compared with the endovascular RSD subgroup. There was a trend toward higher mortality in the endovascular RSD subgroup (6.4% vs 3.1%, p = 0.08). The proportion of patients with poor neurological outcome at last follow-up was significantly higher in the endovascular RSD group (15.4% vs 7.2%, p < 0.01). CONCLUSIONS The treatment of paraclinoid ICA aneurysms using the RSD technique is associated with high aneurysm obliteration rates, good long-term neurological outcome, and low RSD-related morbidity and mortality. Review of the RSD literature showed no evidence of a higher complication rate associated with the Dallas technique compared with similar endovascular methods. On a subgroup analysis of Dallas RSD and endovascular RSD, both groups achieved similar obliteration rates, but a lower RSD-related morbidity was seen in the Dallas technique subgroup. Twenty-five years after its initial publication, RSD remains a useful neurosurgical technique for the management of large and giant paraclinoid aneurysms.

Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM

NASA Astrophysics Data System (ADS)

Jing, Ya-Bing; Liu, Chang-Wen; Bi, Feng-Rong; Bi, Xiao-Yang; Wang, Xia; Shao, Kang

2017-07-01

Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying features. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastICA-SVM achieves higher classification accuracy and makes better generalization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastICA-SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of feature extraction and the fault diagnosis of diesel engines.

Carotid-Falciform Optic Neuropathy: Microsurgical Treatment.

PubMed

Woodall, M Neil; Alleyne, Cargill H

2017-08-01

Several recent reports have implicated vascular ectasia and vessel contact in dysfunction of the visual apparatus. A subset of patients with prechiasmatic visual deterioration have an ectatic internal carotid artery (ICA) that displaces and flattens the optic nerve (ON) rostrally as the ON exits the skull base. We describe a proposed pathophysiologic mechanism and a straightforward surgical technique for dealing with this problem. Via an ipsilateral pterional craniotomy, the bony roof of the optic canal is removed. The falciform ligament is opened in parallel to the ON. Adhesions between the ICA and ON are then dissected, and a Teflon pledget is placed between the ICA and ON to complete the decompression. Patients both in the literature and in this series experienced an improvement in their vision postoperatively. We propose that 3 mechanisms contribute to this caroticofalciform optic neuropathy: 1) mass effect from ICA ectasia, 2) ON irritation from vessel pulsatility, and 3) indirect compression by the falciform ligament from above. This disease process can be treated safely using standard microsurgical techniques with excellent outcomes. Copyright © 2017 Elsevier Inc. All rights reserved.

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

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.

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.

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.

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

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.

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.

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

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.

Rescue strategy for acute carotid stent thrombosis during carotid stenting with distal filter protection using forced arterial suction thrombectomy with a reperfusion catheter of the Penumbra System: a technical note.

PubMed

Kim, Yong-Won; Kang, Dong-Hun; Hwang, Jeong-Hyun; Park, Jaechan; Hwang, Yang-Ha; Kim, Yong-Sun

2013-08-01

Among the procedural complications related to carotid artery stenting (CAS), internal carotid artery (ICA) flow arrest is one of the most drastic complications, as it can cause major ischemic stroke. Acute carotid stent thrombosis (ACST) is a rare etiology of ICA flow arrest during carotid artery stenting with distal filter protection, but the most devastating. Moreover, no definitive management strategy has been established so far for treating ACST. We introduce a rescue management strategy for differential diagnosis of ICA flow arrest and for recanalization of ACST with a simple endovascular mechanical thrombectomy technique. In three cases of ICA flow arrest caused by ACST, selective angiography with a 1.7 F microcatheter provided confirmative diagnosis. Recanalization was then achieved with a Penumbra System (PS) reperfusion catheter using the forced arterial suction thrombectomy (FAST) technique. Successful recanalization with a Thrombolysis In Cerebral Infarction score of 3 was achieved for all three patients. Recanalization was confirmed with follow-up angiography at least 24 h after the procedure. No complications associated with this technique occurred. Based on our preliminary experiences, selective microangiography can be helpful for rapid diagnosis of ACST, and the present mechanical thrombectomy technique, using a modification of the PS, can play a role in adjuvant management or as a last resort for the treatment of ACST during CAS.

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

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.

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

PubMed

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

2014-02-01

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

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.

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.

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

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

NASA Astrophysics Data System (ADS)

Almurshedi, Ahmed; Ismail, Abd Khamim

2015-04-01

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

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

PubMed Central

Castro, Eduardo; Hjelm, R. Devon; Plis, Sergey M.; Dinh, Laurent; Turner, Jessica A.; Calhoun, Vince D.

2016-01-01

Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns. PMID:26891483

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

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.

[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.

Potential Surgical Exposure of the Parapharyngeal Internal Carotid Artery by Endonasal, Transoral, and Transcervical Approaches.

PubMed

Tanjararak, Kangsadarn; Upadhyay, Smita; Thiensri, Thanakorn; Muto, Jun; Roongpuvapaht, Boonsam; Prevedello, Daniel M; Carrau, Ricardo L

2018-06-01

Objectives  Endoscopic and endoscopic-assisted approaches to the parapharyngeal space have been reported; however, their potential for vascular exposure has not been previously assessed. This study aims to compare the potential exposure and control of the parapharyngeal internal carotid artery (ppICA) via various approaches. Design and Main Outcome Measures  Ten cadaveric specimens were dissected bilaterally, exposing the ppICA via endonasal, transoral, and transcervical approaches. Length of the exposed vessel and potential control were assessed (feasibility and time required to place an encircling suture). Results  Endoscopic transoral and transcervical-transmandibular approaches expose a significantly longer segment of the ppICA (6.89 and 7.09 cm) than the transoral and endonasal approaches. Vascular control was achieved via endoscopic-endonasal, endoscopic-transoral, and open techniques in 121.6, 64.8, and 5.2 seconds, respectively. Conclusion  Histopathology, goals of surgery, and familiarity of the surgeon with each technique may ultimately determine the choice of approach; however, this study suggests that exposure of the ppICA by endoscopic-assisted transoral approach is comparable to that of a transcervical-transmandibular approach. Vascular control was feasible under elective circumstances. However, the difficulty varied widely, potentially reflecting the challenges of controlling an injured ppICA. However, one must note that active bleeding obscures the surgical field in ways that may impair ppICA control. Furthermore, the results may not reflect clinical scenarios where tumor distorts the surgical field. Nonetheless, the study suggests that, in properly selected patients, the endoscopic-assisted transoral approach avoids problems associated with unsightly scars, mandibular osteotomy, and facial nerve manipulation, whereas, the transcervical-transmandibular approach offers the swiftest vascular control.

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.

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

PubMed

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

2015-01-01

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

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.

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

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.

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.

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.

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.

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.

Results of microsurgical treatment of large and giant ICA aneurysms using the retrograde suction decompression (RSD) technique: series of 92 patients.

PubMed

Eliava, Shalva S; Filatov, Yuri M; Yakovlev, Sergei B; Shekhtman, Oleg D; Kheireddin, Ali S; Sazonov, Ilya A; Sazonova, Olga B; Okishev, Dmitry N

2010-06-01

Microsurgical treatment of large and giant paraclinoid internal carotid artery (ICA) aneurysms often requires the use of the retrograde suction decompression (RSD) technique to facilitate clipping. Surgical results, functional outcomes at discharge, and technique limitations based on single institution series are presented. Between 1996 and 2009, eighty-three consecutive patients (19 to 68 years, mean 45.5 ± 9.9 years), predominantly women (69 women and 14 men) with large (23 patients, 27.7%) or giant (60 patients, 72.3%) paraclinoid aneurysms were surgically treated with the RSD technique performed by the neck route (62 patients, 74.4%) or later on, by endovascular means (21 patients, 25.3%). Patients were admitted after hemorrhage (48 patients, 57.9%), pseudotumor course (28 patients, 33.7%), mixed symptoms (5 patients, 6%), or asymptomatic (2 patients, 2.4%). In most RSD surgeries (90.4%) aneurysms were successfully excluded: neck was clipped in 57 patients (68.7%) or clipping with ICA reconstruction was achieved in 18 patients (21.7%). In six patients aneurysms were wrapped with glue (7.2%), trapped in one patient (1.2%), and in one patient, ICA balloon deconstruction was performed (1.2%). Good or excellent results (Glasgow Outcome Scale scores 4-5) at discharge were achieved in 69 patients (83.1%), 11 patients (13.3%) remained severely disabled (Glasgow Outcome Scale 3), and 3 patients died (3.6%). Surgical clipping with the RSD method remains a treatment of choice with acceptable outcomes for patients not amenable for endovascular treatment. Copyright © 2010 Elsevier Inc. All rights reserved.

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

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.

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.

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.

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.

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.

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

PubMed Central

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

2018-01-01

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

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.

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

PubMed

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

2010-08-01

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

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

Separation of Doppler radar-based respiratory signatures.

PubMed

Lee, Yee Siong; Pathirana, Pubudu N; Evans, Robin J; Steinfort, Christopher L

2016-08-01

Respiration detection using microwave Doppler radar has attracted significant interest primarily due to its unobtrusive form of measurement. With less preparation in comparison with attaching physical sensors on the body or wearing special clothing, Doppler radar for respiration detection and monitoring is particularly useful for long-term monitoring applications such as sleep studies (i.e. sleep apnoea, SIDS). However, motion artefacts and interference from multiple sources limit the widespread use and the scope of potential applications of this technique. Utilising the recent advances in independent component analysis (ICA) and multiple antenna configuration schemes, this work investigates the feasibility of decomposing respiratory signatures into each subject from the Doppler-based measurements. Experimental results demonstrated that FastICA is capable of separating two distinct respiratory signatures from two subjects adjacent to each other even in the presence of apnoea. In each test scenario, the separated respiratory patterns correlate closely to the reference respiration strap readings. The effectiveness of FastICA in dealing with the mixed Doppler radar respiration signals confirms its applicability in healthcare applications, especially in long-term home-based monitoring as it usually involves at least two people in the same environment (i.e. two people sleeping next to each other). Further, the use of FastICA to separate involuntary movements such as the arm swing from the respiratory signatures of a single subject was explored in a multiple antenna environment. The separated respiratory signal indeed demonstrated a high correlation with the measurements made by a respiratory strap used currently in clinical settings.

Village victories: new motivational techniques in Kenya and Zimbabwe.

PubMed

Miller, N N

1983-01-01

The Institute of Cultural Affairs (ICA) in Kenya and the Zimbabwe Project in Zimbabwe are organizations working to promote local level development in their respective countries and a major challenge to these organizations has been how to change the attitudes and perceptions of the poor in ways that help them help themselves. ICA efforts are carried out in Kenya by several hundred volunteer staff, including 30 expatriates. Most are assigned to 1 of the 21 projects spread across southern Kenya. Since 1975 the ICA has launched projects in over 200 villages. Village clean up, public health, school construction, water development, and agricultural improvement are some of the project categories. Tangible results include starting demonstration farms, field terracing projects, building pit latrines and compost pits, constructing new pathways, roads, and schoolrooms. Many of ICA's efforts are funded by local companies and through Kenyan offices of development organizations. In the field of health, ICA provides training courses at the village level that emphasize preventive care, sanitation, hygiene, nutrition, family planning, first aid, and treatment of common illnesses. ICA's mobilization techniques are based on motivating villagers to help themselves, to "catalyze and energize" the resources at hand. The process begins with a "consult" in which 12 or more ICA staff conduct a 3- or 4-day meeting with villagers to reorient local thinking. A special effort is made to break old attitudes that have held traditional villagers back. The consult is also designed to confront traditional assumptions about what the longterm reality might be. For urban slum villages the focus is on the transient nature of community that serves as low cost housing for thousands of newly arrived migrants. Today the Zimbabwe Project (ZP) is working with former soldiers, although when established in 1978 in Britain its purpose was to assist refugees from the Rhodesian struggle who had fled to Botswana, Mozambique, and Zambia. The organization currently provides training courses and practical advice on how to start cooperative farms and small businesses. Its overall concern is to help ex-combatants become economically self sufficient. To date, some 32,000 soldiers have been demobilized. The idea was to give each soldier support while he was being trained or finding a job. As a group the ex-combatants ZP works with today are young, usually between 20-35. Although both ICA and ZP take motivational approaches, ICA is more inspirational and group oriented and ZP more pragmatic and individual oriented. The ZP is more concerned with cooperative economic processes and with fitting specific individuals back into socially useful roles. The approaches embraced by ICA and ZP can serve as models for local level development elsewhere.

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2006-09-01

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

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

PubMed

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

2015-01-01

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

Blind source separation in retinal videos

NASA Astrophysics Data System (ADS)

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

2003-05-01

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

A 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.

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

PubMed

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

2013-01-01

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

Model-free fMRI group analysis using FENICA.

PubMed

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

2011-03-01

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

Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA

PubMed Central

Michael, Andrew M.; Anderson, Mathew; Miller, Robyn L.; Adalı, Tülay; Calhoun, Vince D.

2014-01-01

Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent interest in resting state fMRI data with potentially higher degree of subject variability makes the evaluation of the above techniques important. In this paper we compare component estimation accuracies of GICA and an improved version of IVA using simulated fMRI datasets. We systematically change the degree of inter-subject spatial variability of components and evaluate estimation accuracy over all spatial maps (SMs) and time courses (TCs) of the decomposition. Our results indicate the following: (1) at low levels of SM variability or when just one SM is varied, both GICA and IVA perform well, (2) at higher levels of SM variability or when more than one SMs are varied, IVA continues to perform well but GICA yields SM estimates that are composites of other SMs with errors in TCs, (3) both GICA and IVA remove spatial correlations of overlapping SMs and introduce artificial correlations in their TCs, (4) if number of SMs is over estimated, IVA continues to perform well but GICA introduces artifacts in the varying and extra SMs with artificial correlations in the TCs of extra components, and (5) in the absence or presence of SMs unique to one subject, GICA produces errors in TCs and IVA estimates are accurate. In summary, our simulation experiments (both simplistic and realistic) and our holistic analyses approach indicate that IVA produces results that are closer to ground truth and thereby better preserves subject variability. The improved version of IVA is now packaged into the GIFT toolbox (http://mialab.mrn.org/software/gift). PMID:25018704

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.

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.

Lithological mapping of Kanjamalai hill using hyperspectral remote sensing tools in Salem district, Tamil Nadu, India

NASA Astrophysics Data System (ADS)

Arulbalaji, Palanisamy; Balasubramanian, Gurugnanam

2017-07-01

This study uses advanced spaceborne thermal emission and reflection radiometer (ASTER) hyperspectral remote sensing techniques to discriminate rock types composing Kanjamalai hill located in the Salem district of Tamil Nadu, India. Kanjamalai hill is of particular interest because it contains economically viable iron ore deposits. ASTER hyperspectral data were subjected to principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) to improve identification of lithologies remotely and to compare these digital data results with published geologic maps. Hyperspectral remote sensing analysis indicates that PCA (R∶G∶B=2∶1∶3), MNF (R∶G∶B=3∶2∶1), and ICA (R∶G∶B=1∶3∶2) provide the best band combination for effective discrimination of lithological rock types composing Kanjamalai hill. The remote sensing-derived lithological map compares favorably with a published geological map from Geological Survey of India and has been verified with ground truth field investigations. Therefore, ASTER data-based lithological mapping provides fast, cost-effective, and accurate geologic data useful for lithological discrimination and identification of ore deposits.

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

NASA Astrophysics Data System (ADS)

E, Jianwei; Bao, Yanling; Ye, Jimin

2017-10-01

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

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

NASA Astrophysics Data System (ADS)

Naritomi, Yusuke; Fuchigami, Sotaro

2013-12-01

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

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.

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.

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

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.

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).

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

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.

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%).

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.

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.

Histology of the distal dural ring.

PubMed

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

2017-09-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-07-01

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

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

PubMed

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

2018-05-11

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

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

PubMed Central

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

2009-01-01

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

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.

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)

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.

A nonlinear circuit architecture for magnetoencephalographic signal analysis.

PubMed

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

2004-01-01

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

Linked independent component analysis for multimodal data fusion.

PubMed

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

2011-02-01

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

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

PubMed Central

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

2014-01-01

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

ICA model order selection of task co-activation networks.

PubMed

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

2013-01-01

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

ICA model order selection of task co-activation networks

PubMed Central

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

2013-01-01

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

Nonstationary signal analysis in episodic memory retrieval

NASA Astrophysics Data System (ADS)

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

2004-04-01

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

Stimulation of ICa by basal PKA activity is facilitated by caveolin-3 in cardiac ventricular myocytes.

PubMed

Bryant, Simon; Kimura, Tomomi E; Kong, Cherrie H T; Watson, Judy J; Chase, Anabelle; Suleiman, M Saadeh; James, Andrew F; Orchard, Clive H

2014-03-01

L-type Ca channels (LTCC), which play a key role in cardiac excitation-contraction coupling, are located predominantly at the transverse (t-) tubules in ventricular myocytes. Caveolae and the protein caveolin-3 (Cav-3) are also present at the t-tubules and have been implicated in localizing a number of signaling molecules, including protein kinase A (PKA) and β2-adrenoceptors. The present study investigated whether disruption of Cav-3 binding to its endogenous binding partners influenced LTCC activity. Ventricular myocytes were isolated from male Wistar rats and LTCC current (ICa) recorded using the whole-cell patch-clamp technique. Incubation of myocytes with a membrane-permeable peptide representing the scaffolding domain of Cav-3 (C3SD) reduced basal ICa amplitude in intact, but not detubulated, myocytes, and attenuated the stimulatory effects of the β2-adrenergic agonist zinterol on ICa. The PKA inhibitor H-89 also reduced basal ICa; however, the inhibitory effects of C3SD and H-89 on basal ICa amplitude were not summative. Under control conditions, myocytes stained with antibody against phosphorylated LTCC (pLTCC) displayed a striated pattern, presumably reflecting localization at the t-tubules. Both C3SD and H-89 reduced pLTCC staining at the z-lines but did not affect staining of total LTCC or Cav-3. These data are consistent with the idea that the effects of C3SD and H-89 share a common pathway, which involves PKA and is maximally inhibited by H-89, and suggest that Cav-3 plays an important role in mediating stimulation of ICa at the t-tubules via PKA-induced phosphorylation under basal conditions, and in response to β2-adrenoceptor stimulation. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

PubMed

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

2016-02-19

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

Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review.

PubMed

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

2011-06-01

The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.

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.

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.

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.

Application of Independent Component Analysis to Legacy UV Quasar Spectra

NASA Astrophysics Data System (ADS)

Richards, Gordon

2017-08-01

We propose to apply a novel analysis technique to UV spectroscopy ofquasars in the HST archive. We endeavor to analyze all of thearchival quasar spectra, but will first focus on those quasars thatalso have optical spectroscopy from SDSS. An archival investigationby Sulentic et al. (2007) revealed 130 known quasars with UV coverageof CIV complementing optical emission line coverage. Today, thesample has grown considerably and now includes COS spectroscopy. Ourproposal includes a proof-of-concept demonstration of the power of atechnique called Independent Component Analysis (ICA). ICA allows usto reduce complexity of of quasar spectra to just a handful ofnumbers. In addition to providing a uniform set of traditional linemeasurements (and carefully calibrated redshifts), we will provide ICAweights to the community with examples of how they can be used to doscience that previously would have been quite difficult. The time isripe for such an investigation because 1) it has been a decade sincethe last significant archival investigation of UV emission lines fromHST quasars, 2) the future is uncertain for obtaining new UV quasarspectroscopy, and 3) the rise of machine learning has provided us withpowerful new tools. Thus our proposed work will provide a true UVlegacy database for quasar-based investigations.

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.

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.

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

Variational Bayesian Learning for Wavelet Independent Component Analysis

NASA Astrophysics Data System (ADS)

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

2005-11-01

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

A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function

PubMed Central

Liu, Jingyu; Demirci, Oguz; Calhoun, Vince D.

2009-01-01

Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings. PMID:19834575

A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function.

PubMed

Liu, Jingyu; Demirci, Oguz; Calhoun, Vince D

2008-01-01

Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings.

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

Use of automatic exposure control in multislice computed tomography of the coronaries: comparison of 16‐slice and 64‐slice scanner data with conventional coronary angiography

PubMed Central

Deetjen, Anja; Möllmann, Susanne; Conradi, Guido; Rolf, Andreas; Schmermund, Axel; Hamm, Christian W; Dill, Thorsten

2007-01-01

Objective To evaluate the radiation‐dose‐reduction potential of automatic exposure control (AEC) in 16‐slice and 64‐slice multislice computed tomography (MSCT) of the coronary arteries (computed tomography angiography, CTA) in patients. The rapid growth in MSCT CTA emphasises the necessity of adjusting technique factors to reduce radiation dose exposure. Design A retrospective data analysis was performed for 154 patients who had undergone MSCT CTA. Group 1 (n = 56) had undergone 16‐slice MSCT without AEC, and group 2 (n = 51), with AEC. In group 1, invasive coronary angiography (ICA) had been performed in addition. Group 3 (n = 47) had been examined using a 64‐slice scanner (with AEC, without ECG‐triggered tube current modulation). Results In group 1, the mean (SD) effective dose (ED) for MSCT CTA was 9.76 (1.84) mSv and for ICA it was 2.6 (1.27) mSv. In group 2, the mean ED for MSCT CTA was 5.83 (1.73) mSv, which signifies a 42.8% dose reduction for CTA by the use of AEC. In comparison to ICA, MSCT CTA without AEC shows a 3.8‐fold increase in radiation dose, and the radiation dose of CTA with AEC was increased by a factor of 1.9. In group 3, the mean ED for MSCT CTA was 13.58 (2.80) mSV. Conclusions This is the first study to show the significant dose‐reduction potential (42.8%) of AEC in MSCT CTA in patients. This relatively new technique can be used to optimise the radiation dose levels in MSCT CTA. PMID:17395667

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

PubMed

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

2018-02-01

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

Internal Carotid Artery Stenosis and Collateral Recruitment in Stroke Patients.

PubMed

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

2017-04-24

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

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

PubMed

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

2018-06-02

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

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

PubMed

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

2015-09-29

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

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.

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.

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.

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.

Novel application of a balloon-anchoring technique for the realignment of a prolapsed pipeline embolization device: a technical report.

PubMed

Crowley, R Webster; Abla, Adib A; Ducruet, Andrew F; McDougall, Cameron G; Albuquerque, Felipe C

2014-07-01

Flow-diverting stents represent a substantial advancement in the treatment of cerebral aneurysms. They can, however, be associated with unique complications that may require management through adjunctive techniques. To present a technical report of a salvage technique used to realign a prolapsed Pipeline Embolization Device (PED) during the treatment of a giant internal carotid artery (ICA) aneurysm. A patient in his late 70s with an incidental giant supraclinoid ICA aneurysm presented for endovascular consideration. Treatment was planned using the PED. Following placement of the device there were two focal areas of incomplete expansion and balloon angioplasty was performed. This manipulation resulted in foreshortening of the distal aspect of the PED which caused the device to prolapse into the aneurysm. After multiple unsuccessful attempts to regain distal access, a salvage technique was attempted in which a balloon was inflated in the middle cerebral artery and, by applying traction, the PED was realigned with the parent artery. After the PED was realigned, direct distal catheter access was achieved and a second Pipeline device was deployed, successfully covering the aneurysm neck with resultant flow stasis. The patient had no postoperative issues and was discharged 2 days later without deficit. The balloon-anchoring technique was successfully used to realign a PED that had prolapsed into a giant ICA aneurysm. This maneuver prevented potentially disastrous complications and allowed the satisfactory completion of the aneurysm embolization. This represents a useful salvage technique that should be considered when encountering a prolapsed stent. 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.

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

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.

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.

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

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.

Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction

PubMed Central

Aguilera, Teodoro; Lozano, Jesús; Paredes, José A.; Álvarez, Fernando J.; Suárez, José I.

2012-01-01

The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose (e-nose) combined with Independent Component Analysis (ICA) as a dimensionality reduction technique, Partial Least Squares (PLS) to predict sensorial descriptors and Artificial Neural Networks (ANNs) for classification purpose. A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification. PMID:22969387

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.

Group III metabotropic glutamate receptors and exocytosed protons inhibit L-type calcium currents in cones but not in rods.

PubMed

Hosoi, Nobutake; Arai, Itaru; Tachibana, Masao

2005-04-20

Light responses of photoreceptors (rods and cones) are transmitted to the second-order neurons (bipolar cells and horizontal cells) via glutamatergic synapses located in the outer plexiform layer of the retina. Although it has been well established that postsynaptic group III metabotropic glutamate receptors (mGluRs) of ON bipolar cells contribute to generating the ON signal, presynaptic roles of group III mGluRs remain to be elucidated at this synaptic connection. We addressed this issue by applying the slice patch-clamp technique to the newt retina. OFF bipolar cells and horizontal cells generate a steady inward current in the dark and a transient inward current at light offset, both of which are mediated via postsynaptic non-NMDA receptors. A group III mGluR-specific agonist, L-2-amino-4-phosphonobutyric acid (L-AP-4), inhibited both the steady and off-transient inward currents but did not affect the glutamate-induced current in these postsynaptic neurons. L-AP-4 inhibited the presynaptic L-type calcium current (ICa) in cones by shifting the voltage dependence of activation to more positive membrane potentials. The inhibition of ICa was most prominent around the physiological range of cone membrane potentials. In contrast, L-AP-4 did not affect L-type ICa in rods. Paired recordings from photoreceptors and the synaptically connected second-order neurons confirmed that L-AP-4 inhibited both ICa and glutamate release in cones but not in rods. Furthermore, we found that exocytosed protons also inhibited ICa in cones but not in rods. Selective modulation of ICa in cones may help broaden the dynamic range of synaptic transfer by controlling the amount of transmitter release from cones.

Inhibitory effects of sevoflurane on pacemaking activity of sinoatrial node cells in guinea-pig heart

PubMed Central

Kojima, Akiko; Kitagawa, Hirotoshi; Omatsu-Kanbe, Mariko; Matsuura, Hiroshi; Nosaka, Shuichi

2012-01-01

BACKGROUND AND PURPOSE The volatile anaesthetic sevoflurane affects heart rate in clinical settings. The present study investigated the effect of sevoflurane on sinoatrial (SA) node automaticity and its underlying ionic mechanisms. EXPERIMENTAL APPROACH Spontaneous action potentials and four ionic currents fundamental for pacemaking, namely, the hyperpolarization-activated cation current (If), T-type and L-type Ca2+ currents (ICa,T and ICa,L, respectively), and slowly activating delayed rectifier K+ current (IKs), were recorded in isolated guinea-pig SA node cells using perforated and conventional whole-cell patch-clamp techniques. Heart rate in guinea-pigs was recorded ex vivo in Langendorff mode and in vivo during sevoflurane inhalation. KEY RESULTS In isolated SA node cells, sevoflurane (0.12–0.71 mM) reduced the firing rate of spontaneous action potentials and its electrical basis, diastolic depolarization rate, in a qualitatively similar concentration-dependent manner. Sevoflurane (0.44 mM) reduced spontaneous firing rate by approximately 25% and decreased If, ICa,T, ICa,L and IKs by 14.4, 31.3, 30.3 and 37.1%, respectively, without significantly affecting voltage dependence of current activation. The negative chronotropic effect of sevoflurane was partly reproduced by a computer simulation of SA node cell electrophysiology. Sevoflurane reduced heart rate in Langendorff-perfused hearts, but not in vivo during sevoflurane inhalation in guinea-pigs. CONCLUSIONS AND IMPLICATIONS Sevoflurane at clinically relevant concentrations slowed diastolic depolarization and thereby reduced pacemaking activity in SA node cells, at least partly due to its inhibitory effect on If, ICa,T and ICa,L. These findings provide an important electrophysiological basis of alterations in heart rate during sevoflurane anaesthesia in clinical settings. PMID:22356456

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

PubMed

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

2015-11-01

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

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2013-05-01

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

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

PubMed Central

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

2016-01-01

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

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.

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

PubMed

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

2018-01-01

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

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

PubMed

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

2014-01-01

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

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.

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

PubMed

LeVan, P; Urrestarazu, E; Gotman, J

2006-04-01

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

Immunochromatographic assay of cadmium levels in oysters.

PubMed

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

2012-08-15

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

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

PubMed

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

2012-01-01

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

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

PubMed

Yao, Linyin; Yi, Xiaoli; Wei, Yongxiang

2013-09-01

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

Mini-dystrophin restores L-type calcium currents in skeletal muscle of transgenic mdx mice

PubMed Central

Friedrich, O; Both, M; Gillis, J M; Chamberlain, J S; Fink, RHA

2004-01-01

L-type calcium currents (iCa) were recorded using the two-microelectrode voltage-clamp technique in single short toe muscle fibres of three different mouse strains: (i) C57/SV129 wild-type mice (wt); (ii) mdx mice (an animal model for Duchenne muscular dystrophy; and (iii) transgenically engineered mini-dystrophin (MinD)-expressing mdx mice. The activation and inactivation properties of iCa were examined in 2- to 18-month-old animals. Ca2+ current densities at 0 mV in mdx fibres increased with age, but were always significantly smaller compared to age-matched wild-type fibres. Time-to-peak (TTP) of iCa was prolonged in mdx fibres compared to wt fibres. MinD fibres always showed similar TTP and current amplitudes compared to age-matched wt fibres. In all three genotypes, the voltage-dependent inactivation and deactivation of iCa were similar. Intracellular resting calcium concentration ([Ca2+]i) and the distribution of dihydropyridine binding sites were also not different in young animals of all three genotypes, whereas iCa was markedly reduced in mdx fibres. We conclude, that dystrophin influences L-type Ca2+ channels via a direct or indirect linkage which may be disrupted in mdx mice and may be crucial for proper excitation–contraction coupling initiating Ca2+ release from the sarcoplasmic reticulum. This linkage seems to be fully restored in the presence of mini-dystrophin. PMID:14594987

Vascular space occupancy (VASO) cerebral blood volume-weighted MRI identifies hemodynamic impairment in patients with carotid artery disease.

PubMed

Donahue, Manus J; van Laar, Peter Jan; van Zijl, Peter C M; Stevens, Robert D; Hendrikse, Jeroen

2009-03-01

To assess the role of vascular space occupancy (VASO) magnetic resonance imaging (MRI), a noninvasive cerebral blood volume (CBV)-weighted technique, for evaluating CBV reactivity in patients with internal carotid artery (ICA) stenosis. VASO reactivity, defined as a signal change in response to hypercapnic stimulus (4-second exhale, 14-second breath-hold), was measured in the left and right ICA flow territories in patients (n=10) with varying degrees of unilateral and bilateral ICA stenosis and in healthy volunteers (n=10). Percent VASO reactivity was more negative (P<0.01) bilaterally in patients (ipsilateral: -3.6+/-1.5%; contralateral: -3.4+/-1.2%) compared with age-matched controls (left: -1.9+/-0.6%; right: -1.9+/-0.8%). Owing to the nature of the VASO contrast mechanism, this more negative VASO reactivity was attributed to autoregulatory CBV effects in patients. A postbreath-hold overshoot, which was absent in healthy volunteers, was observed unilaterally in a subset of patients. More negative VASO reactivity was observed in patients with ICA stenosis and may be a marker of autoregulatory effects. Furthermore, the postbreath-hold overshoot observed in patients is consistent with compensatory microvascular vasoconstriction and may be a marker of hemodynamic impairment. Based on the results of this feasibility study, VASO should be useful for identifying CBV adjustments in patients with steno-occlusive disease of the ICA. Copyright (c) 2009 Wiley-Liss, Inc.

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

PubMed

Oh, Hyung-Geun; Rhee, Eun-Jung

2016-11-01

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

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

PubMed

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

2016-02-01

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

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.

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

[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.

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/

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

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

Internal Carotid Artery Sacrifice for Radical Resection of Skull Base Tumors

PubMed Central

Lawton, Michael T.; Spetzler, Robert F.

1996-01-01

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

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.

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.

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

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.

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

Microsurgical Clipping of an Unruptured Carotid Cave Aneurysm: 3-Dimensional Operative Video.

PubMed

Tabani, Halima; Yousef, Sonia; Burkhardt, Jan-Karl; Gandhi, Sirin; Benet, Arnau; Lawton, Michael T

2017-08-01

Most aneurysms originating from the clinoidal segment of the internal carotid artery (ICA) are nowadays managed conservatively, treated endovascularly with coiling (with or without stenting) or flow diverters. However, microsurgical clip occlusion remains an alternative. This video demonstrates clip occlusion of an unruptured right carotid cave aneurysm measuring 7 mm in a 39-year-old woman. The patient opted for surgery because of concerns about prolonged antiplatelet use associated with endovascular therapy. After patient consent, a standard pterional craniotomy was performed followed by extradural anterior clinoidectomy. After dural opening and sylvian fissure split, a clinoidal flap was opened to enter the extradural space around the clinoidal segment. The dural ring was dissected circumferentially, freeing the medial wall of the ICA down to the sellar region and mobilizing the ICA out of its canal of the clinoidal segment. With the aneurysm neck in view, the aneurysm was clipped with a 45° angled fenestrated clip over the ICA. Indocyanine green angiography confirmed no further filling of the aneurysm and patency of the ICA. Complete aneurysm occlusion was confirmed with postoperative angiography, and the patient had no neurologic deficits (Video 1). This case demonstrates the importance of anterior clinoidectomy and thorough distal dural ring dissection for effective clipping of carotid cave aneurysms. Control of venous bleeding from the cavernous sinus with fibrin glue injection simplifies the dissection, which should minimize manipulation of the optic nerve. Knowledge of this anatomy and proficiency with these techniques is important in an era of declining open aneurysm cases. Copyright © 2017 Elsevier Inc. All rights reserved.

Blind extraction of exoplanetary spectra

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

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.

Reverse waffle cone technique in management of stent dislodgement into intracranial aneurysms.

PubMed

Luo, Chao-Bao; Lai, Yen-Jun; Teng, Michael Mu-Huo; Chang, Feng-Chi; Lin, Chung-Jung; Guo, Wan-Yuo

2013-09-01

Stent-assisted coil embolization (SACE) is a common method to manage intracranial wide-neck aneurysm. Using this technique, a stent must be successfully deployed into the parent artery to cross the aneurysm neck. We describe the reverse waffle cone technique in management of intra-procedural stent dislodgement during SACE of internal carotid artery (ICA) wide-neck aneurysms. Two patients with unruptured wide-neck ICA aneurysms underwent SACE. Intra-procedural forward stent migration occurred during catheterization with proximal stent dislodgement and migration into the aneurysm sac. Navigation of a second stent to bridge the aneurysm neck failed in one patient because the second stent was impeded by the dislodged stent. Using the reverse waffle cone technique, a microcatheter was navigated into the aneurysm sacs. Coils were safely detached into each aneurysm sac without any device assistance. The two wide-neck aneurysms were successfully treated with preservation of flow to the internal carotid arteries. The complication of intra-procedural distal stent migration and dislodgement, with proximal stent prolapse into an aneurysm sac, may not result in a failure to coil the aneurysm. The reverse waffle cone technique provides an effective treatment in the management of this complication. Copyright © 2013 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

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.

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.

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.

Unilateral congenital elongation of the cervical part of the internal carotid artery with kinking and looping: two case reports and review of the literature

PubMed Central

Ovchinnikov, Nikolai A; Rao, Ramesh T; Rao, Suresh R

2007-01-01

Unilateral and bilateral variation in the course and elongation of the cervical (extracranial) part of the internal carotid artery (ICA) leading to its tortuosity, kinking and coiling or looping is not a rare condition, which could be caused by both embryological and acquired factors. Patients with such variations may be asymptomatic in some cases; in others, they can develop cerebrovascular symptoms due to carotid stenosis affecting cerebral circulation. The risk of transient ischemic attacks in patients with carotid stenosis is high and its surgical correction is indicated for the prevention of ischemic stroke. Detection of developmental variations of the ICA and evaluation of its stenotic areas is very important for surgical interventions and involves specific diagnostic imaging techniques for vascular lesions including contrast arteriography, duplex ultrasonography and magnetic resonance angiography. Examination of obtained images in cases of unusual and complicated variations of vascular pattern of the ICA may lead to confusion in interpretation of data. Awareness about details and topographic anatomy of variations of the ICA may serve as a useful guide for both radiologists and vascular surgeons. It may help to prevent diagnostic errors, influence surgical tactics and interventional procedures and avoid complications during the head and neck surgery. Our present study was conducted with a purpose of updating data about developmental variations of the ICA. Dissections of the main neurovascular bundle of the head and neck were performed on a total 14 human adult cadavers (10 – Africans: 7 males & 3 females and 4 – East Indians: all males). Two cases of unilateral congenital elongation of the cervical part of the ICA with kinking and looping and carotid stenoses were found only in African males. Here we present their detailed case reports with review of the literature. PMID:17650347

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.

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

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.

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

PubMed

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

2017-01-01

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

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

PubMed

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

2008-02-15

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

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

PubMed Central

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

2011-01-01

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

Characterization of Strombolian events by using independent component analysis

NASA Astrophysics Data System (ADS)

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

2004-10-01

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

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

PubMed

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

2008-02-15

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

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

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

PubMed

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

2015-09-08

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

Source counting in MEG neuroimaging

NASA Astrophysics Data System (ADS)

Lei, Tianhu; Dell, John; Magee, Ralphy; Roberts, Timothy P. L.

2009-02-01

Magnetoencephalography (MEG) is a multi-channel, functional imaging technique. It measures the magnetic field produced by the primary electric currents inside the brain via a sensor array composed of a large number of superconducting quantum interference devices. The measurements are then used to estimate the locations, strengths, and orientations of these electric currents. This magnetic source imaging technique encompasses a great variety of signal processing and modeling techniques which include Inverse problem, MUltiple SIgnal Classification (MUSIC), Beamforming (BF), and Independent Component Analysis (ICA) method. A key problem with Inverse problem, MUSIC and ICA methods is that the number of sources must be detected a priori. Although BF method scans the source space on a point-to-point basis, the selection of peaks as sources, however, is finally made by subjective thresholding. In practice expert data analysts often select results based on physiological plausibility. This paper presents an eigenstructure approach for the source number detection in MEG neuroimaging. By sorting eigenvalues of the estimated covariance matrix of the acquired MEG data, the measured data space is partitioned into the signal and noise subspaces. The partition is implemented by utilizing information theoretic criteria. The order of the signal subspace gives an estimate of the number of sources. The approach does not refer to any model or hypothesis, hence, is an entirely data-led operation. It possesses clear physical interpretation and efficient computation procedure. The theoretical derivation of this method and the results obtained by using the real MEG data are included to demonstrates their agreement and the promise of the proposed approach.

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.

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

PREFACE: 7th International Conference on Applied Electrostatics (ICAES-2012)

NASA Astrophysics Data System (ADS)

Li, Jie

2013-03-01

ICAES is an important conference organized every four years by the Committee on Electrostatics of the Chinese Physical Society, which serves as a forum for scientists, educators and engineers interested in the fundamentals, applications, disasters and safety of electrostatics, etc. In recent years, new techniques, applications and fundamental theories on electrostatics have developed considerably. ICAES-7, held in Dalian, China, from 17-19 September 2012, aimed to provide a forum for all scholars to report the newest developments in electrostatics, to probe the questions that scholars faced and to discuss fresh ideas related to electrostatics. ICAES-7 was co-organized and hosted by Dalian University of Technology, and was sponsored by the Ministry of Education of China, the National Natural Science Foundation of China, Dalian University of Technology, Nanjing Suman Electronics Co. Ltd (Suman, China), Shekonic (Yangzhou Shuanghong, China) Electric/Mechanical Co. Ltd, and Suzhou TA&A Ultra Clean Technology Co. Ltd. (China). On behalf of the organizing committee of ICAES-7, I express my great appreciation for their support of the conference. Over 160 scholars and engineers from many countries including Croatia, The Czech Republic, D.P.R. Korea, Germany, Japan, Malaysia, Poland, Russia, the United States of America, China attended ICAES-7, and the conference collected and selected 149 papers for publication. The subjects of those papers cover the fundamentals of electrostatics, electrostatic disaster and safety, and electrostatic application (e.g. precipitation, pollutant control, biological treatment, mixture separation and food processing, etc). I cordially thank all authors and attendees for their support, and my appreciation is also given to the conference honorary chair, the organizing committee and advisory committee, and the conference secretaries for their hard work. ICAES-7 is dedicated to the memory of Professor Jen-Shih Chang (professor emeritus in the Faculty of Engineering, McMaster University, Canada), Haitian Scholar of Dalian University of Technology (China), who passed away on 27 February 2011. Professor Chang was active in research fields including the applications of electrostatics, electromagnetic hydrodynamics, plasma environmental pollution control technologies, etc and he contributed much to the development of these fields. Professor Chang was the visiting professor at some Key Universities in China and was the friend of Chinese scholars engaged in electrostatics. Professor Chang was also active in joining and supporting the previous ICAES. We will cherish the memory of Professor Jen-Shih Chang forever. Professor Jie Li Proceedings Editor Dalian, September 2012 Conference photograph

Complex Signal Kurtosis and Independent Component Analysis for Wideband Radio Frequency Interference Detection

NASA Technical Reports Server (NTRS)

Schoenwald, Adam; Mohammed, Priscilla; Bradley, Damon; Piepmeier, Jeffrey; Wong, Englin; Gholian, Armen

2016-01-01

Radio-frequency interference (RFI) has negatively implicated scientific measurements across a wide variation passive remote sensing satellites. This has been observed in the L-band radiometers SMOS, Aquarius and more recently, SMAP [1, 2]. RFI has also been observed at higher frequencies such as K band [3]. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements [4]. This work explores the use of ICA (Independent Component Analysis) as a blind source separation technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

RFI Detection and Mitigation using Independent Component Analysis as a Pre-Processor

NASA Technical Reports Server (NTRS)

Schoenwald, Adam J.; Gholian, Armen; Bradley, Damon C.; Wong, Mark; Mohammed, Priscilla N.; Piepmeier, Jeffrey R.

2016-01-01

Radio-frequency interference (RFI) has negatively impacted scientific measurements of passive remote sensing satellites. This has been observed in the L-band radiometers Soil Moisture and Ocean Salinity (SMOS), Aquarius and more recently, Soil Moisture Active Passive (SMAP). RFI has also been observed at higher frequencies such as K band. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements. This work explores the use of Independent Component Analysis (ICA) as a blind source separation (BSS) technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

[Effect of substance P on the potassium and calcium currents of colonic smooth muscle cells].

PubMed

Tang, Qincai; Luo, Hesheng; Quan, Xiaojing; Fan, Han; Yu, Guang

2015-08-11

To investigate the effect of substance P(SP) on the spontaneous contractile activity of smooth muscle cells,the large-conductance calcium-activated potassium channel currents (IBKCa) and the L-type calcium channel currents (ICaL) in rat smooth muscle cells of the proximal colon. A total of 24 healthy male Wista rats were used in this test. The change of smooth muscle strips spontaneous contraction of rat proximal colon after adding SP was recorded by a physiological signal stystem (RM6240). The IBKCa and ICaL were measured via the whole cell patch-clamp technique. The longitudinal muscle contraction was obviously increased concentration-dependently after adding different concentrations of SP (10(-7)-10(-6) mol/L), so as the circular muscle while adding SP(10(-8)-10(-6) mol/L) (all P<0.05). Compared with the control group, IBKCa was decreased after adding SP(10(-6) mol/L). Under the stimulating voltage of 60 mV, the IBKCa current density was (11.71±1.65) pA/pF, which was significantly lower compared with the control group (14.42±2.89) pA/pF (P<0.05). The ICaL) was apparently increased. Under the stimulating voltage of 0 mV, the ICaL) currents density was (-5.04±0.67) pA/pF, compared with the control group (-4.25±0.46) pA/pF, which was significantly increased (P<0.01). SP can promote the spontaneous contractile activity of colon smooth muscle of rats in vitro.And SP decrease IBKCa representatively while apparently increase ICaL). That is probably one of the mechanism SP regulate the gastrointestinal motility.

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.

Percutaneous Cervical Sympathetic Block for Pain Control after Internal Carotid Artery Dissection. A Report of Two Cases.

PubMed

Saeed, Omar; Khan, Asif A; Herial, Nabeel A; Aytac, Emrah; Qureshi, Adnan I

2017-01-01

Medical treatment of cranio-cervical pain can be suboptimal in patients with internal carotid artery (ICA) dissection. We report the use of cervical sympathetic block for treatment of pain in two patients with ICA dissection. A 58-year-old man and a 43-year-old woman presented with severe cranio-cervical pain associated with left and right ICA dissection confirmed by magnetic resonance imaging and cerebral angiography. Due to suboptimal control of pain with medical treatment, cervical sympathetic block was performed under fluoroscopic guidance using 20 ml of bupivacaine injected lateral to the posterior aspect of sixth vertebral body in both patients. On self-reported pain scale, the 58-year-old man reported improvement in pain intensity from 8/10 pain to 0/10 within 1 h of blockade. The patient remained relatively pain free for the 24-h post blockade. Mild recurrence of pain was noted on Day 2. The 43-year-old woman reported improvement in pain intensity from 6/10 pain to 0/10 within 1 h of blockade. The patient remained pain free for five days with recurrence to previous intensity. Cervical sympathetic blockade in patients with ICA dissection may be an effective option in the event of suboptimal pain control with medical treatment; however, the technique may be limited due to relatively short duration of action.

Successful Resection of a Large Carotid Body Tumor Masquerading Complete Encasement of the Internal Carotid Artery on Preoperative Imaging.

PubMed

Kakkos, Stavros K; Zampakis, Peter; Lampropoulos, George C; Paridis, Leonidas; Kaplanis, Charalampos; Bantouna, Dimitra; Nikolakopoulos, Konstantinos M; Papageorgopoulou, Chrysanthi P; Kalogeropoulou, Christina; Tsolakis, Ioannis A

2018-05-01

A 41-year-old woman presented with a large painful and tender mass of the left side of her neck located just below the angle of the mandible. She was also complaining of frequent attacks of symptoms ranging from dizziness upon resuming the erect position to frank syncope. Color-coded duplex showed a large well-vascularized vascular mass at the level of the carotid bifurcation, suggesting the diagnosis of a carotid body tumor (CBT). A computerized tomographic angiography confirmed the diagnosis of a CBT, which measured 5.7 cm in its craniocaudal axis. The tumor appeared to encase the internal carotid artery (ICA) at the level of its origin, indicating the presence of a Shamblin group 3 tumor. During surgery, the CBT seemingly encased the ICA; however, it was successfully taken off the ICA, by establishing an adventitial dissection place, obviating the need for arterial resection and replacement. A brief intraoperative episode of cardiac arrest was successfully managed. Postoperative course was uneventful, and all symptoms were cured. Pathology confirmed the clinical diagnosis and did not show malignancy. In conclusion, meticulous surgical techniques can spare the ICA from removal or inadvertent injury during CBT surgery and reduce the morbidity often associated with the resection of large or advanced tumors but also cure atypical patient symptoms.

Pre-reconstruction of cervical-to-petrous internal carotid artery: An improved technique for treatment of vascular lesions involving internal carotid artery at the lateral skull base.

PubMed

Li, Fang-Da; Gao, Zhi-Qiang; Ren, Hua-Liang; Liu, Chang-Wei; Song, Xiao-Jun; Li, Yan-Feng; Zheng, Yue-Hong

2016-04-01

Reconstruction of the internal carotid artery (ICA) is an operative challenge for lesions involving the lateral skull base because of excessive blood loss, intraoperative cranial nerve injury, and difficulties in cerebral protection. Between January 2010 and October 2014, 9 patients with vascular lesions at the lateral skull base were treated with a "pre-reconstruction" technique, which means reconstruction of the ICA in advance of excising the lesions. All operations were technically successful with no mortality or strokes. The mean blood loss was 921 ± 210 mL. The mean total clamping time was 18 ± 5 minutes. Among the 5 patients without invasion of specific cranial nerves, no long-term sequelae occurred during the follow-up period ranging from 11 to 54 months. With less blood loss, slighter cranial nerve injuries, and shorter clamping time, the "pre-reconstruction" technique was safe and effective for the treatment of vascular lesions at the lateral skull base. © 2016 Wiley Periodicals, Inc. Head Neck 38: E1562-E1567, 2016. © 2016 Wiley Periodicals, Inc.

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.

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

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

PubMed Central

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

2018-01-01

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

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

PubMed

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

2017-01-01

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

Extending the Kerberos Protocol for Distributed Data as a Service

DTIC Science & Technology

2012-09-20

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

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

PubMed

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

2017-10-01

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

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

PubMed

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

2013-09-19

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

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.

Hypocalcemia Following Resuscitation from Cardiac Arrest Revisited

PubMed Central

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

2009-01-01

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

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

PubMed Central

Provence, Aaron; Malysz, John

2015-01-01

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

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

PubMed

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

2015-09-01

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

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.

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

PubMed

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

2014-06-01

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

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

PubMed Central

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

2014-01-01

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

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

PubMed

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

2016-06-13

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

Extraction of phase information in daily stock prices

NASA Astrophysics Data System (ADS)

Fujiwara, Yoshi; Maekawa, Satoshi

2000-06-01

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

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.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

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

PubMed

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

2012-04-15

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

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

PubMed

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

2008-05-01

A 48-year-old man presented with a pseudoaneurysm at the cervical portion of the left internal carotid artery (ICA) secondary to infection in the deep neck space. Magnetic resonance (MR) imaging demonstrated enhancement of the wall of the ICA and a pseudoaneurysm, considered to be sequelae of infection spread. ICA occlusion occurred on the next day resulting in sudden onset of right hemiparesis and motor aphasia. The ICA pseudoaneurysm shrank gradually and his neurological deficits improved with conservative therapy. One month later, he presented with aneurysm regrowth. The common carotid artery was occluded with Guglielmi detachable coils to block arterial flow into the pseudoaneurysm. There were no neurological complications. Marked enhancement of the ICA wall on computed tomography and MR imaging may indicate the possibility of vascular complications such as rupture, pseudoaneurysm development, or ICA occlusion, and consequent neurological deficits. ICA occlusion caused by spread of infection in the deep neck space may cause accelerated coagulopathy due to ICA wall inflammation.

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.

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.

Expanding the endoscopic transpterygoid corridor to the petroclival region: anatomical study and volumetric comparative analysis.

PubMed

Freeman, Jacob L; Sampath, Raghuram; Quattlebaum, Steven Craig; Casey, Michael A; Folzenlogen, Zach A; Ramakrishnan, Vijay R; Youssef, A Samy

2017-07-21

OBJECTIVE The endoscopic endonasal transmaxillary transpterygoid (TMTP) approach has been the gateway for lateral skull base exposure. Removal of the cartilaginous eustachian tube (ET) and lateral mobilization of the internal carotid artery (ICA) are technically demanding adjunctive steps that are used to access the petroclival region. The gained expansion of the deep working corridor provided by these maneuvers has yet to be quantified. METHODS The TMTP approach with cartilaginous ET removal and ICA mobilization was performed in 5 adult cadaveric heads (10 sides). Accessible portions of the petrous apex were drilled during the following 3 stages: 1) before ET removal, 2) after ET removal but before ICA mobilization, and 3) after ET removal and ICA repositioning. Resection volumes were calculated using 3D reconstructions generated from thin-slice CT scans obtained before and after each step of the dissection. RESULTS The average petrous temporal bone resection volumes at each stage were 0.21 cm 3 , 0.71 cm 3 , and 1.32 cm 3 (p < 0.05, paired t-test). Without ET removal, inferior and superior access to the petrous apex was limited. Furthermore, without ICA mobilization, drilling was confined to the inferior two-thirds of the petrous apex. After mobilization, the resection was extended superiorly through the upper extent of the petrous apex. CONCLUSIONS The transpterygoid corridor to the petroclival region is maximally expanded by the resection of the cartilaginous ET and mobilization of the paraclival ICA. These added maneuvers expanded the deep window almost 6 times and provided more lateral access to the petroclival region with a maximum volume of 1.5 cm 3 . This may result in the ability to resect small-to-moderate sized intradural petroclival lesions up to that volume. Larger lesions may better be approached through an open transcranial approach.

[Determination of soluble solids content in Nanfeng Mandarin by Vis/NIR spectroscopy and UVE-ICA-LS-SVM].

PubMed

Sun, Tong; Xu, Wen-Li; Hu, Tian; Liu, Mu-Hua

2013-12-01

The objective of the present research was to assess soluble solids content (SSC) of Nanfeng mandarin by visible/near infrared (Vis/NIR) spectroscopy combined with new variable selection method, simplify prediction model and improve the performance of prediction model for SSC of Nanfeng mandarin. A total of 300 Nanfeng mandarin samples were used, the numbers of Nanfeng mandarin samples in calibration, validation and prediction sets were 150, 75 and 75, respectively. Vis/NIR spectra of Nanfeng mandarin samples were acquired by a QualitySpec spectrometer in the wavelength range of 350-1000 nm. Uninformative variables elimination (UVE) was used to eliminate wavelength variables that had few information of SSC, then independent component analysis (ICA) was used to extract independent components (ICs) from spectra that eliminated uninformative wavelength variables. At last, least squares support vector machine (LS-SVM) was used to develop calibration models for SSC of Nanfeng mandarin using extracted ICs, and 75 prediction samples that had not been used for model development were used to evaluate the performance of SSC model of Nanfeng mandarin. The results indicate t hat Vis/NIR spectroscopy combinedwith UVE-ICA-LS-SVM is suitable for assessing SSC o f Nanfeng mandarin, and t he precision o f prediction ishigh. UVE--ICA is an effective method to eliminate uninformative wavelength variables, extract important spectral information, simplify prediction model and improve the performance of prediction model. The SSC model developed by UVE-ICA-LS-SVM is superior to that developed by PLS, PCA-LS-SVM or ICA-LS-SVM, and the coefficient of determination and root mean square error in calibration, validation and prediction sets were 0.978, 0.230%, 0.965, 0.301% and 0.967, 0.292%, respectively.

Hybrid systems analysis of the control of burst duration by low-voltage-activated calcium current in leech heart interneurons.

PubMed

Olypher, Andrey; Cymbalyuk, Gennady; Calabrese, Ronald L

2006-12-01

The leech heartbeat CPG is paced by the alternating bursting of pairs of mutually inhibitory heart interneurons that form elemental half-center oscillators. We explore the control of burst duration in heart interneurons using a hybrid system, where a living, pharmacologically isolated, heart interneuron is connected with artificial synapses to a model heart interneuron running in real-time, by focusing on a low-voltage-activated (LVA) calcium current I(CaS). The transition from silence to bursting in this half-center oscillator occurs when the spike frequency of the bursting interneuron declines to a critical level, f(Final), at which the inhibited interneuron escapes owing to a build-up of the hyperpolarization-activated cation current, I(h). We varied I(CaS) inactivation time constant either in the living heart interneuron or in the model heart interneuron. In both cases, varying I(CaS) inactivation time constant did not affect f(Final) of either interneuron, but in the varied interneuron, the time constant of decline of spike frequency during bursts to f(Final) and thus the burst duration varied directly and nearly linearly with I(CaS) inactivation time constant. Bursts of the opposite, nonvaried interneuron did not change. We show also that control of burst duration by I(CaS) inactivation does not require synaptic interaction by reconstituting autonomous bursting in synaptically isolated living interneurons with injected I(CaS). Therefore inactivation of LVA calcium current is critically important for setting burst duration and thus period in a heart interneuron half-center oscillator and is potentially a general intrinsic mechanism for regulating burst duration in neurons.

Attitude towards one's illness vs. attitude towards a surgical operation, displayed by patients diagnosed with asymptomatic abdominal aortic aneurysm and asymptomatic internal carotid artery stenosis.

PubMed

Stanisić, M; Rzepa, T

2012-08-01

Two most frequent asymptomatic diseases qualifying for vascular surgery are abdominal aortic aneurysm (AAA) and internal carotid artery stenosis (ICAS). Emotions experienced by the patient activate processes of dealing with the cognitive dissonance of asymptomatic disease. The aim of this paper was to compare the reasons involved in decision making on surgery in two asymptomatic vascular pathologies. Fifty patients were divided into two groups: the ICAS group-27 (CAS or CEA) and the AAA group-23 (EVAR or open surgical operation (OSR). Specific questionnaire regarding: 1) self-image; 2) attitude to one's illness; 3) reasons for decision on surgery was applied for the study. The χ² test was used to for the analysis. The AAA patients reacted emotionally (88.2%) comparing to ICAS patients reacting "rationally" (59.3%) (α=0.05). In AAA patients attitude towards themselves had worsened (α=0.001) AAA patients were less likely to seek support in decision on surgery (α=0.01). ICAS patients are internally motivated (78.7%), whereas AAA patients are externally motivated (63.9%) (α=0.001). Reasons underlying the decision on surgery, were predominantly rational (55.8%). In the process of decision-making on surgery by asymptomatic patients, evolutionary transformation takes place - the emotional attitude to one's illness leads to rationally evaluated decision. Regardless of the causes the process of making a decision on surgical operation tended to run more smoothly in ICAS patients. The ICAS patients tended to display a rational attitude to their illness. AAA patients displayed a distinctly emotional attitude towards their illness.

Phenotypic and genotypic characterization of biofilm formation among Staphylococcus aureus isolates from clinical specimens, an Atomic Force Microscopic (AFM) study.

PubMed

Bazari, Pelin Aslani Menareh; Honarmand Jahromy, Sahar; Zare Karizi, Shohreh

2017-09-01

Staphylococcus aureus is a major cause of nosocomial infections. Biofilm formation is an important factor for bacterial pathogenesis. Its mechanisms are complex and include of many genes depends on expression of icaADBC operon involved in the synthesis of a polysaccharide intercellular adhesion. The aim of study was to investigate biofilm forming ability of Staphylococcus aureus strains by phenotypic and genotypic methods. Also Atomic Force microscope (AFM) was used to visualize biofilm formation. 140 Isolates were collected from clinical specimens of patients in Milad Hospital, Tehran and diagnosed by biochemical tests. The ability of strains to produce slime was evaluated by CRA method. For diagnosing of bacterial EPS, Indian ink staining were used and finally biofilm surface of 3 isolates observed by AFM. The prevalence of icaA and icaD genes was determined by PCR. By CRA method 15% of samples considered as positive slime producers, 44.28% as intermediate and 40.71% indicative as negative slime producers. 118 staphylococcus aureus strains showed a distinct halo transparent zone but 22 strains showed no halo zone. AFM analysis of Slime positive isolates showed a distinct and complete biofilm formation. In slime negative strain, there was not observed biofilm. The prevalence of icaA, icaD genes was 44.2% and 10% of the isolates had both genes simultaneously. There is a relationship between exopolysaccharide layer and biofilm formation of Staphylococcus aureus isolates. The presence of icaAD genes among isolates is not associated with in vitro formation of biofilm. AFM is a useful tool for observation of bacterial biofilm formation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Tetrodotoxin Blockade on Canine Cardiac L-Type Ca2+ Channels Depends on pH and Redox Potential

PubMed Central

Hegyi, Bence; Komáromi, István; Kistamás, Kornél; Ruzsnavszky, Ferenc; Váczi, Krisztina; Horváth, Balázs; Magyar, János; Bányász, Tamás; Nánási, Péter P.; Szentandrássy, Norbert

2013-01-01

Tetrodotoxin (TTX) is believed to be one of the most selective inhibitors of voltage-gated fast Na+ channels in excitable tissues. Recently, however, TTX has been shown to block L-type Ca2+ current (ICa) in canine cardiac cells. In the present study, the TTX-sensitivity of ICa was studied in isolated canine ventricular myocytes as a function of (1) channel phosphorylation, (2) extracellular pH and (3) the redox potential of the bathing medium using the whole cell voltage clamp technique. Fifty-five micromoles of TTX (IC50 value obtained under physiological conditions) caused 60% ± 2% inhibition of ICa in acidic (pH = 6.4), while only a 26% ± 2% block in alkaline (pH = 8.4) milieu. Similarly, the same concentration of TTX induced 62% ± 6% suppression of ICa in a reductant milieu (containing glutathione + ascorbic acid + dithiothreitol, 1 mM each), in contrast to the 31% ± 3% blockade obtained in the presence of a strong oxidant (100 μM H2O2). Phosphorylation of the channel protein (induced by 3 μM forskolin) failed to modify the inhibiting potency of TTX; an IC50 value of 50 ± 4 μM was found in forskolin. The results are in a good accordance with the predictions of our model, indicating that TTX binds, in fact, to the selectivity filter of cardiac L-type Ca channels. PMID:23771047

Calcium balance in pediatric online hemodiafiltration: Beware of sodium and bicarbonate in the dialysate.

PubMed

Bacchetta, Justine; Sellier-Leclerc, Anne-Laure; Bertholet-Thomas, Aurélia; Carlier, Marie-Christine; Cartier, Régine; Cochat, Pierre; Ranchin, Bruno

2015-11-01

Online hemodiafiltration (oHDF) is increasingly used in children; we treated 28 children since 2009, adapting this technique to pediatric patients. In this service evaluation audit, we assessed plasma electrolytes to evaluate the evolution of total (tCa) and ionized (iCa) during a session, as well as dialysate calcium (dCa) concentrations. Using a 1.25 mmol Ca/L-dialysate, both tCa and iCa decreased during the session, with iCa falling below 1.1 mmol/L in 4/5 patients. In contrast, using a 1.5 mmol Ca/L-dialysate, iCa remained normal in all patients. Major discrepancies were observed between the expected and the measured dCa: 1.25 vs. 1.01 (0.83-1.04), and 1.5 vs. 1.47 (0.85-1.75) mmol/L, respectively (results presented as median [range]). These differences were explained by the modality of reconstituting dialysate: increasing bicarbonates and/or decreasing sodium requested in the dialysate decreases calcium extraction from the acid preparation. Proof of concept was given when requesting in an "ex-vivo" setting modifications in the requested sodium and bicarbonate in dialysate directly on the Fresenius machine. Nephrologists should be aware that "high bicarbonate and/or low sodium" requirements in oHDF decrease calcium in the dialysate. Copyright © 2015 Association Société de néphrologie. Published by Elsevier SAS. All rights reserved.

Functional brain networks in schizophrenia: a review.

PubMed

Calhoun, Vince D; Eichele, Tom; Pearlson, Godfrey

2009-01-01

Functional magnetic resonance imaging (fMRI) has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event-related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA) which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large-scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their inter-relationships with fMRI has great potential to improve our understanding of schizophrenia.

The ICA Communication Audit and Perceived Communication Effectiveness Changes in 16 Audited Organizations.

ERIC Educational Resources Information Center

Brooks, Keith; And Others

1979-01-01

Discusses the benefits of the International Communication Association Communication Audit as a methodology for evaluation of organizational communication processes and outcomes. An "after" survey of 16 audited organizations confirmed the audit as a valid diagnostic methodology and organization development intervention technique which…

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.

Reduction hybrid artifacts of EMG-EOG in electroencephalography evoked by prefrontal transcranial magnetic stimulation

NASA Astrophysics Data System (ADS)

Bai, Yang; Wan, Xiaohong; Zeng, Ke; Ni, Yinmei; Qiu, Lirong; Li, Xiaoli

2016-12-01

Objective. When prefrontal-transcranial magnetic stimulation (p-TMS) performed, it may evoke hybrid artifact mixed with muscle activity and blink activity in EEG recordings. Reducing this kind of hybrid artifact challenges the traditional preprocessing methods. We aim to explore method for the p-TMS evoked hybrid artifact removal. Approach. We propose a novel method used as independent component analysis (ICA) post processing to reduce the p-TMS evoked hybrid artifact. Ensemble empirical mode decomposition (EEMD) was used to decompose signal into multi-components, then the components were separated with artifact reduced by blind source separation (BSS) method. Three standard BSS methods, ICA, independent vector analysis, and canonical correlation analysis (CCA) were tested. Main results. Synthetic results showed that EEMD-CCA outperformed others as ICA post processing step in hybrid artifacts reduction. Its superiority was clearer when signal to noise ratio (SNR) was lower. In application to real experiment, SNR can be significantly increased and the p-TMS evoked potential could be recovered from hybrid artifact contaminated signal. Our proposed method can effectively reduce the p-TMS evoked hybrid artifacts. Significance. Our proposed method may facilitate future prefrontal TMS-EEG researches.

Detection of explosives on the surface of banknotes by Raman hyperspectral imaging and independent component analysis.

PubMed

Almeida, Mariana R; Correa, Deleon N; Zacca, Jorge J; Logrado, Lucio Paulo Lima; Poppi, Ronei J

2015-02-20

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm(-2). Copyright © 2014 Elsevier B.V. All rights reserved.

Ocular Blood Flow in Rabbits under Deep Anesthesia: A Real-Time Measurement Technique and Its Application in Characterizing Retinal Ischemia.

PubMed

Bhatti, Mehwish Saba; Tang, Tong Boon; Chen, Hui Cheng

2018-04-09

In this study, we reported a new technique based on laser speckle flowgraphy to record the ocular blood flow in rabbits under deep anesthesia, and proposed parameters to characterize retinal ischemia. We applied the proposed technique to study the correlation of blood flow between the eyes of normal non-anesthetized animals, and to characterize the occlusion of the internal carotid artery (ICA) and external carotid artery (ECA). We established a correlation in blood flow between the eyes of non-anesthetized animals, and derived two new parameters, namely, the laterality index and vascular perfusion estimate (VPE). Our experimental results from 16 eyes (of 13 New Zealand white rabbits) showed a reduction in ocular blood flow with a significant decrease in the VPE after the occlusion of the ECA (p < 0.001). A low/minimal effect on blood flow was observed with the occlusion of the ICA. In conclusion, we demonstrated a means for the real-time measurement of the ocular blood flow in rabbits under deep anesthesia by using laser speckle flowgraphy and the VPE as an indicator of successful occlusion. The proposed technique might be applicable in quantifying the efficacy of new drugs and interventions for the treatment of retinal ischemia.

Methods of Collection of Biological Information for Fatigue Evaluation during Visual Display Terminals (VDT) Operation

NASA Astrophysics Data System (ADS)

Hachiya, Yuriko; Ogai, Harutoshi; Okazaki, Hiroko; Fujisaki, Takeshi; Uchida, Kazuhiko; Oda, Susumu; Wada, Futoshi; Mori, Koji

A method for the analysis of fatigue parameters has been rarely researched in VDT operation. Up to now, fatigue was evaluated by changing of biological information. If signals regarding fatigue are detected, fatigue can be measured. The purpose of this study proposed experiment and analysis method to extract parameters related to fatigue from the biological information during VDT operation using the Independent Component Analysis (ICA). An experiment had 11 subjects. As for the experiment were light loaded VDT operation and heavy loaded VDT operation. A measurement item were amount of work, a mistake number, subjective symptom, surface skin temperature (forehead and apex nasi), heart rate, skin blood flow of forearm and respiratory rate. In the heavy loaded operation group, mistake number and subjective symptom score were increased to compare with the other. And Two-factor ANOVA was used for analysis. The result of mistake number was confirmed that heavy loaded. After the moving averages of waveshape were calculated, it was made to extract independent components by using the ICA. The results of the ICA suggest that the independent components increase according to accumulation of fatigue. Thus, the independent components would be a possible parameter of fatigue. However, further experiments should continue in order to obtain the conclusive finding of our research.

Polysaccharide intercellular adhesin in biofilm: structural and regulatory aspects

PubMed Central

Arciola, Carla Renata; Campoccia, Davide; Ravaioli, Stefano; Montanaro, Lucio

2015-01-01

Staphylococcus aureus and Staphylococcus epidermidis are the leading etiologic agents of implant-related infections. Biofilm formation is the main pathogenetic mechanism leading to the chronicity and irreducibility of infections. The extracellular polymeric substances of staphylococcal biofilms are the polysaccharide intercellular adhesin (PIA), extracellular-DNA, proteins, and amyloid fibrils. PIA is a poly-β(1-6)-N-acetylglucosamine (PNAG), partially deacetylated, positively charged, whose synthesis is mediated by the icaADBC locus. DNA sequences homologous to ica locus are present in many coagulase-negative staphylococcal species, among which S. lugdunensis, however, produces a biofilm prevalently consisting of proteins. The product of icaA is an N-acetylglucosaminyltransferase that synthetizes PIA oligomers from UDP-N-acetylglucosamine. The product of icaD gives optimal efficiency to IcaA. The product of icaC is involved in the externalization of the nascent polysaccharide. The product of icaB is an N-deacetylase responsible for the partial deacetylation of PIA. The expression of ica locus is affected by environmental conditions. In S. aureus and S. epidermidis ica-independent alternative mechanisms of biofilm production have been described. S. epidermidis and S. aureus undergo to a phase variation for the biofilm production that has been ascribed, in turn, to the transposition of an insertion sequence in the icaC gene or to the expansion/contraction of a tandem repeat naturally harbored within icaC. A role is played by the quorum sensing system, which negatively regulates biofilm formation, favoring the dispersal phase that disseminates bacteria to new infection sites. Interfering with the QS system is a much debated strategy to combat biofilm-related infections. In the search of vaccines against staphylococcal infections deacetylated PNAG retained on the surface of S. aureus favors opsonophagocytosis and is a potential candidate for immune-protection. PMID:25713785

The Effect of Spatial and Temporal Resolution of Cine Phase Contrast MRI on Wall Shear Stress and Oscillatory Shear Index Assessment

PubMed Central

Gijsen, Frank J.; Marquering, Henk; van Ooij, Pim; vanBavel, Ed; Wentzel, Jolanda J.; Nederveen, Aart J.

2016-01-01

Introduction Wall shear stress (WSS) and oscillatory shear index (OSI) are associated with atherosclerotic disease. Both parameters are derived from blood velocities, which can be measured with phase-contrast MRI (PC-MRI). Limitations in spatiotemporal resolution of PC-MRI are known to affect these measurements. Our aim was to investigate the effect of spatiotemporal resolution using a carotid artery phantom. Methods A carotid artery phantom was connected to a flow set-up supplying pulsatile flow. MRI measurement planes were placed at the common carotid artery (CCA) and internal carotid artery (ICA). Two-dimensional PC-MRI measurements were performed with thirty different spatiotemporal resolution settings. The MRI flow measurement was validated with ultrasound probe measurements. Mean flow, peak flow, flow waveform, WSS and OSI were compared for these spatiotemporal resolutions using regression analysis. The slopes of the regression lines were reported in %/mm and %/100ms. The distribution of low and high WSS and OSI was compared between different spatiotemporal resolutions. Results The mean PC-MRI CCA flow (2.5±0.2mL/s) agreed with the ultrasound probe measurements (2.7±0.02mL/s). Mean flow (mL/s) depended only on spatial resolution (CCA:-13%/mm, ICA:-49%/mm). Peak flow (mL/s) depended on both spatial (CCA:-13%/mm, ICA:-17%/mm) and temporal resolution (CCA:-19%/100ms, ICA:-24%/100ms). Mean WSS (Pa) was in inverse relationship only with spatial resolution (CCA:-19%/mm, ICA:-33%/mm). OSI was dependent on spatial resolution for CCA (-26%/mm) and temporal resolution for ICA (-16%/100ms). The regions of low and high WSS and OSI matched for most of the spatiotemporal resolutions (CCA:30/30, ICA:28/30 cases for WSS; CCA:23/30, ICA:29/30 cases for OSI). Conclusion We show that both mean flow and mean WSS are independent of temporal resolution. Peak flow and OSI are dependent on both spatial and temporal resolution. However, the magnitude of mean and peak flow, WSS and OSI, and the spatial distribution of OSI and WSS did not exhibit a strong dependency on spatiotemporal resolution. PMID:27669568

Rotenone-stimulated superoxide release from mitochondrial complex I acutely augments L-type Ca2+ current in A7r5 aortic smooth muscle cells

PubMed Central

Dhagia, Vidhi; Lakhkar, Anand; Patel, Dhara; Wolin, Michael S.; Gupte, Sachin A.

2016-01-01

Voltage-gated L-type Ca2+ current (ICa,L) induces contraction of arterial smooth muscle cells (ASMCs), and ICa,L is increased by H2O2 in ASMCs. Superoxide released from the mitochondrial respiratory chain (MRC) is dismutated to H2O2. We studied whether superoxide per se acutely modulates ICa,L in ASMCs using cultured A7r5 cells derived from rat aorta. Rotenone is a toxin that inhibits complex I of the MRC and increases mitochondrial superoxide release. The superoxide content of mitochondria was estimated using mitochondrial-specific MitoSOX and HPLC methods, and was shown to be increased by a brief exposure to 10 μM rotenone. ICa,L was recorded with 5 mM BAPTA in the pipette solution. Rotenone administration (10 nM to 10 μM) resulted in a greater ICa,L increase in a dose-dependent manner to a maximum of 22.1% at 10 μM for 1 min, which gradually decreased to 9% after 5 min. The rotenone-induced ICa,L increase was associated with a shift in the current-voltage relationship (I-V) to a hyperpolarizing direction. DTT administration resulted in a 17.9% increase in ICa,L without a negative shift in I–V, and rotenone produced an additional increase with a shift. H2O2 (0.3 mM) inhibited ICa,L by 13%, and additional rotenone induced an increase with a negative shift. Sustained treatment with Tempol (4-hydroxy tempo) led to a significant ICa,L increase but it inhibited the rotenone-induced increase. Staurosporine, a broad-spectrum protein kinase inhibitor, partially inhibited ICa,L and completely suppressed the rotenone-induced increase. Superoxide released from mitochondria affected protein kinases and resulted in stronger ICa,L preceding its dismutation to H2O2. The removal of nitric oxide is a likely mechanism for the increase in ICa,L. PMID:26873970

Role of acidosis-induced increases in calcium on PTH secretion in acute metabolic and respiratory acidosis in the dog.

PubMed

López, Ignacio; Aguilera-Tejero, Escolástico; Estepa, José Carlos; Rodríguez, Mariano; Felsenfeld, Arnold J

2004-05-01

Recently, we showed that both acute metabolic acidosis and respiratory acidosis stimulate parathyroid hormone (PTH) secretion in the dog. To evaluate the specific effect of acidosis, ionized calcium (iCa) was clamped at a normal value. Because iCa values normally increase during acute acidosis, we now have studied the PTH response to acute metabolic and respiratory acidosis in dogs in which the iCa concentration was allowed to increase (nonclamped) compared with dogs with a normal iCa concentration (clamped). Five groups of dogs were studied: control, metabolic (clamped and nonclamped), and respiratory (clamped and nonclamped) acidosis. Metabolic (HCl infusion) and respiratory (hypoventilation) acidosis was progressively induced during 60 min. In the two clamped groups, iCa was maintained at a normal value with an EDTA infusion. Both metabolic and respiratory acidosis increased (P < 0.05) iCa values in nonclamped groups. In metabolic acidosis, the increase in iCa was progressive and greater (P < 0.05) than in respiratory acidosis, in which iCa increased by 0.04 mM and then remained constant despite further pH reductions. The increase in PTH values was greater (P < 0.05) in clamped than in nonclamped groups (metabolic and respiratory acidosis). In the nonclamped metabolic acidosis group, PTH values first increased and then decreased from peak values when iCa increased by > 0.1 mM. In the nonclamped respiratory acidosis group, PTH values exceeded (P < 0.05) baseline values only after iCa values stopped increasing at a pH of 7.30. For the same increase in iCa in the nonclamped groups, PTH values increased more in metabolic acidosis. In conclusion, 1) both metabolic acidosis and respiratory acidosis stimulate PTH secretion; 2) the physiological increase in the iCa concentration during the induction of metabolic and respiratory acidosis reduces the magnitude of the PTH increase; 3) in metabolic acidosis, the increase in the iCa concentration can be of sufficient magnitude to reverse the increase in PTH values; and 4) for the same degree of acidosis-induced hypercalcemia, the increase in PTH values is greater in metabolic than in respiratory acidosis.

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

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.

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.

Extracranial internal carotid artery dissection caused by compression from a giant osteophyte due to atlantoaxial osteoarthritis: case report.

PubMed

Ikedo, Taichi; Nakamura, Kazuhito; Sano, Noritaka; Nagata, Manabu; Okada, Yumiko; Kawakami, Taichiro; Murata, Takaho

2017-10-01

Deformed osseous structures have been reported as rare causes of extracranial internal carotid artery (ICA) dissection, including the styloid process and the hyoid bone. Here, the authors describe the first known case of symptomatic ICA dissection caused by a giant osteophyte due to atlantoaxial osteoarthritis. The left ICA was fixed at the skull base and at the ICA portion compressed by the osteophyte, and it was highly stretched and injured between the two portions during neck rotation. The patient was successfully treated with ligation of the affected ICA following balloon test occlusion. Atlantoaxial osteoarthritis should be considered in the differential diagnosis of ICA dissection in patients with a severely deformed cervical spine.

Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study.

PubMed

Yu, Qingbao; Du, Yuhui; Chen, Jiayu; He, Hao; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D

2017-11-01

A key challenge in building a brain graph using fMRI data is how to define the nodes. Spatial brain components estimated by independent components analysis (ICA) and regions of interest (ROIs) determined by brain atlas are two popular methods to define nodes in brain graphs. It is difficult to evaluate which method is better in real fMRI data. Here we perform a simulation study and evaluate the accuracies of a few graph metrics in graphs with nodes of ICA components, ROIs, or modified ROIs in four simulation scenarios. Graph measures with ICA nodes are more accurate than graphs with ROI nodes in all cases. Graph measures with modified ROI nodes are modulated by artifacts. The correlations of graph metrics across subjects between graphs with ICA nodes and ground truth are higher than the correlations between graphs with ROI nodes and ground truth in scenarios with large overlapped spatial sources. Moreover, moving the location of ROIs would largely decrease the correlations in all scenarios. Evaluating graphs with different nodes is promising in simulated data rather than real data because different scenarios can be simulated and measures of different graphs can be compared with a known ground truth. Since ROIs defined using brain atlas may not correspond well to real functional boundaries, overall findings of this work suggest that it is more appropriate to define nodes using data-driven ICA than ROI approaches in real fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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.

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.

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

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.

Serum ionized calcium in dogs with chronic renal failure and metabolic acidosis.

PubMed

Kogika, Marcia M; Lustoza, Marcio D; Notomi, Marcia K; Wirthl, Vera A B F; Mirandola, Regina M S; Hagiwara, Mitika K

2006-12-01

Chronic renal failure (CRF) is a common disease in dogs, and many metabolic disorders can be observed, including metabolic acidosis and calcium and phosphorus disturbances. Acidosis may change the ionized calcium (i-Ca) fraction, usually increasing its concentration. In this study we evaluated the influence of acidosis on the serum concentration of i-Ca in dogs with CRF and metabolic acidosis. Dogs were studied in 2 groups: group I (control group = 40 clinically normal dogs) and group II (25 dogs with CRF and metabolic acidosis). Serum i-Ca was measured by an ion-selective electrode method; other biochemical analytes were measured using routine methods. The i-Ca concentration was significantly lower in dogs in group II than in group I; 56% of the dogs in group II were hypocalcemic. Hypocalcemia was observed in only 8% of dogs in group II when based on total calcium (t-Ca) concentration. No correlation between pH and i-Ca concentration was observed. A slight but significant correlation was detected between i-Ca and serum phosphorus concentration (r = -.284; P = .022), as well as between serum t-Ca and i-Ca concentration (r = .497; P < .0001). The i-Ca concentration in dogs with CRF and metabolic acidosis varied widely from that of t-Ca, showing the importance of determining the biologically active form of calcium. Metabolic acidosis did not influence the increase in i-Ca concentration, so other factors besides acidosis in CRF might alter the i-Ca fraction, such as hyperphosphatemia and other compounds that may form complexes with calcium.

External carotid artery stenosis after internal and common carotid stenting.

PubMed

Siracuse, Jeffrey J; Epelboym, Irene; Li, Boyangzi; Hoque, Rahima; Catz, Diana; Morrissey, Nicholas J

2015-04-01

The external carotid artery (ECA) can be an important collateral for cerebral perfusion in the presence of severe internal carotid artery (ICA) disease. ICA stenting that covers the ECA origin may put the ECA at increased risk of stenosis. Our objective was to determine the rate of ECA stenosis secondary to ICA stenting, determine predictive factors, and describe any subsequent associated symptoms. We retrospectively reviewed clinical data on all ICA stents crossing the origin of the ECA placed by vascular surgeons at our institution. We analyzed patient demographics, comorbidities, stent type and sizes, as well as medication profile to determine predictors of ECA stenosis. Between 2005 and 2013, there were 72 (out of 119 total ICA stenting) patients (mean age 71, 68% male) who underwent placement of ICA stents that also crossed the origin of the ECA. Six patients (8.3%) had a significantly increased ECA stenosis postprocedure. There were no occlusions. All patients with ECA stenosis maintained patency of their ICA stent and were asymptomatic. Age, gender, comorbidities, stent type and size, and medication profile were not associated with ECA stenosis after stenting. ECA stenosis after ICA stenting covering the ECA origin is uncommon and not clinically significant in patients with patent ICA stents. The clinical significance of concurrent ECA and ICA stenosis after stenting is unclear as it is not captured here. The potential for ECA stenosis should not deter stenting across the ECA origin if necessary. Patient and stent factors are not predictive of ECA stenosis. Copyright © 2015 Elsevier Inc. All rights reserved.

Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies vs. usual care in patients with suspected coronary artery disease: the prospective longitudinal trial of FFR(CT): outcome and resource impacts study.

PubMed

Douglas, Pamela S; Pontone, Gianluca; Hlatky, Mark A; Patel, Manesh R; Norgaard, Bjarne L; Byrne, Robert A; Curzen, Nick; Purcell, Ian; Gutberlet, Matthias; Rioufol, Gilles; Hink, Ulrich; Schuchlenz, Herwig Walter; Feuchtner, Gudrun; Gilard, Martine; Andreini, Daniele; Jensen, Jesper M; Hadamitzky, Martin; Chiswell, Karen; Cyr, Derek; Wilk, Alan; Wang, Furong; Rogers, Campbell; De Bruyne, Bernard

2015-12-14

In symptomatic patients with suspected coronary artery disease (CAD), computed tomographic angiography (CTA) improves patient selection for invasive coronary angiography (ICA) compared with functional testing. The impact of measuring fractional flow reserve by CTA (FFRCT) is unknown. At 11 sites, 584 patients with new onset chest pain were prospectively assigned to receive either usual testing (n = 287) or CTA/FFR(CT) (n = 297). Test interpretation and care decisions were made by the clinical care team. The primary endpoint was the percentage of those with planned ICA in whom no significant obstructive CAD (no stenosis ≥50% by core laboratory quantitative analysis or invasive FFR < 0.80) was found at ICA within 90 days. Secondary endpoints including death, myocardial infarction, and unplanned revascularization were independently and blindly adjudicated. Subjects averaged 61 ± 11 years of age, 40% were female, and the mean pre-test probability of obstructive CAD was 49 ± 17%. Among those with intended ICA (FFR(CT)-guided = 193; usual care = 187), no obstructive CAD was found at ICA in 24 (12%) in the CTA/FFR(CT) arm and 137 (73%) in the usual care arm (risk difference 61%, 95% confidence interval 53-69, P< 0.0001), with similar mean cumulative radiation exposure (9.9 vs. 9.4 mSv, P = 0.20). Invasive coronary angiography was cancelled in 61% after receiving CTA/FFR(CT) results. Among those with intended non-invasive testing, the rates of finding no obstructive CAD at ICA were 13% (CTA/FFR(CT)) and 6% (usual care; P = 0.95). Clinical event rates within 90 days were low in usual care and CTA/FFR(CT) arms. Computed tomographic angiography/fractional flow reserve by CTA was a feasible and safe alternative to ICA and was associated with a significantly lower rate of invasive angiography showing no obstructive CAD. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Cardiology.

Stabilizing contact resistance of isotropically conductive adhesives on various metal surfaces by incorporating sacrificial anode materials

NASA Astrophysics Data System (ADS)

Moon, Kyoung-Sik; Liong, Silvia; Li, Haiying; Wong, C. P.

2004-11-01

The contact resistance stability of isotropically conductive adhesives (ICAs) on non-noble metal surfaces under the 85°C/85% relative humidity (RH) aging test was investigated. Previously, we demonstrated that galvanic corrosion has been shown as the main mechanism of the unstable contact resistance of ICAs on non-noble metal surfaces. A sacrificial anode was introduced into the ICA joint for cathodic protection. Zinc, chromium, and magnesium were employed in the ICA formulations as sacrificial anode materials that have much lower electrode-potential values than the metal pad surface, such as tin or tin-based alloys. The effect of particle sizes and loading levels of sacrificial anode materials were studied. Chromium was not as effective in suppressing corrosion as magnesium or zinc because of its strong tendency to self-passivate. The corrosion potential of ICAs was reduced by half with the addition of zinc and magnesium into the ICA formulation. The addition of zinc and magnesium was very effective in controlling galvanic corrosion that takes place in the ICA joints, resulting in stabilized contact resistance of ICAs on Sn, SnPb, and SnAgCu surfaces during the 85°C/85% RH aging test.

Evidence of stimulus correlated empathy modes--Group ICA of fMRI data.

PubMed

Vemuri, Kavita; Surampudi, Bapi Raju

2015-03-01

The analysis of the cognitive processes in response to a narrative as presented in a movie provides an insight into momentary reaction to a single depicted action like a facial expression and an aggregate processing of the entire sequence of events. In this study we report results from fMRI data analyzed by group independent component analysis (ICA) method from a free viewing experiment using a diverse set of movie clips--an animation, a Hollywood and an Indian Hindi movie. The fMRI data were collected from 15 college students as they viewed 5-8 min clips from three movies. The movie clips were rated for depiction of emotional expressions, the emotion as per the narrative and the viewer's own empathy response. The neural correlates attributed to cognitive, motor and emotional empathy were the focus of the study. The methodology of using long duration stimuli in free viewing mode combined with ICA analysis has the potential to tease out spatially distributed but temporally coherent brain activity as demonstrated in this study. The independent components obtained from group ICA method isolated spatial maps with activations which can be safely ascribed to stimulus processing. We found that the activity in the areas attributable to cognitive and motor empathy was comparable for all the three stimuli while certain critical areas for emotional empathy were not noticed for the animation movie. These findings lead to interesting questions on possible differential emotion response in viewer(s) for computer generated actors compared to actors in live-action movies and the role of narrative and exposure to different genre of movies on racial, ethnic and cultural differences in empathy response. Copyright © 2015 Elsevier Inc. All rights reserved.

Alcohol Dose Effects on Brain Circuits During Simulated Driving: An fMRI Study

PubMed Central

Meda, Shashwath A.; Calhoun, Vince D.; Astur, Robert S.; Turner, Beth M.; Ruopp, Kathryn; Pearlson, Godfrey D.

2009-01-01

Driving while intoxicated remains a major public health hazard. Driving is a complex task involving simultaneous recruitment of multiple cognitive functions. The investigators studied the neural substrates of driving and their response to different blood alcohol concentrations (BACs), using functional magnetic resonance imaging (fMRI) and a virtual reality driving simulator. We used independent component analysis (ICA) to isolate spatially independent and temporally correlated driving-related brain circuits in 40 healthy, adult moderate social drinkers. Each subject received three individualized, separate single-blind doses of beverage alcohol to produce BACs of 0.05% (moderate), 0.10% (high), or 0% (placebo). 3 T fMRI scanning and continuous behavioral measurement occurred during simulated driving. Brain function was assessed and compared using both ICA and a conventional general linear model (GLM) analysis. ICA results replicated and significantly extended our previous 1.5T study (Calhoun et al. [2004a]: Neuropsychopharmacology 29:2097–2017). GLM analysis revealed significant dose-related functional differences, complementing ICA data. Driving behaviors including opposite white line crossings and mean speed independently demonstrated significant dose-dependent changes. Behavior-based factors also predicted a frontal-basal-temporal circuit to be functionally impaired with alcohol dosage across baseline scaled, good versus poorly performing drivers. We report neural correlates of driving behavior and found dose-related spatio-temporal disruptions in critical driving-associated regions including the superior, middle and orbito frontal gyri, anterior cingulate, primary/supplementary motor areas, basal ganglia, and cerebellum. Overall, results suggest that alcohol (especially at high doses) causes significant impairment of both driving behavior and brain functionality related to motor planning and control, goal directedness, error monitoring, and memory. PMID:18571794

A network analysis of ¹⁵O-H₂O PET reveals deep brain stimulation effects on brain network of Parkinson's disease.

PubMed

Park, Hae-Jeong; Park, Bumhee; Kim, Hae Yu; Oh, Maeng-Keun; Kim, Joong Il; Yoon, Misun; Lee, Jong Doo; Chang, Jin Woo

2015-05-01

As Parkinson's disease (PD) can be considered a network abnormality, the effects of deep brain stimulation (DBS) need to be investigated in the aspect of networks. This study aimed to examine how DBS of the bilateral subthalamic nucleus (STN) affects the motor networks of patients with idiopathic PD during motor performance and to show the feasibility of the network analysis using cross-sectional positron emission tomography (PET) images in DBS studies. We obtained [¹⁵O]H₂O PET images from ten patients with PD during a sequential finger-to-thumb opposition task and during the resting state, with DBS-On and DBS-Off at STN. To identify the alteration of motor networks in PD and their changes due to STN-DBS, we applied independent component analysis (ICA) to all the cross-sectional PET images. We analysed the strength of each component according to DBS effects, task effects and interaction effects. ICA blindly decomposed components of functionally associated distributed clusters, which were comparable to the results of univariate statistical parametric mapping. ICA further revealed that STN-DBS modifies usage-strengths of components corresponding to the basal ganglia-thalamo-cortical circuits in PD patients by increasing the hypoactive basal ganglia and by suppressing the hyperactive cortical motor areas, ventrolateral thalamus and cerebellum. Our results suggest that STN-DBS may affect not only the abnormal local activity, but also alter brain networks in patients with PD. This study also demonstrated the usefulness of ICA for cross-sectional PET data to reveal network modifications due to DBS, which was not observable using the subtraction method.

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.

Juvenile nasopharyngeal angiofibroma: vascular determinates for operative complications and tumor recurrence.

PubMed

Chan, Kenny H; Gao, Dexiang; Fernandez, Patrick G; Kingdom, Todd T; Kumpe, David A

2014-03-01

Operative complications and tumor recurrence in juvenile nasopharyngeal angiofibroma (JNA) are measurable and meaningful outcomes. This study aimed to assess the association of these two outcomes to various clinical indices and in particular, vascular determinates. Retrospective cohort study. An 18-year retrospective chart review of an academic tertiary center was undertaken. Data from clinical notes, imaging studies, and arteriograms were analyzed. Thirty-seven male (mean age, 14.4 years) patients were included in the study. Tumor stages included: IA (three), IB (three), IIA (14), IIB (three), IIC (five), IIIA (five), and IIIB (four). Four complications (cerebrospinal fluid leak, cerebral vascular accident, and two transient ocular defects) occurred. Eight recurrences occurred within 24 months following surgery. Complications were associated with estimated intraoperative blood loss (EBL) (P = .045). Tumor recurrence was associated with feeding vessels from the contralateral internal carotid artery (ICA) (P = .017). EBL was significantly associated with surgical technique used. EBL, tumor stage, and tumor vascular supply were significantly associated with each other. Vascular factors were associated with JNA complication and tumor recurrence. EBL might affect complications, and contralateral ICA as a feeding vessel might affect recurrence. EBL was influenced by procedure choice and was interrelated to size and vascular supply of the tumor. This study bolsters the need to decrease intraoperative blood loss by preoperative embolization and use of endoscopic removal techniques. Furthermore, when branches of the ICA are found to be feeding vessels, greater surgical attention for a dry surgical field is encouraged. © 2013 The American Laryngological, Rhinological and Otological Society, Inc.

Electrophysiological effects of calcitonin gene-related peptide in bull-frog and guinea-pig atrial myocytes.

PubMed Central

Ono, K; Giles, W R

1991-01-01

1. Electrophysiological effects of calcitonin gene-related peptide (CGRP) on action potentials and corresponding transmembrane currents in single myocytes from bull-frog and guinea-pig atria were studied using a whole-cell voltage-clamp method. 2. CGRP at relatively low concentrations increased the height of the action potential plateau in a dose-dependent manner in both bull-frog and guinea-pig myocytes. In addition, in bull-frog cells CGRP accelerated the early phase of repolarization, thus shortening the overall duration of the action potential. In contrast, in guinea-pig myocytes CGRP prolonged the action potential duration at all concentrations that were studied. 3. Voltage-clamp measurements demonstrated that CGRP increased transmembrane calcium current (ICa) in guinea-pig myocytes without a significant change in its voltage dependence. The ED50 value for this effect on ICa was 1.28 +/- 0.55 X 10(-8) M (n = 4). The time course of the inactivation of ICa was not affected by CGRP. 4. CGRP increased the delayed rectifier K+ current (IK) at relatively low concentrations in bull-frog atria, whereas relatively high concentrations were needed to increase IK in guinea-pig myocytes. This effect was observed even after complete inhibition of ICa. 5. CGRP had no significant effect on the inwardly rectifying background K+ current, IK1, even at very high concentrations. 6. Comparison of the time course of ICa augmentation in bull-frog and guinea-pig myocytes revealed an important difference in the effect of CGRP in these two types of cells. CGRP at maximal concentrations increased ICa transiently in bull-frog myocytes, whereas this response was sustained in guinea-pig myocytes. Isoprenaline (Iso) induced sustained increase in ICa in both species. When ICa was fully activated by Iso, CGRP at high concentrations strongly inhibited ICa in the bull-frog, whereas it had little effect on ICa in guinea-pig myocytes. 7. Intracellular application of GTP gamma S (guanosine 5'-O-(3-thiotriphosphate) 10(-4) M) greatly potentiated the CGRP effect on ICa; in contrast, GDP beta S (guanosine 5'-O-(2-thiodiphosphate), 2 x 10(-3) M) partially inhibited the CGRP-induced augmentation of ICa. Taken together, these results indicate that the stimulation of ICa by CGRP is mediated by a GTP-binding protein. 8. The observed dose-dependent changes in ICa and IK in bull-frog and guinea-pig myocytes can explain the different patterns of CGRP-induced changes in action potential shape in these two myocyte preparations. PMID:1905755

A Novel Anti-Spoofing Solution for Iris Recognition Toward Cosmetic Contact Lens Attack Using Spectral ICA Analysis

PubMed Central

Hsieh, Sheng-Hsun; Wang, Wei; Tien, Chung-Hao

2018-01-01

In this study, we maneuvered a dual-band spectral imaging system to capture an iridal image from a cosmetic-contact-lens-wearing subject. By using the independent component analysis to separate individual spectral primitives, we successfully distinguished the natural iris texture from the cosmetic contact lens (CCL) pattern, and restored the genuine iris patterns from the CCL-polluted image. Based on a database containing 200 test image pairs from 20 CCL-wearing subjects as the proof of concept, the recognition accuracy (False Rejection Rate: FRR) was improved from FRR = 10.52% to FRR = 0.57% with the proposed ICA anti-spoofing scheme. PMID:29509692

mGluR5 stimulating Homer–PIKE formation initiates icariin induced cardiomyogenesis of mouse embryonic stem cells by activating reactive oxygen species

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

Zhou, Limin; Huang, Yujie; Zhang, Yingying

2013-06-10

Icariin (ICA) has been reported to facilitate cardiac differentiation of mouse embryonic stem (ES) cells; however, the mechanism by which ICA induced cardiomyogenesis has not been fully elucidated yet. Here, an underlying signaling network including metabotropic glutamate receptor 5 (mGluR5), Homer, phosphatidylinositol 3-Kinase Enhancer (PIKE), phosphatidylinositol 3-Kinase (PI3K), reactive oxygen species (ROS) and nuclear factor-kappaB (NF-κB) was investigated in ICA induced cardiomyogenesis. Our results showed that the co-expression of mGluR5 together with α-actinin or Troponin T in embryoid bodies (EBs) treated with ICA was elevated to 10.86% and 9.62%, compared with the case in the control (4.04% and 3.45%, respectively).more » Exposure of EBs to ICA for 2 h remarkably increased the dimeric form of mGluR5, which was inhibited by small interfering RNA targeting mGluR5 (si-mGluR5). Moreover, the extracellular glutamate concentration in ICA treatment medium was elevated to 28.9±3.5 μM. Furthermore, the activation of mGluR5 by ICA triggered the formation of Homer–PIKE complex and activated PI3K, stimulating ROS generation and NF-κB nuclear translocation. Knockdown of mGluR5 or inhibition of PI3K by LY294002 blocked ICA induced cardiomyogenesis via repressing mGluR5 pathway, reducing ROS and NF-κB activation. These results revealed that the inducible mechanisms of ICA were related to activate mGluR5 pathway. -- Highlights: • ICA increased mGluR5 expression in cardiac differentiation of ES cells. • ICA enhanced the glutamate level and the receptor mGluR5 dimerization, stimulating the formation of Homer–PIKE complex. • Knockdown of mGluR5 or inhibition of PI3K by LY294002 inhibited ICA induced ROS generation and NF-κB nuclear translocation.« less

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.

Continuous functional magnetic resonance imaging reveals dynamic nonlinearities of "dose-response" curves for finger opposition.

PubMed

Berns, G S; Song, A W; Mao, H

1999-07-15

Linear experimental designs have dominated the field of functional neuroimaging, but although successful at mapping regions of relative brain activation, the technique assumes that both cognition and brain activation are linear processes. To test these assumptions, we performed a continuous functional magnetic resonance imaging (MRI) experiment of finger opposition. Subjects performed a visually paced bimanual finger-tapping task. The frequency of finger tapping was continuously varied between 1 and 5 Hz, without any rest blocks. After continuous acquisition of fMRI images, the task-related brain regions were identified with independent components analysis (ICA). When the time courses of the task-related components were plotted against tapping frequency, nonlinear "dose- response" curves were obtained for most subjects. Nonlinearities appeared in both the static and dynamic sense, with hysteresis being prominent in several subjects. The ICA decomposition also demonstrated the spatial dynamics with different components active at different times. These results suggest that the brain response to tapping frequency does not scale linearly, and that it is history-dependent even after accounting for the hemodynamic response function. This implies that finger tapping, as measured with fMRI, is a nonstationary process. When analyzed with a conventional general linear model, a strong correlation to tapping frequency was identified, but the spatiotemporal dynamics were not apparent.

``Seeing'' electroencephalogram through the skull: imaging prefrontal cortex with fast optical signal

NASA Astrophysics Data System (ADS)

Medvedev, Andrei V.; Kainerstorfer, Jana M.; Borisov, Sergey V.; Gandjbakhche, Amir H.; Vanmeter, John

2010-11-01

Near-infrared spectroscopy is a novel imaging technique potentially sensitive to both brain hemodynamics (slow signal) and neuronal activity (fast optical signal, FOS). The big challenge of measuring FOS noninvasively lies in the presumably low signal-to-noise ratio. Thus, detectability of the FOS has been controversially discussed. We present reliable detection of FOS from 11 individuals concurrently with electroencephalogram (EEG) during a Go-NoGo task. Probes were placed bilaterally over prefrontal cortex. Independent component analysis (ICA) was used for artifact removal. Correlation coefficient in the best correlated FOS-EEG ICA pairs was highly significant (p < 10-8), and event-related optical signal (EROS) was found in all subjects. Several EROS components were similar to the event-related potential (ERP) components. The most robust ``optical N200'' at t = 225 ms coincided with the N200 ERP; both signals showed significant difference between targets and nontargets, and their timing correlated with subject's reaction time. Correlation between FOS and EEG even in single trials provides further evidence that at least some FOS components ``reflect'' electrical brain processes directly. The data provide evidence for the early involvement of prefrontal cortex in rapid object recognition. EROS is highly localized and can provide cost-effective imaging tools for cortical mapping of cognitive processes.

“Seeing” electroencephalogram through the skull: imaging prefrontal cortex with fast optical signal

PubMed Central

Medvedev, Andrei V.; Kainerstorfer, Jana M.; Borisov, Sergey V.; Gandjbakhche, Amir H.; VanMeter, John

2010-01-01

Near-infrared spectroscopy is a novel imaging technique potentially sensitive to both brain hemodynamics (slow signal) and neuronal activity (fast optical signal, FOS). The big challenge of measuring FOS noninvasively lies in the presumably low signal-to-noise ratio. Thus, detectability of the FOS has been controversially discussed. We present reliable detection of FOS from 11 individuals concurrently with electroencephalogram (EEG) during a Go-NoGo task. Probes were placed bilaterally over prefrontal cortex. Independent component analysis (ICA) was used for artifact removal. Correlation coefficient in the best correlated FOS–EEG ICA pairs was highly significant (p < 10−8), and event-related optical signal (EROS) was found in all subjects. Several EROS components were similar to the event-related potential (ERP) components. The most robust “optical N200” at t = 225 ms coincided with the N200 ERP; both signals showed significant difference between targets and nontargets, and their timing correlated with subject’s reaction time. Correlation between FOS and EEG even in single trials provides further evidence that at least some FOS components “reflect” electrical brain processes directly. The data provide evidence for the early involvement of prefrontal cortex in rapid object recognition. EROS is highly localized and can provide cost-effective imaging tools for cortical mapping of cognitive processes. PMID:21198150

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.

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.

Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition.

PubMed

Al-Qazzaz, Noor Kamal; Ali, Sawal; Ahmad, Siti Anom; Escudero, Javier

2017-07-01

The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA) and wavelet transform (WT) as a hybrid preprocessing approach for EEG artifact removal. Three different features were extracted from the cleaned EEG signals: spectral entropy (SpecEn), permutation entropy (PerEn) and Tsallis entropy (TsEn). Two classification schemes were applied - support vector machine (SVM) and k-nearest neighbors (kNN) - with fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique. The FNPAQR dimensionality reduction technique increased the SVM classification accuracy from 82.22% to 90.37% and from 82.6% to 86.67% for kNN. These results suggest that FNPAQR consistently improves the discrimination of VaD, MCI patients and control normal subjects and it could be a useful feature selection to help the identification of patients with VaD and MCI.

Resting-State Functional Magnetic Resonance Imaging for Language Preoperative Planning

PubMed Central

Branco, Paulo; Seixas, Daniela; Deprez, Sabine; Kovacs, Silvia; Peeters, Ronald; Castro, São L.; Sunaert, Stefan

2016-01-01

Functional magnetic resonance imaging (fMRI) is a well-known non-invasive technique for the study of brain function. One of its most common clinical applications is preoperative language mapping, essential for the preservation of function in neurosurgical patients. Typically, fMRI is used to track task-related activity, but poor task performance and movement artifacts can be critical limitations in clinical settings. Recent advances in resting-state protocols open new possibilities for pre-surgical mapping of language potentially overcoming these limitations. To test the feasibility of using resting-state fMRI instead of conventional active task-based protocols, we compared results from fifteen patients with brain lesions while performing a verb-to-noun generation task and while at rest. Task-activity was measured using a general linear model analysis and independent component analysis (ICA). Resting-state networks were extracted using ICA and further classified in two ways: manually by an expert and by using an automated template matching procedure. The results revealed that the automated classification procedure correctly identified language networks as compared to the expert manual classification. We found a good overlay between task-related activity and resting-state language maps, particularly within the language regions of interest. Furthermore, resting-state language maps were as sensitive as task-related maps, and had higher specificity. Our findings suggest that resting-state protocols may be suitable to map language networks in a quick and clinically efficient way. PMID:26869899

Enhanced subject-specific resting-state network detection and extraction with fast fMRI.

PubMed

Akin, Burak; Lee, Hsu-Lei; Hennig, Jürgen; LeVan, Pierre

2017-02-01

Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Self-sustained vibrations in volcanic areas extracted by Independent Component Analysis: a review and new results

NASA Astrophysics Data System (ADS)

de Lauro, E.; de Martino, S.; Falanga, M.; Palo, M.

2011-12-01

We investigate the physical processes associated with volcanic tremor and explosions. A volcano is a complex system where a fluid source interacts with the solid edifice so generating seismic waves in a regime of low turbulence. Although the complex behavior escapes a simple universal description, the phases of activity generate stable (self-sustained) oscillations that can be described as a non-linear dynamical system of low dimensionality. So, the system requires to be investigated with non-linear methods able to individuate, decompose, and extract the main characteristics of the phenomenon. Independent Component Analysis (ICA), an entropy-based technique is a good candidate for this purpose. Here, we review the results of ICA applied to seismic signals acquired in some volcanic areas. We emphasize analogies and differences among the self-oscillations individuated in three cases: Stromboli (Italy), Erebus (Antarctica) and Volcán de Colima (Mexico). The waveforms of the extracted independent components are specific for each volcano, whereas the similarity can be ascribed to a very general common source mechanism involving the interaction between gas/magma flow and solid structures (the volcanic edifice). Indeed, chocking phenomena or inhomogeneities in the volcanic cavity can play the same role in generating self-oscillations as the languid and the reed do in musical instruments. The understanding of these background oscillations is relevant not only for explaining the volcanic source process and to make a forecast into the future, but sheds light on the physics of complex systems developing low turbulence.

Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex.

PubMed

Yeo, B T Thomas; Krienen, Fenna M; Chee, Michael W L; Buckner, Randy L

2014-03-01

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP. © 2013.

Estimates of Segregation and Overlap of Functional Connectivity Networks in the Human Cerebral Cortex

PubMed Central

Yeo, BT Thomas; Krienen, Fenna M; Chee, Michael WL; Buckner, Randy L

2014-01-01

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1,000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP. PMID:24185018

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.

Predictors of antegrade flow at internal carotid artery during carotid artery stenting with proximal protection.

PubMed

Harada, Kei; Kakumoto, Kosuke; Oshikata, Shogo; Fukuyama, Kozo

2018-06-01

Carotid artery stenting (CAS) with proximal occlusion effectively prevent distal cerebral embolism by flow arrest at internal carotid artery (ICA); however, the method can expose antegrade flow at ICA due to incomplete flow arrest. The aim of this study was to identify predictors of antegrade flow during CAS with proximal protection. We retrospectively analyzed clinical and angiographic data among 143 lesions treated with CAS with proximal protection by occluding the common carotid artery (CCA) and external carotid artery (ECA). Flow arrest or antegrade flow at ICA was confirmed by contrast injection during proximal protection. Antegrade flow at ICA was observed in 12 lesions (8.4%). Compared with lesions in which flow arrest of ICA was achieved, the diameter of the superior thyroid artery (STA) was significantly larger (2.4 ± 0.34 vs. 1.4 ± 0.68 mm, p < 0.001), and the rate of ECA branches other than the STA located 0-10 mm above the bifurcation was significantly higher (50 vs. 8.4%, p < 0.001). Results of multivariate analysis revealed that a diameter of the STA ≥ 2.3 mm (OR 44, 95% CI 8.1-237; p < 0.001) and ECA branches other than the STA located 0-10 mm above the bifurcation (OR 6.0, 95% CI 1.1-32; p = 0.036) were independent predictors of antegrade flow. Distal filter protection should be combined with proximal protection for the lesions with antegrade flow to prevent distal migration of the carotid debris.

Specific Immunoassays for Placental Alkaline Phosphatase As a Tumor Marker

PubMed Central

Stinghen, Sérvio T.; Moura, Juliana F.; Zancanella, Patrícia; Rodrigues, Giovanna A.; Pianovski, Mara A.; Lalli, Enzo; Arnold, Dodie L.; Minozzo, João C.; Callefe, Luis G.; Ribeiro, Raul C.; Figueiredo, Bonald C.

2006-01-01

Human placental (hPLAP) and germ cell (PLAP-like) alkaline phosphatases are polymorphic and heat-stable enzymes. This study was designed to develop specific immunoassays for quantifying hPLAP and PLAP-like enzyme activity (EA) in sera of cancer patients, pregnant women, or smokers. Polyclonal sheep anti-hPLAP antibodies were purified by affinity chromatography with whole hPLAP protein (ICA-PLAP assay) or a synthetic peptide (aa 57–71) of hPLAP (ICA-PEP assay); the working range was 0.1–11 U/L and cutoff value was 0.2 U/L EA for nonsmokers. The intra- and interassay coefficients of variation were 3.7%–6.5% (ICA-PLAP assay) and 9.0%–9.9% (ICA-PEP assay). An insignificant cross-reactivity was noted for high levels of unheated intestinal alkaline phosphatase in ICA-PEP assay. A positive correlation between the regression of tumor size and EA was noted in a child with embryonal carcinoma. It can be concluded that ICA-PEP assay is more specific than ICA-PLAP, which is still useful to detect other PLAP/PLAP-like phenotypes. PMID:17489017

Risk factors for neurological worsening and symptomatic watershed infarction in internal carotid artery aneurysm treated by extracranial-intracranial bypass using radial artery graft.

PubMed

Matsukawa, Hidetoshi; Tanikawa, Rokuya; Kamiyama, Hiroyasu; Tsuboi, Toshiyuki; Noda, Kosumo; Ota, Nakao; Miyata, Shiro; Oda, Jumpei; Takeda, Rihee; Tokuda, Sadahisa; Kamada, Kyousuke

2016-08-01

OBJECT The revascularization technique, including bypass created using the external carotid artery (ECA), radial artery (RA), and M2 portion of middle cerebral artery (MCA), has remained indispensable for treatment of complex aneurysms. To date, it remains unknown whether diameters of the RA, superficial temporal artery (STA), and C2 portion of the internal carotid artery (ICA) and intraoperative MCA blood pressure have influences on the outcome and the symptomatic watershed infarction (WI). The aim of the present study was to evaluate the factors for the symptomatic WI and neurological worsening in patients treated by ECA-RA-M2 bypass for complex ICA aneurysm with therapeutic ICA occlusion. METHODS The authors measured the sizes of vessels (RA, C2, M2, and STA) and intraoperative MCA blood pressure (initial, after ICA occlusion, and after releasing the RA graft bypass) in 37 patients. Symptomatic WI was defined as presence of the following: postoperative new neurological deficits, WI on postoperative diffusion-weighted imaging, and ipsilateral cerebral blood flow reduction on SPECT. Neurological worsening was defined as the increase in 1 or more modified Rankin Scale scores. First, the authors performed receiver operating characteristic curve analysis for continuous variables and the binary end point of the symptomatic WI. The clinical, radiological, and physiological characteristics of patients with and without the symptomatic WI were compared using the log-rank test. Then, the authors compared the variables between patients with and without neurological worsening at discharge and at the 12-month follow-up examination or last hospital visit. RESULTS Symptomatic WI was observed in 2 (5.4%) patients. The mean MCA pressure after releasing the RA graft (< 55 mm Hg; p = 0.017), mean (MCA pressure after releasing the RA graft)/(initial MCA pressure) (< 0.70 mm Hg; p = 0.032), and mean cross-sectional area ratio ([RA/C2 diameter](2) < 0.40 mm [p < 0.0001] and [STA/C2 diameter](2) < 0.044 mm [p < 0.0001]) were related to the symptomatic WI. All preoperatively independent patients remained independent (modified Rankin Scale score < 3). After adjusting for age and sex, left operative side (p = 0.0090 and 0.038) and perforating artery ischemia (p = 0.0050 and 0.022) were related to neurological worsening at discharge (11 [29%] patients) and at the 12-month follow-up or last hospital visit (8 [22%] patients). CONCLUSIONS Results of the present study showed that the vessel diameter and intraoperative MCA pressure had impacts on the symptomatic WI and that operative side and perforating artery ischemia were related to neurological worsening in patients with complex ICA aneurysms treated by ECA-RA-M2 bypass.

Extracting factors for interest rate scenarios

NASA Astrophysics Data System (ADS)

Molgedey, L.; Galic, E.

2001-04-01

Factor based interest rate models are widely used for risk managing purposes, for option pricing and for identifying and capturing yield curve anomalies. The movements of a term structure of interest rates are commonly assumed to be driven by a small number of orthogonal factors such as SHIFT, TWIST and BUTTERFLY (BOW). These factors are usually obtained by a Principal Component Analysis (PCA) of historical bond prices (interest rates). Although PCA diagonalizes the covariance matrix of either the interest rates or the interest rate changes, it does not use both covariance matrices simultaneously. Furthermore higher linear and nonlinear correlations are neglected. These correlations as well as the mean reverting properties of the interest rates become crucial, if one is interested in a longer time horizon (infrequent hedging or trading). We will show that Independent Component Analysis (ICA) is a more appropriate tool than PCA, since ICA uses the covariance matrix of the interest rates as well as the covariance matrix of the interest rate changes simultaneously. Additionally higher linear and nonlinear correlations may be easily incorporated. The resulting factors are uncorrelated for various time delays, approximately independent but nonorthogonal. This is in contrast to the factors obtained from the PCA, which are orthogonal and uncorrelated for identical times only. Although factors from the ICA are nonorthogonal, it is sufficient to consider only a few factors in order to explain most of the variation in the original data. Finally we will present examples that ICA based hedges outperforms PCA based hedges specifically if the portfolio is sensitive to structural changes of the yield curve.

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

PubMed

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

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.

[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.

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.

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.

Dual-energy x-ray image decomposition by independent component analysis

NASA Astrophysics Data System (ADS)

Jiang, Yifeng; Jiang, Dazong; Zhang, Feng; Zhang, Dengfu; Lin, Gang

2001-09-01

The spatial distributions of bone and soft tissue in human body are separated by independent component analysis (ICA) of dual-energy x-ray images. It is because of the dual energy imaging modelí-s conformity to the ICA model that we can apply this method: (1) the absorption in body is mainly caused by photoelectric absorption and Compton scattering; (2) they take place simultaneously but are mutually independent; and (3) for monochromatic x-ray sources the total attenuation is achieved by linear combination of these two absorption. Compared with the conventional method, the proposed one needs no priori information about the accurate x-ray energy magnitude for imaging, while the results of the separation agree well with the conventional one.

Icariin combined with human umbilical cord mesenchymal stem cells significantly improve the impaired kidney function in chronic renal failure.

PubMed

Li, Wen; Wang, Li; Chu, Xiaoqian; Cui, Huantian; Bian, Yuhong

2017-04-01

At present, the main therapy for chronic renal failure (CRF) is dialysis and renal transplantation, but neither obtains satisfactory results. Human umbilical cord mesenchymal stem cells (huMSCs) are isolated from the fetal umbilical cord which has a high self-renewal and multi-directional differentiation potential. Icariin (ICA), a kidney-tonifying Chinese Medicine can enhance the multipotency of huMSCs. Therefore, this work seeks to employ the use of ICA-treated huMSCs for the treatment of chronic renal failure. Blood urea nitrogen and creatinine (Cr) analyses showed amelioration of functional parameters in ICA-treated huMSCs for the treatment of CRF rats at 3, 7, and 14 days after transplantation. ICA-treated huMSCs can obviously increase the number of cells in injured renal tissues at 3, 7, and 14 days after transplantation by optical molecular imaging system. Hematoxylin-eosin staining demonstrated that ICA-treated huMSCs reduced the levels of fibrosis in CRF rats at 14 days after transplantation. Superoxide dismutase and Malondialdehyde analyses showed that ICA-treated huMSCs reduced the oxidative damage in CRF rats. Moreover, transplantation with ICA-treated huMSCs decreased inflammatory responses, promoted the expression of growth factors, and protected injured renal tissues. Taken together, our findings suggest that ICA-treated huMSCs could improve the kidney function in CRF rats.

Analysis on unevenness of skin color using the melanin and hemoglobin components separated by independent component analysis of skin color image

NASA Astrophysics Data System (ADS)

Ojima, Nobutoshi; Fujiwara, Izumi; Inoue, Yayoi; Tsumura, Norimichi; Nakaguchi, Toshiya; Iwata, Kayoko

2011-03-01

Uneven distribution of skin color is one of the biggest concerns about facial skin appearance. Recently several techniques to analyze skin color have been introduced by separating skin color information into chromophore components, such as melanin and hemoglobin. However, there are not many reports on quantitative analysis of unevenness of skin color by considering type of chromophore, clusters of different sizes and concentration of the each chromophore. We propose a new image analysis and simulation method based on chromophore analysis and spatial frequency analysis. This method is mainly composed of three techniques: independent component analysis (ICA) to extract hemoglobin and melanin chromophores from a single skin color image, an image pyramid technique which decomposes each chromophore into multi-resolution images, which can be used for identifying different sizes of clusters or spatial frequencies, and analysis of the histogram obtained from each multi-resolution image to extract unevenness parameters. As the application of the method, we also introduce an image processing technique to change unevenness of melanin component. As the result, the method showed high capabilities to analyze unevenness of each skin chromophore: 1) Vague unevenness on skin could be discriminated from noticeable pigmentation such as freckles or acne. 2) By analyzing the unevenness parameters obtained from each multi-resolution image for Japanese ladies, agerelated changes were observed in the parameters of middle spatial frequency. 3) An image processing system modulating the parameters was proposed to change unevenness of skin images along the axis of the obtained age-related change in real time.

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…

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.

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.

The blockade of excitation/contraction coupling by nifedipine in patch-clamped rat skeletal muscle cells in culture.

PubMed

Cognard, C; Rivet, M; Raymond, G

1990-04-01

The effects of the dihydropyridine derivative, nifedipine, well known as a blocker of calcium channels, were tested on cultured rat myoballs. Membrane currents and contractions were simultaneously recorded by means of the patch-clamp technique and a photoelectric transducing method. High concentrations of nifedipine (5 microM) inhibited the contractile responses and inward calcium current (ICa) elicited by long depolarizations. In the absence of ICa (1.5 mM cadmium in the bath), nifedipine inhibited both the ICa-independent contractile component and the outward current, supposed to depend on the intracellular calcium released during contraction. At low concentrations (0.5 microM) the blocking effects of nifedipine could be strongly enhanced by shifting the membrane potential towards less negative values (-60 mV) for 50 s prior to the test pulse. A blocking effect of nifedipine, at a usually ineffective concentration (0.1 microM), could also be observed when long-lasting (3 min) prepulses to 0 mV were applied from a reference membrane potential of -60 mV. This effect could be relieved by long-lasting cell hyperpolarizations (-90 mV). The blocking effects of nifedipine unrelated to ICa could be interpreted as an action on a molecule (voltage sensor) in the T-tubule membrane involved in the excitation/contraction coupling process and as a preferential binding of the dihydropyridine derivative on the inactivated form of this molecule, favored by the weak negative potentials or long-lasting depolarizations. The results provide data in favor of the existence of strong similarities between the calcium channels and voltage sensors since their operation was inhibited in a voltage-dependent manner by nifedipine.

[Anthropometric measures of central abdominal fat and discriminant capacity for metabolic syndrome in a Spanish population].

PubMed

Bellido, Diego; López de la Torre, Martín; Carreira, José; de Luis, Daniel; Bellido, Virginia; Soto, Alfonso; Luengo, Luis M; Hernández, Antonio; Vidal, Josep; Becerra, Antonio; Ballesteros, María

2013-01-01

The metabolic syndrome (MS) carries an increased risk of cardiovascular disease and diabetes mellitus. Insulin resistance is probably the mechanism underlying the changes detected in lipid and carbohydrate metabolism in these patients, who have, as a common anthropometric feature, a predominantly increased abdominal fat distribution. A total of 3316 patients were studied, of whom 63.40% were female and 36.60 male, with a mean age of 42.36±14.63 years, and a body mass index (BMI) of 32.76±6.81kg/m(2). Weight, height and waist circumference (CC) were measured using standard techniques. The waist/height (ICA) was calculated using two indicators, expressed as waist in cm divided by height in m(2), and as waist divided by height, both in cm. The prevalence of metabolic syndrome in the sample was 33.70%. In order to assess the predictive ability of BMI, ICA and CC to detect the existence of MS, receiver operating curves (ROC) were constructed and the areas under the curve (AUC) calculated for each anthropometric parameter. An AUC of 0.724 (95%CI: 0.706 to 0.742), P<.001, was obtained for CC, 0.709 (95%CI: 0.691 to 0.728), P<.001 for ICA with height in m(2), and 0.729 (95%CI: 0.711 to 0.747), P<.001 for ICA with height in cm, and for the BMI it was 0.680 (95%CI 0.661-0.699), P<.001. Anthropometric indices that assess abdominal fat distribution have a better predictive capacity for detecting MS, compared to total adiposity indicators such as BMI. Copyright © 2013 Elsevier España, S.L. y SEA. All rights reserved.

Comparison of Biofilm Formation Capacities of Two Clinical Isolates of Staphylococcus Epidermidis with and without icaA and icaD Genes on Intraocular Lenses.

PubMed

Kıvanç, Sertaç Argun; Kıvanç, Merih; Kılıç, Volkan; Güllülü, Gülay; Özmen, Ahmet Tuncer

2017-04-01

To compare biofilm formations of two Staphylococcus epidermidis (S. epidermidis) isolates with known biofilm formation capacities on four different intraocular lenses (IOL) that have not been studied before. Two isolates obtained from ocular surfaces and identified in previous studies and stored at -86 °C in 15% glycerol in the microbiology laboratory of the Anadolu University Department of Biology were purified and used in the study. The isolates were S. epidermidis KA 15.8 (ICA+), a known biofilm producer isolate positive for icaA, icaD and bap genes, and S. epidermidis KA 14.5 (ICA-), known as a non-biofilm producer isolate negative for icaA, icaD and bap genes. The biofilm formation capacities of the 2 isolates on 4 different IOLs were compared. Two of the IOLs were acrylic (UD613 [IOL A], Turkey; SA60AT [IOL B], USA), and the other two were polymethyl methacrylate (PMMA) (B60130C [IOL C], India; B55125C [IOL D], India). Bacterial enumeration and optical density measurements were done from biofilms that formed on the IOLs. Biofilms were imaged using scanning electron microscopy. Mean bacterial counts on the IOLs were 7.1±0.4 log 10 CFU/mL with the ICA+ isolate, and 6.7±0.8 log 10 CFU/mL with the ICA- isolate; there were no statistically significant differences. Biofilm formation was lower with acrylic lenses than PMMA lenses with both isolates (p=0.009 and p=0.013). The highest biofilm production was obtained on IOL C (PMMA) (p<0.001) and the lowest was obtained on IOL A (hydrophilic acrylic) (p<0.001). Bacterial counts after biofilm formation were lower on acrylic lenses, especially hydrophilic acrylic with hydrophobic properties. Further animal and in vivo studies are required to support the findings of this study.

Transmembrane Na+ and Ca2+ electrochemical gradients in cardiac muscle and their relationship to force development

PubMed Central

1982-01-01

Na+- and CA2+-sensitive microelectrodes were used to measure intracellular Na+ and Ca2+ activities (alpha iCa) of sheep ventricular muscle and Purkinje strands to study the interrelationship between Na+ and Ca2+ electrochemical gradients (delta muNa and delta muCa) under various conditions. In ventricular muscle, alpha iNa was 6.4 +/- 1.2 mM and alpha iCa was 87 +/- 20 nM ([Ca/+] = 272 nM). A graded decrease of external Na+ activity (alpha oNa) resulted in decrease of alpha iNa, and increase of alpha iCa. There was increase of twitch tension in low- alpha oNa solutions, and occasional increase of resting tension in 40% alpha oNa. Increase of external Ca2+ (alpha oCa) resulted in increase of alpha iCa and decrease of alpha iNa. Decrease of alpha oCa resulted in decrease of alpha iCa and increase of alpha iNa. The apparent resting Na-Ca energy ratio (delta muCa/delta muNa) was between 2.43 and 2.63. When the membrane potential (Vm) was depolarized by 50 mM K+ in ventricular muscle, Vm depolarized by 50 mV, alpha iNa decreased, and alpha iCa increased, with the development of a contracture. The apparent energy coupling ratio did not change with depolarization. 5 x 10(-6) M ouabain induced a large increase in alpha iNa ad alpha iCa, accompanied by an increase in twitch and resting tension. Under the conditions we have studied, delta muNa and delta muCa appeared to be coupled and n was nearly constant at 2.5, as would be expected if the Na-Ca exchange system was able to set the steady level of alpha iCa. Tension threshold was about 230 nM alpha iCa. The magnitude of twitch tension was directly related to alpha iCa. PMID:6292328

Comparison of Biofilm Formation Capacities of Two Clinical Isolates of Staphylococcus Epidermidis with and without icaA and icaD Genes on Intraocular Lenses

PubMed Central

Kıvanç, Sertaç Argun; Kıvanç, Merih; Kılıç, Volkan; Güllülü, Gülay; Özmen, Ahmet Tuncer

2017-01-01

Objectives: To compare biofilm formations of two Staphylococcus epidermidis (S. epidermidis) isolates with known biofilm formation capacities on four different intraocular lenses (IOL) that have not been studied before. Materials and Methods: Two isolates obtained from ocular surfaces and identified in previous studies and stored at -86 °C in 15% glycerol in the microbiology laboratory of the Anadolu University Department of Biology were purified and used in the study. The isolates were S. epidermidis KA 15.8 (ICA+), a known biofilm producer isolate positive for icaA, icaD and bap genes, and S. epidermidis KA 14.5 (ICA-), known as a non-biofilm producer isolate negative for icaA, icaD and bap genes. The biofilm formation capacities of the 2 isolates on 4 different IOLs were compared. Two of the IOLs were acrylic (UD613 [IOL A], Turkey; SA60AT [IOL B], USA), and the other two were polymethyl methacrylate (PMMA) (B60130C [IOL C], India; B55125C [IOL D], India). Bacterial enumeration and optical density measurements were done from biofilms that formed on the IOLs. Biofilms were imaged using scanning electron microscopy. Results: Mean bacterial counts on the IOLs were 7.1±0.4 log10 CFU/mL with the ICA+ isolate, and 6.7±0.8 log10 CFU/mL with the ICA- isolate; there were no statistically significant differences. Biofilm formation was lower with acrylic lenses than PMMA lenses with both isolates (p=0.009 and p=0.013). The highest biofilm production was obtained on IOL C (PMMA) (p<0.001) and the lowest was obtained on IOL A (hydrophilic acrylic) (p<0.001). Conclusion: Bacterial counts after biofilm formation were lower on acrylic lenses, especially hydrophilic acrylic with hydrophobic properties. Further animal and in vivo studies are required to support the findings of this study. PMID:28405479

Computational modeling of inhibition of voltage-gated Ca channels: identification of different effects on uterine and cardiac action potentials.

PubMed

Tong, Wing-Chiu; Ghouri, Iffath; Taggart, Michael J

2014-01-01

The uterus and heart share the important physiological feature whereby contractile activation of the muscle tissue is regulated by the generation of periodic, spontaneous electrical action potentials (APs). Preterm birth arising from premature uterine contractions is a major complication of pregnancy and there remains a need to pursue avenues of research that facilitate the use of drugs, tocolytics, to limit these inappropriate contractions without deleterious actions on cardiac electrical excitation. A novel approach is to make use of mathematical models of uterine and cardiac APs, which incorporate many ionic currents contributing to the AP forms, and test the cell-specific responses to interventions. We have used three such models-of uterine smooth muscle cells (USMC), cardiac sinoatrial node cells (SAN), and ventricular cells-to investigate the relative effects of reducing two important voltage-gated Ca currents-the L-type (ICaL) and T-type (ICaT) Ca currents. Reduction of ICaL (10%) alone, or ICaT (40%) alone, blunted USMC APs with little effect on ventricular APs and only mild effects on SAN activity. Larger reductions in either current further attenuated the USMC APs but with also greater effects on SAN APs. Encouragingly, a combination of ICaL and ICaT reduction did blunt USMC APs as intended with little detriment to APs of either cardiac cell type. Subsequent overlapping maps of ICaL and ICaT inhibition profiles from each model revealed a range of combined reductions of ICaL and ICaT over which an appreciable diminution of USMC APs could be achieved with no deleterious action on cardiac SAN or ventricular APs. This novel approach illustrates the potential for computational biology to inform us of possible uterine and cardiac cell-specific mechanisms. Incorporating such computational approaches in future studies directed at designing new, or repurposing existing, tocolytics will be beneficial for establishing a desired uterine specificity of action.

Computational modeling of inhibition of voltage-gated Ca channels: identification of different effects on uterine and cardiac action potentials

PubMed Central

Tong, Wing-Chiu; Ghouri, Iffath; Taggart, Michael J.

2014-01-01

The uterus and heart share the important physiological feature whereby contractile activation of the muscle tissue is regulated by the generation of periodic, spontaneous electrical action potentials (APs). Preterm birth arising from premature uterine contractions is a major complication of pregnancy and there remains a need to pursue avenues of research that facilitate the use of drugs, tocolytics, to limit these inappropriate contractions without deleterious actions on cardiac electrical excitation. A novel approach is to make use of mathematical models of uterine and cardiac APs, which incorporate many ionic currents contributing to the AP forms, and test the cell-specific responses to interventions. We have used three such models—of uterine smooth muscle cells (USMC), cardiac sinoatrial node cells (SAN), and ventricular cells—to investigate the relative effects of reducing two important voltage-gated Ca currents—the L-type (ICaL) and T-type (ICaT) Ca currents. Reduction of ICaL (10%) alone, or ICaT (40%) alone, blunted USMC APs with little effect on ventricular APs and only mild effects on SAN activity. Larger reductions in either current further attenuated the USMC APs but with also greater effects on SAN APs. Encouragingly, a combination of ICaL and ICaT reduction did blunt USMC APs as intended with little detriment to APs of either cardiac cell type. Subsequent overlapping maps of ICaL and ICaT inhibition profiles from each model revealed a range of combined reductions of ICaL and ICaT over which an appreciable diminution of USMC APs could be achieved with no deleterious action on cardiac SAN or ventricular APs. This novel approach illustrates the potential for computational biology to inform us of possible uterine and cardiac cell-specific mechanisms. Incorporating such computational approaches in future studies directed at designing new, or repurposing existing, tocolytics will be beneficial for establishing a desired uterine specificity of action. PMID:25360118

Slime-producing Staphylococcus epidermidis and S. aureus in acute bacterial conjunctivitis in soft contact lens wearers.

PubMed

Catalanotti, Piergiorgio; Lanza, Michele; Del Prete, Antonio; Lucido, Maria; Catania, Maria Rosaria; Gallè, Francesca; Boggia, Daniela; Perfetto, Brunella; Rossano, Fabio

2005-10-01

In recent years, an increase in ocular pathologies related to soft contact lens has been observed. The most common infectious agents were Staphylococcus spp. Some strains produce an extracellular polysaccharidic slime that can cause severe infections. Polysaccharide synthesis is under genetic control and involves a specific intercellular adhesion (ica) locus, in particular, icaA and icaD genes. Conjunctival swabs from 97 patients with presumably bacterial bilateral conjunctivitis, wearers of soft contact lenses were examined. We determined the ability of staphylococci to produce slime, relating it to the presence of icaA and icaD genes. We also investigated the antibiotic susceptibility and Pulsed Field Gel Electrophoresis (PFGE) patterns of the clinical isolates. We found that 74.1% of the S. epidermidis strains and 61.1% of the S. aureus strains isolated were slime producers and showed icaA and icaD genes. Both S. epidermidis and S. aureus slime-producing strains exhibited more surface hydrophobicity than non-producing slime strains. The PFGE patterns overlapped in S. epidermidis strains with high hydrophobicity. The similar PFGE patterns were not related to biofilm production. We found scarce matching among the Staphylococcus spp. studied, slime production, surface hydrophobicity and antibiotic susceptibility.

Glucose & sodium chloride induced biofilm production & ica operon in clinical isolates of staphylococci.

PubMed

Agarwal, Astha; Jain, Amita

2013-01-01

All colonizing and invasive staphylococcal isolates may not produce biofilm but may turn biofilm producers in certain situations due to change in environmental factors. This study was done to test the hypothesis that non biofilm producing clinical staphylococci isolates turn biofilm producers in presence of sodium chloride (isotonic) and high concentration of glucose, irrespective of presence or absence of ica operon. Clinical isolates of 100 invasive, 50 colonizing and 50 commensal staphylococci were tested for biofilm production by microtiter plate method in different culture media (trypticase soy broth alone or supplemented with 0.9% NaCl/ 5 or 10% glucose). All isolates were tested for the presence of ica ADBC genes by PCR. Biofilm production significantly increased in the presence of glucose and saline, most, when both glucose and saline were used together. All the ica positive staphylococcal isolates and some ica negative isolates turned biofilm producer in at least one of the tested culture conditions. Those remained biofilm negative in different culture conditions were all ica negative. The present results showed that the use of glucose or NaCl or combination of both enhanced biofilm producing capacity of staphylococcal isolates irrespective of presence or absence of ica operon.

MEG-SIM: a web portal for testing MEG analysis methods using realistic simulated and empirical data.

PubMed

Aine, C J; Sanfratello, L; Ranken, D; Best, E; MacArthur, J A; Wallace, T; Gilliam, K; Donahue, C H; Montaño, R; Bryant, J E; Scott, A; Stephen, J M

2012-04-01

MEG and EEG measure electrophysiological activity in the brain with exquisite temporal resolution. Because of this unique strength relative to noninvasive hemodynamic-based measures (fMRI, PET), the complementary nature of hemodynamic and electrophysiological techniques is becoming more widely recognized (e.g., Human Connectome Project). However, the available analysis methods for solving the inverse problem for MEG and EEG have not been compared and standardized to the extent that they have for fMRI/PET. A number of factors, including the non-uniqueness of the solution to the inverse problem for MEG/EEG, have led to multiple analysis techniques which have not been tested on consistent datasets, making direct comparisons of techniques challenging (or impossible). Since each of the methods is known to have their own set of strengths and weaknesses, it would be beneficial to quantify them. Toward this end, we are announcing the establishment of a website containing an extensive series of realistic simulated data for testing purposes ( http://cobre.mrn.org/megsim/ ). Here, we present: 1) a brief overview of the basic types of inverse procedures; 2) the rationale and description of the testbed created; and 3) cases emphasizing functional connectivity (e.g., oscillatory activity) suitable for a wide assortment of analyses including independent component analysis (ICA), Granger Causality/Directed transfer function, and single-trial analysis.

MEG-SIM: A Web Portal for Testing MEG Analysis Methods using Realistic Simulated and Empirical Data

PubMed Central

Aine, C. J.; Sanfratello, L.; Ranken, D.; Best, E.; MacArthur, J. A.; Wallace, T.; Gilliam, K.; Donahue, C. H.; Montaño, R.; Bryant, J. E.; Scott, A.; Stephen, J. M.

2012-01-01

MEG and EEG measure electrophysiological activity in the brain with exquisite temporal resolution. Because of this unique strength relative to noninvasive hemodynamic-based measures (fMRI, PET), the complementary nature of hemodynamic and electrophysiological techniques is becoming more widely recognized (e.g., Human Connectome Project). However, the available analysis methods for solving the inverse problem for MEG and EEG have not been compared and standardized to the extent that they have for fMRI/PET. A number of factors, including the non-uniqueness of the solution to the inverse problem for MEG/EEG, have led to multiple analysis techniques which have not been tested on consistent datasets, making direct comparisons of techniques challenging (or impossible). Since each of the methods is known to have their own set of strengths and weaknesses, it would be beneficial to quantify them. Toward this end, we are announcing the establishment of a website containing an extensive series of realistic simulated data for testing purposes (http://cobre.mrn.org/megsim/). Here, we present: 1) a brief overview of the basic types of inverse procedures; 2) the rationale and description of the testbed created; and 3) cases emphasizing functional connectivity (e.g., oscillatory activity) suitable for a wide assortment of analyses including independent component analysis (ICA), Granger Causality/Directed transfer function, and single-trial analysis. PMID:22068921

Evolution reversed: the ability to bind iron restored to the N-lobe of the murine inhibitor of carbonic anhydrase by strategic mutagenesis.

PubMed

Mason, Anne B; Judson, Gregory L; Bravo, Maria Cristina; Edelstein, Andrew; Byrne, Shaina L; James, Nicholas G; Roush, Eric D; Fierke, Carol A; Bobst, Cedric E; Kaltashov, Igor A; Daughtery, Margaret A

2008-09-16

The murine inhibitor of carbonic anhydrase (mICA) is a member of the superfamily related to the bilobal iron transport protein transferrin (TF), which binds a ferric ion within a cleft in each lobe. Although the gene encoding ICA in humans is classified as a pseudogene, an apparently functional ICA gene has been annotated in mice, rats, cows, pigs, and dogs. All ICAs lack one (or more) of the amino acid ligands in each lobe essential for high-affinity coordination of iron and the requisite synergistic anion, carbonate. The reason why ICA family members have lost the ability to bind iron is potentially related to acquiring a new function(s), one of which is inhibition of certain carbonic anhydrase (CA) isoforms. A recombinant mutant of the mICA (W124R/S188Y) was created with the goal of restoring the ligands required for both anion (Arg124) and iron (Tyr188) binding in the N-lobe. Absorption and fluorescence spectra definitively show that the mutant binds ferric iron in the N-lobe. Electrospray ionization mass spectrometry confirms the presence of both ferric iron and carbonate. At the putative endosomal pH of 5.6, iron is released by two slow processes indicative of high-affinity coordination. Induction of specific iron binding implies that (1) the structure of mICA resembles those of other TF family members and (2) the N-lobe can adopt a conformation in which the cleft closes when iron binds. Because the conformational change in the N-lobe indicated by metal binding does not impact the inhibitory activity of mICA, inhibition of CA was tentatively assigned to the C-lobe. Proof of this assignment is provided by limited trypsin proteolysis of porcine ICA.

Comparison of pre-filter and post-filter ionised calcium monitoring in continuous veno-venous hemodiafiltration (CVVHD-F) with citrate anti-coagulation.

PubMed

Brain, Matthew J; Roodenburg, Owen S; McNeil, John

2017-01-01

It is widespread practice during citrate anticoagulated renal replacement therapy to monitor circuit ionised calcium (iCa2+) to evaluate the effectiveness of anticoagulation. Whether the optimal site to sample the blood path is before or after the haemofilter is a common question. Using a prospectively collected observational dataset from intensive care patients receiving pre-dilution continuous veno-venous haemodiafiltration (CVVHD-F) with integrated citrate anticoagulation we compared paired samples of pre and post filter iCa2+ where the target range was 0.3-0.5 mmol.L-1 as well as concurrently collected arterial iCa2+. Two nested mixed methods linear models were fitted to the data describing post vs pre filter iCa2+, and the relationship of pre, post and arterial samples. An 11 bed general intensive care unit. 450 grouped samples from 152 time periods in seven patients on CRRT with citrate anticoagulation. The relationship of post to pre-filter iCa2+ was not 1:1 with post = 0.082 + 0.751 x pre-filter iCa2+ (95% CI intercept: 0.015-0.152, slope 0.558-0.942). Variation was greatest between patients rather than between circuits within the same patient or citrate dose. Compared to arterial iCa2+ there was no significant difference between pre and post-filter sampling sites (F-value 0.047, p = 0.827). These results demonstrate that there is minimal difference between pre and post filter samples for iCa2+ monitoring of circuit anticoagulation in citrate patients relative to the arterial iCa2+ in CVVHD-F however compared to pre-filter sampling, post filter sampling has a flatter response and greater variation.

Compensatory patterns of collateral flow in stroke patients with unilateral and bilateral carotid stenosis.

PubMed

Fang, Hui; Song, Bo; Cheng, Bo; Wong, Ka Sing; Xu, Yu Ming; Ho, Stella Sin Yee; Chen, Xiang Yan

2016-03-18

Collateral pathways are important in maintaining adequate cerebral blood flow in patients with carotid stenosis. We aimed to evaluate the hemodynamic patterns in relation to carotid stenosis in acute stroke patients. Consecutive 586 stroke patients in a hospital based cohort were included in the present study. Carotid duplex was performed to identify patients with absolute minimal diameter reductions of 50% or greater in their internal carotid arteries (ICAs). Color velocity imaging quantification ultrasound (CVIQ) was used to measure extracranial arterial blood flow volume (BFV) in bilateral common carotid arteries (CCAs) and bilateral vertebral arteries (VAs). The absolute values of BFV and the ratios were compared between patients with and without ICA stenosis. Among 586 acute ischemic stroke patients (mean age: 67.5 ± 12.4y), ICA stenosis was detected in 112 patients (19.1%), including unilateral ICA stenosis in 81 patients (13.8%) and bilateral ICA stenosis in 31 patients (5.3%). Among patients with unilateral ICA stenosis, the BFV in contralateral CCA was significantly higher than that in ipsilateral CCA (325.5 ± 99.8 mL/min vs. 242.2 ± 112.2 mL/min, P < 0.001). Among patients with bilateral ICA stenosis, the sum of BFV in bilateral VAs accounted for 22% of the whole cerebral blood flow, which was significantly higher than that in those without ICA stenosis (14.8%, P < 0.001) or with unilateral ICA stenosis (16.9%, P = 0.007). In patients with unilateral carotid stenosis, contralateral carotid blood flow increases to compensate decreased blood flow, while posterior circulation may compensate for the decreased brain perfusion in those with bilateral carotid stenosis.

Computed tomography versus invasive coronary angiography: design and methods of the pragmatic randomised multicentre DISCHARGE trial.

PubMed

Napp, Adriane E; Haase, Robert; Laule, Michael; Schuetz, Georg M; Rief, Matthias; Dreger, Henryk; Feuchtner, Gudrun; Friedrich, Guy; Špaček, Miloslav; Suchánek, Vojtěch; Fuglsang Kofoed, Klaus; Engstroem, Thomas; Schroeder, Stephen; Drosch, Tanja; Gutberlet, Matthias; Woinke, Michael; Maurovich-Horvat, Pál; Merkely, Béla; Donnelly, Patrick; Ball, Peter; Dodd, Jonathan D; Quinn, Martin; Saba, Luca; Porcu, Maurizio; Francone, Marco; Mancone, Massimo; Erglis, Andrejs; Zvaigzne, Ligita; Jankauskas, Antanas; Sakalyte, Gintare; Harań, Tomasz; Ilnicka-Suckiel, Malgorzata; Bettencourt, Nuno; Gama-Ribeiro, Vasco; Condrea, Sebastian; Benedek, Imre; Čemerlić Adjić, Nada; Adjić, Oto; Rodriguez-Palomares, José; Garcia Del Blanco, Bruno; Roditi, Giles; Berry, Colin; Davis, Gershan; Thwaite, Erica; Knuuti, Juhani; Pietilä, Mikko; Kępka, Cezary; Kruk, Mariusz; Vidakovic, Radosav; Neskovic, Aleksandar N; Díez, Ignacio; Lecumberri, Iñigo; Geleijns, Jacob; Kubiak, Christine; Strenge-Hesse, Anke; Do, The-Hoang; Frömel, Felix; Gutiérrez-Ibarluzea, Iñaki; Benguria-Arrate, Gaizka; Keiding, Hans; Katzer, Christoph; Müller-Nordhorn, Jacqueline; Rieckmann, Nina; Walther, Mario; Schlattmann, Peter; Dewey, Marc

2017-07-01

More than 3.5 million invasive coronary angiographies (ICA) are performed in Europe annually. Approximately 2 million of these invasive procedures might be reduced by noninvasive tests because no coronary intervention is performed. Computed tomography (CT) is the most accurate noninvasive test for detection and exclusion of coronary artery disease (CAD). To investigate the comparative effectiveness of CT and ICA, we designed the European pragmatic multicentre DISCHARGE trial funded by the 7th Framework Programme of the European Union (EC-GA 603266). In this trial, patients with a low-to-intermediate pretest probability (10-60 %) of suspected CAD and a clinical indication for ICA because of stable chest pain will be randomised in a 1-to-1 ratio to CT or ICA. CT and ICA findings guide subsequent management decisions by the local heart teams according to current evidence and European guidelines. Major adverse cardiovascular events (MACE) defined as cardiovascular death, myocardial infarction and stroke as a composite endpoint will be the primary outcome measure. Secondary and other outcomes include cost-effectiveness, radiation exposure, health-related quality of life (HRQoL), socioeconomic status, lifestyle, adverse events related to CT/ICA, and gender differences. The DISCHARGE trial will assess the comparative effectiveness of CT and ICA. • Coronary artery disease (CAD) is a major cause of morbidity and mortality. • Invasive coronary angiography (ICA) is the reference standard for detection of CAD. • Noninvasive computed tomography angiography excludes CAD with high sensitivity. • CT may effectively reduce the approximately 2 million negative ICAs in Europe. • DISCHARGE addresses this hypothesis in patients with low-to-intermediate pretest probability for CAD.

Comparison of pre-filter and post-filter ionised calcium monitoring in continuous veno-venous hemodiafiltration (CVVHD-F) with citrate anti-coagulation

PubMed Central

Roodenburg, Owen S.; McNeil, John

2017-01-01

Background It is widespread practice during citrate anticoagulated renal replacement therapy to monitor circuit ionised calcium (iCa2+) to evaluate the effectiveness of anticoagulation. Whether the optimal site to sample the blood path is before or after the haemofilter is a common question. Methods Using a prospectively collected observational dataset from intensive care patients receiving pre-dilution continuous veno-venous haemodiafiltration (CVVHD-F) with integrated citrate anticoagulation we compared paired samples of pre and post filter iCa2+ where the target range was 0.3–0.5 mmol.L-1 as well as concurrently collected arterial iCa2+. Two nested mixed methods linear models were fitted to the data describing post vs pre filter iCa2+, and the relationship of pre, post and arterial samples. Setting An 11 bed general intensive care unit. Participants 450 grouped samples from 152 time periods in seven patients on CRRT with citrate anticoagulation. Results The relationship of post to pre-filter iCa2+ was not 1:1 with post = 0.082 + 0.751 x pre-filter iCa2+ (95% CI intercept: 0.015–0.152, slope 0.558–0.942). Variation was greatest between patients rather than between circuits within the same patient or citrate dose. Compared to arterial iCa2+ there was no significant difference between pre and post-filter sampling sites (F-value 0.047, p = 0.827) Conclusion These results demonstrate that there is minimal difference between pre and post filter samples for iCa2+ monitoring of circuit anticoagulation in citrate patients relative to the arterial iCa2+ in CVVHD-F however compared to pre-filter sampling, post filter sampling has a flatter response and greater variation. PMID:29272278

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.

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

Carotid DSA based CFD simulation in assessing the patient with asymptomatic carotid stenosis: a preliminary study.

PubMed

Zhang, Dong; Xu, Pengcheng; Qiao, Hongyu; Liu, Xin; Luo, Liangping; Huang, Wenhua; Zhang, Heye; Shi, Changzheng

2018-03-12

Cerebrovascular events are frequently associated with hemodynamic disturbance caused by internal carotid artery (ICA) stenosis. It is challenging to determine the ischemia-related carotid stenosis during the intervention only using digital subtracted angiography (DSA). Inspired by the performance of well-established FFRct technique in hemodynamic assessment of significant coronary stenosis, we introduced a pressure-based carotid arterial functional assessment (CAFA) index generated from computational fluid dynamic (CFD) simulation in DSA data, and investigated its feasibility in the assessment of hemodynamic disturbance preliminarily using pressure-wired measurement and arterial spin labeling (ASL) MRI as references. The cerebral multi-delay multi-parametric ASL-MRI and carotid DSA including trans-stenotic pressure-wired measurement were implemented on a 65-year-old man with asymptomatic unilateral (left) ICA stenosis. A CFD simulation using simplified boundary condition was performed in DSA data to calculate the CAFA index. The cerebral blood flow (CBF) and arterial transit time (ATT) of ICA territories were acquired. CFD simulation showed good correlation (r = 0.839, P = 0.001) with slight systematic overestimation (mean difference - 0.007, standard deviation 0.017) compared with pressure-wired measurement. No significant difference was observed between them (P = 0.09). Though the narrowing degree of in the involved ICA was about 70%, the simulated and measured CAFA (0.942/0.937) revealed a functionally nonsignificant stenosis which was also verified by a compensatory final CBF (fronto-temporal/fronto-parietal region: 51.58/45.62 ml/100 g/min) and slightly prolonged ATT (1.23/1.4 s) in the involved territories, together with a normal left-right percentage difference (2.1-8.85%). The DSA based CFD simulation showed good consistence with invasive approach and could be used as a cost-saving and efficient way to study the relationship between hemodynamic disorder caused by ICA stenosis and subsequent perfusion variations in brain. Further research should focus on the role of noninvasive pressure-based CAFA in screening asymptomatic ischemia-causing carotid stenosis.

Removal of BCG artifacts from EEG recordings inside the MR scanner: a comparison of methodological and validation-related aspects.

PubMed

Vanderperren, Katrien; De Vos, Maarten; Ramautar, Jennifer R; Novitskiy, Nikolay; Mennes, Maarten; Assecondi, Sara; Vanrumste, Bart; Stiers, Peter; Van den Bergh, Bea R H; Wagemans, Johan; Lagae, Lieven; Sunaert, Stefan; Van Huffel, Sabine

2010-04-15

Multimodal approaches are of growing interest in the study of neural processes. To this end much attention has been paid to the integration of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data because of their complementary properties. However, the simultaneous acquisition of both types of data causes serious artifacts in the EEG, with amplitudes that may be much larger than those of EEG signals themselves. The most challenging of these artifacts is the ballistocardiogram (BCG) artifact, caused by pulse-related electrode movements inside the magnetic field. Despite numerous efforts to find a suitable approach to remove this artifact, still a considerable discrepancy exists between current EEG-fMRI studies. This paper attempts to clarify several methodological issues regarding the different approaches with an extensive validation based on event-related potentials (ERPs). More specifically, Optimal Basis Set (OBS) and Independent Component Analysis (ICA) based methods were investigated. Their validation was not only performed with measures known from previous studies on the average ERPs, but most attention was focused on task-related measures, including their use on trial-to-trial information. These more detailed validation criteria enabled us to find a clearer distinction between the most widely used cleaning methods. Both OBS and ICA proved to be able to yield equally good results. However, ICA methods needed more parameter tuning, thereby making OBS more robust and easy to use. Moreover, applying OBS prior to ICA can optimize the data quality even more, but caution is recommended since the effect of the additional ICA step may be strongly subject-dependent. Copyright 2010 Elsevier Inc. All rights reserved.

Ontario multidetector computed tomographic coronary angiography study: field evaluation of diagnostic accuracy.

PubMed

Chow, Benjamin J W; Freeman, Michael R; Bowen, James M; Levin, Leslie; Hopkins, Robert B; Provost, Yves; Tarride, Jean-Eric; Dennie, Carole; Cohen, Eric A; Marcuzzi, Dan; Iwanochko, Robert; Moody, Alan R; Paul, Narinder; Parker, John D; O'Reilly, Daria J; Xie, Feng; Goeree, Ron

2011-06-13

Computed tomographic coronary angiography (CTCA) has gained clinical acceptance for the detection of obstructive coronary artery disease. Although single-center studies have demonstrated excellent accuracy, multicenter studies have yielded variable results. The true diagnostic accuracy of CTCA in the "real world" remains uncertain. We conducted a field evaluation comparing multidetector CTCA with invasive CA (ICA) to understand CTCA's diagnostic accuracy in a real-world setting. A multicenter cohort study of patients awaiting ICA was conducted between September 2006 and June 2009. All patients had either a low or an intermediate pretest probability for coronary artery disease and underwent CTCA and ICA within 10 days. The results of CTCA and ICA were interpreted visually by local expert observers who were blinded to all clinical data and imaging results. Using a patient-based analysis (diameter stenosis ≥50%) of 169 patients, the sensitivity, specificity, positive predictive value, and negative predictive value were 81.3% (95% confidence interval [CI], 71.0%-89.1%), 93.3% (95% CI, 85.9%-97.5%), 91.6% (95% CI, 82.5%-96.8%), and 84.7% (95% CI, 76.0%-91.2%), respectively; the area under receiver operating characteristic curve was 0.873. The diagnostic accuracy varied across centers (P < .001), with a sensitivity, specificity, positive predictive value, and negative predictive value ranging from 50.0% to 93.2%, 92.0% to 100%, 84.6% to 100%, and 42.9% to 94.7%, respectively. Compared with ICA, CTCA appears to have good accuracy; however, there was variability in diagnostic accuracy across centers. Factors affecting institutional variability need to be better understood before CTCA is universally adopted. Additional real-world evaluations are needed to fully understand the impact of CTCA on clinical care. clinicaltrials.gov Identifier: NCT00371891.

Computed Tomographic Distinction of Intimal and Medial Calcification in the Intracranial Internal Carotid Artery.

PubMed

Kockelkoren, Remko; Vos, Annelotte; Van Hecke, Wim; Vink, Aryan; Bleys, Ronald L A W; Verdoorn, Daphne; Mali, Willem P Th M; Hendrikse, Jeroen; Koek, Huiberdina L; de Jong, Pim A; De Vis, Jill B

2017-01-01

Intracranial internal carotid artery (iICA) calcification is associated with stroke and is often seen as a proxy of atherosclerosis of the intima. However, it was recently shown that these calcifications are predominantly located in the tunica media and internal elastic lamina (medial calcification). Intimal and medial calcifications are thought to have a different pathogenesis and clinical consequences and can only be distinguished through ex vivo histological analysis. Therefore, our aim was to develop CT scoring method to distinguish intimal and medial iICA calcification in vivo. First, in both iICAs of 16 cerebral autopsy patients the intimal and/or medial calcification area was histologically assessed (142 slides). Brain CT images of these patients were matched to the corresponding histological slides to develop a CT score that determines intimal or medial calcification dominance. Second, performance of the CT score was assessed in these 16 patients. Third, reproducibility was tested in a separate cohort. First, CT features of the score were circularity (absent, dot(s), <90°, 90-270° or 270-360°), thickness (absent, ≥1.5mm, or <1.5mm), and morphology (indistinguishable, irregular/patchy or continuous). A high sum of features represented medial and a lower sum intimal calcifications. Second, in the 16 patients the concordance between the CT score and the dominant calcification type was reasonable. Third, the score showed good reproducibility (kappa: 0.72 proportion of agreement: 0.82) between the categories intimal, medial or absent/indistinguishable. The developed CT score shows good reproducibility and can differentiate reasonably well between intimal and medial calcification dominance in the iICA, allowing for further (epidemiological) studies on iICA calcification.

Internal Carotid Artery Agenesis with an Intercavernous Anastomosis: A Rare Case.

PubMed

Erdogan, Mucahid; Senadim, Songul; Ince Yasinoglu, K Nur; Selcuk, H Hakan; Atakli, H Dilek

2017-10-01

Agenesis of the internal carotid artery (ICA) is a rare vascular anomaly that was first observed postmortem. Various anastomoses supply the distal vessels at the site of agenesis. Of these anastomoses, an intercavernous anastomosis is very rare. This paper presents a patient with ischemic stroke in whom we discovered left ICA agenesis and an ipsilateral intercavernous anastomosis. A 58-year-old man with a history of myocardial infarction and diabetes mellitus presented with sudden-onset difficulty in speaking, numbness on the left side of the face, and weakness of the left arm and leg. Neurological examination revealed dysarthria, left facial paralysis, left hemiparesis, and bilateral absence of the plantar reflexes. Diffusion-weighted magnetic resonance imaging showed a right middle cerebral artery (MCA) infarction. On cranial and cervical magnetic resonance angiography, the left ICA could not be seen distal to the bifurcation; the left MCA was supplied through an intercavernous anastomosis between the right ICA and the left ICA. Cranial computed tomography (CT) revealed the absence of the left carotid canal. Digital subtraction angiography led to a diagnosis of left ICA agenesis with an intercavernous anastomosis. The patient was discharged on acetylsalicylic acid and warfarin. ICA agenesis with an intercavernous anastomosis is a rare vascular anomaly that should be differentiated from secondary causes of ICA stenosis and occlusions by showing agenesis of the carotid canal on cranial CT. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Icariin Promotes Tendon-Bone Healing during Repair of Rotator Cuff Tears: A Biomechanical and Histological Study.

PubMed

Ye, Chenyi; Zhang, Wei; Wang, Shengdong; Jiang, Shuai; Yu, Yuanbin; Chen, Erman; Xue, Deting; Chen, Jianzhong; He, Rongxin

2016-10-25

To investigate whether the systematic administration of icariin (ICA) promotes tendon-bone healing after rotator cuff reconstruction in vivo, a total of 64 male Sprague Dawley rats were used in a rotator cuff injury model and underwent rotator cuff reconstruction (bone tunnel suture fixation). Rats from the ICA group ( n = 32) were gavage-fed daily with ICA at 0.125 mg/g, while rats in the control group ( n = 32) received saline only. Micro-computed tomography, biomechanical tests, serum ELISA (calcium; Ca, alkaline phosphatase; AP, osteocalcin; OCN) and histological examinations (Safranin O and Fast Green staining, type I, II and III collagen (Col1, Col2, and Col3), CD31, and vascular endothelial growth factor (VEGF)) were analyzed two and four weeks after surgery. In the ICA group, the serum levels of AP and OCN were higher than in the control group. More Col1-, Col2-, CD31-, and VEGF-positive cells, together with a greater degree of osteogenesis, were detected in the ICA group compared with the control group. During mechanical testing, the ICA group showed a significantly higher ultimate failure load than the control group at both two and four weeks. Our results indicate that the systematic administration of ICA could promote angiogenesis and tendon-bone healing after rotator cuff reconstruction, with superior mechanical strength compared with the controls. Treatment for rotator cuff injury using systematically-administered ICA could be a promising strategy.

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering.

PubMed

Yang, Guang; Raschke, Felix; Barrick, Thomas R; Howe, Franklyn A

2015-09-01

To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method. In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis. © 2014 Wiley Periodicals, Inc.

Single-trial EEG-informed fMRI reveals spatial dependency of BOLD signal on early and late IC-ERP amplitudes during face recognition.

PubMed

Wirsich, Jonathan; Bénar, Christian; Ranjeva, Jean-Philippe; Descoins, Médéric; Soulier, Elisabeth; Le Troter, Arnaud; Confort-Gouny, Sylviane; Liégeois-Chauvel, Catherine; Guye, Maxime

2014-10-15

Simultaneous EEG-fMRI has opened up new avenues for improving the spatio-temporal resolution of functional brain studies. However, this method usually suffers from poor EEG quality, especially for evoked potentials (ERPs), due to specific artifacts. As such, the use of EEG-informed fMRI analysis in the context of cognitive studies has particularly focused on optimizing narrow ERP time windows of interest, which ignores the rich diverse temporal information of the EEG signal. Here, we propose to use simultaneous EEG-fMRI to investigate the neural cascade occurring during face recognition in 14 healthy volunteers by using the successive ERP peaks recorded during the cognitive part of this process. N170, N400 and P600 peaks, commonly associated with face recognition, were successfully and reproducibly identified for each trial and each subject by using a group independent component analysis (ICA). For the first time we use this group ICA to extract several independent components (IC) corresponding to the sequence of activation and used single-trial peaks as modulation parameters in a general linear model (GLM) of fMRI data. We obtained an occipital-temporal-frontal stream of BOLD signal modulation, in accordance with the three successive IC-ERPs providing an unprecedented spatio-temporal characterization of the whole cognitive process as defined by BOLD signal modulation. By using this approach, the pattern of EEG-informed BOLD modulation provided improved characterization of the network involved than the fMRI-only analysis or the source reconstruction of the three ERPs; the latter techniques showing only two regions in common localized in the occipital lobe. Copyright © 2014 Elsevier Inc. All rights reserved.

Method for multimodal analysis of independent source differences in schizophrenia: combining gray matter structural and auditory oddball functional data.

PubMed

Calhoun, V D; Adali, T; Giuliani, N R; Pekar, J J; Kiehl, K A; Pearlson, G D

2006-01-01

The acquisition of both structural MRI (sMRI) and functional MRI (fMRI) data for a given study is a very common practice. However, these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform independent component analysis across image modalities, specifically, gray matter images and fMRI activation images as well as a joint histogram visualization technique. Joint independent component analysis (jICA) is used to decompose a matrix with a given row consisting of an fMRI activation image resulting from auditory oddball target stimuli and an sMRI gray matter segmentation image, collected from the same individual. We analyzed data collected on a group of schizophrenia patients and healthy controls using the jICA approach. Spatially independent joint-components are estimated and resulting components were further analyzed only if they showed a significant difference between patients and controls. The main finding was that group differences in bilateral parietal and frontal as well as posterior temporal regions in gray matter were associated with bilateral temporal regions activated by the auditory oddball target stimuli. A finding of less patient gray matter and less hemodynamic activity for target detection in these bilateral anterior temporal lobe regions was consistent with previous work. An unexpected corollary to this finding was that, in the regions showing the largest group differences, gray matter concentrations were larger in patients vs. controls, suggesting that more gray matter may be related to less functional connectivity in the auditory oddball fMRI task. Hum Brain Mapp, 2005. (c) 2005 Wiley-Liss, 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.…

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.

Guided Exploration of Genomic Risk for Gray Matter Abnormalities in Schizophrenia Using Parallel Independent Component Analysis with Reference

PubMed Central

Chen, Jiayu; Calhoun, Vince D.; Pearlson, Godfrey D.; Perrone-Bizzozero, Nora; Sui, Jing; Turner, Jessica A.; Bustillo, Juan R; Ehrlich, Stefan; Sponheim, Scott R.; Cañive, José M.; Ho, Beng-Choon; Liu, Jingyu

2013-01-01

One application of imaging genomics is to explore genetic variants associated with brain structure and function, presenting a new means of mapping genetic influences on mental disorders. While there is growing interest in performing genome-wide searches for determinants, it remains challenging to identify genetic factors of small effect size, especially in limited sample sizes. In an attempt to address this issue, we propose to take advantage of a priori knowledge, specifically to extend parallel independent component analysis (pICA) to incorporate a reference (pICA-R), aiming to better reveal relationships between hidden factors of a particular attribute. The new approach was first evaluated on simulated data for its performance under different configurations of effect size and dimensionality. Then pICA-R was applied to a 300-participant (140 schizophrenia (SZ) patients versus 160 healthy controls) dataset consisting of structural magnetic resonance imaging (sMRI) and single nucleotide polymorphism (SNP) data. Guided by a reference SNP set derived from ANK3, a gene implicated by the Psychiatric Genomic Consortium SZ study, pICA-R identified one pair of SNP and sMRI components with a significant loading correlation of 0.27 (p = 1.64×10−6). The sMRI component showed a significant group difference in loading parameters between patients and controls (p = 1.33×10−15), indicating SZ-related reduction in gray matter concentration in prefrontal and temporal regions. The linked SNP component also showed a group difference (p = 0.04) and was predominantly contributed to by 1,030 SNPs. The effect of these top contributing SNPs was verified using association test results of the Psychiatric Genomic Consortium SZ study, where the 1,030 SNPs exhibited significant SZ enrichment compared to the whole genome. In addition, pathway analyses indicated the genetic component majorly relating to neurotransmitter and nervous system signaling pathways. Given the simulation and experiment results, pICA-R may prove a promising multivariate approach for use in imaging genomics to discover reliable genetic risk factors under a scenario of relatively high dimensionality and small effect size. PMID:23727316

A lower baseline glomerular filtration rate predicts high mortality and newly cerebrovascular accidents in acute ischemic stroke patients.

PubMed

Dong, Kai; Huang, Xiaoqin; Zhang, Qian; Yu, Zhipeng; Ding, Jianping; Song, Haiqing

2017-02-01

Chronic kidney disease (CKD) is gradually recognized as an independent risk factor for cardiovascular and cardio-/cerebrovascular disease. This study aimed to examine the association of the estimated glomerular filtration rate (eGFR) and clinical outcomes at 3 months after the onset of ischemic stroke in a hospitalized Chinese population.Totally, 972 patients with acute ischemic stroke were enrolled into this study. Modified of Diet in Renal Disease (MDRD) equations were used to calculate eGFR and define CKD. The site and degree of the stenosis were examined. Patients were followed-up for 3 months. Endpoint events included all-cause death and newly ischemic events. The multivariate logistic model was used to determine the association between renal dysfunction and patients' outcomes.Of all patients, 130 patients (13.4%) had reduced eGFR (<60 mL/min/1.73 m), and 556 patients had a normal eGFR (≥90 mL/min/1.73 m). A total of 694 patients suffered from cerebral artery stenosis, in which 293 patients only had intracranial artery stenosis (ICAS), 110 only with extracranial carotid atherosclerotic stenosis (ECAS), and 301 with both ICAS and ECAS. The patients with eGFR <60 mL/min/1.73m had a higher proportion of death and newly ischemic events compared with those with a relatively normal eGFR. Multivariate analysis revealed that a baseline eGFR <60 mL/min/1.73 m increased the risk of mortality by 3.089-fold and newly ischemic events by 4.067-fold. In further analysis, a reduced eGFR was associated with increased rates of mortality and newly events both in ICAS patients and ECAS patients. However, only an increased risk of newly events was found as the degree of renal function deteriorated in ICAS patients (odds ratio = 8.169, 95% confidence interval = 2.445-14.127).A low baseline eGFR predicted a high mortality and newly ischemic events at 3 months in ischemic stroke patients. A low baseline eGFR was also a strong independent predictor for newly ischemic events in ICAS patients.

Long-term follow-up analysis of microsurgical clip ligation and endovascular coil embolization for dorsal wall blister aneurysms of the internal carotid artery.

PubMed

Brown, Mason A; Guandique, Cristian F; Parish, Jonathan; McMillan, Aubrey C; Lehnert, Stephen; Mansour, Nassir; Tu, Michael; Bohnstedt, Bradley N; Payner, Troy D; Leipzig, Thomas J; DeNardo, Andrew J; Scott, John A; Cohen-Gadol, Aaron A

2017-05-01

Blister aneurysms at non-branching sites of the dorsal internal carotid artery (dICA) are fragile, rare, and often difficult to treat. The purpose of this study is to address the demographics, treatment modalities, and long-term outcome of patients treated for dICA blister aneurysms. A retrospective review of medical records identified all consecutive patients who presented with a blister aneurysm from 2002 to 2011 at our institution. Eighteen patients (M=7, F=11; mean age: 48.4±15.1years; range: 15-65years) harbored a total of 43 aneurysms, 25 of which were dorsal wall blister aneurysms of the ICA. Eleven (61.1%) patients presented with aneurysmal subarachnoid hemorrhage (aSAH), and 10 (55.6%) patients had multiple aneurysms at admission. Twelve patients had 18 aneurysms that were treated microsurgically. Five (41.7%) of these patients had a single recurrence that was retreated with subsequent repeat clip ligation. Six patients had 7 blister aneurysms that were treated with endovascularly. One (16.7%) of these patients had a single recurrence that was retreated with subsequent coil embolization. Postoperative vasospasm occurred in 8 (44.4%) patients, one of whom suffered from a stroke. This is one of the largest single-institution dICA blister aneurysm studies to date. There was no detected significant difference between microsurgical clip ligation and endovascular coil embolization in terms of surgical outcome. These blister aneurysms demonstrate a propensity to be associated with multiple cerebral aneurysms. Strict clinical and angiographic long-term follow-up may be warranted. Blister aneurysms are focal wall defects covered by a thin layer of fibrous tissue and adventitia, lacking the usual collagenous layer. Due to their pathologically thin vessel wall, blister aneurysms are prone to rupture. The management of these rare and fragile aneurysms presents a number of challenges. Here, we address the long-term outcome of patients treated for blister aneurysms at non-branching sites of the dICA. The presented data and analysis is imperative to determine the necessary strict long-term clinical and angiographic follow-up. Copyright © 2017 Elsevier Ltd. All rights reserved.

Innovative high-performance liquid chromatography method development for the screening of 19 antimalarial drugs based on a generic approach, using design of experiments, independent component analysis and design space.

PubMed

Debrus, B; Lebrun, P; Kindenge, J Mbinze; Lecomte, F; Ceccato, A; Caliaro, G; Mbay, J Mavar Tayey; Boulanger, B; Marini, R D; Rozet, E; Hubert, Ph

2011-08-05

An innovative methodology based on design of experiments (DoE), independent component analysis (ICA) and design space (DS) was developed in previous works and was tested out with a mixture of 19 antimalarial drugs. This global LC method development methodology (i.e. DoE-ICA-DS) was used to optimize the separation of 19 antimalarial drugs to obtain a screening method. DoE-ICA-DS methodology is fully compliant with the current trend of quality by design. DoE was used to define the set of experiments to model the retention times at the beginning, the apex and the end of each peak. Furthermore, ICA was used to numerically separate coeluting peaks and estimate their unbiased retention times. Gradient time, temperature and pH were selected as the factors of a full factorial design. These retention times were modelled by stepwise multiple linear regressions. A recently introduced critical quality attribute, namely the separation criterion (S), was also used to assess the quality of separations rather than using the resolution. Furthermore, the resulting mathematical models were also studied from a chromatographic point of view to understand and investigate the chromatographic behaviour of each compound. Good adequacies were found between the mathematical models and the expected chromatographic behaviours predicted by chromatographic theory. Finally, focusing at quality risk management, the DS was computed as the multidimensional subspace where the probability for the separation criterion to lie in acceptance limits was higher than a defined quality level. The DS was computed propagating the prediction error from the modelled responses to the quality criterion using Monte Carlo simulations. DoE-ICA-DS allowed encountering optimal operating conditions to obtain a robust screening method for the 19 considered antimalarial drugs in the framework of the fight against counterfeit medicines. Moreover and only on the basis of the same data set, a dedicated method for the determination of three antimalarial compounds in a pharmaceutical formulation was optimized to demonstrate both the efficiency and flexibility of the methodology proposed in the present study. Copyright © 2011 Elsevier B.V. All rights reserved.

Multivariate genetic determinants of EEG oscillations in schizophrenia and psychotic bipolar disorder from the BSNIP study

PubMed Central

Narayanan, B; Soh, P; Calhoun, V D; Ruaño, G; Kocherla, M; Windemuth, A; Clementz, B A; Tamminga, C A; Sweeney, J A; Keshavan, M S; Pearlson, G D

2015-01-01

Schizophrenia (SZ) and psychotic bipolar disorder (PBP) are disabling psychiatric illnesses with complex and unclear etiologies. Electroencephalogram (EEG) oscillatory abnormalities in SZ and PBP probands are heritable and expressed in their relatives, but the neurobiology and genetic factors mediating these abnormalities in the psychosis dimension of either disorder are less explored. We examined the polygenic architecture of eyes-open resting state EEG frequency activity (intrinsic frequency) from 64 channels in 105 SZ, 145 PBP probands and 56 healthy controls (HCs) from the multisite BSNIP (Bipolar-Schizophrenia Network on Intermediate Phenotypes) study. One million single-nucleotide polymorphisms (SNPs) were derived from DNA. We assessed eight data-driven EEG frequency activity derived from group-independent component analysis (ICA) in conjunction with a reduced subset of 10 422 SNPs through novel multivariate association using parallel ICA (para-ICA). Genes contributing to the association were examined collectively using pathway analysis tools. Para-ICA extracted five frequency and nine SNP components, of which theta and delta activities were significantly correlated with two different gene components, comprising genes participating extensively in brain development, neurogenesis and synaptogenesis. Delta and theta abnormality was present in both SZ and PBP, while theta differed between the two disorders. Theta abnormalities were also mediated by gene clusters involved in glutamic acid pathways, cadherin and synaptic contact-based cell adhesion processes. Our data suggest plausible multifactorial genetic networks, including novel and several previously identified (DISC1) candidate risk genes, mediating low frequency delta and theta abnormalities in psychoses. The gene clusters were enriched for biological properties affecting neural circuitry and involved in brain function and/or development. PMID:26101851

Evaluation of the adequacy of information from research on infant mortality in Recife, Pernambuco, Brazil.

PubMed

Oliveira, Conceição Maria de; Guimarães, Maria José Bezerra; Bonfim, Cristine Vieira do; Frias, Paulo Germano; Antonino, Verônica Cristina Sposito; Guimarães, Aline Luzia Sampaio; Medeiros, Zulma Maria

2018-03-01

This study is an evaluation of infant death research in Recife, Pernambuco (PE). It is a cross-sectional study with 120 variables grouped into six dimensions (prenatal, birth, child care, family characteristics, occurrence of death, and conclusion and recommendations), weighted by consensus technique. The research was classifiedas adequate, partially adequate or inadequate according to a composite indicator assessment (ICA). There was dissension on 11 variables (9 in prenatal dimension, one in labor and birth, and 1 in the conclusions and recommendations). Of the 568 deaths studied, 56.2% have adequate research. The occurrence of death was the best-evaluated dimension and prenatal the poorest. The preparation of the ICA enables professionals and managers of child health policies to identify bottlenecks in the investigation of infant deaths for better targeting of actions, and contributing to the discussion about surveillance in other cities and states.

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.

Application of Remote Sensing in Geological Mapping, Case Study al Maghrabah Area - Hajjah Region, Yemen

NASA Astrophysics Data System (ADS)

Al-Nahmi, F.; Saddiqi, O.; Hilali, A.; Rhinane, H.; Baidder, L.; El arabi, H.; Khanbari, K.

2017-11-01

Remote sensing technology plays an important role today in the geological survey, mapping, analysis and interpretation, which provides a unique opportunity to investigate the geological characteristics of the remote areas of the earth's surface without the need to gain access to an area on the ground. The aim of this study is achievement a geological map of the study area. The data utilizes is Sentinel-2 imagery, the processes used in this study, the OIF Optimum Index Factor is a statistic value that can be used to select the optimum combination of three bands in a satellite image. It's based on the total variance within bands and correlation coefficient between bands, ICA Independent component analysis (3, 4, 6) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals, MNF Minimum Noise Fraction (1, 2, 3) is used to determine the inherent dimensionality of image data to segregate noise in the data and to reduce the computational requirements for subsequent processing, Optimum Index Factor is a good method for choosing the best band for lithological mapping. ICA, MNF, also a practical way to extract the structural geology maps. The results in this paper indicate that, the studied area can be divided into four main geological units: Basement rocks (Meta volcanic, Meta sediments), Sedimentary rocks, Intrusive rocks, volcanic rocks. The method used in this study offers great potential for lithological mapping, by using Sentinel-2 imagery, the results were compared with existing geologic maps and were superior and could be used to update the existing maps.

Regional Geological Mapping in the Graham Land of Antarctic Peninsula Using LANDSAT-8 Remote Sensing Data

NASA Astrophysics Data System (ADS)

Pour, A. B.; Hashim, M.; Park, Y.

2017-10-01

Geological investigations in Antarctica confront many difficulties due to its remoteness and extreme environmental conditions. In this study, the applications of Landsat-8 data were investigated to extract geological information for lithological and alteration mineral mapping in poorly exposed lithologies in inaccessible domains such in Antarctica. The north-eastern Graham Land, Antarctic Peninsula (AP) was selected in this study to conduct a satellite-based remote sensing mapping technique. Continuum Removal (CR) spectral mapping tool and Independent Components Analysis (ICA) were applied to Landsat-8 spectral bands to map poorly exposed lithologies at regional scale. Pixels composed of distinctive absorption features of alteration mineral assemblages associated with poorly exposed lithological units were detected by applying CR mapping tool to VNIR and SWIR bands of Landsat-8.Pixels related to Si-O bond emission minima features were identified using CR mapping tool to TIR bands in poorly mapped andunmapped zones in north-eastern Graham Land at regional scale. Anomaly pixels in the ICA image maps related to spectral featuresof Al-O-H, Fe, Mg-O-H and CO3 groups and well-constrained lithological attributions from felsic to mafic rocks were detectedusing VNIR, SWIR and TIR datasets of Landsat-8. The approach used in this study performed very well for lithological andalteration mineral mapping with little available geological data or without prior information of the study region.

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.

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.

Transposition of the paraclival carotid artery: a novel concept of self-retaining vascular retraction during endoscopic endonasal skull base surgery technical report.

PubMed

Freeman, Jacob L; Sampath, Raghuram; Casey, Michael A; Quattlebaum, Steven Craig; Ramakrishnan, Vijay R; Youssef, A Samy

2016-08-01

Fixed retraction of the internal carotid artery (ICA) has previously been described for use during transcranial microscopic surgery. We report the novel use of a self-retaining microvascular retractor for static repositioning and protection of the ICA during expanded endonasal endoscopic approaches to the paramedian skull base. The transmaxillary, transpterygoid approach was performed in five cadaver heads (ten sides). The self-retaining microvascular retractor was used to laterally reposition the pterygopalatine fossa contents during exposure of the pterygoid base/plates and the paraclival ICA to expose the petrous apex. Maximum ICA retraction distance was measured in the x-axis for all ten sides. The average horizontal distance of ICA retraction measured at the mid-paraclival segment for all ten sides was 4.75 mm. In all cases, the carotid artery was repositioned without injury to the vessel or disruption of the surrounding neurovascular structures. Static repositioning of the ICA and other delicate neurovascular structures was effectively performed during endonasal, endoscopic cadaveric surgery of the skull base and has potential merits in live patients.

High resolution iridocorneal angle imaging system by axicon lens assisted gonioscopy.

PubMed

Perinchery, Sandeep Menon; Shinde, Anant; Fu, Chan Yiu; Jeesmond Hong, Xun Jie; Baskaran, Mani; Aung, Tin; Murukeshan, Vadakke Matham

2016-07-29

Direct visualization and assessment of the iridocorneal angle (ICA) region with high resolution is important for the clinical evaluation of glaucoma. However, the current clinical imaging systems for ICA do not provide sufficient structural details due to their poor resolution. The key challenges in achieving high quality ICA imaging are its location in the anterior region of the eye and the occurrence of total internal reflection due to refractive index difference between cornea and air. Here, we report an indirect axicon assisted gonioscopy imaging probe with white light illumination. The illustrated results with this probe shows significantly improved visualization of structures in the ICA including TM region, compared to the current available tools. It could reveal critical details of ICA and expected to aid management by providing information that is complementary to angle photography and gonioscopy.

High resolution iridocorneal angle imaging system by axicon lens assisted gonioscopy

PubMed Central

Perinchery, Sandeep Menon; Shinde, Anant; Fu, Chan Yiu; Jeesmond Hong, Xun Jie; Baskaran, Mani; Aung, Tin; Murukeshan, Vadakke Matham

2016-01-01

Direct visualization and assessment of the iridocorneal angle (ICA) region with high resolution is important for the clinical evaluation of glaucoma. However, the current clinical imaging systems for ICA do not provide sufficient structural details due to their poor resolution. The key challenges in achieving high quality ICA imaging are its location in the anterior region of the eye and the occurrence of total internal reflection due to refractive index difference between cornea and air. Here, we report an indirect axicon assisted gonioscopy imaging probe with white light illumination. The illustrated results with this probe shows significantly improved visualization of structures in the ICA including TM region, compared to the current available tools. It could reveal critical details of ICA and expected to aid management by providing information that is complementary to angle photography and gonioscopy. PMID:27471000

High resolution iridocorneal angle imaging system by axicon lens assisted gonioscopy

NASA Astrophysics Data System (ADS)

Perinchery, Sandeep Menon; Shinde, Anant; Fu, Chan Yiu; Jeesmond Hong, Xun Jie; Baskaran, Mani; Aung, Tin; Murukeshan, Vadakke Matham

2016-07-01

Direct visualization and assessment of the iridocorneal angle (ICA) region with high resolution is important for the clinical evaluation of glaucoma. However, the current clinical imaging systems for ICA do not provide sufficient structural details due to their poor resolution. The key challenges in achieving high quality ICA imaging are its location in the anterior region of the eye and the occurrence of total internal reflection due to refractive index difference between cornea and air. Here, we report an indirect axicon assisted gonioscopy imaging probe with white light illumination. The illustrated results with this probe shows significantly improved visualization of structures in the ICA including TM region, compared to the current available tools. It could reveal critical details of ICA and expected to aid management by providing information that is complementary to angle photography and gonioscopy.

Accuracy of Carotid Duplex Criteria in Diagnosis of Significant Carotid Stenosis in Asian Patients.

PubMed

Dharmasaroja, Pornpatr A; Uransilp, Nattaphol; Watcharakorn, Arvemas; Piyabhan, Pritsana

2018-03-01

Extracranial carotid stenosis can be diagnosed by velocity criteria of carotid duplex. Whether they are accurately applied to define severity of internal carotid artery (ICA) stenosis in Asian patients needs to be proved. The purpose of this study was to evaluate the accuracy of 2 carotid duplex velocity criteria in defining significant carotid stenosis. Carotid duplex studies and magnetic resonance angiography were reviewed. Criteria 1 was recommended by the Society of Radiologists in Ultrasound; moderate stenosis (50%-69%): peak systolic velocity (PSV) 125-230 cm/s, diastolic velocity (DV) 40-100 cm/s; severe stenosis (>70%): PSV greater than 230 cm/s, DV greater than 100 cm/s. Criteria 2 used PSV greater than 140 cm/s, DV less than 110 cm/s to define moderate stenosis (50%-75%) and PSV greater than 140 cm/s, DV greater than 110 cm/s for severe stenosis (76%-95%). A total of 854 ICA segments were reviewed. There was moderate stenosis in 72 ICAs, severe stenosis in 50 ICAs, and occlusion in 78 ICAs. Criteria 2 had slightly lower sensitivity, whereas higher specificity and accuracy than criteria 1 were observed in detecting moderate stenosis (criteria 1: sensitivity 95%, specificity 83%, accuracy 84%; criteria 2: sensitivity 92%, specificity 92%, and accuracy 92%). However, in detection of severe ICA stenosis, no significant difference in sensitivity, specificity, and accuracy was found (criteria 1: sensitivity 82%, specificity 99.57%, accuracy 98%; criteria 2: sensitivity 86%, specificity 99.68%, and accuracy 99%). In the subgroup of moderate stenosis, the criteria using ICA PSV greater than 140 cm/s had higher specificity and accuracy than the criteria using ICA PSV 125-230 cm/s. However, there was no significant difference in detection of severe stenosis or occlusion of ICA. Copyright © 2018 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Association of Hypercalcemia Before Treatment With Hypocalcemia After Treatment in Dogs With Primary Hyperparathyroidism.

PubMed

Dear, J D; Kass, P H; Della Maggiore, A M; Feldman, E C

2017-03-01

Development of hypocalcemia after treatment of hyperparathyroidism results in increased costs and risk of poorer outcomes. Previous studies have shown conflicting data about predictors of hypocalcemia after these procedures. The objective of this study was to investigate whether ionized calcium (iCa) concentrations before treatment are predictive of hypocalcemia or its clinical signs after surgical removal or heat ablation in dogs with primary hyperparathyroidism. Fifty-four dogs with primary hyperparathyroidism (29 female, 25 male; 49 retrospective, 5 prospective). Dogs were enrolled if they met the inclusion criteria: persistent hypercalcemia (iCa >1.41 mmol/L) due to primary hyperparathyroidism and absence of preemptive calcitriol treatment. All dogs were treated with parathyroidectomy (n = 37) or percutaneous ultrasound-guided heat ablation (n = 17). After treatment, iCa was monitored twice daily until plateau or intervention. There was a moderate correlation between before-treatment hypercalcemia and after-treatment hypocalcemia. The prospective study was terminated due to ethical concerns given findings in the retrospective section. All dogs were placed into groups according to their pretreatment iCa: 1.46-1.61 mmol/L, 1.62-1.71 mmol/L, iCa 1.72-1.81 mmol/L, or >1.81 mmol/L. After treatment, the mean lowest iCa for each group, respectively, was 1.19, 1.18, 1.13, and 1.01 mmol/L. There was a significant association between higher group and proportion of dogs with iCa <1.00 mmol/L (P = .014). This study demonstrates a moderate correlation between iCa concentration before treatment and hypocalcemia after treatment. Dogs with higher initial iCa concentrations should be treated to prevent rapid decline and development of clinical hypocalcemia. Copyright © 2017 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Enhanced, rapid occlusion of carotid and vertebral arteries using the AMPLATZER Vascular Plug II device: the Duke Cerebrovascular Center experience in 8 patients with 22 AMPLATZER Vascular Plug II devices.

PubMed

Mihlon, Frank; Agrawal, Abishek; Nimjee, Shahid M; Ferrell, Andrew; Zomorodi, Ali R; Smith, Tony P; Britz, Gavin W

2015-01-01

Therapeutic embolization of the common carotid artery (CCA), internal carotid artery (ICA), and vertebral artery (VA) is necessary in the treatment of a subset of chronic arteriovenous fistulas (AVFs), hemorrhages, highly vascularized neoplasms before resection, and giant aneurysms. There are currently no reports of the use of the AMPLATZER Vascular Plug II (AVP II) device to occlude the CCA, ICA, or VA. The objective of this article is to present the Duke Cerebrovascular Center experience using the AVP II device in neurointerventional applications. This case series is a retrospective review of all of the cases at Duke University Hospital in which an AVP II device was used in the CCA, ICA, or VA up to September 2012. The AVP II device was often used in conjunction with embolization coils or as multiple AVP II devices deployed in tandem. During 2010-2012, 8 cases meeting criteria were performed. These included 2 chronic VA to internal jugular AVFs, 1 hemorrhagic CCA to internal jugular AVF secondary to invasive head and neck squamous cell carcinoma, 1 ICA hemorrhage secondary to invasive head and neck squamous cell carcinoma, 1 ICA hemorrhage secondary to trauma, 1 ruptured ICA aneurysm, 1 giant petrous ICA aneurysm, and 1 case of cervical vertebral sarcoma requiring preoperative VA embolization. Successful occlusion of the target vessel was achieved in all 8 cases. There was 1 major complication that consisted of a watershed distribution cerebral infarct; however, this was related to emergent occlusion of the ICA in the setting of intracranial hemorrhage and was not a problem intrinsic to the AVP II device. The AVP II device is relatively large, self-expanding vascular occlusion device that safely allows enhanced, rapid take-down of the CCA, ICA, and VA with low risk of distal migration. Copyright © 2015 Elsevier Inc. All rights reserved.

Spontaneous Recanalization After Carotid Artery Dissection: The Case for an Ultrasound-Only Monitoring Strategy

PubMed Central

Lumsden, Sarah; Rosta, Gabor; Bismuth, Jean; Lumsden, Alan B.; Garami, Zsolt

2017-01-01

Dissection of the internal carotid artery (ICA) accounts for 5% to 25% of ischemic strokes in young adults. We report a case of spontaneous recanalization of a traumatic ICA dissection in which carotid duplex (CDU) and transcranial color-coded duplex ultrasound (TCCD) were used. A 47-year-old male presented with intermittent episodes of headache, blurry vision, anisocoria, and loss of taste sensation following a whiplash injury while body surfing. Magnetic resonance angiogram (MRA) of the neck revealed absent flow in the cavernous ICA and a clot at the skull base. Carotid duplex, used to further evaluate flow, demonstrated reverberating color Doppler and spectrum signal. A TCCD showed ICA occlusion and smaller-caliber intracranial ICA. The patient reported for follow-up after 1 month on anticoagulation therapy. Upon his return, CDU and TCCD were normal and the ICA showed normal color and spectrum signals. Computed tomography angiogram confirmed ultrasound findings of a dramatic improvement of ICA patency. Additionally, the patient reported that his headaches had resolved. Extracranial CDU and TCCD are useful for monitoring patient progress in cases of spontaneous recanalization following carotid artery dissection. These inexpensive and noninvasive imaging modalities proved to be critical in the initial and follow-up evaluations of the extracranial and intracranial vascular system, providing a strong alternative to expensive magnetic resonance imaging and invasive angiograms and offering more hemodynamic information than “static” MRA. PMID:29744017

Association of Inter-arm Blood Pressure Difference with Asymptomatic Intracranial and Extracranial Arterial Stenosis in Hypertension Patients.

PubMed

Wang, Yan; Zhang, Jin; Qian, Yuesheng; Tang, Xiaofeng; Ling, Huawei; Chen, Kemin; Li, Yan; Gao, Pingjin; Zhu, Dingliang

2016-07-14

Inter-arm blood pressure (BP) difference has been associated with ischemic stroke. Local atherosclerosis of stroke differ among vulnerable individuals, whereas intracranial arterial stenosis (ICAS) is more frequently affected Asians, and extracranial arterial stenosis (ECAS) is more prevalent among whites. We hereby sought to explore the association of inter-arm BP difference with ICAS and ECAS in stroke-free hypertensive patients in Chinese population. All the 885 subjects were evaluated of ICAS and ECAS through computerized tomographic angiography. Both arm BP was measured simultaneously by Vascular Profiler-1000 device. In the continuous study, ICAS was significantly associated with age, male, average brachial SBP, diabetes, anti-hypertensive treatment and inter-arm DBP difference. ECAS was associated with age, inter-arm SBP and LDL. In the categorical study, subjects with the top quartile of inter-arm DBP difference (≥4 mmHg) showed significantly higher risk of ICAS (OR = 2.109; 95% CI, 1.24-3.587). And the participants with the top quartile of inter-arm SBP difference (≥6 mmHg) showed significantly higher risk of ECAS (OR = 2.288; 95% CI, 1.309-3.998). In conclusion, we reported a diverse association of inter-arm SBP/DBP difference with the ICAS/ECAS. Inter-arm DBP difference might be the early symbol of ICAS in Chinese population, which need further verification in long-term cohort study.

Standardizing Imperata cylindrica--source material for quality allergen preparations.

PubMed

Bijli, Kaiser M; Singh, B P; Sridhara, Susheela; Gaur, S N; Arora, Naveen

2002-02-01

Tropical countries experience wide variations in daytime temperature and relative humidity. This affects the quality of the source material used for allergen extracts. The present study was undertaken to standardize the processing and preservation conditions of Imperata cylindrica grass pollen. I. cylindrica (Ic) inflorescence were freeze-dried, pollens sieved out and stored at -70 degrees C (IcA). Alternatively, the inflorescence were dried at room temperature and then at 37 degrees C, pollens sieved out and stored at 4 degrees C (IcB). The extracts prepared in PBS were analyzed in vivo by skin tests and in vitro by immunochemical methods. Reduced SDS-PAGE revealed 37 protein bands in IcA extract and 23 in IcB extract. IgE immunoblot with a pool of sera from Ic hypersensitive patients showed 30 allergenic bands in IcA and 14 in IcB. Immunoblot using anti-Ic rabbit sera revealed 33 antigenic bands in IcA and 22 in IcB. In both blots, the IcA extract exhibited sharp bands and the IcB extract exhibited diffuse bands. ELISA, ELISA inhibition and skin test procedures showed that IcA extracts had a higher potency than IcB extracts. Extracts prepared from -70 degrees C processed and preserved pollens (IcA) are allergenically more potent and contain a greater number of major and minor allergens than IcB extracts.

Anatomical nuances of the internal carotid artery in relation to the quadrangular space.

PubMed

Dolci, Ricardo L L; Ditzel Filho, Leo F S; Goulart, Carlos R; Upadhyay, Smita; Buohliqah, Lamia; Lazarini, Paulo R; Prevedello, Daniel M; Carrau, Ricardo L

2018-01-01

OBJECTIVE The aim of this study was to evaluate the anatomical variations of the internal carotid artery (ICA) in relation to the quadrangular space (QS) and to propose a classification system based on the results. METHODS A total of 44 human cadaveric specimens were dissected endonasally under direct endoscopic visualization. During the dissection, the anatomical variations of the ICA and their relationship with the QS were noted. RESULTS The space between the paraclival ICAs (i.e., intercarotid space) can be classified as 1 of 3 different shapes (i.e., trapezoid, square, or hourglass) based on the trajectory of the ICAs. The ICA trajectories also directly influence the volumetric area of the QS. Based on its geometry, the QS was classified as one of the following: 1) Type A has the smallest QS area and is associated with a trapezoid intercarotid space, 2) Type B corresponds to the expected QS area (not minimized or enlarged) and is associated with a square intercarotid space, and 3) Type C has the largest QS area and is associated with an hourglass intercarotid space. CONCLUSIONS The different trajectories of the ICAs can modify the area of the QS and may be an essential parameter to consider for preoperative planning and defining the most appropriate corridor to reach Meckel's cave. In addition, ICA trajectories should be considered prior to surgery to avoid injuring the vessels.

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.

Rationale and design of the dual-energy computed tomography for ischemia determination compared to "gold standard" non-invasive and invasive techniques (DECIDE-Gold): A multicenter international efficacy diagnostic study of rest-stress dual-energy computed tomography angiography with perfusion.

PubMed

Truong, Quynh A; Knaapen, Paul; Pontone, Gianluca; Andreini, Daniele; Leipsic, Jonathon; Carrascosa, Patricia; Lu, Bin; Branch, Kelley; Raman, Subha; Bloom, Stephen; Min, James K

2015-10-01

Dual-energy CT (DECT) has potential to improve myocardial perfusion for physiologic assessment of coronary artery disease (CAD). Diagnostic performance of rest-stress DECT perfusion (DECTP) is unknown. DECIDE-Gold is a prospective multicenter study to evaluate the accuracy of DECT to detect hemodynamic (HD) significant CAD, as compared to fractional flow reserve (FFR) as a reference standard. Eligible participants are subjects with symptoms of CAD referred for invasive coronary angiography (ICA). Participants will undergo DECTP, which will be performed by pharmacological stress, and participants will subsequently proceed to ICA and FFR. HD-significant CAD will be defined as FFR ≤ 0.80. In those undergoing myocardial stress imaging (MPI) by positron emission tomography (PET), single photon emission computed tomography (SPECT) or cardiac magnetic resonance (CMR) imaging, ischemia will be graded by % ischemic myocardium. Blinded core laboratory interpretation will be performed for CCTA, DECTP, MPI, ICA, and FFR. Primary endpoint is accuracy of DECTP to detect ≥1 HD-significant stenosis at the subject level when compared to FFR. Secondary and tertiary endpoints are accuracies of combinations of DECTP at the subject and vessel levels compared to FFR and MPI. DECIDE-Gold will determine the performance of DECTP for diagnosing ischemia.

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

A comparison of the metabolism of the abortifacient compounds from Ponderosa Pine needles in conditioned versus naive cattle

USDA-ARS?s Scientific Manuscript database

Isocupressic acid (ICA) is the abortifacient compound in ponderosa pine needles, which can cause late term abortions in cattle. However, cattle rapidly metabolize ICA to agathic acid and subsequent metabolites. When pine needles are dosed orally to cattle, no ICA is detected in their serum while a...

Proceedings of the ICA Conference (4th, Washington, D.C., November 11-13, 1970).

ERIC Educational Resources Information Center

ICA Information, 1971

1971-01-01

The proceedings of the Fourth Conference of the Intergovernmental Council for ADP-ICA, are contained in this, the 7th issue of the ICA-Information, the Council's official publication. The proceedings include the edited minutes of the sessions, which covered the following topics: Management information systems for the government, Policy…

Evaluation of cerebral blood flow changes in focal cerebral ischemia rats by using transcranial Doppler ultrasonography.

PubMed

Li, Le; Ke, Zheng; Tong, Kai Yu; Ying, Michael

2010-04-01

Ischemic stroke is typically characterized by the disruption of cerebral blood flow. This study aimed to consecutively evaluate the cerebral blood flow changes in a focal ischemia rat model during the occlusion-reperfusion procedure and along the recovery stage after stroke. In 12 Sprague Dawley (SD) rats, a middle cerebral artery occlusion/reperfusion (MCAo/r) surgery was conducted, which combines a permanent occlusion of the right common carotid artery (CCA), external carotid artery (ECA) and a transient occlusion of the right internal carotid artery (ICA) and middle cerebral artery (MCA) with a monofilament introduced from the proximal ICA towards the distal right ICA then removed after 90 min. Blood flow velocity (BFV) from the concerned arteries were measured using ultrasonography (13-4 MHz) at the basal stage before the surgery, after the reperfusion stage and during the post-stroke status. At reperfusion stage and after, BFV increased significantly in the left ICA and in the basilar artery (BA) (starting from post-24 h, p < 0.05 vs. basal). Moreover, BFV were reversed in the distal right ICA and reflow was recorded in the right MCA. Time-average maximum BFV in the right MCA at reperfusion and post-stroke 24-96 h was decreased significantly (p < 0.05 vs. basal). The reversed flow in the right ICA was enabled by the settlement of the collateral supply through the circle of Willis which consisted in higher BFV in the opposite ICA and in the BA still 24 h, although the proximal right ICA remain occluded. Ultrasound measurement of BFV helps to provide information on the redistribution of the blood flow supply after the onset of stroke. Copyright 2010 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Emergency reconstructive endovascular management of intraoperative complications involving the internal carotid artery from trans-sphenoidal surgery.

PubMed

Griauzde, Julius; Gemmete, Joseph J; Pandey, Aditya S; McKean, Erin L; Sullivan, Stephen E; Chaudhary, Neeraj

2015-01-01

To report our experience with intraoperative complications involving the internal carotid artery (ICA) during trans-sphenoidal surgery and their outcome with reconstructive endovascular management. A retrospective review was conducted of patients with an ICA injury related to trans-sphenoidal surgery from 2000 to 2012. Demographic data, clinical charts, indications for treatment, radiographic images, lesion characteristics, operative notes, endovascular procedure notes and post-procedure hospital course were reviewed. Three men and one woman of mean age of 52 years (range 33-74) were identified. The lesions included two macroadenomas, one meningioma and one chondrosarcoma. Risk factors for ICA rupture included two patients with carotid dehiscence, one with sphenoid septal attachment to the ICA, two with revision surgery, one with prior radiation to the tumor, one with bromocriptine treatment and two with acromegaly. In three patients, covered stent placement achieved hemostasis at the site of injury within the ICA. One patient developed delayed bleeding 6 h after covered stent placement and underwent successful endovascular occlusion of the ICA but died 6 days after the injury. The fourth patient had an intraoperative ICA stroke requiring suction thrombectomy, thrombolysis, stent placement and evacuation of an epidural hematoma. At 1-year follow-up, two patients had a modified Rankin score (mRS) and National Institute of Health Stroke Scale (NIHSS) score of 0; in the patient who had a stroke the mRS score was 1 and the NIHSS score 2. Endovascular management with arterial reconstruction is helpful in the treatment of ICA injuries during trans-sphenoidal surgery. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A novel PKC-ι inhibitor abrogates cell proliferation and induces apoptosis in neuroblastoma.

PubMed

Pillai, Prajit; Desai, Shraddha; Patel, Rekha; Sajan, Mini; Farese, Robert; Ostrov, David; Acevedo-Duncan, Mildred

2011-05-01

Protein Kinase C-iota (PKC-ι), an atypical protein kinase C isoform manifests its potential as an oncogene by targeting various aspects of cancer cells such as growth, invasion and survival. PKC-ι confers resistance to drug-induced apoptosis in cancer cells. The acquisition of drug resistance is a major obstacle to good prognosis in neuroblastoma. The focus of this research was to identify the efficacy of [4-(5-amino-4-carbamoylimidazol-1-yl)-2,3-dihydroxycyclopentyl] methyl dihydrogen phosphate (ICA-1) as a novel PKC-ι inhibitor in neuroblastoma cell proliferation and apoptosis. ICA-1 specifically inhibits the activity of PKC-ι but not that of PKC-zeta (PKC-ζ), the closely related atypical PKC family member. The IC(50) for the kinase activity assay was approximately 0.1μM which is 1000 times less than that of aurothiomalate, a known PKC-ι inhibitor. Cyclin dependent kinase 7 (Cdk7) phosphorylates cyclin dependent kinases (cdks) and promotes cell proliferation. Our data shows that PKC-ι is an in vitro Cdk7 kinase and the phosphorylation of Cdk7 by PKC-ι was potently inhibited by ICA-1. Furthermore, our data shows that neuroblastoma cells proliferate via a PKC-ι/Cdk7/cdk2 cell signaling pathway and ICA-1 mediates its antiproliferative effects by inhibiting this pathway. ICA-1 (0.1μM) inhibited the in vitro proliferation of BE(2)-C neuroblastoma cells by 58% (P=0.01). Additionally, ICA-1 also induced apoptosis in neuroblastoma cells. Interestingly, ICA-1 did not affect the proliferation of normal neuronal cells suggesting its potential as chemotherapeutic with low toxicity. Hence, our results emphasize the potential of ICA-1 as a novel PKC-ι inhibitor and chemotherapeutic agent for neuroblastoma. Published by Elsevier Ltd.

Current understanding of chronic total occlusion of the internal carotid artery

PubMed Central

Xu, Baofeng; Li, Chao; Guo, Yunbao; Xu, Kan; Yang, Yi; Yu, Jinlu

2018-01-01

At present, there is limited understanding of chronic total occlusion (CTO) of the internal carotid artery (ICA). Therefore, the present report collected related cases from PubMed and reviewed the literature. Cerebral vessels may form collateral circulation immediately or gradually following CTO of the ICA. The natural history of CTO of the ICA includes a variety of outcomes, all of which are biased toward a non-benign progressive process and are characterized by insufficient cerebral perfusion, embolus detachment and cognitive dysfunction. The majority of cases of CTO of the ICA require treatment. In early studies, the results of external-ICA bypass were unsatisfactory, while recanalization is now considered the only viable option. The current treatment indications mainly depend on the degree of injury to the cerebrovascular reserve and the extent to which the oxygen extraction fraction is increased. The length, height and duration of ICA occlusion are also relevant, though more frequently, the condition depends on multiple factors. Endovascular interventional recanalization, carotid endarterectomy (CEA) and hybrid surgery may be conducted in a select group of patients. As novel materials are developed, the success rate of simple recanalization may gradually increase; however, hybrid surgery may be more representative of the current trend, as advanced CEA can remove carotid atherosclerosis plaques, thus reducing the technological demands of the subsequent interventional recanalization. There are many complications that may result from recanalization following CTO of the ICA, including hyperperfusion and technical errors; therefore, the operation must be conducted carefully. If the recanalization is successful, it typically results in a stable improvement of patient condition in the long term. However, despite these conclusions, more studies are required in the future to further improve current understanding of CTO of the ICA. PMID:29435269

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module.

PubMed

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

2015-08-01

An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p <; 0.05).

Separation of the low-frequency atmospheric variability into non-Gaussian multidimensional sources by Independent Subspace Analysis

NASA Astrophysics Data System (ADS)

Pires, Carlos; Ribeiro, Andreia

2016-04-01

An efficient nonlinear method of statistical source separation of space-distributed non-Gaussian distributed data is proposed. The method relies in the so called Independent Subspace Analysis (ISA), being tested on a long time-series of the stream-function field of an atmospheric quasi-geostrophic 3-level model (QG3) simulating the winter's monthly variability of the Northern Hemisphere. ISA generalizes the Independent Component Analysis (ICA) by looking for multidimensional and minimally dependent, uncorrelated and non-Gaussian distributed statistical sources among the rotated projections or subspaces of the multivariate probability distribution of the leading principal components of the working field whereas ICA restrict to scalar sources. The rationale of that technique relies upon the projection pursuit technique, looking for data projections of enhanced interest. In order to accomplish the decomposition, we maximize measures of the sources' non-Gaussianity by contrast functions which are given by squares of nonlinear, cross-cumulant-based correlations involving the variables spanning the sources. Therefore sources are sought matching certain nonlinear data structures. The maximized contrast function is built in such a way that it provides the minimization of the mean square of the residuals of certain nonlinear regressions. The issuing residuals, followed by spherization, provide a new set of nonlinear variable changes that are at once uncorrelated, quasi-independent and quasi-Gaussian, representing an advantage with respect to the Independent Components (scalar sources) obtained by ICA where the non-Gaussianity is concentrated into the non-Gaussian scalar sources. The new scalar sources obtained by the above process encompass the attractor's curvature thus providing improved nonlinear model indices of the low-frequency atmospheric variability which is useful since large circulation indices are nonlinearly correlated. The non-Gaussian tested sources (dyads and triads, respectively of two and three dimensions) lead to a dense data concentration along certain curves or surfaces, nearby which the clusters' centroids of the joint probability density function tend to be located. That favors a better splitting of the QG3 atmospheric model's weather regimes: the positive and negative phases of the Arctic Oscillation and positive and negative phases of the North Atlantic Oscillation. The leading model's non-Gaussian dyad is associated to a positive correlation between: 1) the squared anomaly of the extratropical jet-stream and 2) the meridional jet-stream meandering. Triadic sources coming from maximized third-order cross cumulants between pairwise uncorrelated components reveal situations of triadic wave resonance and nonlinear triadic teleconnections, only possible thanks to joint non-Gaussianity. That kind of triadic synergies are accounted for an Information-Theoretic measure: the Interaction Information. The dominant model's triad occurs between anomalies of: 1) the North Pole anomaly pressure 2) the jet-stream intensity at the Eastern North-American boundary and 3) the jet-stream intensity at the Eastern Asian boundary. Publication supported by project FCT UID/GEO/50019/2013 - Instituto Dom Luiz.

Investigation of the effect of biologically active threo-Ds-isocitric acid on oxidative stress in Paramecium caudatum.

PubMed

Morgunov, Igor G; Karpukhina, Olga V; Kamzolova, Svetlana V; Samoilenko, Vladimir A; Inozemtsev, Anatoly N

2018-01-02

The effect of biologically active form (threo-Ds-) of isocitric acid (ICA) on oxidative stress was studied using the infusorian Paramecium caudatum stressed by hydrogen peroxide and salts of some heavy metals (Cu, Pb, Zn, and Cd). ICA at concentrations between 0.5 and 10 mM favorably influenced the infusorian cells with oxidative stress induced by the toxicants studied. The maximal antioxidant effect of ICA was observed at its concentration 10 mM irrespective of the toxicant used (either H 2 O 2 or heavy metal ions). ICA was found to be a more active antioxidant than ascorbic acid. Biologically active pharmaceutically pure threo-Ds-ICA was produced through cultivation of the yeast Yarrowia lipolytica and isolated from the culture liquid in the form of crystalline monopotassium salt with a purity of 99.9%.

Traumatic pseudoaneurysm of the intracavernous ICA presenting with massive epistaxis: imaging diagnosis and endovascular treatment.

PubMed

Han, M H; Sung, M W; Chang, K H; Min, Y G; Han, D H; Han, M C

1994-03-01

Traumatic pseudoaneurysm of the intracavernous internal carotid artery (ICA) is a very rare cause of epistaxis but is a life-threatening clinical situation when left untreated. The authors have experienced four cases of traumatic pseudoaneurysm involving the intracavernous ICA. Delayed massive epistaxes developed 1 to 8 months after trauma and initial transient epistaxis in all four patients. Three of the cases were successfully managed by the detachable balloon occlusion (DBO) of the ICA along with the aneurysm openings. In one case, a large pseudoaneurysm destroying a large area of the central skull base with peripheral blood clot was demonstrated on computed tomography, magnetic resonance imaging, and angiography; this patient died due to massive epistaxis before the trial of DBO. Imaging findings of pseudoaneurysms involving the intracavernous ICA in the four cases are described, and the role of endovascular treatment is discussed.

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

TMS-EEG: From basic research to clinical applications

NASA Astrophysics Data System (ADS)

Hernandez-Pavon, Julio C.; Sarvas, Jukka; Ilmoniemi, Risto J.

2014-11-01

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful technique for non-invasively studying cortical excitability and connectivity. The combination of TMS and EEG has widely been used to perform basic research and recently has gained importance in different clinical applications. In this paper, we will describe the physical and biological principles of TMS-EEG and different applications in basic research and clinical applications. We will present methods based on independent component analysis (ICA) for studying the TMS-evoked EEG responses. These methods have the capability to remove and suppress large artifacts, making it feasible, for instance, to study language areas with TMS-EEG. We will discuss the different applications and limitations of TMS and TMS-EEG in clinical applications. Potential applications of TMS are presented, for instance in neurosurgical planning, depression and other neurological disorders. Advantages and disadvantages of TMS-EEG and its variants such as repetitive TMS (rTMS) are discussed in comparison to other brain stimulation and neuroimaging techniques. Finally, challenges that researchers face when using this technique will be summarized.

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.

Immunochromatographic strip assay for detection of bioactive Ganoderma triterpenoid, ganoderic acid A in Ganoderma lingzhi.

PubMed

Sakamoto, Seiichi; Kikkawa, Nao; Kohno, Toshitaka; Shimizu, Kuniyoshi; Tanaka, Hiroyuki; Morimoto, Satoshi

2016-10-01

Ganoderic acid A (GAA) is one of the major Ganoderma triterpenes produced by medicinal mushroom belonging to the genus Ganoderma (Ganodermataceae). Due to its interesting pharmacological activities, Ganoderma species have been traditionally used in China for the treatment of various diseases. Herein, we developed a colloidal gold-based immunochromatographic strip assay (ICA) for the rapid detection of GAA using highly specific monoclonal antibody against GAA (MAb 12A) conjugated with gold nanoparticles. Using the developed ICA, the detection of GAA can be completed within 15min after dipping the test strip into an analyte solution with the limit of detection (LOD) for GAA of ~500ng/mL. In addition, this system makes it possible to perform a semi-quantitative analysis of GAA in Ganoderma lingzhi, where high reliability was evaluated by enzyme-linked immunosorbent assay (ELISA). The newly developed ICA can potentially be applied to the standardization of Ganoderma using GAA as an index because GAA is major triterpenoid present much in the mushroom. Copyright © 2016. Published by Elsevier B.V.

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

Hauth, Elke A. M.; Drescher, Robert; Forsting, Michael

We present a patient with a symptomatic, high-grade stenosis of the internal carotid artery (ICA) and contraindication for open surgery. Endovascular treatment was attempted, but stent placement was not possible. In view of good collateral flow to the related hemisphere, embolization of the stenosis of the ICA with Guglielmi detachable coils (GDCs) was performed to occlude the vessel. No complications occurred during the procedure or in the 1-year follow-up period. In cases where open surgery or endovascular treatment of a stenosis of the ICA are contraindicated or not possible, therapeutic occlusion of the stenotic ICA could be an alternative treatmentmore » option in patients with good collateral flow.« less

Social justice and intercountry adoptions: the role of the U.S. social work community.

PubMed

Roby, Jini L; Rotabi, Karen; Bunkers, Kelley M

2013-10-01

Using social justice as the conceptual foundation, the authors present the structural barriers to socially just intercountry adoptions (ICAs) that can exploit and oppress vulnerable children and families participating in ICAs. They argue that such practices threaten the integrity of social work practice in that arena and the survival of ICA as a placement option. Government structures, disparity of power between countries and families on both sides, perceptions regarding poverty, cultural incompetence, misconceptions about orphans and orphanages, lack of knowledge about the impact of institution-based care, and the profit motive are driving forces behind the growing shadow of unethical ICAs. The U.S. social work community has a large role and responsibility in addressing these concerns as the United States receives the most children adopted through ICAs of all receiving countries. In addition to the centrality of social justice as a core value of the profession, the responsibility to carry out ethical and socially just ICA has recently increased as a matter of law, under the implementation legislation to the Hague Convention on Intercountry Adoption. While acknowledging that these issues are complex, authors provide suggestions for corrective policy and practice measures.

Coiling of a Ruptured Large Internal Carotid Artery Aneurysm via Extracranial-Intracranial Saphenous Vein Bypass Graft Just After Proximal Ligation of the Internal Carotid Artery.

PubMed

Tajima, Hayato; Araki, Yoshio; Izumi, Takashi; Nishihori, Masahiro; Okamoto, Sho; Wakabayashi, Toshihiko

2017-02-01

Combined direct and endovascular surgery has been performed to treat large to giant internal carotid artery (ICA) aneurysms. This report describes successful treatment of a large ICA aneurysm by coiling of the aneurysm via an extracranial-intracranial saphenous vein (SV) graft just after bypass and ICA proximal ligation. A 66-year-old woman presented with a left ICA supraclinoid aneurysm with progressive visual field defect and impaired visual acuity in the left eye. While waiting for scheduled surgery, she experienced a subarachnoid hemorrhage. An extracranial-intracranial high-flow bypass using an SV graft and proximal ligation of the ICA were performed. Coiling of the aneurysm was immediately performed successfully via the SV bypass graft. The patient experienced no new neurologic deficit after this treatment. Follow-up radiologic evaluations using magnetic resonance imaging and magnetic resonance angiography revealed complete aneurysm occlusion. Aneurysm coiling via an extracranial-intracranial SV bypass graft could offer an alternative when an antegrade access route to the ICA is not used because of prior parent artery ligation. Copyright © 2016 Elsevier Inc. All rights reserved.

Intracellular Cs+ activates the PKA pathway, revealing a fast, reversible, Ca2+-dependent inactivation of L-type Ca2+ current.

PubMed

Brette, Fabien; Lacampagne, Alain; Sallé, Laurent; Findlay, Ian; Le Guennec, Jean-Yves

2003-08-01

Inactivation of the L-type Ca2+ current (ICaL) was studied in isolated guinea pig ventricular myocytes with different ionic solutions. Under basal conditions, ICaL of 82% of cells infused with Cs+-based intracellular solutions showed enhanced amplitude with multiphasic decay and diastolic depolarization-induced facilitation. The characteristics of ICaL in this population of cells were not due to contamination by other currents or an artifact. These phenomena were reduced by ryanodine, caffeine, cyclopiazonic acid, the protein kinase A inhibitor H-89, and the cAMP-dependent protein kinase inhibitor. Forskolin and isoproterenol increased ICaL by only approximately 60% in these cells. Cells infused with either N-methyl-d-glucamine or K+-based intracellular solutions did not show multiphasic decay or facilitation under basal conditions. Isoproterenol increased ICaL by approximately 200% in these cells. In conclusion, we show that multiphasic inactivation of ICaL is due to Ca2+-dependent inactivation that is reversible on a time scale of tens of milliseconds. Cs+ seems to activate the cAMP-dependent protein kinase pathway when used as a substitute for K+ in the pipette solution.

Competitive immunochromatographic assay for the detection of the organophosphorus pesticide chlorpyrifos

PubMed Central

Kim, Young Ah; Lee, Eun-Hye; Kim, Kwang-Ok; Lee, Yong Tae; Hammock, Bruce D.; Lee, Hye-Sung

2014-01-01

An immunochromatographic assay (ICA) based on competitive antigen-coated format using colloidal gold as the label was developed for the detection of the organophosphorus insecticide chlorpyrifos. The ICA test strip consisted of a membrane with a detection zone, a sample pad and an absorbent pad. The membrane was separately coated with chlorpyrifos hapten-OVA conjugate (test line) and anti-mouse IgG (control line). Based on the fact that the competition is between the migrating analyte in the sample and the analyte hapten immobilized on the test strip for the binding sites of the antibody-colloidal gold (Ab-CG) conjugate migrating on the test strip, this study suggests that the relative migration speed between the two migrating substances is a critically important factor for the sensitive detection by competitive ICA. This criterion was utilized for the confirmation of appropriateness of a nitrocellulose (NC) membrane for chlorpyrifos ICA. The detection limit of the ICA for chlorpyrifos standard and chlorpyrifos spiked into agricultural samples were 10 and 50 ng mL−1, respectively. The assay time for the ICA test was less than 10 min, suitable for rapid on-site testing of chlorpyrifos. PMID:21504817

Differential Activation of Vascular Smooth Muscle Kv7.4, Kv7.5, and Kv7.4/7.5 Channels by ML213 and ICA-069673

PubMed Central

Brueggemann, Lyubov I.; Haick, Jennifer M.; Cribbs, Leanne L.

2014-01-01

Recent research suggests that smooth muscle cells express Kv7.4 and Kv7.5 voltage-activated potassium channels, which contribute to maintenance of their resting membrane voltage. New pharmacologic activators of Kv7 channels, ML213 (N-mesitybicyclo[2.2.1]heptane-2-carboxamide) and ICA-069673 N-(6-chloropyridin-3-yl)-3,4-difluorobenzamide), have been reported to discriminate among channels formed from different Kv7 subtypes. We compared the effects of ML213 and ICA-069673 on homomeric human Kv7.4, Kv7.5, and heteromeric Kv7.4/7.5 channels exogenously expressed in A7r5 vascular smooth muscle cells. We found that, despite its previous description as a selective activator of Kv7.2 and Kv7.4, ML213 significantly increased the maximum conductance of homomeric Kv7.4 and Kv7.5, as well as heteromeric Kv7.4/7.5 channels, and induced a negative shift of their activation curves. Current deactivation rates decreased in the presence of the ML213 (10 μM) for all three channel combinations. Mutants of Kv7.4 (W242L) and Kv7.5 (W235L), previously found to be insensitive to another Kv7 channel activator, retigabine, were also insensitive to ML213 (10 μM). In contrast to ML213, ICA-069673 robustly activated Kv7.4 channels but was significantly less effective on homomeric Kv7.5 channels. Heteromeric Kv7.4/7.5 channels displayed intermediate responses to ICA-069673. In each case, ICA-069673 induced a negative shift of the activation curves without significantly increasing maximal conductance. Current deactivation rates decreased in the presence of ICA-069673 in a subunit-specific manner. Kv7.4 W242L responded to ICA-069673-like wild-type Kv7.4, but a Kv7.4 F143A mutant was much less sensitive to ICA-069673. Based on these results, ML213 and ICA-069673 likely bind to different sites and are differentially selective among Kv7.4, Kv7.5, and Kv7.4/7.5 channel subtypes. PMID:24944189

Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks.

PubMed

Calhoun, Vince D; Kiehl, Kent A; Pearlson, Godfrey D

2008-07-01

Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called "resting state networks"); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer's disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail. (c) 2008 Wiley-Liss, Inc.

Modulation of Temporally Coherent Brain Networks Estimated Using ICA at Rest and During Cognitive Tasks

PubMed Central

Calhoun, Vince D.; Kiehl, Kent A.; Pearlson, Godfrey D.

2009-01-01

Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called “resting state networks”); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer’s disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail. PMID:18438867

Unilateral agenesis of internal carotid artery associated with congenital combined pituitary hormone deficiency and pituitary stalk interruption without HESX1, LHX4 or OTX2 mutation: a case report.

PubMed

Lamine, Faïza; Kanoun, Faouzi; Chihaoui, Melika; Saveanu, Alexandru; Menif, Emna; Barlier, Anne; Enjalbert, Alain; Brue, Thierry; Slimane, Hédia

2012-12-01

Agenesis of internal carotid artery (ICA) is an unusual finding in subjects with congenital Combined Pituitary hormone deficiency (CPHD) with only nine cases being reported to date but to our best knowledge none of them was genetically investigated. A 10-years old girl presented with severe growth failure (height 103 cm) with substantial bone age delay (3 years). She had no history of perinatal insults or familial CPHD. There was no evidence of mental retardation or craniofacial dysmorphism or ophtalmological abnormalities. She was first diagnosed with GH and TSH deficiency. Cerebral magnetic resonance imaging (MRI) showed hypoplastic anterior pituitary, flat sella turcica, absent pituitary stalk with ectopic posterior pituitary as well as agenesis of the left ICA and the left carotid canal. Genomic analysis of pituitary transcription factor HESX1, LHX4 and OTX2 showed no mutations. Treatment with GH and thyroxine was started. The patient remained free of neurovascular symptoms for 5 years but she presented at the age of 15 years with delayed puberty related to an evolving gonadotropin deficiency. ICA agenesis associated with CPHD is unusual and is often asymptomatic in children. Since the CPHD with pituitary stalk interruption cannot be due to HESX1, LHX4 or OTX2 mutation in our case, other pathogenetic mechanisms may be responsible for CPHD associated with unilateral ICA agenesis.

Association of Inter-arm Blood Pressure Difference with Asymptomatic Intracranial and Extracranial Arterial Stenosis in Hypertension Patients

PubMed Central

Wang, Yan; Zhang, Jin; Qian, Yuesheng; Tang, Xiaofeng; Ling, Huawei; Chen, Kemin; Li, Yan; Gao, Pingjin; Zhu, Dingliang

2016-01-01

Inter-arm blood pressure (BP) difference has been associated with ischemic stroke. Local atherosclerosis of stroke differ among vulnerable individuals, whereas intracranial arterial stenosis (ICAS) is more frequently affected Asians, and extracranial arterial stenosis (ECAS) is more prevalent among whites. We hereby sought to explore the association of inter-arm BP difference with ICAS and ECAS in stroke-free hypertensive patients in Chinese population. All the 885 subjects were evaluated of ICAS and ECAS through computerized tomographic angiography. Both arm BP was measured simultaneously by Vascular Profiler-1000 device. In the continuous study, ICAS was significantly associated with age, male, average brachial SBP, diabetes, anti-hypertensive treatment and inter-arm DBP difference. ECAS was associated with age, inter-arm SBP and LDL. In the categorical study, subjects with the top quartile of inter-arm DBP difference (≥4 mmHg) showed significantly higher risk of ICAS (OR = 2.109; 95% CI, 1.24–3.587). And the participants with the top quartile of inter-arm SBP difference (≥6 mmHg) showed significantly higher risk of ECAS (OR = 2.288; 95% CI, 1.309–3.998). In conclusion, we reported a diverse association of inter-arm SBP/DBP difference with the ICAS/ECAS. Inter-arm DBP difference might be the early symbol of ICAS in Chinese population, which need further verification in long-term cohort study. PMID:27412818

The critical role of the external carotid artery in cerebral perfusion of patients with total occlusion of the internal carotid artery.

PubMed

Dalainas, I; Avgerinos, E D; Daskalopoulos, M E; Papapetrou, A; Papasideris, C P; Katsikas, V; Xiromeritis, K; Moulakakis, K; Gianakopoulos, T; Liapis, C D

2012-02-01

The ipsilateral external carotid artery (ECA) can potentially provide an important collateral pathway for cerebral blood flow in the presence of occlusion or severe stenosis of the internal carotid artery (ICA), recovering up to 15% of the middle cerebral arterial flow. The aim of the study is to elucidate the role of ECA in cerebral flow of patients with total ICA occlusion. Retrospective study of prospectively collected data of 139 patients with total ICA occlusions. The patients were divided to symptomatic and asymptomatic and were categorized in four subgroups according to the stenosis rates: A) ipsilateral ECA<70% and contralateral internal carotid artery stenosis <70%; B) ipsilateral ECA stenosis <70% and contralateral internal carotid artery stenosis ≥70%; C) ipsilateral ECA stenosis ≥70% and contralateral ICA stenosis <70%; D) ipsilateral ECA stenosis ≥70% and contralateral ICA stenosis ≥75%. Fifty eight (41.7%) patients were asymptomatic. The highest rate (48.2%) of asymptomatic patients was in Group A. Among patients with strokes, the highest rate belonged in groups C and D (44.4% and 50% respectively) where ipsilateral ECA stenosis was ≥70% irrespectively of the contralateral ICA patency. Ipsilateral external carotid artery stenosis ≥70% proved to be and independent risk factor for symptom presentation (P=0.013). The study reveals the significant role of ECA patency in cerebral flow in patients with ICA occlusion.

Ion acoustic waves at comet 67P/Churyumov-Gerasimenko. Observations and computations

NASA Astrophysics Data System (ADS)

Gunell, H.; Nilsson, H.; Hamrin, M.; Eriksson, A.; Odelstad, E.; Maggiolo, R.; Henri, P.; Vallieres, X.; Altwegg, K.; Tzou, C.-Y.; Rubin, M.; Glassmeier, K.-H.; Stenberg Wieser, G.; Simon Wedlund, C.; De Keyser, J.; Dhooghe, F.; Cessateur, G.; Gibbons, A.

2017-04-01

Context. On 20 January 2015 the Rosetta spacecraft was at a heliocentric distance of 2.5 AU, accompanying comet 67P/Churyumov-Gerasimenko on its journey toward the Sun. The Ion Composition Analyser (RPC-ICA), other instruments of the Rosetta Plasma Consortium, and the ROSINA instrument made observations relevant to the generation of plasma waves in the cometary environment. Aims: Observations of plasma waves by the Rosetta Plasma Consortium Langmuir probe (RPC-LAP) can be explained by dispersion relations calculated based on measurements of ions by the Rosetta Plasma Consortium Ion Composition Analyser (RPC-ICA), and this gives insight into the relationship between plasma phenomena and the neutral coma, which is observed by the Comet Pressure Sensor of the Rosetta Orbiter Spectrometer for Ion and Neutral Analysis instrument (ROSINA-COPS). Methods: We use the simple pole expansion technique to compute dispersion relations for waves on ion timescales based on the observed ion distribution functions. These dispersion relations are then compared to the waves that are observed. Data from the instruments RPC-LAP, RPC-ICA and the mutual impedance probe (RPC-MIP) are compared to find the best estimate of the plasma density. Results: We find that ion acoustic waves are present in the plasma at comet 67P/Churyumov-Gerasimenko, where the major ion species is H2O+. The bulk of the ion distribution is cold, kBTI = 0.01 eV when the ion acoustic waves are observed. At times when the neutral density is high, ions are heated through acceleration by the solar wind electric field and scattered in collisions with the neutrals. This process heats the ions to about 1 eV, which leads to significant damping of the ion acoustic waves. Conclusions: In conclusion, we show that ion acoustic waves appear in the H2O+ plasmas at comet 67P/Churyumov-Gerasimenko and how the interaction between the neutral and ion populations affects the wave properties. Computer code for the dispersion analysis is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/600/A3

Analyzing Transient Turbuelnce in a Stenosed Carotid Artery by Proper Orthogonal Decomposition

NASA Astrophysics Data System (ADS)

Grinberg, Leopold; Yakhot, Alexander; Karniadakis, George

2009-11-01

High resolution 3D simulation (involving 100M degrees of freedom) were employed to study transient turbulent flow in a carotid arterial bifurcation with a stenosed internal carotid artery (ICA). In the performed simulation an intermittent (in space and time) laminar-turbulent-laminar regime was observed. The simulation reveals the mechanism of the onset of turbulent flow in the stenosed ICA where the narrowing in the artery generates a strong jet flow. Time- and space-window Proper Orthogonal Decomposition (POD) was applied to quantify the different flow regimes in the occluded artery. A simplified version of the POD analysis that utilizes 2D slices only - more appropriate in the clinical setting - was also investigated.

75 FR 45560 - Airworthiness Directives; Rolls-Royce plc (RR) RB211-Trent 800 Series Turbofan Engines

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-03

... limitations section (ALS) of your instructions for continued airworthiness (ICA) to incorporate Task 05-10-01... ALS of your ICA by incorporating any revision of the Rolls-Royce Trent 800 TLM dated prior to the June... ALS of the ICA, any revision of the Rolls-Royce Trent 800 TLM earlier than the June 15, 2009. Other...

Experimental study of hemodynamics in the Circle of Willis.

PubMed

Zhu, Guangyu; Yuan, Qi; Yang, Jian; Yeo, Joon

2015-01-01

The Circle of Willis (CoW) is an important collateral pathway of the cerebral blood flow. An experimental study of the cerebral blood flow (CBF) distribution in different anatomical variations may help to a better understanding of the collateral mechanism of the CoW. An in-vitro test rig was developed to simulate the physiological cerebral blood flow in the CoW. Ten anatomical variations were considered in this study, include a set of different degrees of stenosis in L-ICA and L-ICA occlusion coexist with common anatomical variations. Volume flow rates of efferent arteries and pressure signals at the end of communicating arteries of each case were recorded. Physiological pressure waveforms were applied as inlet boundary condition. In the development of L-ICA stenosis, the total CBF decreases with the increase of stenosis degree. The blood supply of ipsilateral middle cerebral artery (MCA) was affected most by the stenosis of L-ICA. Anterior communicating artery (ACoA) and ipsilateral posterior communicating artery (PCoA) function as important collateral pathways of cerebral collateral circulation when unilateral stenosis occurred. The blood supply of anterior cerebral circulation was compensated by the posterior cerebral circulation through ipsilateral PCoA when L-ICA stenosis degree is greater than 40% and the affected side was compensated immediately by the unaffected side through ACoA. Blood flow of the anterior circulation and the total CBF reached the minimum among all cases studied when L-ICA occlusion coexist with the absence of PCoA. The results demonstrated the flow distribution patterns of the CoW under anatomical variations and clarified the collateral mechanism of the CoW. The flow ACoA is the most sensitive indexes to the morphology change of ipsilateral ICA. The relative independence of the circulation in anterior and posterior sections of the CoW is not broken and the function of ipsilateral PCoA is not activated until a severe stenosis of unilateral ICA occurs. PCoA is the most important collateral pathway of the collateral circulation and the missing of PCoA has the highest risk of stroke when the ipsilateral ICA has severe stenosis. These findings may provide the basis for future therapeutic and diagnosis applications.

Monitoring alert and drowsy states by modeling EEG source nonstationarity

NASA Astrophysics Data System (ADS)

Hsu, Sheng-Hsiou; Jung, Tzyy-Ping

2017-10-01

Objective. As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different brain states and using EEG data to quantitatively assess the state changes. Approach. This study hypothesizes that brain activities under different states, e.g. levels of alertness, can be modeled as distinct compositions of statistically independent sources using independent component analysis (ICA). This study presents a framework to quantitatively assess the EEG source nonstationarity and estimate levels of alertness. The framework was tested against EEG data collected from 10 subjects performing a sustained-attention task in a driving simulator. Main results. Empirical results illustrate that EEG signals under alert versus drowsy states, indexed by reaction speeds to driving challenges, can be characterized by distinct ICA models. By quantifying the goodness-of-fit of each ICA model to the EEG data using the model deviation index (MDI), we found that MDIs were significantly correlated with the reaction speeds (r  =  -0.390 with alertness models and r  =  0.449 with drowsiness models) and the opposite correlations indicated that the two models accounted for sources in the alert and drowsy states, respectively. Based on the observed source nonstationarity, this study also proposes an online framework using a subject-specific ICA model trained with an initial (alert) state to track the level of alertness. For classification of alert against drowsy states, the proposed online framework achieved an averaged area-under-curve of 0.745 and compared favorably with a classic power-based approach. Significance. This ICA-based framework provides a new way to study changes of brain states and can be applied to monitoring cognitive or mental states of human operators in attention-critical settings or in passive brain-computer interfaces.

Tracing the signatures of heavy ions in the plasma environment of 67P/Churyumov-Gerasimenko: Ion Composition Analyzer (ICA) observations

NASA Astrophysics Data System (ADS)

Nicolaou, G.; Yamauchi, M.; Wieser, M.; Nilsson, H.; Behar, E.; Stenberg Wieser, G.

2016-12-01

The Ion Composition Analyzer (ICA) on board ROSETTA is a part of the Rosetta Plasma Consortium (RPC). It is designed to measure the 3-D velocity distribution function of the plasma ions in the environment of the comet 67P/Churyumov-Gerasimenko. Besides the solar wind plasma ions, ICA detected the heavy ions of cometary origin at both low energy (< 100 eV) and at the keV range. So far, ICA distinguished ions of water origin but in principle it should be able to detect CO2+. However, we have not yet succeeded to separate CO2+ ions from O+ or H2O+ ion, mainly due to non-uniform sensitivity and noise level at different mass-channels//azimuthal-sectors, and high cross talk. In May 2016, when ROSETTA was relatively close to the comet (between 6 and 20 km), we observe a second plasma ion population in a higher energy per charge range ( 60-200 eV/q) than the water group ions at 30- 50eV/q. To examine whether this secondary population is still the water group or other ions, such as CO2+, we cleaned the raw data by correcting the non-uniform sensitivity assuming that the noise level should be uniform over different channels. After such a simple cleaning we already found that the mass peak at low energy and that for higher-energy are occasionally similar but in some cases are quite different. Furthermore, we investigate few cases where the low-energy mass peak seems to consist of different Gaussian slopes, indicating that this peak could be composed of two mass peaks. In this presentation we show our techniques we follow to process the data, and we show how we identify the secondary ion component from the May 2016 data.

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

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.

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.