ArticleCognitive neurodynamics2016
Children with well controlled epilepsy possess different spatio-temporal patterns of causal network connectivity during a visual working memory task.
Article in Cognitive neurodynamics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- AI-Driven Electrographic Seizure Classification and Seizure Onset Detection Using Image- and Time-Series-Based Approaches.IEEE transactions on bio-medical engineering · 2026Article
- Evolution of brain network dynamics in early Parkinson's disease with mild cognitive impairment.Cognitive neurodynamics · 2023Article
- Hybrid machine learning method for a connectivity-based epilepsy diagnosis with resting-state EEG.Medical & biological engineering & computing · 2022Article
- Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach.AIMS neuroscience · 2020Article
- Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods.Cognitive neurodynamics · 2019Article
- Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis.Cognitive neurodynamics · 2019Article
- Simulation of retinal ganglion cell response using fast independent component analysis.Cognitive neurodynamics · 2018Article
- Aging-related changes of EEG synchronization during a visual working memory task.Cognitive neurodynamics · 2018Article
- Estimation of effective connectivity using multi-layer perceptron artificial neural network.Cognitive neurodynamics · 2018Article
- Altered Functional and Causal Connectivity of Cerebello-Cortical Circuits between Multiple System Atrophy (Parkinsonian Type) and Parkinson's Disease.Frontiers in aging neuroscience · 2017Article
- Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.IEEE transactions on bio-medical engineering · 2016Article
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4 authors.
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Abstract
Using spectral Granger causality (GC) we identified distinct spatio-temporal causal connectivity (CC) patterns in groups of control and epileptic children during the execution of a one-back matching visual discrimination working memory task. Differences between control and epileptic groups were determined for both GO and NOGO conditions. The analysis was performed on a set of 19-channel EEG cortical activity signals. We show that for the GO task, the highest brain activity in terms of the density of the CC networks is observed in α band for the control group while for the epileptic group the CC network seems disrupted as reflected by the small number of connections. For the NOGO task, the denser CC network was observed in θ band for the control group while widespread differences between the control and the epileptic group were located bilaterally at the left temporal-midline and parietal areas. In order to test the discriminative power of our analysis, we performed a pattern analysis approach based on fuzzy classification techniques. The performance of the classification scheme was evaluated using permutation tests. The analysis demonstrated that, on average, 87.6 % of the subjects were correctly classified in control and epileptic. Thus, our findings may provide a helpful insight on the mechanisms pertaining to the cognitive response of children with well controlled epilepsy and could potentially serve as "functional" biomarkers for early diagnosis.
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