Zhi-Hao Guo1, Bao-Tian Zhao1, Sheela Toprani2, Wen-Han Hu3, Chao Zhang1, Xiu Wang1, Lin Sang4, Yan-Shan Ma4, Xiao-Qiu Shao5, Babak Razavi2, Josef Parvizi2, Robert Fisher6, Jian-Guo Zhang7, Kai Zhang8. 1. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 2. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA, USA. 3. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China. 4. Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China. 5. Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 6. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA, USA. Electronic address: rfisher@stanford.edu. 7. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China. Electronic address: zjguo73@126.com. 8. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China. Electronic address: zhangkai62035@163.com.
Abstract
OBJECTIVE: The goal of this study was to investigate the spatial extent and functional organization of the epileptogenic network through cortico-cortical evoked potentials (CCEPs) in patients being evaluated with intracranial stereoelectroencephalography. METHODS: We retrospectively included 25 patients. We divided the recorded sites into three regions: epileptogenic zone (EZ); propagation zone (PZ); and noninvolved zone (NIZ). The root mean square of the amplitudes was calculated to reconstruct effective connectivity network. We also analyzed the N1/N2 amplitudes to explore the responsiveness influenced by epileptogenicity. Prognostic analysis was performed by comparing intra-region and inter-region connectivity between seizure-free and non-seizure-free groups. RESULTS: Our results confirmed that stimulation of the EZ caused the strongest responses on other sites within and outside the EZ. Moreover, we found a hierarchical connectivity pattern showing the highest connectivity strength within EZ, and decreasing connectivity gradient from EZ, PZ to NIZ. Prognostic analysis indicated a stronger intra-EZ connection in the seizure-free group. CONCLUSION: The EZ showed highest excitability and dominantly influenced other regions. Quantitative CCEPs can be useful in mapping epileptic networks and predicting surgical outcome. SIGNIFICANCE: The generated computational connectivity model may enhance our understanding of epileptogenic networks and provide useful information for surgical planning and prognosis prediction.
OBJECTIVE: The goal of this study was to investigate the spatial extent and functional organization of the epileptogenic network through cortico-cortical evoked potentials (CCEPs) in patients being evaluated with intracranial stereoelectroencephalography. METHODS: We retrospectively included 25 patients. We divided the recorded sites into three regions: epileptogenic zone (EZ); propagation zone (PZ); and noninvolved zone (NIZ). The root mean square of the amplitudes was calculated to reconstruct effective connectivity network. We also analyzed the N1/N2 amplitudes to explore the responsiveness influenced by epileptogenicity. Prognostic analysis was performed by comparing intra-region and inter-region connectivity between seizure-free and non-seizure-free groups. RESULTS: Our results confirmed that stimulation of the EZ caused the strongest responses on other sites within and outside the EZ. Moreover, we found a hierarchical connectivity pattern showing the highest connectivity strength within EZ, and decreasing connectivity gradient from EZ, PZ to NIZ. Prognostic analysis indicated a stronger intra-EZ connection in the seizure-free group. CONCLUSION: The EZ showed highest excitability and dominantly influenced other regions. Quantitative CCEPs can be useful in mapping epileptic networks and predicting surgical outcome. SIGNIFICANCE: The generated computational connectivity model may enhance our understanding of epileptogenic networks and provide useful information for surgical planning and prognosis prediction.
Authors: Masahiro Sawada; Ralph Adolphs; Brian J Dlouhy; Rick L Jenison; Ariane E Rhone; Christopher K Kovach; Jeremy D W Greenlee; Matthew A Howard Iii; Hiroyuki Oya Journal: Nat Commun Date: 2022-08-20 Impact factor: 17.694