Abstract
Directed connectivity between brain sources identified from scalp electroencephalography (EEG) can shed light on the brain’s information flows and provide a biomarker of neurological disorders. However, as volume conductance results in scalp activity being a mix of activities originating from multiple sources, the correct interpretation of their connectivity is a formidable challenge despite source localization being applied with some success. Traditional connectivity approaches rely on statistical assumptions that usually do not hold for EEG, calling for a model-free approach. We investigated several types of Artificial Neural Networks in estimating Directed Connectivity between Reconstructed EEG Sources and assessed their accuracy with respect to several ground truths. We show that a Long Short-Term Memory neural network with Non-Uniform Embedding yields the most promising results due to its relative robustness to differing dipole locations. We conclude that certain network architectures can compete with the already established methods for brain connectivity analysis.
Highlights
A challenging problem in neuroimaging is to estimate directed connectivity between brain regions reconstructed from scalp EEG recordings but important to unveil their joint dynamics
Measures based upon these are Precision, Sensitivity/Recall, and F1-score (Figure 6), which we used for comparing Temporal Causal Discovery Framework (TCDF), Long ShortTerm Memory network (LSTM)-NUE, Conv2D and Time-Reversed Granger Causality (TRGC)
TRGC outperformed all Artificial Neural Networks (ANNs) in terms of Sensitivity but, statistically, no differences in Precision were found given that the main effect of the connectivity method was only marginally significant
Summary
A challenging problem in neuroimaging is to estimate directed connectivity between brain regions reconstructed from scalp EEG recordings but important to unveil their joint dynamics. In a comprehensive set of simulations, [1] studied the influence of several inverse solutions, the depth of the sources, their reciprocal distance, and the Signal-to-Noise Ratio (SNR) of the recordings They found that all these factors had a significant impact on the resulting connectivity pattern and that the number of spurious connectivity estimations depends heavily on the combinations of these factors. Even when bivariate GC is extended towards multiple time series by conditioning on these other variables, it is still possible that the found influence is caused by a linear mixture of non-interacting sources. This is because the signal measured from one electrode usually contains contributions of several sources [7]. The location of of the dipoles and their connectivity while keeping noise level and the choice of the inverse solution constant
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