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Zhou C, Xie F, Wang D, Huang X, Guo D, Du Y, Xiao L, Liu D, Xiao B, Yang Z, Feng L. Preoperative structural-functional coupling at the default mode network predicts surgical outcomes of temporal lobe epilepsy. Epilepsia 2024; 65:1115-1127. [PMID: 38393301 DOI: 10.1111/epi.17921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVE Structural-functional coupling (SFC) has shown great promise in predicting postsurgical seizure recurrence in patients with temporal lobe epilepsy (TLE). In this study, we aimed to clarify the global alterations in SFC in TLE patients and predict their surgical outcomes using SFC features. METHODS This study analyzed presurgical diffusion and functional magnetic resonance imaging data from 71 TLE patients and 48 healthy controls (HCs). TLE patients were categorized into seizure-free (SF) and non-seizure-free (nSF) groups based on postsurgical recurrence. Individual functional connectivity (FC), structural connectivity (SC), and SFC were quantified at the regional and modular levels. The data were compared between the TLE and HC groups as well as among the TLE, SF, and nSF groups. The features of SFC, SC, and FC were categorized into three datasets: the modular SFC dataset, regional SFC dataset, and SC/FC dataset. Each dataset was independently integrated into a cross-validated machine learning model to classify surgical outcomes. RESULTS Compared with HCs, the visual and subcortical modules exhibited decoupling in TLE patients (p < .05). Multiple default mode network (DMN)-related SFCs were significantly higher in the nSF group than in the SF group (p < .05). Models trained using the modular SFC dataset demonstrated the highest predictive performance. The final prediction model achieved an area under the receiver operating characteristic curve of .893 with an overall accuracy of .887. SIGNIFICANCE Presurgical hyper-SFC in the DMN was strongly associated with postoperative seizure recurrence. Furthermore, our results introduce a novel SFC-based machine learning model to precisely classify the surgical outcomes of TLE.
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Affiliation(s)
- Chunyao Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Fangfang Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Dongcui Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaoting Huang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Danni Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yangsa Du
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ling Xiao
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Dingyang Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhiquan Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Li Feng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, Xiangya Hospital, Central South University (Jiangxi Branch), Nanchang, China
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Jiang Y, Li W, Li J, Li X, Zhang H, Sima X, Li L, Wang K, Li Q, Fang J, Jin L, Gong Q, Yao D, Zhou D, Luo C, An D. Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images. Nat Commun 2024; 15:2221. [PMID: 38472252 DOI: 10.1038/s41467-024-46629-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 03/05/2024] [Indexed: 03/14/2024] Open
Abstract
Artificial intelligence provides an opportunity to try to redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional MRI from 296 individuals with focal epilepsy originating from the temporal lobe (TLE) and 91 healthy controls, we show phenotypic heterogeneity in the pathophysiological progression of TLE. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify two hippocampus-predominant phenotypes, characterized by atrophy beginning in the left or right hippocampus; a third cortex-predominant phenotype, characterized by hippocampus atrophy after the neocortex; and a fourth phenotype without atrophy but amygdala enlargement. These four subtypes are replicated in the independent validation cohort (109 individuals). These subtypes show differences in neuroanatomical signature, disease progression and epilepsy characteristics. Five-year follow-up observations of these individuals reveal differential seizure outcomes among subtypes, indicating that specific subtypes may benefit from temporal surgery or pharmacological treatment. These findings suggest a diverse pathobiological basis underlying focal epilepsy that potentially yields to stratification and prognostication - a necessary step for precise medicine.
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Affiliation(s)
- Yuchao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
| | - Wei Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Geriatrics, West China Hospital, Sichuan University, China National Clinical Research Center for Geriatric Medicine, Chengdu, China
| | - Jinmei Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiuli Li
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Heng Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiutian Sima
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Luying Li
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Kang Wang
- Epilepsy Center, Department of Neurology, The first affiliated hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qifu Li
- Department of Neurology, The first affiliated hospital, Hainan Medical University and the Key Laboratory of Brain Science Research and Transformation in Tropical Environment of Hainan Province, Haikou, Hainan, China
| | - Jiajia Fang
- Department of Neurology, The fourth affiliated hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang, China
| | - Lu Jin
- Psychological Medicine Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, China
| | - Dong Zhou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and technology, University of Electronic Science and Technology of China, Chengdu, China.
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China.
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, China.
| | - Dongmei An
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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Zheng B, Abdulrazeq H, Shao B, Liu DD, Leary O, Lauro PM, Bartolini L, Blum AS, Asaad WF. Seizure and anatomical outcomes of repeat laser amygdalohippocampotomy for temporal lobe epilepsy: A single-institution case series. Epilepsy Behav 2023; 146:109365. [PMID: 37523797 DOI: 10.1016/j.yebeh.2023.109365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/12/2023] [Accepted: 07/20/2023] [Indexed: 08/02/2023]
Abstract
OBJECTIVE In patients with treatment-refractory temporal lobe epilepsy (TLE), a single stereotactic laser interstitial thermotherapy (LITT) procedure is sometimes insufficient to ablate epileptogenic tissue, particularly the medial structures often implicated in TLE. In patients with seizure recurrence after initial ablation, the extent to which a second ablation may achieve improved seizure outcomes is uncertain. The objective of this study was to investigate the feasibility and potential efficacy of repeat LITT amygdalohippocampotomy as a worthwhile strategy for intractable temporal lobe epilepsy by quantifying changes to targeted mesial temporal lobe structures and seizure outcomes. METHODS Patients who underwent two LITT procedures for drug-resistant mesial TLE at our institution were included in the study. Lesion volumes for both procedures were calculated by comparing post-ablation intraoperative sequences to preoperative anatomy. Clinical outcomes after the initial procedure and repeat procedure were classified according to Engel scores. RESULTS Five consecutive patients were included in this retrospective case series: 3 with right- and 2 with left-sided TLE. The median interval between LITT procedures was 294 days (range: 227-1918). After the first LITT, 3 patients experienced class III outcomes, 1 experienced a class IV, and 1 experienced a class IB outcome. All patients achieved increased seizure freedom after a second procedure, with class I outcomes (3 IA, 2 IB). CONCLUSIONS Repeat LITT may be sufficient to achieve satisfactory seizure outcomes in some individuals who might otherwise be considered for more aggressive resection or palliative neuromodulation. A larger study to establish the potential value of repeat LITT amygdalohippocampotomy vs. other re-operation strategies for persistent, intractable temporal lobe epilepsy is worth pursuing.
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Affiliation(s)
- Bryan Zheng
- The Warren Alpert Medical School of Brown University, Providence, RI, USA; Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA
| | - Hael Abdulrazeq
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA.
| | - Belinda Shao
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA
| | - David D Liu
- Department of Neurosurgery, Brigham and Womens Hospital, Boston, MA, USA
| | - Owen Leary
- The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Peter M Lauro
- The Warren Alpert Medical School of Brown University, Providence, RI, USA; Department of Neuroscience, Brown University, Providence, RI, USA
| | - Luca Bartolini
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA; Deparment of Neurology, Rhode Island Hospital, Providence, RI, USA; Deparment of Pediatrics, Hasbro Children's Hospital, Providence, RI, USA
| | - Andrew S Blum
- Deparment of Neurology, Rhode Island Hospital, Providence, RI, USA
| | - Wael F Asaad
- Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA; Department of Neuroscience, Brown University, Providence, RI, USA; The Carney Institute for Brain Science, Brown University, Providence, RI, USA; The Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, USA
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Li Z, Jiang C, Gao Q, Xiang W, Qi Z, Peng K, Lin J, Wang W, Deng B, Wang W. The relationship between the interictal epileptiform discharge source connectivity and cortical structural couplings in temporal lobe epilepsy. Front Neurol 2023; 14:1029732. [PMID: 36846133 PMCID: PMC9948620 DOI: 10.3389/fneur.2023.1029732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 01/09/2023] [Indexed: 02/05/2023] Open
Abstract
Objective The objective of this study was to explore the relation between interictal epileptiform discharge (IED) source connectivity and cortical structural couplings (SCs) in temporal lobe epilepsy (TLE). Methods High-resolution 3D-MRI and 32-sensor EEG data from 59 patients with TLE were collected. Principal component analysis was performed on the morphological data on MRI to obtain the cortical SCs. IEDs were labeled from EEG data and averaged. The standard low-resolution electromagnetic tomography analysis was performed to locate the source of the average IEDs. Phase-locked value was used to evaluate the IED source connectivity. Finally, correlation analysis was used to compare the IED source connectivity and the cortical SCs. Results The features of the cortical morphology in left and right TLE were similar across four cortical SCs, which could be mainly described as the default mode network, limbic regions, connections bilateral medial temporal, and connections through the ipsilateral insula. The IED source connectivity at the regions of interest was negatively correlated with the corresponding cortical SCs. Significance The cortical SCs were confirmed to be negatively related to IED source connectivity in patients with TLE as detected with MRI and EEG coregistered data. These findings suggest the important role of intervening IEDs in treating TLE.
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Affiliation(s)
- Zhensheng Li
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China
| | - Che Jiang
- Department of Neurosurgery, General Hospital of Southern Theater Command, Guangzhou, China
| | - Quwen Gao
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China
| | - Wei Xiang
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China
| | - Zijuan Qi
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China
| | - Kairun Peng
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China
| | - Jian Lin
- Department of Neurosurgery, General Hospital of Southern Theater Command, Guangzhou, China
| | - Wei Wang
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bingmei Deng
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China,Bingmei Deng ✉
| | - Weimin Wang
- Department of Neurosurgery, General Hospital of Southern Theater Command, Guangzhou, China,*Correspondence: Weimin Wang ✉
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Association Between Anatomical Location of Surgically Induced Lesions and Postoperative Seizure Outcome in Temporal Lobe Epilepsy. Neurology 2023; 100:265. [PMID: 35835562 PMCID: PMC10499423 DOI: 10.1212/wnl.0000000000200503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 02/28/2022] [Indexed: 02/01/2023] Open
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Brain structural connectivity sub typing in unilateral temporal lobe epilepsy. Brain Imaging Behav 2022; 16:2220-2228. [PMID: 35674920 DOI: 10.1007/s11682-022-00691-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2022] [Indexed: 11/02/2022]
Abstract
To categorize and clinically characterize subtypes of brain structural connectivity patterns in unilateral temporal lobe epilepsy (TLE). Voxel based morphometry (VBM) and surfaced based morphometry (SBM) analysis were used to detect brain structural alterations associated with TLE from MRI data. Principal component analysis (PCA) was performed to identify subtypes of brain structural connectivity patterns. Correlation analysis was used to explore associations between PC scores and clinical characteristics. A total of 59 patients with TLE and 100 healthy adults were included in this study. Widespread cortical atrophy was shown in both left and right TLE (P < 0.05, FWE corrected). Six principal components (PCs) that explained more than 70% of the variance were extracted for left and right TLE, reflecting patterns of brain structural connectivity. PCs representing perisylvian connectivity were positively correlated with verbal IQ (left TLE: r = 0.696, P < 0.001; right TLE: r = 0.484, P = 0.012) and total IQ (left TLE r = 0.608, P < 0.001) and negatively correlated with disease duration (r = -0.448, P = 0.009). In left TLE, the PC in the ipsilateral mesial temporal region was negatively correlated with age at onset (r = -0.382, P = 0.028). In right TLE, the PC representing the default mode network was negatively correlated with number of antiepileptic drugs (r = -0.407, P = 0.039). This study categorized subtypes of unilateral TLE based on brain structural connectivity patterns. Findings may provide insight into seizure pathways, the pathophysiology of epilepsy, including comorbidities such as cognitive impairment, and help predict treatment outcomes.
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King-Stephens D. Is Node Disconnection the Key for Improving LITT Outcome? Epilepsy Curr 2022; 22:173-175. [DOI: 10.1177/15357597221086394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Sinclair B, Cahill V, Seah J, Kitchen A, Vivash LE, Chen Z, Malpas CB, O'Shea MF, Desmond PM, Hicks RJ, Morokoff AP, King JA, Fabinyi GC, Kaye AH, Kwan P, Berkovic SF, Law M, O'Brien TJ. Machine Learning Approaches for Imaging-Based Prognostication of the Outcome of Surgery for Mesial Temporal Lobe Epilepsy. Epilepsia 2022; 63:1081-1092. [PMID: 35266138 PMCID: PMC9545680 DOI: 10.1111/epi.17217] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 02/09/2022] [Accepted: 03/07/2022] [Indexed: 11/29/2022]
Abstract
Objectives Around 30% of patients undergoing surgical resection for drug‐resistant mesial temporal lobe epilepsy (MTLE) do not obtain seizure freedom. Success of anterior temporal lobe resection (ATLR) critically depends on the careful selection of surgical candidates, aiming at optimizing seizure freedom while minimizing postoperative morbidity. Structural MRI and FDG‐PET neuroimaging are routinely used in presurgical assessment and guide the decision to proceed to surgery. In this study, we evaluate the potential of machine learning techniques applied to standard presurgical MRI and PET imaging features to provide enhanced prognostic value relative to current practice. Methods Eighty two patients with drug resistant MTLE were scanned with FDG‐PET pre‐surgery and T1‐weighted MRI pre‐ and postsurgery. From these images the following features of interest were derived: volume of temporal lobe (TL) hypometabolism, % of extratemporal hypometabolism, presence of contralateral TL hypometabolism, presence of hippocampal sclerosis, laterality of seizure onset volume of tissue resected and % of temporal lobe hypometabolism resected. These measures were used as predictor variables in logistic regression, support vector machines, random forests and artificial neural networks. Results In the study cohort, 24 of 82 (28.3%) who underwent an ATLR for drug‐resistant MTLE did not achieve Engel Class I (i.e., free of disabling seizures) outcome at a minimum of 2 years of postoperative follow‐up. We found that machine learning approaches were able to predict up to 73% of the 24 ATLR surgical patients who did not achieve a Class I outcome, at the expense of incorrect prediction for up to 31% of patients who did achieve a Class I outcome. Overall accuracies ranged from 70% to 80%, with an area under the receiver operating characteristic curve (AUC) of .75–.81. We additionally found that information regarding overall extent of both total and significantly hypometabolic tissue resected was crucial to predictive performance, with AUC dropping to .59–.62 using presurgical information alone. Incorporating the laterality of seizure onset and the choice of machine learning algorithm did not significantly change predictive performance. Significance Collectively, these results indicate that "acceptable" to "good" patient‐specific prognostication for drug‐resistant MTLE surgery is feasible with machine learning approaches utilizing commonly collected imaging modalities, but that information on the surgical resection region is critical for optimal prognostication.
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Affiliation(s)
- Benjamin Sinclair
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Varduhi Cahill
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.,Academic Neurology Unit, University of Sheffield, Royal Hallamshire Hospital, Sheffield, United Kingdom.,Division of Neuroscience and Experimental Psychology, School of Biological Sciences, University of Manchester, Manchester, United Kingdom.,Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Jarrel Seah
- Department of Radiology, Alfred Health, Melbourne, Victoria, Australia
| | - Andy Kitchen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Lucy E Vivash
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Zhibin Chen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Charles B Malpas
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department Neurology, Alfred Health, Melbourne, Victoria, Australia.,Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Melbourne, Victoria, Australia.,Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Marie F O'Shea
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia.,Comprehensive Epilepsy Program, Austin Health, Melbourne, Victoria, Australia
| | - Patricia M Desmond
- Department of Radiology, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Rodney J Hicks
- Peter MacCallum Cancer Centre and the Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew P Morokoff
- Department of Surgery, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - James A King
- Department of Surgery, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Gavin C Fabinyi
- Department of Surgery, University of Melbourne, Austin Hospital, Melbourne, Victoria, Australia
| | - Andrew H Kaye
- Department of Neurosurgery, Hadassah Hebrew University Hospital, Jerusalem, Israel
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department Neurology, Alfred Health, Melbourne, Victoria, Australia.,Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Samuel F Berkovic
- Epilepsy Research Centre, University of Melbourne, Austin Hospital, Melbourne, Victoria, Australia.,Comprehensive Epilepsy Program, Austin Health, Melbourne, Victoria, Australia
| | - Meng Law
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Radiology, Alfred Health, Melbourne, Victoria, Australia
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department Neurology, Alfred Health, Melbourne, Victoria, Australia.,Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Melbourne, Victoria, Australia
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