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Reports Summarize Biomedicine and Biomedical Engineering Study Results from New Jersey Institute of Technology (Adaptive Covariance Estimation of...

July 9, 2014



Reports Summarize Biomedicine and Biomedical Engineering Study Results from New Jersey Institute of Technology (Adaptive Covariance Estimation of Non-Stationary Processes and its Application to Infer Dynamic Connectivity From fMRI)

By a News Reporter-Staff News Editor at Biotech Week -- Data detailed on Biotechnology have been presented. According to news reporting originating from Newark, New Jersey, by NewsRx correspondents, research stated, "Time-varying covariance is an important metric to measure the statistical dependence between non-stationary biological processes. Time-varying covariance is conventionally estimated from short-time data segments within a window having a certain bandwidth, but it is difficult to choose an appropriate bandwidth to estimate covariance with different degrees of non-stationarity."

Our news editors obtained a quote from the research from the New Jersey Institute of Technology, "This paper introduces a local polynomial regression (LPR) method to estimate time-varying covariance and performs an asymptotic analysis of the LPR covariance estimator to show that both the estimation bias and variance are functions of the bandwidth and there exists an optimal bandwidth to minimize the mean square error (MSE) locally. A data-driven variable bandwidth selection method, namely the intersection of confidence intervals (ICI), is adopted in LPR for adaptively determining the local optimal bandwidth that minimizes the MSE. Experimental results on simulated signals show that the LPR-ICI method can achieve robust and reliable performance in estimating time-varying covariance with different degrees of variations and under different noise scenarios, making it a powerful tool to study the dynamic relationship between non-stationary biomedical signals. Further, we apply the LPR-ICI method to estimate time-varying covariance of functional magnetic resonance imaging (fMRI) signals in a visual task for the inference of dynamic functional brain connectivity."

According to the news editors, the research concluded: "The results show that the LPR-ICI method can effectively capture the transient connectivity patterns from fMRI."

For more information on this research see: Adaptive Covariance Estimation of Non-Stationary Processes and its Application to Infer Dynamic Connectivity From fMRI. IEEE Transactions on Biomedical Circuits and Systems, 2014;8(2):228-239. IEEE Transactions on Biomedical Circuits and Systems can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - www.ieee.org/; IEEE Transactions on Biomedical Circuits and Systems - ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4156126)

The news editors report that additional information may be obtained by contacting Z.N. Fu, New Jersey Inst Technol, Dept. of Biomed Engn, Newark, NJ 07103, United States. Additional authors for this research include S.C. Chan, X. Di, B. Biswal and Z.G. Zhang (see also Biotechnology).

Keywords for this news article include: Biotechnology, Newark, New Jersey, United States, North and Central America

Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2014, NewsRx LLC


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Source: Biotech Week


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