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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.
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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.
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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.
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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.
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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.
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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.)
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Wang Z, Mo J, Zhang J, Feng T, Zhang K. Surface-Based Neuroimaging Pattern of Multiple System Atrophy. Acad Radiol 2023; 30:2999-3009. [PMID: 37495425 DOI: 10.1016/j.acra.2023.04.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 07/28/2023]
Abstract
RATIONALE AND OBJECTIVES Overlapping parkinsonism, cerebellar ataxia, and pyramidal signs render challenges in the clinical diagnosis of multiple system atrophy (MSA). The neuroimaging pattern is valuable to understand its pathophysiology and improve its diagnostic effect. MATERIALS AND METHODS We retrospectively obtained magnetic resonance imaging and susceptibility-weighted imaging in patients with MSA (including parkinsonian type [MSA-P] and cerebellar type [MSA-C]), Parkinson's disease, and normal controls. We quantified neuroimaging features to identify the optimal threshold for diagnosis. Furthermore, we explore neuroimaging patterns of MSA by mapping the subcortical morphological alterations and constructing a diagnostic model. RESULTS Compared to controls, normalized putaminal volume significantly decreased in patients with MSA-P (P < .001) and normalized pontine volume significantly decreased in patients with MSA-C (P < .001). The Youden index of the threshold-based clinical prediction model was 0.871-0.928 in patients with MSA. The neuroimaging pattern in patients with MSA was jointly located in the lateral putamen, and the neuroimaging pattern prediction model achieved a classification accuracy of 83.9%-100%. CONCLUSION The quantitative neuroimaging features and surface-based morphologic anomalies represent the markers of MSA and open new avenues for personalized clinical diagnosis.
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Affiliation(s)
- Zhan Wang
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (Z.W., T.F.); China National Clinical Research Center for Neurological Disease, NCRC-ND, Beijing, China (Z.W., T.F.)
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Beijing Key Laboratory of Neurostimulation, Beijing, China (J.M., J.Z., K.Z.)
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Beijing Key Laboratory of Neurostimulation, Beijing, China (J.M., J.Z., K.Z.)
| | - Tao Feng
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (Z.W., T.F.); China National Clinical Research Center for Neurological Disease, NCRC-ND, Beijing, China (Z.W., T.F.)
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Beijing Key Laboratory of Neurostimulation, Beijing, China (J.M., J.Z., K.Z.).
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5
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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.
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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
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Mo J, Zhang J, Hu W, Wang X, Zhao B, Zhang K, Zhang C. Neural underpinnings of default mode network on empathy revealed by intracranial stereoelectroencephalography. Psychiatry Clin Neurosci 2022; 76:659-666. [PMID: 36117401 DOI: 10.1111/pcn.13470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/26/2022] [Accepted: 08/30/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Empathy is the ability to understand and share the feelings of others. It is fundamental to emotional intelligence and social iterations. Neuroimaging studies have demonstrated that empathy activates brain regions associated with the social cognition network. AIM To explore the neural underpinnings of empathy revealed by stereoelectroencephalography utilizing recurrence quantification analysis (RQA). METHODS This retrospective cohort included 38 epilepsy patients with stereoelectroencephalography implantation. RQA metrics were applied to parameterize the network organization of default mode network (DMN) brain regions. The relationships between DMN, seizure burden activity, and empathy, as measured using the Interpersonal Reactivity Index, were examined using partial least-square regression and mediation analysis. RESULTS RQA metrics with DMN (R2 = 0.75, PBonferroni < 0.001) and its subsystems (medial temporal subsystem: R2 = 0.53, PBonferroni < 0.001; core subsystem: R2 = 0.70, PBonferroni < 0.001; dorsal medial subsystem: R2 = 0.48, PBonferroni < 0.001) were positively correlated with empathy scores. Of 13 RQA metrics, the mean diagonal line length, entropy of the diagonal line lengths, trapping time, maximal vertical line length, and recurrence time of second type were found to be statistically higher in patient cohorts with reportedly high empathy. Furthermore, DMN characteristics (b path: F = 3.69, P = 0.04), rather than seizure burdens (direct effect: t = 0.33, P = 0.74, c' = - 0.007), mediated empathy status. CONCLUSION The present study used various RQA metrics to parameterize the network organization of DMN and determine the neural underpinning of DMN for empathy modulation.
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Affiliation(s)
- Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
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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.
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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
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Mo J, Zhang J, Hu W, Sang L, Zheng Z, Zhou W, Wang H, Zhu J, Zhang C, Wang X, Zhang K. Automated Detection and Surgical Planning for Focal Cortical Dysplasia with Multicenter Validation. Neurosurgery 2022; 91:799-807. [PMID: 36135782 DOI: 10.1227/neu.0000000000002113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 06/20/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND In patients with surgically amenable focal cortical dysplasia (FCD), subtle neuroimaging representation and the risk of open surgery lead to gaps in surgical treatment and delays in surgery. OBJECTIVE To construct an integrated platform that can accurately detect FCD and automatically establish trajectory planning for magnetic resonance-guided laser interstitial thermal therapy. METHODS This multicenter study included retrospective patients to train the automated detection model, prospective patients for model evaluation, and an additional cohort for construction of the automated trajectory planning algorithm. For automated detection, we evaluated the performance and generalization of the conventional neural network in different multicenter cohorts. For automated trajectory planning, feasibility/noninferiority and safety score were calculated to evaluate the clinical value. RESULTS Of the 260 patients screened for eligibility, 202 were finally included. Eighty-eight patients were selected for conventional neural network training, 88 for generalizability testing, and 26 for the establishment of an automated trajectory planning algorithm. The model trained using preprocessed and multimodal neuroimaging displayed the best performance in diagnosing FCD (figure of merit = 0.827 and accuracy range = 75.0%-91.7% across centers). None of the clinical variables had a significant effect on prediction performance. Moreover, the automated trajectory was feasible and noninferior to the manual trajectory (χ2 = 3.540, P = .060) and significantly safer (overall: test statistic = 30.423, P < .001). CONCLUSION The integrated platform validated based on multicenter, prospective cohorts exhibited advantages of easy implementation, high performance, and generalizability, thereby indicating its potential in the diagnosis and minimally invasive treatment of FCD.
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Affiliation(s)
- Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 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
| | - Wenjing Zhou
- Epilepsy Center, Tsinghua University Yuquan Hospital, Beijing, China
| | - Haixiang Wang
- Epilepsy Center, Tsinghua University Yuquan Hospital, Beijing, China
| | - Junming Zhu
- Epilepsy Center, Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
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9
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Mo J, Wang Y, Zhang J, Cai L, Liu Q, Hu W, Sang L, Zhang C, Wang X, Shao X, Zhang K. Metabolic phenotyping of hand automatisms in mesial temporal lobe epilepsy. EJNMMI Res 2022; 12:32. [PMID: 35657491 PMCID: PMC9166918 DOI: 10.1186/s13550-022-00902-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 05/09/2022] [Indexed: 11/26/2022] Open
Abstract
Purpose Hand automatisms (HA) are common clinical manifestations in mesial temporal lobe epilepsy. However, the location of the symptomatogenic zone (EZ) in HA as well as the networks involved, are still unclear. To have a better understanding of HA underlying mechanisms, we analyzed images from interictal [18F] fluorodeoxyglucose-positron emission tomography (FDG-PET) in patients with mesial temporal lobe epilepsy (mTLE). Methods We retrospectively recruited 79 mTLE patients and 18 healthy people that substituted the control group for the analysis. All patients underwent anterior temporal lobectomy and were seizure-free. Based on the semiology of the HA occurrence, the patients were divided into three subgroups: patients with unilateral HA (Uni-HA), with bilateral HA (Bil-HA) and without HA (None-HA). We performed the intergroup comparison analysis of the interictal FDG-PET images and compared the functional connectivity within metabolic communities. Results Our analysis showed that the metabolic patterns varied among the different groups. The Uni-HA subgroup had significant differences in the extratemporal lobe brain areas, mostly in the ipsilateral supplementary motor area (SMA) and middle cingulate cortex (MCC) when compared to the healthy control group. The Bil-HA subgroup demonstrated that the bilateral SMA and MCC areas were differentially affected, whereas in the None-HA subgroup the differences were evident in limited brain areas. The metabolic network involving HA showed a constrained network embedding the SMA and MCC brain regions. Furthermore, the increased metabolic synchronization between SMA and MCC was significantly correlated with HA. Conclusion The metabolic pattern of HA was most conspicuous in SMA and MCC brain regions. Increased metabolic synchronization within SMA and MCC was considered as the major EZ of HA. Metabolic pattern analysis allowed allocation of the symptomatogenic zone (EZ) and brain network of hand automatisms (HA) in mesial temporal lobe epilepsy (mTLE). The involved network of bilateral HA was larger than the unilateral one, probably due to the occurrence of contralateral dystonic posturing. Increased metabolic synchronization within supplementary motor area (SMA) and middle cingulate cortex (MCC) regions were engaged in the representation and modulation of HA, suggesting these regions as the EZ for HA.
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Affiliation(s)
- Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yao Wang
- Pediatric Epilepsy Center, Peking University First Hospital, Peking University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lixin Cai
- Pediatric Epilepsy Center, Peking University First Hospital, Peking University, Beijing, China
| | - Qingzhu Liu
- Pediatric Epilepsy Center, Peking University First Hospital, Peking University, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lin Sang
- Epilepsy Center, Peking University First Hospital Fengtai Hospital, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. .,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
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Mo J, Dong W, Cui T, Chen C, Shi W, Hu W, Zhang C, Wang X, Zhang K, Shao X. Whole-brain metabolic pattern analysis in patients with anti-LGI1 encephalitis. Eur J Neurol 2022; 29:2376-2385. [PMID: 35514068 DOI: 10.1111/ene.15384] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/07/2022] [Accepted: 04/21/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND PURPOSE Faciobrachial dystonic seizures (FBDS) and hyponatraemia are the distinct clinical features of autoimmune encephalitis (AE) caused by antibodies against leucine-rich glioma-inactivated 1 (LGI1). The pathophysiological pattern and neural mechanisms underlying these symptoms remain largely unexplored. METHODS We included 30 patients with anti-LGI1 AE and 30 controls from a retrospective observational cohort. Whole-brain metabolic pattern analysis was performed to assess the pathological network of anti-LGI1 AE, as well as the symptomatic networks of FBDS. Logistic regression was applied to explore independent predictors of FBDS. Finally, we applied multiple regression model to investigate the hyponatraemia-associated brain network and its effect on serum sodium levels. RESULTS The pathological network of anti-LGI1 AE involved a hypermetabolism in cerebellum, subcortical structures, and Rolandic area, as well as a hypometabolism in the medial prefrontal cortex. The symptomatic network of FBDS shown a hypometabolism in cerebellum and Rolandic area (PFDR < 0.05). Hypometabolism in the cerebellum was an independent predictor of FBDS (P < 0.001). Hyponatraemia-associated network highlighted a negative effect on caudate nucleus, frontal and temporal white matter. Serum sodium level had the negative trend with metabolism of hypothalamus (Pearson's R = -0.180, P = 0.342) but the mediation was not detected (path c' = -7.238, 95% CI = -30.947 to 16.472). CONCLUSIONS Our results provide insights into the whole-brain metabolic patterns of patients with anti-LGI1 AE, including the symptomatic network FBDS and the hyponatraemia-associated brain network, which is conducive to understanding the neural mechanisms and evaluating disease progress of anti-LGI1 AE.
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Affiliation(s)
- Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Wenyu Dong
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Disease, NCRC-, ND, Beijing, China
| | - Tao Cui
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Disease, NCRC-, ND, Beijing, China
| | - Chao Chen
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Disease, NCRC-, ND, Beijing, China
| | - Weixiong Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Disease, NCRC-, ND, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Disease, NCRC-, ND, Beijing, China
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11
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Mo J, Zhang J, Hu W, Shao X, Sang L, Zheng Z, Zhang C, Wang Y, Wang X, Liu C, Zhao B, Zhang K. Neuroimaging gradient alterations and epileptogenic prediction in focal cortical dysplasia Ⅲa. J Neural Eng 2022; 19. [PMID: 35405671 DOI: 10.1088/1741-2552/ac6628] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 04/10/2022] [Indexed: 11/12/2022]
Abstract
INTRODUCTION Focal cortical dysplasia Type Ⅲa (FCD Ⅲa) is a highly prevalent temporal lobe epilepsy but the seizure outcomes are not satisfactory after epilepsy surgery. Hence, quantitative neuroimaging, epileptogenic alterations, as well as their values in guiding surgery are worth exploring. METHODS We examined 69 patients with pathologically verified FCD Ⅲa using multimodal neuroimaging and stereoelectroencephalography (SEEG). Among them, 18 received postoperative imaging which showed the extent of surgical resection and 9 underwent SEEG implantation. We also explored neuroimaging gradient alterations along with the distance to the temporal pole. Subsequently, the machine learning regression model was employed to predict whole-brain epileptogenicity. Lastly, the correlation between neuroimaging or epileptogenicity and surgical cavities was assessed. RESULTS FCD Ⅲa displayed neuroimaging gradient alterations on the temporal neocortex, morphology-signal intensity decoupling, low similarity of intra-morphological features and high similarity of intra-signal intensity features. The support vector regression model was successfully applied at the whole-brain level to calculate the continuous epileptogenic value at each vertex (mean-squared error = 13.8 ± 9.8). CONCLUSION Our study investigated the neuroimaging gradient alterations and epileptogenicity of FCD Ⅲa, along with their potential values in guiding suitable resection range and in predicting postoperative seizure outcomes. The conclusions from this study may facilitate an accurate presurgical examination of FCD Ⅲa. However, further investigation including a larger cohort is necessary to confirm the results.
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Affiliation(s)
- Jiajie Mo
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Jianguo Zhang
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Wenhan Hu
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Xiaoqiu Shao
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Lin Sang
- Peking University First Hospital Fengtai Hospital, No. 99 South 4th Fengtai Road, Fengtai District, Beijing, 100070, CHINA
| | - Zhong Zheng
- Peking University First Hospital Fengtai Hospital, No. 99 South 4th Fengtai Road, Fengtai District, Beijing, 100070, CHINA
| | - Chao Zhang
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Yao Wang
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Xiu Wang
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Chang Liu
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Baotian Zhao
- Beijing Tiantan Hospital, , Beijing, 100070, CHINA
| | - Kai Zhang
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
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12
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Ayub MA, Soman S. Editorial for "Neuroimaging Phenotyping and Structural-Metabolic-Epileptogenic Correlations in the Temporal Neocortex of Focal Cortical Dysplasia IIIa". J Magn Reson Imaging 2021; 54:936-937. [PMID: 33890322 DOI: 10.1002/jmri.27644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 03/31/2021] [Indexed: 11/09/2022] Open
Affiliation(s)
- Muhammad A Ayub
- Department of Radiology, Beth Israel Deaconess Medical Center Harvard Medical School, Boston, Massachusetts, USA
| | - Salil Soman
- Department of Radiology, Beth Israel Deaconess Medical Center Harvard Medical School, Boston, Massachusetts, USA
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