期刊
BRAIN TOPOGRAPHY
卷 32, 期 4, 页码 675-695出版社
SPRINGER
DOI: 10.1007/s10548-017-0609-4
关键词
Dynamic functional connectivity; Imaginary part of coherency; Generalized partial directed coherence; Frequency-domain granger causality; Surrogate data; EEG
资金
- grant Functional connectivity and neuroplasticity in physiological and pathological aging, PRIN 20102011 [2010SH7H3F_006]
- grant Breaking the Nonuniqueness Barrier in Electromagnetic Neuroimaging (BREAKBEN), H2020-FETO-PEN-2014-2015/H2020-FETOPEN-2014-2015-RIA [686865]
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.
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