Scalp EEG brain functional connectivity networks in pediatric epilepsy

Sargolzaei, Saman; Cabrerizo, Mercedes; Goryawala, Mohammed; Eddin, Anas Salah; Adjouadi, Malek

doi:10.1016/j.compbiomed.2014.10.018

Cited by 58 publications

(59 citation statements)

References 53 publications

(65 reference statements)

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“…In previous studies, decreased FC has often been reported in patients with different types of epilepsy, [26,32,33] suggesting that the epileptiform activity interrupts the functional interactions of the brain. Interestingly, more obvious FC was observed in the patients with BECTS than in the healthy controls.…”

Section: Discussionmentioning

confidence: 99%

Altered functional and effective connectivity in anticorrelated intrinsic networks in children with benign childhood epilepsy with centrotemporal spikes

et al. 2016

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Section: Discussionmentioning

confidence: 99%

Altered functional and effective connectivity in anticorrelated intrinsic networks in children with benign childhood epilepsy with centrotemporal spikes

et al. 2016

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“…A number of studies have looked into various metrics of connectivity among multiple brain regions from EEG/MEG [47], [104]–[112] and from fMRI [113]–[120]. In addition to Granger causality and DCM approaches, graph theoretic methods have also been used to study brain functional connectivity from ECoG [121] and EEG [122], [123]. …”

Section: Discussionmentioning

confidence: 99%

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

Sohrabpour

Worrell

et al. 2016

IEEE Trans. Biomed. Eng.

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Objective Combined source imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a non-invasive fashion. Source imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source imaging algorithms to both find the network nodes (regions of interest) and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Methods Source imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from inter-ictal and ictal signals recorded by EEG and/or MEG. Results Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ~20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Conclusion Our study indicates that combined source imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). Significance The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

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“…A precise localization of the epileptogenic focus in patients with neocortical epilepsy who underwent surgery has been possible using connectivity analysis of resting-state MEG data irrespective of the presence or absence of epileptic spikes (Krishnan et al 2015). Furthermore, functional connectivity networks have successfully been used to discriminate children with epilepsy from controls blindly to clinical data, with an accuracy of 88.8 %, a sensitivity of 81.8 %, and 100 % specificity (Sargolzaei et al 2015). Although more research is needed, it is promising that brain connectivity analysis can provide valuable information for an early diagnosis of epilepsy or for identification of the epileptogenic zone in patients without epileptiform discharges or without structural lesions.…”

Section: Functional Connectivity In the Clinical Diagnosis Of Epilepsymentioning

confidence: 99%

The Role of Functional Networks in Neuropsychiatric Disorders

Canuet¹,

Aoki

Ishii

et al. 2016

Multimodal Oscillation-Based Connectivity Theory

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Scalp EEG brain functional connectivity networks in pediatric epilepsy

Cited by 58 publications

References 53 publications

Altered functional and effective connectivity in anticorrelated intrinsic networks in children with benign childhood epilepsy with centrotemporal spikes

Altered functional and effective connectivity in anticorrelated intrinsic networks in children with benign childhood epilepsy with centrotemporal spikes

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

The Role of Functional Networks in Neuropsychiatric Disorders

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