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Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN’s ability to generate appropriate input–output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of “Causality coefficient” is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called “CREANN” (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals. [ABSTRACT FROM AUTHOR]

Objective: Recent evidence mainly based on hemodynamic measures suggests that the impairment of functional connections between different brain areas may help to clarify the neuronal dysfunction occurring in patients with disorders of consciousness (DOC). The aim of this study was to evaluate effective EEG connectivity in a cohort of 18 patients in a chronic vegetative state (VS) observed years after the occurrence of hypoxic (eight) and traumatic or hemorrhagic brain insult., Methods: we analysed the EEG signals recorded under resting conditions using a frequency domain linear index of connectivity (partial directed coherence: PDC) estimated from a multivariate autoregressive model. The results were compared with those obtained in ten healthy controls., Results: Our findings indicated significant connectivity changes in EEG activities in delta and alpha bands. The VS patients showed a significant and widespread decrease in delta band connectivity, whereas the alpha activity was hyper-connected in the central and posterior cortical regions., Conclusion: These changes suggest the occurrence of severe circuitry derangements probably due to the loose control of the subcortical connections. The alpha hyper-synchronisation may be due to simplified networks mainly involving the short-range connections between intrinsically oscillatory cortical neurons that generate aberrant EEG alpha sources. This increased connectivity may be interpreted as a reduction in information capacity, implying an increasing prevalence of stereotypic activity patterns., Significance: Our observations suggest a remarkable rearrangement of connectivity in patients with long-standing VS. We hypothesize that in persistent VS, after a first period characterized by a breakdown of cortical connectivity, neurodegenerative processes, largely independent from the type of initial insult, lead to cortex de-afferentation and to a severe reduction of possible cortical activity patterns and states., (Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)