The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Fengyu Cong - One of the best experts on this subject based on the ideXlab platform.
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transient seizure onset network for localization of epileptogenic zone effective connectivity and graph theory based analyses of ecog data in temporal lobe epilepsy
Journal of Neurology, 2019Co-Authors: Fengyu Cong, Xiaoli Li, Tapani Ristaniemi, Yuping Wang, Ruihua ZhangAbstract:Objective Abnormal and dynamic epileptogenic networks cause difficulties for clinical Epileptologists in the localization of the seizure onset zone (SOZ) and the epileptogenic zone (EZ) in preoperative assessments of patients with refractory epilepsy. The aim of this study is to investigate the characteristics of time-varying effective connectivity networks in various non-seizure and seizure periods, and to propose a quantitative approach for accurate localization of SOZ and EZ.
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transient seizure onset network for localization of epileptogenic zone effective connectivity and graph theory based analyses of ecog data in temporal lobe epilepsy
Journal of Neurology, 2019Co-Authors: Ye Ren, Fengyu Cong, Tapani Ristaniemi, Yuping Wang, Ruihua ZhangAbstract:Objective Abnormal and dynamic epileptogenic networks cause difficulties for clinical Epileptologists in the localization of the seizure onset zone (SOZ) and the epileptogenic zone (EZ) in preoperative assessments of patients with refractory epilepsy. The aim of this study is to investigate the characteristics of time-varying effective connectivity networks in various non-seizure and seizure periods, and to propose a quantitative approach for accurate localization of SOZ and EZ. Methods We used electrocorticogram recordings in the temporal lobe and hippocampus from seven patients with temporal lobe epilepsy to characterize the effective connectivity dynamics at a high temporal resolution using the full-frequency adaptive directed transfer function (ffADTF) measure and five graph metrics, i.e., the out-degree (OD), closeness centrality (CC), betweenness centrality (BC), clustering coefficient (C), and local efficiency (LE). The ffADTF effective connectivity network was calculated and described in five frequency bands (δ, θ, α, β, and γ) and five seizure periods (pre-seizure, early seizure, mid-seizure, late seizure, and post-seizure). The cortical areas with high values of graph metrics in the transient seizure onset network were compared with the SOZ and EZ identified by clinical Epileptologists and the results of epilepsy resection surgeries. Results Origination and propagation of epileptic activity were observed in the high time resolution ffADTF effective connectivity network throughout the entire seizure period. The seizure-specific transient seizure onset ffADTF network that emerged at seizure onset time remained for approximately 20-50 ms with strong connections generated from both SOZ and EZ. The values of graph metrics in the SOZ and EZ were significantly larger than that in the other cortical areas. More cortical areas with the highest mean of graph metrics were the same as the clinically determined SOZ in the low-frequency δ and θ bands and in Engel Class I patients than in higher frequency α, β, and γ bands and in Engel Class II and III patients. The OD and C were more likely to localize the SOZ and EZ than CC, BC, and LE in the transient seizure onset network. Conclusion The high temporal resolution ffADTF effective connectivity analysis combined with the graph theoretical analysis helps us to understand how epileptic activity is generated and propagated during the seizure period. The newly discovered seizure-specific transient seizure onset network could be an important biomarker and a promising tool for more precise localization of the SOZ and EZ in preoperative evaluations.
Ruihua Zhang - One of the best experts on this subject based on the ideXlab platform.
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transient seizure onset network for localization of epileptogenic zone effective connectivity and graph theory based analyses of ecog data in temporal lobe epilepsy
Journal of Neurology, 2019Co-Authors: Fengyu Cong, Xiaoli Li, Tapani Ristaniemi, Yuping Wang, Ruihua ZhangAbstract:Objective Abnormal and dynamic epileptogenic networks cause difficulties for clinical Epileptologists in the localization of the seizure onset zone (SOZ) and the epileptogenic zone (EZ) in preoperative assessments of patients with refractory epilepsy. The aim of this study is to investigate the characteristics of time-varying effective connectivity networks in various non-seizure and seizure periods, and to propose a quantitative approach for accurate localization of SOZ and EZ.
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transient seizure onset network for localization of epileptogenic zone effective connectivity and graph theory based analyses of ecog data in temporal lobe epilepsy
Journal of Neurology, 2019Co-Authors: Ye Ren, Fengyu Cong, Tapani Ristaniemi, Yuping Wang, Ruihua ZhangAbstract:Objective Abnormal and dynamic epileptogenic networks cause difficulties for clinical Epileptologists in the localization of the seizure onset zone (SOZ) and the epileptogenic zone (EZ) in preoperative assessments of patients with refractory epilepsy. The aim of this study is to investigate the characteristics of time-varying effective connectivity networks in various non-seizure and seizure periods, and to propose a quantitative approach for accurate localization of SOZ and EZ. Methods We used electrocorticogram recordings in the temporal lobe and hippocampus from seven patients with temporal lobe epilepsy to characterize the effective connectivity dynamics at a high temporal resolution using the full-frequency adaptive directed transfer function (ffADTF) measure and five graph metrics, i.e., the out-degree (OD), closeness centrality (CC), betweenness centrality (BC), clustering coefficient (C), and local efficiency (LE). The ffADTF effective connectivity network was calculated and described in five frequency bands (δ, θ, α, β, and γ) and five seizure periods (pre-seizure, early seizure, mid-seizure, late seizure, and post-seizure). The cortical areas with high values of graph metrics in the transient seizure onset network were compared with the SOZ and EZ identified by clinical Epileptologists and the results of epilepsy resection surgeries. Results Origination and propagation of epileptic activity were observed in the high time resolution ffADTF effective connectivity network throughout the entire seizure period. The seizure-specific transient seizure onset ffADTF network that emerged at seizure onset time remained for approximately 20-50 ms with strong connections generated from both SOZ and EZ. The values of graph metrics in the SOZ and EZ were significantly larger than that in the other cortical areas. More cortical areas with the highest mean of graph metrics were the same as the clinically determined SOZ in the low-frequency δ and θ bands and in Engel Class I patients than in higher frequency α, β, and γ bands and in Engel Class II and III patients. The OD and C were more likely to localize the SOZ and EZ than CC, BC, and LE in the transient seizure onset network. Conclusion The high temporal resolution ffADTF effective connectivity analysis combined with the graph theoretical analysis helps us to understand how epileptic activity is generated and propagated during the seizure period. The newly discovered seizure-specific transient seizure onset network could be an important biomarker and a promising tool for more precise localization of the SOZ and EZ in preoperative evaluations.
Kristl Vonck - One of the best experts on this subject based on the ideXlab platform.
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ictal onset localization through connectivity analysis of intracranial eeg signals in patients with refractory epilepsy
Epilepsia, 2013Co-Authors: Pieter Van Mierlo, Evelien Carrette, Hans Hallez, Robrecht Raedt, Alfred Meurs, Stefaan Vandenberghe, Dirk Van Roost, Paul Boon, Steven Staelens, Kristl VonckAbstract:Summary Purpose Fifteen percent to 25% of patients with refractory epilepsy require invasive video–electroencephalography (EEG) monitoring (IVEM) to precisely delineate the ictal-onset zone. This delineation based on the recorded intracranial EEG (iEEG) signals occurs visually by the Epileptologist and is therefore prone to human mistakes. The purpose of this study is to investigate whether effective connectivity analysis of intracranially recorded EEG during seizures provides an objective method to localize the ictal-onset zone. Methods In this study data were analyzed from eight patients who underwent IVEM at Ghent University Hospital in Belgium. All patients had a focal ictal onset and were seizure-free following resective surgery. The effective connectivity pattern was calculated during the first 20 s of ictal rhythmic iEEG activity. The out-degree, which is reflective of the number of outgoing connections, was calculated for each electrode contact for every single seizure during these 20 s. The seizure specific out-degrees were summed per patient to obtain the total out-degree. The electrode contact with the highest total out-degree was considered indicative of localization of the ictal-onset zone. This result was compared to the conclusion of the visual analysis of the Epileptologist and the resected brain region segmented from postoperative magnetic resonance imaging (MRI). Key findings In all eight patients the electrode contact with the highest total out-degree was among the contacts identified by the Epileptologist as the ictal onset. This contact, that we named “the driver,” always laid within the resected brain region. Furthermore, the patient-specific connectivity patterns were consistent over the majority of seizures. Significance In this study we demonstrated the feasibility of correctly localizing the ictal-onset zone from iEEG recordings by using effective connectivity analysis during the first 20 s of ictal rhythmic iEEG activity.
German Mato - One of the best experts on this subject based on the ideXlab platform.
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two types of ictal phase amplitude couplings in epilepsy patients revealed by spectral harmonicity of intracerebral eeg recordings
Clinical Neurophysiology, 2020Co-Authors: Damian Dellavale, Eugenio Urdapilleta, Nuria Campora, Osvaldo Matias Velarde, Silvia Kochen, German MatoAbstract:Abstract Objective Spectral harmonicity of the ictal activity was analyzed regarding two clinically relevant aspects, (1) as a confounding factor producing ‘spurious’ phase-amplitude couplings (PAC) which may lead to wrong conclusions about the underlying ictal mechanisms, and (2) its role in how good PAC is in correspondence to the seizure onset zone (SOZ) classification performed by the Epileptologists. Methods PAC patterns observed in intracerebral electroencephalography (iEEG) recordings were retrospectively studied during seizures of seven patients with pharmacoresistant focal epilepsy. The time locked index (TLI) measure was introduced to quantify the degree of harmonicity between frequency bands associated to the emergence of PAC during epileptic seizures. Results (1) Harmonic and non harmonic PAC patterns coexist during the seizure dynamics in iEEG recordings with macroelectrodes. (2) Harmonic PAC patterns are an emergent property of the periodic non sinusoidal waveform constituting the epileptiform activity. (3) The TLI metric allows to distinguish the non harmonic PAC pattern, which has been previously associated with the ictal core through the paroxysmal depolarizing shifts mechanism of seizure propagation. Conclusions Our results suggest that the spectral harmonicity of the ictal activity plays a relevant role in the visual analysis of the iEEG recordings performed by the Epileptologists to define the SOZ, and that it should be considered for the proper interpretation of ictal mechanisms. Significance The proposed harmonicity analysis can be used to improve the delineation of the SOZ by reliably identifying non harmonic PAC patterns emerging from fully recruited cortical and subcortical areas.
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spectral harmonicity distinguishes two types of ictal phase amplitude cross frequency couplings in patients candidate to epilepsy surgery
bioRxiv, 2020Co-Authors: Damian Dellavale, Eugenio Urdapilleta, Nuria Campora, Osvaldo Matias Velarde, Silvia Kochen, German MatoAbstract:Objective: Spectral harmonicity of the ictal activity was analyzed regarding two clinically relevant aspects, (1) as a confounding factor producing 9spurious9 phase-amplitude couplings (PAC) which may lead to wrong conclusions about the underlying ictal mechanisms, and (2) its role in how good PAC is in correspondence to the seizure onset zone (SOZ) classification performed by the Epileptologists. Methods: PAC patterns observed in intracerebral electroencephalography (iEEG) recordings were retrospectively studied during seizures of seven patients with pharmacoresistant focal epilepsy. The time locked index (TLI) measure was introduced to quantify the degree of harmonicity between frequency bands associated to the emergence of PAC during epileptic seizures. Results: (1) Harmonic and non harmonic PAC patterns coexist during the seizure dynamics in iEEG recordings with macroelectrodes. (2) Harmonic PAC patterns are an emergent property of the periodic non sinusoidal waveform constituting the epileptiform activity. (3) The TLI metric allows to distinguish the non harmonic PAC pattern, which has been previously associated with the ictal core through the paroxysmal depolarizing shifts mechanism of seizure propagation. Conclusions: Our results suggest that the spectral harmonicity of the ictal activity plays a relevant role in the visual analysis of the iEEG recordings performed by the Epileptologists to define the SOZ, and that it should be considered for the proper interpretation of ictal mechanisms. Significance: The proposed harmonicity analysis can be used to improve the delineation of the SOZ by reliably identifying non harmonic PAC patterns emerging from fully recruited cortical and subcortical areas.
Pieter Van Mierlo - One of the best experts on this subject based on the ideXlab platform.
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ictal onset localization through connectivity analysis of intracranial eeg signals in patients with refractory epilepsy
Epilepsia, 2013Co-Authors: Pieter Van Mierlo, Evelien Carrette, Hans Hallez, Robrecht Raedt, Alfred Meurs, Stefaan Vandenberghe, Dirk Van Roost, Paul Boon, Steven Staelens, Kristl VonckAbstract:Summary Purpose Fifteen percent to 25% of patients with refractory epilepsy require invasive video–electroencephalography (EEG) monitoring (IVEM) to precisely delineate the ictal-onset zone. This delineation based on the recorded intracranial EEG (iEEG) signals occurs visually by the Epileptologist and is therefore prone to human mistakes. The purpose of this study is to investigate whether effective connectivity analysis of intracranially recorded EEG during seizures provides an objective method to localize the ictal-onset zone. Methods In this study data were analyzed from eight patients who underwent IVEM at Ghent University Hospital in Belgium. All patients had a focal ictal onset and were seizure-free following resective surgery. The effective connectivity pattern was calculated during the first 20 s of ictal rhythmic iEEG activity. The out-degree, which is reflective of the number of outgoing connections, was calculated for each electrode contact for every single seizure during these 20 s. The seizure specific out-degrees were summed per patient to obtain the total out-degree. The electrode contact with the highest total out-degree was considered indicative of localization of the ictal-onset zone. This result was compared to the conclusion of the visual analysis of the Epileptologist and the resected brain region segmented from postoperative magnetic resonance imaging (MRI). Key findings In all eight patients the electrode contact with the highest total out-degree was among the contacts identified by the Epileptologist as the ictal onset. This contact, that we named “the driver,” always laid within the resected brain region. Furthermore, the patient-specific connectivity patterns were consistent over the majority of seizures. Significance In this study we demonstrated the feasibility of correctly localizing the ictal-onset zone from iEEG recordings by using effective connectivity analysis during the first 20 s of ictal rhythmic iEEG activity.