Fusion Between EEG Rhythm And MRI Information And Its Application In Epilepsy Study

Electroencephalography(EEG)and functional magnetic resonance imaging(fMRI),as two noninvasive techniques commonly used in neuroinformation research,has been widely used in cognitive neuroscience,neurological and psychiatric diseases,as well as psychological research,to detect brain neural activity at the macro level.Combining EEG and fMRI signals can take advantage of the complementarity of these two technologies,thus probing the spatiotemporal information and providing feasibility for exploring the origin and modulation mechanism of neural activity.Based on the simultaneous EEG and fMRI data,the work in this dissertation expanded the EEG-fMRI fusion framework to detect the relationship between neural oscillation and blood oxygen level-dependent(BOLD)signals in the brain,and further explore the rhythmic characteristics and spatial patterns related to the epileptic activity.Finally,a novel network-based epileptic discharge and modulation model hypothesis was proposed.The main works and findings include the following five parts:1.Starting with the global spatiotemporal features of EEG and the foundation problem of EEG reference,we analyzed the relationship between the mass center of EEG(EEG-CM)and the MRI spatial patterns.Two quantitative EEG indexes,the laterality and the propagation velocity of EEG-CM were proposed,and the travelling patterns of the resting-state EEG rhythm and task-based P300 component were examined.Furthermore,based on the simultaneous EEG-fMRI data,we detected the relationship between EEGCM and the intrinsic brain networks,as well as the white matter fiber tracts in brain.The results show that resting-state rhythmic EEG-CM and the P300-CM have specific spatiotemporal patterns.Moreover,the laterality of EEG-CM travelling is related to the combination of certain intrinsic networks in brain,and the propagation velocity is associated with certain white matter fiber tracts,such as the corpus callosum and corona radiata.The analysis of spatiotemporal EEG-CM and MRI spatial connectivity demonstrates that EEG features are valid representations arising from the subcortical and cortical structure and function organization,thus being helpful for understanding the information processing and resource allocation in brain,and providing basis for the following epilepsy application.2.BOLD-fMRI activity informed by network variation of scalp EEG was estimated in juvenile myoclonic epilepsy based on the EEG-fMRI network fusion method.Furthermore,EEG-fMRI modulatory interaction model was used to explore the couplings between brain regions related to the epileptic activity,and the dependence on the EEG network dynamics.This modulation analysis is useful for detecting the contribution of the EEG representations to fMRI spatial connectivity.The results show the validity of epileptic activity prediction using the high EEG network variation,and the connections among thalamus,frontal areas,sensorimotor areas,and cerebellum were modulated by EEG network organization.These directed connections may be important representations relative to the origin and propagation of epilepsy.This study expands the traditional EEGfMRI fusion framework,and demonstrates the thalamocortical and corticocortical circuits in epilepsy,thus providing potential method for the clinical detection of epileptic activity.3.Furthermore,the EEG-fMRI modulation interaction analysis was extended to investigate the thalamocortical couplings dependent on the modulation of rhythmic dynamics.This section focused on the rhythmic dependence of the couplings between thalamus and the frontal-related networks,and analyzed the alteration of this modulatory dependence in different epilepsy subtypes.The results show that brain rhythms have different modulation effect on the couplings from thalamus to default mode network(DMN),sensorimotor network(SMN)and frontoparietal network(FPN).Moreover,juvenile myoclonic epilepsy(JME)and frontal lobe epilepsy(FLE)have distinct thalamocortical uncoupling patterns,and these rhythm-dependent uncouplings have associations with corticocortical connectivity.These modulation analyses build links between the epilepsy circuit and cortical oscillations,and demonstrate the dependence of thalamus-DMN and thalamus-SMN uncouplings on alpha rhythm in generalized epilepsy.4.From the temporal and spatial aspects,the covariation pattern of spontaneous alpha rhythm and BOLD signals in epileptic brain was investigated.In terms of temporal covariation,temporal representation consistency method was proposed to detect the covariation mode between temporal dynamics of alpha and local BOLD signals,and the influence of alpha dynamics on the connectivity strength of the whole brain was also investigated.In terms of spatial covariation,stable synchronization networks were constructed during epileptic discharges.The relationship of the network characteristics between alpha and the key nodes in epileptic discharge circuit was revealed.The results show that alpha could temporally regulate the focal activity and functional connectivity involving thalamus,caudate and frontal regions.In addition,the network strength of precuneus and thalamus in the synchronization network during the discharges showed correlation with the decreased alpha network integration.These results illustrate the network patterns in different discharge stages,and imply that thalamus and regions in default mode network are key nodes for alpha rhythm regulation due to the epileptic activity and spatial hyperconnectivity.5.Finally,based on the causal analysis among the dynamic network connectivities in subcortical-cortical circuit in epilepsy,and combining the EEG-fMRI fusion results in previous chapters,we proposed the epileptic discharge and modulation model hypothesis.The model describes the changes of subcortical-cortical brain circuits in the terms of network connectivity during the discharges,including the triggering role of the hyperconnectivity in local cortical regions before discharges,the involvement of the thalamocortical and corticocortical alterations during discharges,and the hyperconnectivity in the sensorimotor areas after discharges.Moreover,two possible modulation pathways are suggested by investigating the subcortical circuits.In addition,the alteration and modulation of EEG rhythm oscillation is first fused into the proposed discharge and modulation model hypothesis,thus providing new ideas for understanding the pathogenesis of epilepsy.In summary,this thesis expands the fusion analysis of EEG rhythm characteristics and fMRI spatiotemporal information,and aims to explore the neuropathological mechanism of epileptic brain associated with the abnormal neural oscillations.The contents start from the basis of the fusion of EEG and MRI,and then gradually refined according to the pathological features of epilepsy,summarizing into the epileptic discharge and modulation model hypothesis.This model explains the origin,process and modulation mechanism of epileptic discharges from the perspective of network connection,and integrated the modulations of the EEG rhythmic oscillations.The results can deepen the understanding of the pathogenesis mechanism of epilepsy and have scientific significance for clinical intervention of epilepsy.