A Comparative Study of the Robustness of Frequency-Domain Connectivity Measures to Finite Data Length
| العنوان: | A Comparative Study of the Robustness of Frequency-Domain Connectivity Measures to Finite Data Length |
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| المؤلفون: | Vittorio Pizzella, Alberto Sorrentino, Michele Piana, Sara Sommariva, Laura Marzetti |
| بيانات النشر: | Springer, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Imaginary part of coherency, Computer science, Quantitative Biology - Quantitative Methods, Generalized partial directed coherence, 050105 experimental psychology, Surrogate data, 03 medical and health sciences, Random Allocation, 0302 clinical medicine, Granger causality, Statistics, False positive paradox, FOS: Mathematics, Coherence (signal processing), Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, Computer Simulation, Mathematics - Numerical Analysis, EEG, Quantitative Methods (q-bio.QM), Dynamic functional connectivity, Radiological and Ultrasound Technology, Computer simulation, 05 social sciences, Brain, Reproducibility of Results, Electroencephalography, Numerical Analysis (math.NA), Neurology, Autoregressive model, Frequency domain, FOS: Biological sciences, Quantitative Biology - Neurons and Cognition, Frequency-domain granger causality, Neurons and Cognition (q-bio.NC), Neurology (clinical), Anatomy, 030217 neurology & neurosurgery, Algorithms |
| الوصف: | In this work we use numerical simulation to investigate how the temporal length of the data affects the reliability of the estimates of brain connectivity from EEG time--series. We assume that the neural sources follow a stable MultiVariate AutoRegressive model, and consider three connectivity metrics: Imaginary part of Coherency (IC), generalized Partial Directed Coherence (gPDC) and frequency--domain Granger Causality (fGC). In order to assess the statistical significance of the estimated values, we use the surrogate data test by generating phase--randomized and autoregressive surrogate data. We first consider the ideal case where we know the source time courses exactly. Here we show how, expectedly, even exact knowledge of the source time courses is not sufficient to provide reliable estimates of the connectivity when the number of samples gets small; however, while gPDC and fGC tend to provide a larger number of false positives, the IC becomes less sensitive to the presence of connectivity. Then we proceed with more realistic simulations, where the source time courses are estimated using eLORETA, and the EEG signal is affected by biological noise of increasing intensity. Using the ideal case as a reference, we show that the impact of biological noise on IC estimates is qualitatively different from the impact on gPDC and fGC. 32 pages, 13 figures |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aeb671c40dbf915a10bb359075286d05 https://hdl.handle.net/11567/884322 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....aeb671c40dbf915a10bb359075286d05 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |