Epilepsy often accompanies cognitive impairments,which are featured by dynamics of EEG data.The eigenmode method,combined with functional networks derived from EEG data,provides a valid method to investigate dynamical...Epilepsy often accompanies cognitive impairments,which are featured by dynamics of EEG data.The eigenmode method,combined with functional networks derived from EEG data,provides a valid method to investigate dynamical characteristics of the brain’s integration and segregation while establishing connections with cognitive function.Based on the transfer entropy method,we utilize the eigenmode approach to analyze SEEG data from epilepsy patients,which extends the theory of eigenmode hierarchical modules to directed functional networks.This work mainly refines and employs the dynamical characteristics from the eigenmodes of the epilepsy directional functional networks,including integration and segregation theories and proposes the network’s functional recombination rate feature.Results indicate that directed functional networks constructed through transfer entropy can also manifest the phenomenon of hierarchical modules in brain functional modes.In addition,during epileptic seizures,higher layers of overall integration features and increased functional recombination rates are observed.Furthermore,alterations in the aggregation of prominent nodes within the eigenmodes of epilepsy patients are noted during seizure episodes.This paper provides an improved method for the analysis of dynamical features of directed epilepsy network,which may potentially provide help and new understanding for the analysis of functional features of epilepsy.展开更多
基金supported by the National Natural Science Foundation of China(Grants Nos.12332004,12202027,12272092,11932003).
文摘Epilepsy often accompanies cognitive impairments,which are featured by dynamics of EEG data.The eigenmode method,combined with functional networks derived from EEG data,provides a valid method to investigate dynamical characteristics of the brain’s integration and segregation while establishing connections with cognitive function.Based on the transfer entropy method,we utilize the eigenmode approach to analyze SEEG data from epilepsy patients,which extends the theory of eigenmode hierarchical modules to directed functional networks.This work mainly refines and employs the dynamical characteristics from the eigenmodes of the epilepsy directional functional networks,including integration and segregation theories and proposes the network’s functional recombination rate feature.Results indicate that directed functional networks constructed through transfer entropy can also manifest the phenomenon of hierarchical modules in brain functional modes.In addition,during epileptic seizures,higher layers of overall integration features and increased functional recombination rates are observed.Furthermore,alterations in the aggregation of prominent nodes within the eigenmodes of epilepsy patients are noted during seizure episodes.This paper provides an improved method for the analysis of dynamical features of directed epilepsy network,which may potentially provide help and new understanding for the analysis of functional features of epilepsy.
