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Kurt Barbé

As seen in: Wiley Online Library, PLOS ONE
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Articles

LiMMCov: An interactive research tool for efficiently selecting covariance structures in linear mixed models using insights from time series analysis

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By Perseverence Savieri, Vrije Universiteit Brussel, Lara Stas, Kurt Barbé
| PLOS ONE
2. Methods 2.1.1 Deriving correlation matrices using Yule-Walker equations. Time series analysis is used to analyse data points collected or observed at successive points in time [35]. These analyses aim to understand underlying patterns, forecast future values, or model the dependency structure within the data. A key concept in time series analysis is the autocorrelation function, which quantifies the degree of correlation between observations at different time lags [32].
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Neurofilament light chain: A possible fluid biomarker in the intrahippocampal kainic acid mouse model for chronic epilepsy?

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By Marie-Laure Custers, Maxime Vande Vyver, Lea Kaltenböck, Kurt Barbé
| Wiley Online Library Verified
Abstract Objective In the management of epilepsy, there is an ongoing quest to discover new biomarkers to improve the diagnostic process, the monitoring of disease progression, and the evaluation of treatment responsiveness. In this regard, biochemical traceability in biofluids is notably absent in contrast to other diseases. In the present preclinical study, we investigated the potential of neurofilament light chain (NfL) as a possible diagnostic and response fluid biomarker for epilepsy.
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