1
|
Guo ZH, Zhang JG, Shao XQ, Hu WH, Sang L, Zheng Z, Zhang C, Wang X, Li CD, Mo JJ, Zhang K. Neural network mapping of gelastic behavior in children with hypothalamus hamartoma. World J Pediatr 2024; 20:735-745. [PMID: 37938453 PMCID: PMC11269438 DOI: 10.1007/s12519-023-00763-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 09/17/2023] [Indexed: 11/09/2023]
Abstract
BACKGROUND Hypothalamus hamartomas (HHs) are rare, congenital, tumor-like, and nonprogressive malformations resulting in drug-resistant epilepsy, mainly affecting children. Gelastic seizures (GS) are an early hallmark of epilepsy with HH. The aim of this study was to explore the disease progression and the underlying physiopathological mechanisms of pathological laughter in HH. METHODS We obtained clinical information and metabolic images of 56 HH patients and utilized ictal semiology evaluation to stratify the specimens into GS-only, GS-plus, and no-GS subgroups and then applied contrasted trajectories inference (cTI) to calculate the pseudotime value and evaluate GS progression. Ordinal logistic regression was performed to identify neuroimaging-clinical predictors of GS, and then voxelwise lesion network-symptom mapping (LNSM) was applied to explore GS-associated brain regions. RESULTS cTI inferred the specific metabolism trajectories of GS progression and revealed increased complexity from GS to other seizure types. This was further validated via actual disease duration (Pearson R = 0.532, P = 0.028). Male sex [odds ratio (OR) = 2.611, P = 0.013], low age at seizure onset (OR = 0.361, P = 0.005), high normalized HH metabolism (OR = - 1.971, P = 0.037) and severe seizure burden (OR = - 0.006, P = 0.032) were significant neuroimaging clinical predictors. LNSM revealed that the dysfunctional cortico-subcortico-cerebellar network of GS and the somatosensory cortex (S1) represented a negative correlation. CONCLUSIONS This study sheds light on the clinical characteristics and progression of GS in children with HH. We identified distinct subtypes of GS and demonstrated the involvement of specific brain regions at the cortical-subcortical-cerebellar level. These valuable results contribute to our understanding of the neural correlates of GS.
Collapse
Affiliation(s)
- Zhi-Hao Guo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jian-Guo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiao-Qiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wen-Han Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lin Sang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Zhong Zheng
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chun-De Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
| | - Jia-Jie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
| |
Collapse
|
2
|
Lucas A, Revell A, Davis KA. Artificial intelligence in epilepsy - applications and pathways to the clinic. Nat Rev Neurol 2024; 20:319-336. [PMID: 38720105 DOI: 10.1038/s41582-024-00965-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 06/06/2024]
Abstract
Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.
Collapse
Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew Revell
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
3
|
Wang F, Hong ST, Zhang Y, Xing Z, Lin YX. 18F-FDG-PET/CT for Localizing the Epileptogenic Focus in Patients with Different Types of Focal Cortical Dysplasia. Neuropsychiatr Dis Treat 2024; 20:211-220. [PMID: 38333612 PMCID: PMC10849898 DOI: 10.2147/ndt.s442459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/28/2023] [Indexed: 02/10/2024] Open
Abstract
Purpose To determine the diagnostic and localization value of 18F-fluorodeoxyglucose-positron emission tomography (PET)/computed tomography (CT) in patients with focal cortical dysplasia (FCD) who underwent epilepsy surgery. Methods One hundred and eight patients with pathologically proven FCD who underwent surgery for refractory epilepsy were retrospectively analyzed. All patients underwent magnetic resonance imaging (MRI), 18F-FDG-PET/CT, and video electroencephalography. An MRI diagnosis of FCD was defined as MRI+. A PET/CT diagnosis of FCD was defined as PET/CT+. Results MRI and PET/CT detected FCD in 20.37% and 93.52% of patients, respectively. The difference was significant. Twenty-one patients were MRI+/PET+, 80 were MRI-/PET+, six were MRI-/PET-, and one was MRI+/PET-. The MRI positivity rate was lowest in patients with FCD type IIIa (5.6%, P < 0.05). Prevalence of MRI-/PET+ was highest in patients with FCD type IIIa (88.89%, P < 0.05). Conclusion PET/CT is superior to MRI in detecting FCD. FCD type IIIa was more likely than other types to show MRI-/PET+. This suggests that PET/CT has particular diagnostic value for FCD type IIIa patients with negative MRI findings.
Collapse
Affiliation(s)
- Feng Wang
- Neurosurgery Department, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People’s Republic of China
| | - Shu-Ting Hong
- Neurosurgery Department, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People’s Republic of China
| | - Ying Zhang
- Nuclear Medicine Department, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People’s Republic of China
| | - Zhen Xing
- Radiology Department, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People’s Republic of China
| | - Yuan-Xiang Lin
- Neurosurgery Department, the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People’s Republic of China
| |
Collapse
|
4
|
Zhao X, Hu X, Guo Z, Hu W, Zhang C, Mo J, Zhang K. Deep Learning Approaches for Imaging-Based Automated Segmentation of Tuberous Sclerosis Complex. J Clin Med 2024; 13:680. [PMID: 38337374 PMCID: PMC10856546 DOI: 10.3390/jcm13030680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/22/2023] [Accepted: 01/17/2024] [Indexed: 02/12/2024] Open
Abstract
The present study presents a novel approach for identifying epileptogenic tubers in patients with tuberous sclerosis complex (TSC) and automating tuber segmentation using a three-dimensional convolutional neural network (3D CNN). The study retrospectively included 31 TSC patients whose lesions were manually annotated from multiparametric neuroimaging data. Epileptogenic tubers were determined via presurgical evaluation and stereoelectroencephalography recording. Neuroimaging metrics were extracted and compared between epileptogenic and non-epileptogenic tubers. Additionally, five datasets with different preprocessing strategies were used to construct and train 3D CNNs for automated tuber segmentation. The normalized positron emission tomography (PET) metabolic value was significantly lower in epileptogenic tubers defined via presurgical evaluation (p = 0.001). The CNNs showed high performance for localizing tubers, with an accuracy between 0.992 and 0.994 across the five datasets. The automated segmentations were highly correlated with clinician-based features. The neuroimaging characteristics for epileptogenic tubers were demonstrated, increasing surgical confidence in clinical practice. The validated deep learning detection algorithm yielded a high performance in determining tubers with an excellent agreement with reference clinician-based segmentation. Collectively, when coupled with our investigation of minimal input requirements, the approach outlined in this study represents a clinically invaluable tool for the management of TSC.
Collapse
Affiliation(s)
- Xuemin Zhao
- Department of Neurophysiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100071, China;
| | - Xu Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
- Department of Neurosurgery, Wuxi Taihu Hospital, Wuxi Clinical College of Anhui Medical University, Wuxi 214000, China
| | - Zhihao Guo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China; (X.H.); (Z.G.); (W.H.); (C.Z.)
| |
Collapse
|
5
|
Mo J, Guo Z, Wang X, Zhang J, Hu W, Shao X, Sang L, Zheng Z, Zhang C, Zhang K. Magnetic resonance-guided laser interstitial thermal therapy vs. open surgery for drug-resistant mesial temporal lobe epilepsy: a propensity score matched retrospective cohort study. Int J Surg 2024; 110:306-314. [PMID: 37800596 PMCID: PMC10793731 DOI: 10.1097/js9.0000000000000811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 09/18/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) and traditional open surgery (OS) are effective and safe options for patients with drug-resistant mesial temporal lobe epilepsy (DR-mTLE). However, their superiority in seizure control and preservation of functional abilities remains unclear. This study aimed to compare the surgical outcomes of MRgLITT and OS. MATERIALS AND METHODS This multicenter retrospective cohort study included patients with DR-mTLE who underwent MRgLITT or OS at three centres between 2015 and 2023. The data on patient demographics, presurgical non-invasive evaluation, stereoelectroencephalography (SEEG) implantation, memory alteration, and seizure outcomes were collected. Propensity score matching (PSM) analysis was conducted for the comparison of seizure control and functional preservation between two surgical approaches. RESULTS Of the 244 individuals who met the study criteria, 33 underwent MRgLITT and 211 OS. The median (interquartile range) age at seizure onset was 22.0 (13.0) and 12.3 (10.0) years in the MRgLITT and OS groups, respectively. The first PSM, based on demographic and non-invasive information, resulted in 26 matched pairs for the primary analysis. There were no significant differences in memory preservation ( P = 0.95) or surgical outcomes ( P = 0.96) between the groups. The second PSM, based on demographics and SEEG implantation, yielded 32 matched pairs for the sensitivity analysis, showing similar results. Subset analysis of early and late MRgLITT cases revealed no statistically significant differences in the proportion of patients with memory decline ( P = 0.42) or seizure control ( P = 1.00). Patients who underwent SEEG implantation were 96% less likely to achieve seizure freedom after MRgLITT ( P = 0.02). CONCLUSION Minimally invasive MRgLITT is associated with memory preservation and seizure control, similar to traditional OS. MRgLITT is effective and safe for DR-mTLE and is relevant for future prospective randomized trials on dominant-side mTLE, providing practical implications for guiding neurosurgeons in the selection of surgical approaches.
Collapse
Affiliation(s)
- Jiajie Mo
- Departments ofNeurosurgery
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University
| | - Zhihao Guo
- Departments ofNeurosurgery
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University
| | - Xiu Wang
- Departments ofNeurosurgery
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University
| | - Jianguo Zhang
- Departments ofNeurosurgery
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University
| | - Wenhan Hu
- Departments ofNeurosurgery
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University
| | - Xiaoqiu Shao
- Neurology, Beijing Tiantan Hospital
- China National Clinical Research Center for Neurological Disease, NCRC-ND
| | - Lin Sang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Zhong Zheng
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Chao Zhang
- Departments ofNeurosurgery
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University
| | - Kai Zhang
- Departments ofNeurosurgery
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University
| |
Collapse
|
6
|
Mo J, Dong W, Sang L, Zheng Z, Guo Q, Zhou X, Zhou W, Wang H, Meng X, Yao Y, Wang F, Hu W, Zhang K, Shao X. Multimodal imaging-based diagnostic approach for MRI-negative posterior cortex epilepsy. Ther Adv Neurol Disord 2023; 16:17562864231212254. [PMID: 38021475 PMCID: PMC10657531 DOI: 10.1177/17562864231212254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Background Posterior cortex epilepsy (PCE) primarily comprises seizures originating from the occipital, parietal, and/or posterior edge of the temporal lobe. Electroclinical dissociation and subtle imaging representation render the diagnosis of PCE challenging. Improved methods for accurately identifying patients with PCE are necessary. Objectives To develop a novel voxel-based image postprocessing method for better visual identification of the neuroimaging abnormalities associated with PCE. Design Multicenter, retrospective study. Methods Clinical and imaging features of 165 patients with PCE were retrospectively reviewed and collected from five epilepsy centers. A total of 37 patients (32.4% female, 20.2 ± 8.9 years old) with magnetic resonance imaging (MRI)-negative PCE were finally included for analysis. Image postprocessing features were calculated over a neighborhood for each voxel in the multimodality data. The postprocessed maps comprised structural deformation, hyperintense signal, and hypometabolism. Five raters from three different centers were blinded to the clinical diagnosis and determined the neuroimaging abnormalities in the postprocessed maps. Results The average accuracy of correct identification was 55.7% (range from 43.2 to 62.2%) and correct lateralization was 74.1% (range from 64.9 to 81.1%). The Cronbach's alpha was 0.766 for the correct identification and 0.683 for the correct lateralization with similar results of the interclass correlation coefficient, thus indicating reliable agreement between the raters. Conclusion The image postprocessing method developed in this study can potentially improve the visual detection of MRI-negative PCE. The technique could lead to an increase in the number of patients with PCE who could benefit from the surgery.
Collapse
Affiliation(s)
- Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenyu Dong
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Disease, NCRC-ND, Beijing, China
| | - Lin Sang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Zhong Zheng
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Qiang Guo
- Epilepsy Center, Guangdong Sanjiu Brain Hospital, Guangzhou, China
| | - Xiuming Zhou
- Epilepsy Center, Guangdong Sanjiu Brain Hospital, Guangzhou, China
| | - Wenjing Zhou
- Epilepsy Center, Tsinghua University Yuquan Hospital, Beijing, China
| | - Haixiang Wang
- Epilepsy Center, Tsinghua University Yuquan Hospital, Beijing, China
| | - Xianghong Meng
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Yi Yao
- Department of Functional Neurosurgery, Xiamen Humanity Hospital, Fujian Medical University, Xiamen, China
| | - Fengpeng Wang
- Department of Functional Neurosurgery, Xiamen Humanity Hospital, Fujian Medical University, Xiamen, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Disease, NCRC-ND, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing 100070, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing 100070, China
| |
Collapse
|
7
|
Guo Z, Mo J, Zhang C, Zhang J, Hu W, Zhang K. Brain-clinical signatures for vagus nerve stimulation response. CNS Neurosci Ther 2022; 29:855-865. [PMID: 36415145 PMCID: PMC9928539 DOI: 10.1111/cns.14021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/18/2022] [Accepted: 10/20/2022] [Indexed: 11/25/2022] Open
Abstract
AIM Vagus nerve stimulation (VNS) is a valuable treatment for drug-resistant epilepsy (DRE) without the indication of surgical resection. The clinical heterogeneity of DRE has limited the optimal indication of choice and diagnosis prediction. The study aimed to explore the correlations of brain-clinical signatures with the clinical phenotype and VNS responsiveness. METHODS A total of 89 DRE patients, including VNS- (n = 44) and drug-treated (n = 45) patients, were retrospectively recruited. The brain-clinical signature consisted of demographic information and brain structural deformations, which were measured using deformation-based morphometry and presented as Jacobian determinant maps. The efficacy and presurgical differences between these two cohorts were compared. Then, the potential of predicting VNS response using brain-clinical signature was investigated according to the different prognosis evaluation approaches. RESULTS The seizure reduction was higher in the VNS-treated group (42.50%) as compared to the drug-treated group (12.09%) (p = 0.11). Abnormal imaging representation, showing encephalomalacia (pcorrected = 0.03), was commonly observed in the VNS-treated group (p = 0.04). In the patients treated with VNS, the mild/subtle brain abnormalities indicated higher seizure frequency (p = 0.03) and worse VNS response (p = 0.04). The partial least square regression analysis showed a moderate prediction potential of brain-clinical signature for VNS response (p < 0.01). The increase in the pre-VNS seizure frequency and structural etiology could indicate a worse prognosis (higher McHugh classification). CONCLUSION The brain-clinical signature illustrated its clinical potential in predicting the VNS response, which might allow clinicians to personalize treatment decisions for DRE patients.
Collapse
Affiliation(s)
- Zhihao Guo
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina,Department of NeurosurgeryBeijing Neurosurgical Institute, Capital Medical UniversityBeijingChina
| | - Jiajie Mo
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina,Department of NeurosurgeryBeijing Neurosurgical Institute, Capital Medical UniversityBeijingChina
| | - Chao Zhang
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina,Department of NeurosurgeryBeijing Neurosurgical Institute, Capital Medical UniversityBeijingChina
| | - Jianguo Zhang
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina,Department of NeurosurgeryBeijing Neurosurgical Institute, Capital Medical UniversityBeijingChina,Beijing Key Laboratory of NeurostimulationBeijingChina
| | - Wenhan Hu
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina,Department of NeurosurgeryBeijing Neurosurgical Institute, Capital Medical UniversityBeijingChina,Beijing Key Laboratory of NeurostimulationBeijingChina
| | - Kai Zhang
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina,Department of NeurosurgeryBeijing Neurosurgical Institute, Capital Medical UniversityBeijingChina,Beijing Key Laboratory of NeurostimulationBeijingChina
| |
Collapse
|