Progressive topological disorganization of brain network in focal epilepsy

Park, Kang Min; Lee, B. I.; Shin, Kyoungsoon; Ha, Sam Yeol; Park, J.; Kim, T. H.; Mun, Chi-Woong; Kim, S. E.

doi:10.1111/ane.12899

Cited by 35 publications

(38 citation statements)

References 34 publications

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“…We also found a similar tendency TA B L E 1 Differences of the global structural connectivity in patients with JME and healthy controls in another study with a newly diagnosed focal epilepsy based on diffusion tensor imaging, and this is suggestive of the presence of disrupted topological organization of brain network in focal epilepsy as well as generalized epilepsy. 14 despite the lack of statistical differences. Several previous studies with healthy controls have investigated structural and functional relationships in a variety of brain networks, and taken together, this work shows a general agreement that functionally connected regions are also structurally well connected.…”

Section: Discussionmentioning

confidence: 94%

Structural and functional connectivity in newly diagnosed juvenile myoclonic epilepsy

Lee

Park

2019

Acta Neurol Scand

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Objectives We aimed to evaluate structural and functional connectivity of patients with newly diagnosed juvenile myoclonic epilepsy (JME) compared to healthy subjects. Methods We enrolled 36 patients with a diagnosis of JME, who were newly diagnosed and drug‐naïve. They underwent T1‐weighted imaging, and structural volumes were calculated using FreeSurfer software. In addition, EEG data were obtained from all of them. Structural and functional connectivity matrices were estimated by calculating the structural volumes and EEG amplitude correlation, respectively. Then, the connectivity measures were calculated using the BRAPH program. We also enrolled healthy subjects to compare its structural and functional connectivity with the patients with JME. Results We observed that patients with JME exhibited significantly different functional and structural connectivity compared to healthy control subjects. In the global structural connectivity, global efficiency, and local efficiency, and small‐worldness index were decreased, whereas characteristic path length was increased in patients with JME. Betweenness centrality of cingulate, precentral, superior parietal, and superior frontal cortex was increased in patients with JME whereas that of hippocampus was decreased. In the functional connectivity, the betweenness centrality of the fronto‐central electrodes was significantly increased. Conclusions This study reports that the structural connectivity and functional connectivity in patients with JME are significantly different from those in healthy control subjects, even those with newly diagnosed drug‐naive state. The patients with JME exhibited disrupted topological disorganization of the global brain network and hub reorganization in structural and functional connectivity. These alterations are implicated in the pathogenesis of JME and suggestive of network disease.

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Section: Discussionmentioning

confidence: 94%

Structural and functional connectivity in newly diagnosed juvenile myoclonic epilepsy

Lee

Park

2019

Acta Neurol Scand

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“…The first model states that epileptic activity itself causes changes in the brain network organization. This theory is supported by the finding that network abnormalities become more prominent as the duration of epilepsy increases (Qiu et al, 2017;Park et al, 2018). The second model focuses on the epilepsy origin primarily and states that the underlying disease etiology is directly responsible for the disruption of neural networks, which in the end may lead to seizure generation.…”

Section: Mechanistic Concepts For Network Alteration In Epilepsymentioning

confidence: 97%

The etiological classification of the epilepsies: A brain network underpinning?

Slinger

Otte

Noorlag

et al. 2019

Preprint

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Objective: the current epilepsy classification is primarily clinical driven and lacks a mechanistic basis. A mechanistic basis of the classification, and within the classification especially the etiology layer, may help to better understand epilepsy and the associated comorbidities. It may also be helpful in guiding epilepsy treatment. With this study we aimed to investigate if there is a modelled mechanistic underpinning for the etiological epilepsy classification by assessing the association between epilepsy etiology and brain network topology.Methods: to that aim we assessed the association between epilepsy etiology and brain network topology.We included children referred to our outpatient first seizure clinic with suspected epilepsy who had a standard interictal EEG recording. From these EEGs, functional networks were constructed based on eyesclosed resting state time-series. Networks were characterized using measures of segregation, integration, centrality, and network strength. Principal component analyses were used to assess whether patients with epilepsy of similar etiology cluster together based on their functional brain network topology.Results: in total, 228 children with epilepsy were included. Another 402 children served as control subjects.We were not able to detect a correlation between epilepsy etiology and functional brain network topology.We also did not find a difference in brain network topology between the controls and patients with epilepsy.Conclusions: our results do not support the presence of a brain network underpinning for the etiological epilepsy classification. This may support the hypothesis that brain network abnormalities in epilepsy are a result of ongoing seizure activity rather than the epilepsy etiology itself. Further in-depth analyses of network measures and longitudinal studies are needed to confirm this hypothesis.

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“…To obtain ideal results in a shorter detection period, the existing approach is to analyze the EEG information interaction between different brain regions, that is, to build brain networks to provide the possibility to distinguish epilepsy patients from healthy controls (Hassan et al, 2017;Najm, 2018;Park et al, 2018). According to the brain network, disruptions of its connectivity are likely to generate brain disorders such as epilepsy, migraine, and schizophrenia (Van den Heuvel and Fornito, 2014;Van Mierlo et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Analogically, epileptic discharges are likely to cause abnormal brain network wiring and dynamics. Therefore, exploring the differences in topological features between epileptic and healthy brain networks has become a common method to detect epilepsy, but the detection effect is not always satisfactory (De Lathauwer et al, 2000;Booth, 2005;Subramaniyam and Hyttinen, 2013;Preti et al, 2014;Najm, 2018;Park et al, 2018;Rosch et al, 2018;Li and Cao, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Feature Tensor-Based Epileptic Detection Model Based on Improved Edge Removal Approach for Directed Brain Networks

Song

Huo

et al. 2020

Front. Neurosci.

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Electroencephalograph (EEG) plays a significant role in the diagnostics process of epilepsy, but the detection rate is unsatisfactory when the length of interictal EEG signals is relatively short. Although the deliberate attacking theories for undirected brain network based on node removal method can extract potential network features, the node removal method fails to sufficiently consider the directionality of brain electrical activities. To solve the problems above, this study proposes a feature tensor-based epileptic detection method of directed brain networks. First, a directed functional brain network is constructed by calculating the transfer entropy of EEG signals between different electrodes. Second, the edge removal method is used to imitate the disruptions of brain connectivity, which may be related to the disorder of brain diseases, to obtain a sequence of residual networks. After that, topological features of these residual networks are extracted based on graph theory for constructing a five-way feature tensor. To exploit the inherent interactions among multiple modes of the feature tensor, this study uses the Tucker decomposition method to get a core tensor which is finally reshaped into a vector and input into the support vectors machine (SVM) classifier. Experiment results suggest that the proposed method has better epileptic screening performance for short-term interictal EEG data.

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Progressive topological disorganization of brain network in focal epilepsy

Abstract: We newly found a progressive topological disorganization of the brain network in focal epilepsy. In addition, we demonstrated disrupted topological organization in focal epilepsy, shifting toward a more random state.

Cited by 35 publications

References 34 publications

Structural and functional connectivity in newly diagnosed juvenile myoclonic epilepsy

Structural and functional connectivity in newly diagnosed juvenile myoclonic epilepsy

The etiological classification of the epilepsies: A brain network underpinning?

A Feature Tensor-Based Epileptic Detection Model Based on Improved Edge Removal Approach for Directed Brain Networks

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