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Li Y, Qin B, Chen Q, Chen J. Impaired Functional Homotopy and Topological Properties Within the Default Mode Network of Children With Generalized Tonic-Clonic Seizures: A Resting-State fMRI Study. Front Neurosci 2022; 16:833837. [PMID: 35720710 PMCID: PMC9201640 DOI: 10.3389/fnins.2022.833837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 04/27/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction The aim of the present study was to examine interhemispheric functional connectivity (FC) and topological organization within the default-mode network (DMN) in children with generalized tonic-clonic seizures (GTCS). Methods Resting-state functional MRI was collected in 24 children with GTCS and 34 age-matched typically developing children (TDC). Between-group differences in interhemispheric FC were examined by an automated voxel-mirrored homotopic connectivity (VMHC) method. The topological properties within the DMN were also analyzed using graph theoretical approaches. Consistent results were detected and the VMHC values were extracted as features in machine learning for subject classification. Results Children with GTCS showed a significant decrease in VMHC in the DMN, including the hippocampal formation (HF), lateral temporal cortex (LTC), and angular and middle frontal gyrus. Although the patients exhibited efficient small-world properties of the DMN similar to the TDC, significant changes in regional topological organization were found in the patients, involving the areas of the bilateral temporal parietal junction, bilateral LTC, left temporal pole, and HF. Within the DMN, disrupted interhemispheric FC was found between the bilateral HF and LTC, which was consistent with the VMHC results. The VMHC values in bilateral HF and LTC were significantly correlated with clinical information in patients. Support vector machine analysis using average VMHC information in the bilateral HF and LTC as features achieved a correct classification rate of 89.34% for the classification. Conclusion These results indicate that decreased homotopic coordination in the DMN can be used as an effective biomarker to reflect seizure effects and to distinguish children with GTCSs from TDC.
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Affiliation(s)
- Yongxin Li
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
- *Correspondence: Yongxin Li,
| | - Bing Qin
- Department of Neurosurgery, Epilepsy Center, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children’s Hospital, Shenzhen, China
- Qian Chen,
| | - Jiaxu Chen
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
- Jiaxu Chen,
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Swinnen L, Chatzichristos C, Jansen K, Lagae L, Depondt C, Seynaeve L, Vancaester E, Van Dycke A, Macea J, Vandecasteele K, Broux V, De Vos M, Van Paesschen W. Accurate detection of typical absence seizures in adults and children using a two-channel electroencephalographic wearable behind the ears. Epilepsia 2021; 62:2741-2752. [PMID: 34490891 PMCID: PMC9292701 DOI: 10.1111/epi.17061] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 07/09/2021] [Accepted: 08/23/2021] [Indexed: 01/17/2023]
Abstract
OBJECTIVE Patients with absence epilepsy sensitivity <10% of their absences. The clinical gold standard to assess absence epilepsy is a 24-h electroencephalographic (EEG) recording, which is expensive, obtrusive, and time-consuming to review. We aimed to (1) investigate the performance of an unobtrusive, two-channel behind-the-ear EEG-based wearable, the Sensor Dot (SD), to detect typical absences in adults and children; and (2) develop a sensitive patient-specific absence seizure detection algorithm to reduce the review time of the recordings. METHODS We recruited 12 patients (median age = 21 years, range = 8-50; seven female) who were admitted to the epilepsy monitoring units of University Hospitals Leuven for a 24-h 25-channel video-EEG recording to assess their refractory typical absences. Four additional behind-the-ear electrodes were attached for concomitant recording with the SD. Typical absences were defined as 3-Hz spike-and-wave discharges on EEG, lasting 3 s or longer. Seizures on SD were blindly annotated on the full recording and on the algorithm-labeled file and consequently compared to 25-channel EEG annotations. Patients or caregivers were asked to keep a seizure diary. Performance of the SD and seizure diary were measured using the F1 score. RESULTS We concomitantly recorded 284 absences on video-EEG and SD. Our absence detection algorithm had a sensitivity of .983 and false positives per hour rate of .9138. Blind reading of full SD data resulted in sensitivity of .81, precision of .89, and F1 score of .73, whereas review of the algorithm-labeled files resulted in scores of .83, .89, and .87, respectively. Patient self-reporting gave sensitivity of .08, precision of 1.00, and F1 score of .15. SIGNIFICANCE Using the wearable SD, epileptologists were able to reliably detect typical absence seizures. Our automated absence detection algorithm reduced the review time of a 24-h recording from 1-2 h to around 5-10 min.
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Affiliation(s)
- Lauren Swinnen
- Laboratory for Epilepsy Research, KU Leuven and Department of Neurology, University Hospitals, Leuven, Belgium
| | - Christos Chatzichristos
- Department of Electrical Engineering, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Katrien Jansen
- Department Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Lieven Lagae
- Department Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Chantal Depondt
- Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Laura Seynaeve
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.,Neuroprotection and Neuromodulation, Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | | | | | - Jaiver Macea
- Laboratory for Epilepsy Research, KU Leuven and Department of Neurology, University Hospitals, Leuven, Belgium
| | - Kaat Vandecasteele
- Department of Electrical Engineering, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Victoria Broux
- Laboratory for Epilepsy Research, KU Leuven and Department of Neurology, University Hospitals, Leuven, Belgium
| | - Maarten De Vos
- Department of Electrical Engineering, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.,Department Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, KU Leuven and Department of Neurology, University Hospitals, Leuven, Belgium
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