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Doss DJ, Shless JS, Bick SK, Makhoul GS, Negi AS, Bibro CE, Rashingkar R, Gummadavelli A, Chang C, Gallagher MJ, Naftel RP, Reddy SB, Williams Roberson S, Morgan VL, Johnson GW, Englot DJ. The interictal suppression hypothesis is the dominant differentiator of seizure onset zones in focal epilepsy. Brain 2024; 147:3009-3017. [PMID: 38874456 PMCID: PMC11370787 DOI: 10.1093/brain/awae189] [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: 10/20/2023] [Revised: 04/19/2024] [Accepted: 05/16/2024] [Indexed: 06/15/2024] Open
Abstract
Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8-13 Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment.
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Affiliation(s)
- Derek J Doss
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
| | - Jared S Shless
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Sarah K Bick
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Ghassan S Makhoul
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
| | - Aarushi S Negi
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Camden E Bibro
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Rohan Rashingkar
- Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA
| | - Abhijeet Gummadavelli
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Catie Chang
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - Martin J Gallagher
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Robert P Naftel
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Shilpa B Reddy
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Shawniqua Williams Roberson
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Radiology and Biomedical Imaging, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA
- Department of Radiology and Biomedical Imaging, Vanderbilt University Medical Center, Nashville, TN 37235, USA
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Simula S, Makhalova J, Pizzo F, Garnier E, Damiani G, Mercadal B, Chiara Biagi M, Salvador R, Medina-Villalon S, Ruffini G, Wendling F, George Bénar C, Bartolomei F. Impact of transcranial electrical stimulation on simultaneous stereoelectroencephalography recordings: A randomized sham-controlled study. Clin Neurophysiol 2024; 166:211-222. [PMID: 39182340 DOI: 10.1016/j.clinph.2024.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 07/31/2024] [Accepted: 08/05/2024] [Indexed: 08/27/2024]
Abstract
OBJECTIVE Clinical exploitation of transcranial electrical stimulation for focal epilepsy treatment lacks quantification of the underlying neurophysiological changes. This study explores the immediate effects of transcranial alternating (tACS) and direct (tDCS) current stimulation on local and network brain activity using simultaneous stereoelectroencephalography (SEEG) recordings. METHODS Patients were randomized for personalized tACS (n = 5) or tDCS (n = 6). Active stimulation (20 min) was preceded by sham stimulation (20 min). Changes in interictal epileptiform discharges (IED), functional connectivity (FC) and power spectral density (PSD) were quantified against baseline. RESULTS Results demonstrated variable responses. Spike rate decreased in 2/6 subjects following sham and tDCS, while 2/6 showed an increase. Alpha power and aperiodic PSD components generally increased during and after tDCS but decreased following tACS. FC changes varied among subjects and were detectable even during sham sessions. CONCLUSIONS Strong variability suggests that tES does not have a univocal effect on immediate changes in IED or FC, possibly due to the single session format and challenges in affecting subcortical areas. SIGNIFICANCE This is the first study to examine intracranial FC changes during tACS and tDCS, highlighting the importance of sham comparisons and individual variability in tES response, offering valuable insights into its application for epilepsy treatment.
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Affiliation(s)
- Sara Simula
- Aix-Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille 13005, France
| | - Julia Makhalova
- Aix-Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille 13005, France; APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille 13005, France
| | - Francesca Pizzo
- Aix-Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille 13005, France; APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille 13005, France
| | - Elodie Garnier
- Aix-Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille 13005, France
| | | | | | | | | | - Samuel Medina-Villalon
- Aix-Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille 13005, France; APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille 13005, France
| | | | | | - Christian George Bénar
- Aix-Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille 13005, France
| | - Fabrice Bartolomei
- Aix-Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille 13005, France; APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille 13005, France.
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3
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Ye X, Hu P, Yang B, Yang Y, Gao D, Zeng GQ, Wang K. Using scalp EEG to predict seizure recurrence and electrical status epilepticus in children with idiopathic focal epilepsy. Seizure 2024; 118:8-16. [PMID: 38613879 DOI: 10.1016/j.seizure.2024.03.013] [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: 02/01/2024] [Revised: 03/17/2024] [Accepted: 03/27/2024] [Indexed: 04/15/2024] Open
Abstract
PURPOSE Some individuals with idiopathic focal epilepsy (IFE) experience recurring seizures accompanied by the evolution of electrical status epilepticus during sleep (ESES). Here, we aimed to develop a predictor for the early detection of seizure recurrence with ESES in children with IFE using resting state electroencephalogram (EEG) data. METHODS The study group included 15 IFE patients who developed seizure recurrence with ESES. There were 17 children in the control group who did not experience seizure recurrence with ESES during at least 2-year follow-up. We used the degree value of the partial directed coherence (PDC) from the EEG data to predict seizure recurrence with ESES via 6 machine learning (ML) algorithms. RESULTS Among the models, the Xgboost Classifier (XGBC) model achieved the highest specificity of 0.90, and a remarkable sensitivity and accuracy of 0.80 and 0.85, respectively. The CATC showed balanced performance with a specificity of 0.85, sensitivity of 0.73, and an accuracy of 0.80, with an AUC equal to 0.78. For both of these models, F4, Fz and T4 were the overlaps of the top 4 features. CONCLUSIONS Considering its high classification accuracy, the XGBC model is an effective and quantitative tool for predicting seizure recurrence with ESES evolution in IFE patients. We developed an ML-based tool for predicting the development of IFE using resting state EEG data. This could facilitate the diagnosis and treatment of patients with IFE.
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Affiliation(s)
- Xiaofei Ye
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Neurology, Children's Hospital of Fudan University/Anhui Hospital, Hefei, China
| | - Panpan Hu
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bin Yang
- Department of Neurology, Children's Hospital of Fudan University/Anhui Hospital, Hefei, China
| | - Yang Yang
- Department of Neurology, Children's Hospital of Fudan University/Anhui Hospital, Hefei, China
| | - Ding Gao
- Department of Neurology, Children's Hospital of Fudan University/Anhui Hospital, Hefei, China
| | - Ginger Qinghong Zeng
- Institute of Advanced Technology, University of Science and Technology of China, Hefei, China.
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China.
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Doss DJ, Johnson GW, Englot DJ. Imaging and Stereotactic Electroencephalography Functional Networks to Guide Epilepsy Surgery. Neurosurg Clin N Am 2024; 35:61-72. [PMID: 38000842 PMCID: PMC10676462 DOI: 10.1016/j.nec.2023.09.001] [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] [Indexed: 11/26/2023]
Abstract
Epilepsy surgery is a potentially curative treatment of drug-resistant epilepsy that has remained underutilized both due to inadequate referrals and incomplete localization hypotheses. The complexity of patients evaluated for epilepsy surgery has increased, thus new approaches are necessary to treat these patients. The paradigm of epilepsy surgery has evolved to match this challenge, now considering the entire seizure network with the goal of disrupting it through resection, ablation, neuromodulation, or a combination. The network paradigm has the potential to aid in identification of the seizure network as well as treatment selection.
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Affiliation(s)
- Derek J Doss
- Department of Biomedical Engineering, Vanderbilt University, PMB 351631, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Vanderbilt University Institute of Imaging Science (VUIIS), 1161 21st Avenue South, Medical Center North AA-1105, Nashville, TN 37232, USA; Vanderbilt Institute for Surgery and Engineering (VISE), 1161 21st Avenue South, MCN S2323, Nashville, TN 37232, USA
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, PMB 351631, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Vanderbilt University Institute of Imaging Science (VUIIS), 1161 21st Avenue South, Medical Center North AA-1105, Nashville, TN 37232, USA; Vanderbilt Institute for Surgery and Engineering (VISE), 1161 21st Avenue South, MCN S2323, Nashville, TN 37232, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, PMB 351631, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Vanderbilt University Institute of Imaging Science (VUIIS), 1161 21st Avenue South, Medical Center North AA-1105, Nashville, TN 37232, USA; Vanderbilt Institute for Surgery and Engineering (VISE), 1161 21st Avenue South, MCN S2323, Nashville, TN 37232, USA; Department of Neurological Surgery, Vanderbilt University Medical Center, 1161 21st Avenue South, T4224 Medical Center North, Nashville, TN 37232, USA; Department of Electrical and Computer Engineering, Vanderbilt University, PMB 351824, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Department of Radiological Sciences, Vanderbilt University Medical Center, 1161 21st Avenue South, Nashville, TN 37232, USA.
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5
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Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. EBioMedicine 2023; 97:104848. [PMID: 37898096 PMCID: PMC10630610 DOI: 10.1016/j.ebiom.2023.104848] [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: 06/15/2023] [Revised: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 10/30/2023] Open
Abstract
BACKGROUND When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. METHODS We retrospectively investigated data from 43 patients (42% female) with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. FINDINGS Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p = 0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. INTERPRETATION Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. FUNDING This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
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Affiliation(s)
- Jonathan J Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia; Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Division of Neurology, Department of Medicine, Queen's University, Kingston, Canada
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
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6
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Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. ARXIV 2023:arXiv:2304.03192v3. [PMID: 37064531 PMCID: PMC10104180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Background When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. Methods We retrospectively investigated data from 43 patients with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. Findings Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p=0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. Interpretation Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. Funding This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
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Affiliation(s)
- Jonathan J. Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H. Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A. Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W. McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B. Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
- Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C. Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P. Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Division of Neurology, Department of Medicine, Queen’s University, Kingston, Canada
| | - John S. Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N. Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
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Rijal S, Corona L, Perry MS, Tamilia E, Madsen JR, Stone SSD, Bolton J, Pearl PL, Papadelis C. Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy. Sci Rep 2023; 13:9622. [PMID: 37316544 PMCID: PMC10267141 DOI: 10.1038/s41598-023-36551-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Abstract
Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Highly connected nodes in these networks are epilepsy surgery targets. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography can quantify brain regions epileptogenicity and predict surgical outcome in children with drug resistant epilepsy (DRE). We computed FC between electrodes on different states (i.e. interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and frequency bands. We then estimated the electrodes' nodal strength. We compared nodal strength between states, inside and outside resection for good- (n = 22, Engel I) and poor-outcome (n = 9, Engel II-IV) patients, respectively, and tested their utility to predict the epileptogenic zone and outcome. We observed a hierarchical epileptogenic organization among states for nodal strength: lower FC during interictal and pre-ictal states followed by higher FC during ictal and post-ictal states (p < 0.05). We further observed higher FC inside resection (p < 0.05) for good-outcome patients on different states and bands, and no differences for poor-outcome patients. Resection of nodes with high FC was predictive of outcome (positive and negative predictive values: 47-100%). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in patients with DRE.
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Affiliation(s)
- Sakar Rijal
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - Ludovica Corona
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA.
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA.
- School of Medicine, Texas Christian University, Fort Worth, TX, 76129, USA.
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8
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Lagarde S, Bénar CG, Wendling F, Bartolomei F. Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG. Brain Connect 2022; 12:850-869. [PMID: 35972755 PMCID: PMC9807250 DOI: 10.1089/brain.2021.0190] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Introduction: Focal epilepsies are diseases of neuronal excitability affecting macroscopic networks of cortical and subcortical neural structures. These networks ("epileptogenic networks") can generate pathological electrophysiological activities during seizures, and also between seizures (interictal period). Many works attempt to describe these networks by using quantification methods, particularly based on the estimation of statistical relationships between signals produced by brain regions, namely functional connectivity (FC). Results: FC has been shown to be greatly altered during seizures and in the immediate peri-ictal period. An increasing number of studies have shown that FC is also altered during the interictal period depending on the degree of epileptogenicity of the structures. Furthermore, connectivity values could be correlated with other clinical variables including surgical outcome. Significance: This leads to a conceptual change and to consider epileptic areas as both hyperexcitable and abnormally connected. These data open the door to the use of interictal FC as a marker of epileptogenicity and as a complementary tool for predicting the effect of surgery. Aim: In this article, we review the available data concerning interictal FC estimated from intracranial electroencephalograhy (EEG) in focal epilepsies and discuss it in the light of data obtained from other modalities (EEG imaging) and modeling studies.
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Affiliation(s)
- Stanislas Lagarde
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, Marseille, France.,Address correspondence to: Stanislas Lagarde, Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, 264 Rue Saint-Pierre, 13005 Marseille, France
| | | | | | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, Marseille, France
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