1
|
Ferrario R, Parisi A, Tallarita G, Parente A, Pastori C, Giovagnoli AR. Sensitivity to moral and conventional rules in temporal lobe epilepsy. Epilepsy Behav 2024; 158:109889. [PMID: 38936307 DOI: 10.1016/j.yebeh.2024.109889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/06/2024] [Accepted: 06/08/2024] [Indexed: 06/29/2024]
Abstract
OBJECTIVES Sensitivity to moral and conventional rules (SMCR) is supported by bilateral brain networks and psychosocial input both of which may be altered in temporal lobe epilepsy (TLE). This study evaluated the components of SMCR in patients with TLE, aiming to clarify their preservation and link to psychopathological and cognitive aspects. METHODS Adult patients with unilateral TLE and healthy controls were evaluated using neuropsychological tests for SMCR, memory, language, and executive functions, the Empathy Questionnaire (EQ), and the Symptom Checklist-90-R (SCL-90-R). RESULTS The SMCR test items showed good reliability and validity, yielding the Severity and Rules factors distinct from the Executive, Lexical and Memory factors. Patients with right TLE scored worse in moral rules recognition than controls, but this difference was nullified by a significant influence for age and sex. The Severity and Rules factors related to semantic fluency and age and, respectively, TLE side and psychoticism. However, these factors did predict TLE membership. CONCLUSIONS In adult patients with TLE, the SMCR test reflects a distinct cognitive domain. Conventional rules are well-retained, while moral reasoning may be only affected in right TLE if unfavorable demographics coexist. Although age, TLE side, semantic abilities, and psychoticism cooperate to determine SMCR, impairment of such domain is not a distinctive feature of TLE.
Collapse
Affiliation(s)
- Rosalba Ferrario
- Unit of Neurology and Neuropathology, IRCCS Foundation "Carlo Besta" Neurological Institute, Milano, Italy
| | - Alessandra Parisi
- Unit of Neurology and Neuropathology, IRCCS Foundation "Carlo Besta" Neurological Institute, Milano, Italy
| | - Giulia Tallarita
- Unit of Neurology and Neuropathology, IRCCS Foundation "Carlo Besta" Neurological Institute, Milano, Italy
| | - Annalisa Parente
- Unit of Neurology and Neuropathology, IRCCS Foundation "Carlo Besta" Neurological Institute, Milano, Italy
| | - Chiara Pastori
- Unit of Clinical and Experimental Epileptology, IRCCS Foundation "Carlo Besta" Neurological Institute, Milano, Italy
| | - Anna Rita Giovagnoli
- Unit of Neurology and Neuropathology, IRCCS Foundation "Carlo Besta" Neurological Institute, Milano, Italy.
| |
Collapse
|
2
|
Guney T, Demirel M, Celebi U, Aytemir K, Tezer FI, Coteli C, Yorgun H, Saygi S. Ictal Asystole During Focal Seizures Due to Left Occipital Glioneuronal Tumor: A Report of Case Treated With Cardiac Neuromodulation. Clin EEG Neurosci 2024; 55:586-590. [PMID: 38403965 DOI: 10.1177/15500594241234831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Ictal asystole (IA) is a rare but potentially life-threatening complication of focal epilepsy. The sudden onset of loss of consciousness and drop attacks in a patient with chronic epilepsy should suggest the possibility of this complication. Once the diagnosis is established, rapid management should be considered, especially in high-risk cases. The approach does not differ between temporal and extratemporal lobe epilepsies. Strategies can be aimed at preventing the emergence of cortical epileptic activity from the beginning (surgery, antiseizure therapy), neutralizing negative chronotropic effects on the heart (cardiac neuromodulation), or restarting the heart rhythm with a pacemaker. Pacemaker implantation is not a completely complication-free treatment, and living with a device that requires care and follow-up throughout life makes alternative treatment methods more valid for young patients with many years to live or cases that could benefit from surgery. In this article, we present a patient with a left occipital glioneuronal tumor and drug-resistant occipital lobe epilepsy. IA was documented by long-term video EEG monitoring (VEM). During about 2 years of follow-up after a cardiac neuromodulation procedure, there were no drop attacks or asystole with seizures, confirmed by long-term VEM.
Collapse
Affiliation(s)
- Tuba Guney
- School of Medicine, Department of Neurology, Hacettepe University, Ankara, Turkey
| | - Mert Demirel
- School of Medicine, Department of Neurology, Hacettepe University, Ankara, Turkey
| | - Ulufer Celebi
- School of Medicine, Department of Neurology, Hacettepe University, Ankara, Turkey
| | - Kudret Aytemir
- School of Medicine, Department of Cardiology, Hacettepe University, Ankara, Turkey
| | - F Irsel Tezer
- School of Medicine, Department of Neurology, Hacettepe University, Ankara, Turkey
| | - Cem Coteli
- School of Medicine, Department of Cardiology, Hacettepe University, Ankara, Turkey
| | - Hikmet Yorgun
- School of Medicine, Department of Cardiology, Hacettepe University, Ankara, Turkey
| | - Serap Saygi
- School of Medicine, Department of Neurology, Hacettepe University, Ankara, Turkey
| |
Collapse
|
3
|
Kim YT, Kim H, So M, Kong J, Kim KT, Hong JH, Son Y, Sa JK, Do S, Han JH, Kim JB. Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity. Neuroimage 2024; 297:120749. [PMID: 39033787 DOI: 10.1016/j.neuroimage.2024.120749] [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: 04/08/2024] [Revised: 07/12/2024] [Accepted: 07/18/2024] [Indexed: 07/23/2024] Open
Abstract
Differential diagnosis of acute loss of consciousness (LOC) is crucial due to the need for different therapeutic strategies despite similar clinical presentations among etiologies such as nonconvulsive status epilepticus, metabolic encephalopathy, and benzodiazepine intoxication. While altered functional connectivity (FC) plays a pivotal role in the pathophysiology of LOC, there has been a lack of efforts to develop differential diagnosis artificial intelligence (AI) models that feature the distinctive FC change patterns specific to each LOC cause. Three approaches were applied for extracting features for the AI models: three-dimensional FC adjacency matrices, vectorized FC values, and graph theoretical measurements. Deep learning using convolutional neural networks (CNN) and various machine learning algorithms were implemented to compare classification accuracy using electroencephalography (EEG) data with different epoch sizes. The CNN model using FC adjacency matrices achieved the highest accuracy with an AUC of 0.905, with 20-s epoch data being optimal for classifying the different LOC causes. The high accuracy of the CNN model was maintained in a prospective cohort. Key distinguishing features among the LOC causes were found in the delta and theta brain wave bands. This research advances the understanding of LOC's underlying mechanisms and shows promise for enhancing diagnosis and treatment selection. Moreover, the AI models can provide accurate LOC differentiation with a relatively small amount of EEG data in 20-s epochs, which may be clinically useful.
Collapse
Affiliation(s)
- Young-Tak Kim
- Department of Biomedical Sciences, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Hayom Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Mingyeong So
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jooheon Kong
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Keun-Tae Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Je Hyeong Hong
- Department of Electronic Engineering, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Yunsik Son
- Department of Computer Science and Engineering, Dongguk University, 30, Pildong-ro 1-gil, Jung-gu, Seoul, 04620, Republic of Korea
| | - Jason K Sa
- Department of Biomedical Sciences, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea; Department of Biomedical Informatics, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Synho Do
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 125 Nashua Street, Suite 2210, Boston, MA, 02114, United States; KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea; Kempner Institute, Harvard University, 150 Western Avenue, Boston, MA, 02134, United States
| | - Jae-Ho Han
- Department of Brain and Cognitive Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jung Bin Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
| |
Collapse
|
4
|
Rajaraman RR, Smith RJ, Oana S, Daida A, Shrey DW, Nariai H, Lopour BA, Hussain SA. Computational EEG attributes predict response to therapy for epileptic spasms. Clin Neurophysiol 2024; 163:39-46. [PMID: 38703698 DOI: 10.1016/j.clinph.2024.03.035] [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: 09/27/2023] [Revised: 03/10/2024] [Accepted: 03/28/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVE We set out to evaluate whether response to treatment for epileptic spasms is associated with specific candidate computational EEG biomarkers, independent of clinical attributes. METHODS We identified 50 children with epileptic spasms, with pre- and post-treatment overnight video-EEG. After EEG samples were preprocessed in an automated fashion to remove artifacts, we calculated amplitude, power spectrum, functional connectivity, entropy, and long-range temporal correlations (LRTCs). To evaluate the extent to which each feature is independently associated with response and relapse, we conducted logistic and proportional hazards regression, respectively. RESULTS After statistical adjustment for the duration of epileptic spasms prior to treatment, we observed an association between response and stronger baseline and post-treatment LRTCs (P = 0.042 and P = 0.004, respectively), and higher post-treatment entropy (P = 0.003). On an exploratory basis, freedom from relapse was associated with stronger post-treatment LRTCs (P = 0.006) and higher post-treatment entropy (P = 0.044). CONCLUSION This study suggests that multiple EEG features-especially LRTCs and entropy-may predict response and relapse. SIGNIFICANCE This study represents a step toward a more precise approach to measure and predict response to treatment for epileptic spasms.
Collapse
Affiliation(s)
- Rajsekar R Rajaraman
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA
| | - Rachel J Smith
- Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Shingo Oana
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel W Shrey
- Division of Pediatric Neurology, University of California, Irvine, Irvine, CA, USA; Department of Neurology, Children's Hospital of Orange County, Orange, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Shaun A Hussain
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA.
| |
Collapse
|
5
|
Millán AP, van Straaten ECW, Stam CJ, Nissen IA, Idema S, Van Mieghem P, Hillebrand A. Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study. Netw Neurosci 2024; 8:437-465. [PMID: 38952815 PMCID: PMC11142635 DOI: 10.1162/netn_a_00361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/18/2024] [Indexed: 07/03/2024] Open
Abstract
Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study (N = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. We illustrate here the clinical applicability of ESSES with a pseudo-prospective study (N = 34) with a blind setting (to the resection strategy and surgical outcome) that emulated presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after any resection by finding patient-specific model-based optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan overlapped more with the model-based optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated with a fully independent cohort and without the need for iEEG recordings.
Collapse
Affiliation(s)
- Ana P. Millán
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam, The Netherlands
- Institute “Carlos I” for Theoretical and Computational Physics, and Electromagnetism and Matter Physics Department, University of Granada, Granada, Spain
| | - Elisabeth C. W. van Straaten
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Ida A. Nissen
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam, The Netherlands
| | - Sander Idema
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurosurgery, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Cancer Biology and Immonology, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| |
Collapse
|
6
|
van den Bongard F, Gowik JK, Coenen J, Jakobsmeyer R, Reinsberger C. Exercise-induced central and peripheral sympathetic activity in a community-based group of epilepsy patients differ from healthy controls. Exp Brain Res 2024; 242:1301-1310. [PMID: 38551692 PMCID: PMC11108887 DOI: 10.1007/s00221-024-06792-0] [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: 09/28/2023] [Accepted: 01/22/2024] [Indexed: 05/23/2024]
Abstract
Ictal and interictal activity within the autonomic nervous system is characterized by a sympathetic overshoot in people with epilepsy. This autonomic dysfunction is assumed to be driven by alterations in the central autonomic network. In this study, exercise-induced changes of the interrelation of central and peripheral autonomic activity in patients with epilepsy was assessed. 21 patients with epilepsy (16 seizure-free), and 21 healthy matched controls performed an exhaustive bicycle ergometer test. Immediately before and after the exercise test, resting state electroencephalography measurements (Brain Products GmbH, 128-channel actiCHamp) of 5 min were carried out to investigate functional connectivity assessed by phase locking value in source space for whole brain, central autonomic network and visual network. Additionally, 1-lead ECG (Brain products GmbH) was performed to analyze parasympathetic (root mean square of successive differences (RMSSD) of the heart rate variability) and sympathetic activity (electrodermal activity (meanEDA)). MeanEDA increased (p < 0.001) and RMSSD decreased (p < 0.001) from pre to post-exercise in both groups. Correlation coefficients of meanEDA and central autonomic network functional connectivity differed significantly between the groups (p = 0.004) after exercise. Both patients with epilepsy and normal control subjects revealed the expected physiological peripheral autonomic responses to acute exhaustive exercise, but alterations of the correlation between central autonomic and peripheral sympathetic activity may indicate a different sympathetic reactivity after exercise in patients with epilepsy. The clinical relevance of this finding and its modulators (seizures, anti-seizure medication, etc.) still needs to be elucidated.
Collapse
Affiliation(s)
| | - Julia Kristin Gowik
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany
| | - Jessica Coenen
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany
| | - Rasmus Jakobsmeyer
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany
| | - Claus Reinsberger
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany.
- Division of Sports Neurology & Neurosciences, Department of Neurology, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
7
|
Ke M, Luo X, Guo Y, Zhang J, Ren X, Liu G. Alterations in spatiotemporal characteristics of dynamic networks in juvenile myoclonic epilepsy. Neurol Sci 2024:10.1007/s10072-024-07506-8. [PMID: 38704479 DOI: 10.1007/s10072-024-07506-8] [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: 11/23/2023] [Accepted: 03/27/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND Juvenile myoclonic epilepsy (JME) is characterized by altered patterns of brain functional connectivity (FC). However, the nature and extent of alterations in the spatiotemporal characteristics of dynamic FC in JME patients remain elusive. Dynamic networks effectively encapsulate temporal variations in brain imaging data, offering insights into brain network abnormalities and contributing to our understanding of the seizure mechanisms and origins. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 37 JME patients and 37 healthy counterparts. Forty-seven network nodes were identified by group-independent component analysis (ICA) to construct the dynamic network. Ultimately, patients' and controls' spatiotemporal characteristics, encompassing temporal clustering and variability, were contrasted at the whole-brain, large-scale network, and regional levels. RESULTS Our findings reveal a marked reduction in temporal clustering and an elevation in temporal variability in JME patients at the whole-brain echelon. Perturbations were notably pronounced in the default mode network (DMN) and visual network (VN) at the large-scale level. Nodes exhibiting anomalous were predominantly situated within the DMN and VN. Additionally, there was a significant correlation between the severity of JME symptoms and the temporal clustering of the VN. CONCLUSIONS Our findings suggest that excessive temporal changes in brain FC may affect the temporal structure of dynamic brain networks, leading to disturbances in brain function in patients with JME. The DMN and VN play an important role in the dynamics of brain networks in patients, and their abnormal spatiotemporal properties may underlie abnormal brain function in patients with JME in the early stages of the disease.
Collapse
Affiliation(s)
- Ming Ke
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China.
| | - Xiaofei Luo
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Yi Guo
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Juli Zhang
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Xupeng Ren
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Guangyao Liu
- Department of Nuclear Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730030, China.
| |
Collapse
|
8
|
Gong R, Bickel S, Tostaeva G, Lado FA, Metha AD, Kuzniecky RI, Bonilha L, Gleichgerrcht E. Optimizing Surgical Planning for Epilepsy Patients With Multimodal Neuroimaging and Neurophysiology Integration: A Case Study. J Clin Neurophysiol 2024; 41:317-321. [PMID: 38376938 PMCID: PMC11073903 DOI: 10.1097/wnp.0000000000001071] [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: 02/21/2024] Open
Abstract
SUMMARY Current preoperative evaluation of epilepsy can be challenging because of the lack of a comprehensive view of the network's dysfunctions. To demonstrate the utility of our multimodal neurophysiology and neuroimaging integration approach in the presurgical evaluation, we present a proof-of-concept for using this approach in a patient with nonlesional frontal lobe epilepsy who underwent two resective surgeries to achieve seizure control. We conducted a post-hoc investigation using four neuroimaging and neurophysiology modalities: diffusion tensor imaging, resting-state functional MRI, and stereoelectroencephalography at rest and during seizures. We computed region-of-interest-based connectivity for each modality and applied betweenness centrality to identify key network hubs across modalities. Our results revealed that despite seizure semiology and stereoelectroencephalography indicating dysfunction in the right orbitofrontal region, the maximum overlap on the hubs across modalities extended to right temporal areas. Notably, the right middle temporal lobe region served as an overlap hub across diffusion tensor imaging, resting-state functional MRI, and rest stereoelectroencephalography networks and was only included in the resected area in the second surgery, which led to long-term seizure control of this patient. Our findings demonstrated that transmodal hubs could help identify key areas related to epileptogenic network. Therefore, this case presents a promising perspective of using a multimodal approach to improve the presurgical evaluation of patients with epilepsy.
Collapse
Affiliation(s)
- Ruxue Gong
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Stephan Bickel
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Gelana Tostaeva
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Fred A. Lado
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Ashesh Dinesh Metha
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | | | - Leonardo Bonilha
- School of Medicine Columbia, University of South Carolina, Columbia, SC, USA
| | | |
Collapse
|
9
|
Russo A, Mazzone S, Landolina L, Colucci R, Baccari F, Fetta A, Boni A, Cordelli DM. Efficacy and Safety of Pulse Intravenous Methylprednisolone in Pediatric Epileptic Encephalopathies: Timing and Networks Consideration. J Clin Med 2024; 13:2497. [PMID: 38731025 PMCID: PMC11084200 DOI: 10.3390/jcm13092497] [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: 03/05/2024] [Revised: 04/10/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024] Open
Abstract
Background: Epileptic encephalopathies (EE) are characterized by severe drug-resistant seizures, early onset, and unfavorable developmental outcomes. This article discusses the use of intravenous methylprednisolone (IVMP) pulse therapy in pediatric patients with EE to evaluate its efficacy and tolerability. Methods: This is a retrospective study from 2020 to 2023. Inclusion criteria were ≤18 years at the time of IVMP pulse therapy and at least 6 months of follow-up. Efficacy and outcome, defined as seizure reduction > 50% (responder rate), were evaluated at 6 and 9 months of therapy, and 6 months after therapy suspension; quality of life (QoL) was also assessed. Variables predicting positive post-IVMP outcomes were identified using statistical analysis. Results: The study included 21 patients, with a responder rate of 85.7% at 6 and 9 months of therapy, and 80.9% at 6 months after therapy suspension. Variables significantly predicting favorable outcome were etiology (p = 0.0475) and epilepsy type (p = 0.0475), with the best outcome achieved in patients with genetic epilepsy and those with encephalopathy related to electrical status epilepticus during slow-wave sleep (ESES). All patients evidenced improvements in QoL at the last follow-up, with no relevant adverse events reported. Conclusions: Our study confirmed the efficacy and high tolerability of IVMP pulse therapy in pediatric patients with EE. Genetic epilepsy and ESES were positive predictors of a favorable clinical outcome. QOL, EEG tracing, and postural-motor development showed an improving trend as well. IVMP pulse therapy should be considered earlier in patients with EE.
Collapse
Affiliation(s)
- Angelo Russo
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
| | - Serena Mazzone
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
| | - Laura Landolina
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
| | - Roberta Colucci
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
| | - Flavia Baccari
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOS Epidemiologia e Biostatistica, 40139 Bologna, Italy;
| | - Anna Fetta
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
| | - Antonella Boni
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
| | - Duccio Maria Cordelli
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
| |
Collapse
|
10
|
Zanin M, Aktürk T, Yıldırım E, Yerlikaya D, Yener G, Güntekin B. Reconstructing brain functional networks through identifiability and deep learning. Netw Neurosci 2024; 8:241-259. [PMID: 38562295 PMCID: PMC10923503 DOI: 10.1162/netn_a_00353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/17/2023] [Indexed: 04/04/2024] Open
Abstract
We propose a novel approach for the reconstruction of functional networks representing brain dynamics based on the idea that the coparticipation of two brain regions in a common cognitive task should result in a drop in their identifiability, or in the uniqueness of their dynamics. This identifiability is estimated through the score obtained by deep learning models in supervised classification tasks and therefore requires no a priori assumptions about the nature of such coparticipation. The method is tested on EEG recordings obtained from Alzheimer's and Parkinson's disease patients, and matched healthy volunteers, for eyes-open and eyes-closed resting-state conditions, and the resulting functional networks are analysed through standard topological metrics. Both groups of patients are characterised by a reduction in the identifiability of the corresponding EEG signals, and by differences in the patterns that support such identifiability. Resulting functional networks are similar, but not identical to those reconstructed by using a correlation metric. Differences between control subjects and patients can be observed in network metrics like the clustering coefficient and the assortativity in different frequency bands. Differences are also observed between eyes open and closed conditions, especially for Parkinson's disease patients.
Collapse
Affiliation(s)
- Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain
| | - Tuba Aktürk
- Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - Ebru Yıldırım
- Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey
| | - Deniz Yerlikaya
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Görsev Yener
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
- School of Medicine, Izmir University of Economics, Izmir, Turkey
- Brain Dynamics Multidisciplinary Research Center, Dokuz Eylül University, Izmir, Turkey
| | - Bahar Güntekin
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey
| |
Collapse
|
11
|
Stam CJ. Hub overload and failure as a final common pathway in neurological brain network disorders. Netw Neurosci 2024; 8:1-23. [PMID: 38562292 PMCID: PMC10861166 DOI: 10.1162/netn_a_00339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 09/26/2023] [Indexed: 04/04/2024] Open
Abstract
Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.
Collapse
Affiliation(s)
- Cornelis Jan Stam
- Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
12
|
Kobayashi K, Taylor KN, Shahabi H, Krishnan B, Joshi A, Mackow MJ, Feldman L, Zamzam O, Medani T, Bulacio J, Alexopoulos AV, Najm I, Bingaman W, Leahy RM, Nair DR. Effective connectivity relates seizure outcome to electrode placement in responsive neurostimulation. Brain Commun 2024; 6:fcae035. [PMID: 38390255 PMCID: PMC10882982 DOI: 10.1093/braincomms/fcae035] [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: 09/12/2022] [Revised: 09/06/2023] [Accepted: 02/19/2024] [Indexed: 02/24/2024] Open
Abstract
Responsive neurostimulation is a closed-loop neuromodulation therapy for drug resistant focal epilepsy. Responsive neurostimulation electrodes are placed near ictal onset zones so as to enable detection of epileptiform activity and deliver electrical stimulation. There is no standard approach for determining the optimal placement of responsive neurostimulation electrodes. Clinicians make this determination based on presurgical tests, such as MRI, EEG, magnetoencephalography, ictal single-photon emission computed tomography and intracranial EEG. Currently functional connectivity measures are not being used in determining the placement of responsive neurostimulation electrodes. Cortico-cortical evoked potentials are a measure of effective functional connectivity. Cortico-cortical evoked potentials are generated by direct single-pulse electrical stimulation and can be used to investigate cortico-cortical connections in vivo. We hypothesized that the presence of high amplitude cortico-cortical evoked potentials, recorded during intracranial EEG monitoring, near the eventual responsive neurostimulation contact sites is predictive of better outcomes from its therapy. We retrospectively reviewed 12 patients in whom cortico-cortical evoked potentials were obtained during stereoelectroencephalography evaluation and subsequently underwent responsive neurostimulation therapy. We studied the relationship between cortico-cortical evoked potentials, the eventual responsive neurostimulation electrode locations and seizure reduction. Directional connectivity indicated by cortico-cortical evoked potentials can categorize stereoelectroencephalography electrodes as either receiver nodes/in-degree (an area of greater inward connectivity) or projection nodes/out-degree (greater outward connectivity). The follow-up period for seizure reduction ranged from 1.3-4.8 years (median 2.7) after responsive neurostimulation therapy started. Stereoelectroencephalography electrodes closest to the eventual responsive neurostimulation contact site tended to show larger in-degree cortico-cortical evoked potentials, especially for the early latency cortico-cortical evoked potentials period (10-60 ms period) in six out of 12 patients. Stereoelectroencephalography electrodes closest to the responsive neurostimulation contacts (≤5 mm) also had greater significant out-degree in the early cortico-cortical evoked potentials latency period than those further away (≥10 mm) (P < 0.05). Additionally, significant correlation was noted between in-degree cortico-cortical evoked potentials and greater seizure reduction with responsive neurostimulation therapy at its most effective period (P < 0.05). These findings suggest that functional connectivity determined by cortico-cortical evoked potentials may provide additional information that could help guide the optimal placement of responsive neurostimulation electrodes.
Collapse
Affiliation(s)
- Katsuya Kobayashi
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Kenneth N Taylor
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Hossein Shahabi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Balu Krishnan
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Anand Joshi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Michael J Mackow
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Lauren Feldman
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Omar Zamzam
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Takfarinas Medani
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Juan Bulacio
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | | | - Imad Najm
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - William Bingaman
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Richard M Leahy
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Dileep R Nair
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| |
Collapse
|
13
|
Park KM, Park S, Hur YJ. Brain network reconstruction of abnormal functional connectivity in Lennox-Gastaut syndrome according to drug responsiveness: A retrospective study. Epilepsy Res 2024; 200:107312. [PMID: 38309034 DOI: 10.1016/j.eplepsyres.2024.107312] [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: 11/30/2023] [Revised: 01/08/2024] [Accepted: 01/25/2024] [Indexed: 02/05/2024]
Abstract
OBJECTIVE Functional network effects of resective or palliative epilepsy surgery in Lennox-Gastaut syndrome (LGS) patients are different according to the seizure outcome. This study aimed to clarify whether the response to antiseizure medications (ASM) can affect to alteration of brain network connectivity. METHODS In this retrospective study, 37 patients with LGS who underwent 1st electroencephalography (EEG) and 40 healthy controls were enrolled. Among them, 24 LGS patients had follow-up EEG data and were classified as drug responders and non-responders according to the ASM response. Graphical theoretical analysis was used to assess functional connectivity using resting-state EEG. RESULTS The 1st EEG showed a decreased radius in patients with LGS compared with that in healthy controls (3.987 vs. 4.279, P = 0.003). Follow-up EEG data of patients with LGS revealed significant differences in functional connectivity depending on the ASM response. On follow-up EEG, non-responders (n = 11) demonstrated significant increases in global network parameters, whereas responders (n = 13) showed no significant difference in functional connectivity compared with healthy controls. CONCLUSIONS The functional connectivity patterns in patients with LGS differed from those in healthy controls. Functional connectivity in drug-responsive patients with LGS tended to preserve the network of brain connections in a pattern similar to that in healthy controls, whereas non-responders showed more disrupted functional connectivity.
Collapse
Affiliation(s)
- Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Pusan, Republic of Korea
| | - Soyoung Park
- Department of Pediatrics, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Republic of Korea; Yonsei University College of Medicine, Graduate School, Seoul, Republic of Korea
| | - Yun Jung Hur
- Department of Pediatrics, Haeundae Paik Hospital, Inje University College of Medicine, Pusan, Republic of Korea.
| |
Collapse
|
14
|
Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
Collapse
Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
| |
Collapse
|
15
|
Nanda P, Richardson RM. Evolution of Stereo-Electroencephalography at Massachusetts General Hospital. Neurosurg Clin N Am 2024; 35:87-94. [PMID: 38000845 DOI: 10.1016/j.nec.2023.09.007] [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] [Indexed: 11/26/2023]
Abstract
The practice of invasive monitoring for presurgical epilepsy workup has evolved at Massachusetts General Hospital (MGH) in parallel to the evolution in the field's understanding of epilepsy as a network disorder. Implantations have shifted from an emphasis on singularly finding single foci for the purpose of resection to a network-hypothesis-driven approach aiming to delineate patients' seizure networks with the goal of developing surgical interventions that disrupt critical nodes of these networks. Here, the authors review all invasive monitoring cases at MGH from April 2016 through June 2023 to describe how this paradigm shift has taken form.
Collapse
Affiliation(s)
- Pranav Nanda
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurosurgery, Harvard Medical School, Boston, MA 02115, USA.
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurosurgery, Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
16
|
Lainscsek C, Salami P, Carvalho VR, Mendes EMAM, Fan M, Cash SS, Sejnowski TJ. Network-motif delay differential analysis of brain activity during seizures. CHAOS (WOODBURY, N.Y.) 2023; 33:123136. [PMID: 38156987 PMCID: PMC10757649 DOI: 10.1063/5.0165904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024]
Abstract
Delay Differential Analysis (DDA) is a nonlinear method for analyzing time series based on principles from nonlinear dynamical systems. DDA is extended here to incorporate network aspects to improve the dynamical characterization of complex systems. To demonstrate its effectiveness, DDA with network capabilities was first applied to the well-known Rössler system under different parameter regimes and noise conditions. Network-motif DDA, based on cortical regions, was then applied to invasive intracranial electroencephalographic data from drug-resistant epilepsy patients undergoing presurgical monitoring. The directional network motifs between brain areas that emerge from this analysis change dramatically before, during, and after seizures. Neural systems provide a rich source of complex data, arising from varying internal states generated by network interactions.
Collapse
Affiliation(s)
| | - Pariya Salami
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | | | - Eduardo M. A. M. Mendes
- Laboratório de Modelagem, Análise e Controle de Sistemas Não Lineares, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil
| | - Miaolin Fan
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | | |
Collapse
|
17
|
Zhang M, Yu H, Cao G, Huang J, Lu Y, Zhang J, Liu N, Zhang W, Cheng Y, Kang G, Cai L. Enhanced focal cortical dysplasia detection in pediatric frontal lobe epilepsy with asymmetric radiomic and morphological features. Front Neurosci 2023; 17:1289897. [PMID: 38033536 PMCID: PMC10684781 DOI: 10.3389/fnins.2023.1289897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 10/25/2023] [Indexed: 12/02/2023] Open
Abstract
Objective Focal cortical dysplasia (FCD) is the most common pathological cause for pediatric epilepsy, with frontal lobe epilepsy (FLE) being the most prevalent in the pediatric population. We attempted to utilize radiomic and morphological methods on MRI and PET to detect FCD in children with FLE. Methods Thirty-seven children with FLE and 20 controls were included in the primary cohort, and a five-fold cross-validation was performed. In addition, we validated the performance in an independent site of 12 FLE children. A two-stage experiments including frontal lobe and subregions were employed to detect the lesion area of FCD, incorporating the asymmetric feature between the left and right hemispheres. Specifically, for the radiomics approach, we used gray matter (GM), white matter (WM), GM and WM, and the gray-white matter boundary regions of interest to extract features. Then, we employed a Multi-Layer Perceptron classifier to achieve FCD lesion localization based on both radiomic and morphological methods. Results The Multi-Layer Perceptron model based on the asymmetric feature exhibited excellent performance both in the frontal lobe and subregions. In the primary cohort and independent site, the radiomics analysis with GM and WM asymmetric features had the highest sensitivity (89.2 and 91.7%) and AUC (98.9 and 99.3%) in frontal lobe. While in the subregions, the GM asymmetric features had the highest sensitivity (85.6 and 79.7%). Furthermore, relying on the highest sensitivity of GM and WM asymmetric features in frontal lobe, when integrated with the subregions results, our approach exhibited overlaps with GM asymmetric features (55.4 and 52.4%), as well as morphological asymmetric features (54.4 and 53.8%), both in the primary cohort and at the independent site. Significance This study demonstrates that a two-stage design based on the asymmetry of radiomic and morphological features can improve FCD detection. Specifically, incorporating regions of interest for GM, WM, GM, and WM, and the gray-white matter boundary significantly enhances the localization capabilities for lesion detection within the radiomics approach.
Collapse
Affiliation(s)
- Manli Zhang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Hao Yu
- Department of Pediatric Epilepsy Center, Peking University First Hospital, Beijing, China
| | - Gongpeng Cao
- Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Jinguo Huang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
- School of Automation, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yanzhu Lu
- Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Jing Zhang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Nana Liu
- Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Wenjing Zhang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yintao Cheng
- Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Guixia Kang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Lixin Cai
- Department of Pediatric Epilepsy Center, Peking University First Hospital, Beijing, China
| |
Collapse
|
18
|
Woodfield J, Chin RFM, van Schooneveld MMJ, van den Heuvel M, Bastin ME, Braun KPJ. The association of structural connectome efficiency with cognition in children with epilepsy. Epilepsy Behav 2023; 148:109462. [PMID: 37844437 DOI: 10.1016/j.yebeh.2023.109462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/21/2023] [Accepted: 09/26/2023] [Indexed: 10/18/2023]
Abstract
OBJECTIVE Cognitive impairment is common in children with epilepsy (CWE), but understanding the underlying pathological processes is challenging. We aimed to investigate the association of structural brain network organisation with cognition. METHODS This was a retrospective cohort study of CWE without structural brain abnormalities, comparing whole brain network characteristics between those with cognitive impairment and those with intact cognition. We created structural whole-brain connectomes from anatomical and diffusion tensor magnetic resonance imaging using the number of streamlines and tract-averaged fractional anisotropy. We assessed the differences in average path length and global network efficiency between children with cognitive impairment and those without,using multivariable analyses to account for possible clinical group differences. RESULTS Twenty-eight CWE and cognitive impairment had lower whole brain network global efficiency compared with 34 children with intact cognition (0.54, standard deviation (SD):0.003 vs. 0.56, SD:0.002, p < 0.001), which is equivalent to longer normalized network average path lengths (1.14, SD:0.05 vs. 1.10, SD:0.02, p = 0.003). In multivariable logistic regression cognitive impairment was not significantly associated with age of onset, duration of epilepsy, or number of antiseizure medications, but was independently associated with daily seizures (p = 0.04) and normalized average path length (p = 0.007). CONCLUSIONS Higher structural network average path length and lower global network efficiency may be imaging biomarkers of cognitive impairment in epilepsy. Understanding what leads to changes in structural connectivity could aid identification of modifiable risk factors for cognitive impairment. These findings are only applicable to the specific cohort studied, and further confirmation in other cohorts is required.
Collapse
Affiliation(s)
- Julie Woodfield
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Department of Clinical Neurosciences, NHS Lothian, Edinburgh, United Kingdom; Muir Maxwell Epilepsy Centre, University of Edinburgh, Edinburgh, United Kingdom.
| | - Richard F M Chin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Muir Maxwell Epilepsy Centre, University of Edinburgh, Edinburgh, United Kingdom; Royal Hospital for Children and Young People, NHS Lothian, Edinburgh, United Kingdom
| | | | - Martijn van den Heuvel
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Kees P J Braun
- Department of Paediatric Neurology, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
19
|
da Silveira RV, Li LM, Castellano G. Texture-based brain networks for characterization of healthy subjects from MRI. Sci Rep 2023; 13:16421. [PMID: 37775531 PMCID: PMC10541866 DOI: 10.1038/s41598-023-43544-6] [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: 04/12/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023] Open
Abstract
Brain networks have been widely used to study the relationships between brain regions based on their dynamics using, e.g. fMRI or EEG, and to characterize their real physical connections using DTI. However, few studies have investigated brain networks derived from structural properties; and those have been based on cortical thickness or gray matter volume. The main objective of this work was to investigate the feasibility of obtaining useful information from brain networks derived from structural MRI, using texture features. We also wanted to verify if texture brain networks had any relation with established functional networks. T1-MR images were segmented using AAL and texture parameters from the gray-level co-occurrence matrix were computed for each region, for 760 subjects. Individual texture networks were used to evaluate the structural connections between regions of well-established functional networks; assess possible gender differences; investigate the dependence of texture network measures with age; and single out brain regions with different texture-network characteristics. Although around 70% of texture connections between regions belonging to the default mode, attention, and visual network were greater than the mean connection value, this effect was small (only between 7 and 15% of these connections were larger than one standard deviation), implying that texture-based morphology does not seem to subside function. This differs from cortical thickness-based morphology, which has been shown to relate to functional networks. Seventy-five out of 86 evaluated regions showed significant (ANCOVA, p < 0.05) differences between genders. Forty-four out of 86 regions showed significant (ANCOVA, p < 0.05) dependence with age; however, the R2 indicates that this is not a linear relation. Thalamus and putamen showed a very unique texture-wise structure compared to other analyzed regions. Texture networks were able to provide useful information regarding gender and age-related differences, as well as for singling out specific brain regions. We did not find a morphological texture-based subsidy for the evaluated functional brain networks. In the future, this approach will be extended to neurological patients to investigate the possibility of extracting biomarkers to help monitor disease evolution or treatment effectiveness.
Collapse
Affiliation(s)
- Rafael Vinícius da Silveira
- Department of Cosmic Rays and Chronology, Gleb Wataghin Physics Institute, University of Campinas - UNICAMP, R. Sérgio Buarque de Holanda, 777, Cidade Universitária Zeferino Vaz, Campinas, SP, 13083-859, Brazil.
- Brazilian Institute of Neuroscience and Neurotechnology - BRAINN, Campinas, SP, 13083-887, Brazil.
| | - Li Min Li
- Department of Neurology, School of Medical Sciences, University of Campinas - UNICAMP, R. Tessália Vieira de Camargo, 126, Cidade Universitária Zeferino Vaz, Campinas, SP, 13083-887, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology - BRAINN, Campinas, SP, 13083-887, Brazil
| | - Gabriela Castellano
- Department of Cosmic Rays and Chronology, Gleb Wataghin Physics Institute, University of Campinas - UNICAMP, R. Sérgio Buarque de Holanda, 777, Cidade Universitária Zeferino Vaz, Campinas, SP, 13083-859, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology - BRAINN, Campinas, SP, 13083-887, Brazil
| |
Collapse
|
20
|
Matarrese MAG, Loppini A, Fabbri L, Tamilia E, Perry MS, Madsen JR, Bolton J, Stone SSD, Pearl PL, Filippi S, Papadelis C. Spike propagation mapping reveals effective connectivity and predicts surgical outcome in epilepsy. Brain 2023; 146:3898-3912. [PMID: 37018068 PMCID: PMC10473571 DOI: 10.1093/brain/awad118] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/23/2023] [Indexed: 04/06/2023] Open
Abstract
Neurosurgical intervention is the best available treatment for selected patients with drug resistant epilepsy. For these patients, surgical planning requires biomarkers that delineate the epileptogenic zone, the brain area that is indispensable for the generation of seizures. Interictal spikes recorded with electrophysiological techniques are considered key biomarkers of epilepsy. Yet, they lack specificity, mostly because they propagate across brain areas forming networks. Understanding the relationship between interictal spike propagation and functional connections among the involved brain areas may help develop novel biomarkers that can delineate the epileptogenic zone with high precision. Here, we reveal the relationship between spike propagation and effective connectivity among onset and areas of spread and assess the prognostic value of resecting these areas. We analysed intracranial EEG data from 43 children with drug resistant epilepsy who underwent invasive monitoring for neurosurgical planning. Using electric source imaging, we mapped spike propagation in the source domain and identified three zones: onset, early-spread and late-spread. For each zone, we calculated the overlap and distance from surgical resection. We then estimated a virtual sensor for each zone and the direction of information flow among them via Granger causality. Finally, we compared the prognostic value of resecting these zones, the clinically-defined seizure onset zone and the spike onset on intracranial EEG channels by estimating their overlap with resection. We observed a spike propagation in source space for 37 patients with a median duration of 95 ms (interquartile range: 34-206), a spatial displacement of 14 cm (7.5-22 cm) and a velocity of 0.5 m/s (0.3-0.8 m/s). In patients with good surgical outcome (25 patients, Engel I), the onset had higher overlap with resection [96% (40-100%)] than early-spread [86% (34-100%), P = 0.01] and late-spread [59% (12-100%), P = 0.002], and it was also closer to resection than late-spread [5 mm versus 9 mm, P = 0.007]. We found an information flow from onset to early-spread in 66% of patients with good outcomes, and from early-spread to onset in 50% of patients with poor outcome. Finally, resection of spike onset, but not area of spike spread or the seizure onset zone, predicted outcome with positive predictive value of 79% and negative predictive value of 56% (P = 0.04). Spatiotemporal mapping of spike propagation reveals information flow from onset to areas of spread in epilepsy brain. Surgical resection of the spike onset disrupts the epileptogenic network and may render patients with drug resistant epilepsy seizure-free without having to wait for a seizure to occur during intracranial monitoring.
Collapse
Affiliation(s)
- Margherita A G Matarrese
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
| | - Alessandro Loppini
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Lorenzo Fabbri
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Simonetta Filippi
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
- School of Medicine, Texas Christian University, Fort Worth, TX, USA
| |
Collapse
|
21
|
Kim SH, Kim H, Kim JB. Differences in functional network between focal onset nonconvulsive status epilepticus and toxic metabolic encephalopathy: application to machine learning models for differential diagnosis. Cogn Neurodyn 2023; 17:845-853. [PMID: 37522045 PMCID: PMC10374505 DOI: 10.1007/s11571-022-09877-0] [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: 01/07/2022] [Revised: 05/19/2022] [Accepted: 08/19/2022] [Indexed: 11/29/2022] Open
Abstract
We aimed to compare network properties between focal-onset nonconvulsive status epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of periodic discharge using graph theoretical analysis, and to evaluate the applicability of graph measures as markers for the differential diagnosis between focal-onset NCSE and TME, using machine learning algorithms. Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were analyzed. Epochs with nonictal periodic discharges were selected, and the coherence in each frequency band was analyzed. Graph theoretical analysis was performed to compare brain network properties between the groups. Eight different traditional machine learning methods were implemented to evaluate the utility of graph theoretical measures as input features to discriminate between the two conditions. The average degree (in delta, alpha, beta, and gamma bands), strength (in delta band), global efficiency (in delta and alpha bands), local efficiency (in delta band), clustering coefficient (in delta band), and transitivity (in delta band) were higher in TME than in NCSE. TME showed lower modularity (in delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. Machine learning algorithms based on EEG global graph measures classified NCSE and TME with high accuracy, and gradient boosting was the most accurate classification model with an area under the receiver operating characteristics curve of 0.904. Our findings on differences in network properties may provide novel insights that graph measures reflecting the network properties could be quantitative markers for the differential diagnosis between focal-onset NCSE and TME.
Collapse
Affiliation(s)
- Seong Hwan Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Hayom Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jung Bin Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
22
|
Kim H, Wang IN, Park JS, Kim KT, Kong J, Kim JB, Kim DJ. Inherent seizure susceptibility in patients with antihistamine-induced acute symptomatic seizure: a resting-state EEG analysis. Sci Rep 2023; 13:9146. [PMID: 37277514 DOI: 10.1038/s41598-023-36415-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 06/03/2023] [Indexed: 06/07/2023] Open
Abstract
We compared neural activities and network properties between the antihistamine-induced seizures (AIS) and seizure-free groups, with the hypothesis that patients with AIS might have inherently increased neural activities and network properties that are easily synchronized. Resting-state electroencephalography (EEG) data were collected from 27 AIS patients and 30 healthy adults who had never had a seizure. Power spectral density analysis was used to compare neural activities in each localized region. Functional connectivity (FC) was measured using coherence, and graph theoretical analyses were performed to compare network properties between the groups. Machine learning algorithms were applied using measurements found to be different between the groups in the EEG analyses as input features. Compared with the seizure-free group, the AIS group showed a higher spectral power in the entire regions of the delta, theta, and beta bands, as well as in the frontal areas of the alpha band. The AIS group had a higher overall FC strength, as well as a shorter characteristic path length in the theta band and higher global efficiency, local efficiency, and clustering coefficient in the beta band than the seizure-free group. The Support Vector Machine, k-Nearest Neighbor, and Random Forest models distinguished the AIS group from the seizure-free group with a high accuracy of more than 99%. The AIS group had seizure susceptibility considering both regional neural activities and functional network properties. Our findings provide insights into the underlying pathophysiological mechanisms of AIS and may be useful for the differential diagnosis of new-onset seizures in the clinical setting.
Collapse
Affiliation(s)
- Hayom Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - In-Nea Wang
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Jun-Su Park
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Keun-Tae Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jooheon Kong
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jung Bin Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
| | - Dong-Joo Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
- NeuroTx, Co., Ltd., Seoul, Republic of Korea.
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea.
| |
Collapse
|
23
|
Uher D, Drenthen GS, Schijns OEMG, Colon AJ, Hofman PAM, van Lanen RHGJ, Hoeberigs CM, Jansen JFA, Backes WH. Advances in Image Processing for Epileptogenic Zone Detection with MRI. Radiology 2023; 307:e220927. [PMID: 37129491 DOI: 10.1148/radiol.220927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.
Collapse
Affiliation(s)
- Daniel Uher
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Gerhard S Drenthen
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Olaf E M G Schijns
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Albert J Colon
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Paul A M Hofman
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Rick H G J van Lanen
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Christianne M Hoeberigs
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Jacobus F A Jansen
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Walter H Backes
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| |
Collapse
|
24
|
Nogales A, García-Tejedor ÁJ, Chazarra P, Ugalde-Canitrot A. Discriminating and understanding brain states in children with epileptic spasms using deep learning and graph metrics analysis of brain connectivity. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 232:107427. [PMID: 36870168 DOI: 10.1016/j.cmpb.2023.107427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Epilepsy is a brain disorder consisting of abnormal electrical discharges of neurons resulting in epileptic seizures. The nature and spatial distribution of these electrical signals make epilepsy a field for the analysis of brain connectivity using artificial intelligence and network analysis techniques since their study requires large amounts of data over large spatial and temporal scales. For example, to discriminate states that would otherwise be indistinguishable from the human eye. This paper aims to identify the different brain states that appear concerning the intriguing seizure type of epileptic spasms. Once these states have been differentiated, an attempt is made to understand their corresponding brain activity. METHODS The representation of brain connectivity can be done by graphing the topology and intensity of brain activations. Graph images from different instants within and outside the actual seizure are used as input to a deep learning model for classification purposes. This work uses convolutional neural networks to discriminate the different states of the epileptic brain based on the appearance of these graphs at different times. Next, we apply several graph metrics as an aid to interpret what happens in the brain regions during and around the seizure. RESULTS Results show that the model consistently finds distinctive brain states in children with epilepsy with focal onset epileptic spasms that are indistinguishable under the expert visual inspection of EEG traces. Furthermore, differences are found in brain connectivity and network measures in each of the different states. CONCLUSIONS Computer-assisted discrimination using this model can detect subtle differences in the various brain states of children with epileptic spasms. The research reveals previously undisclosed information regarding brain connectivity and networks, allowing for a better understanding of the pathophysiology and evolving characteristics of this particular seizure type. From our data, we speculate that the prefrontal, premotor, and motor cortices could be more involved in a hypersynchronized state occurring in the few seconds immediately preceding the visually evident EEG and clinical ictal features of the first spasm in a cluster. On the other hand, a disconnection in centro-parietal areas seems a relevant feature in the predisposition and repetitive generation of epileptic spasms within clusters.
Collapse
Affiliation(s)
- Alberto Nogales
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain.
| | - Álvaro J García-Tejedor
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain
| | - Pedro Chazarra
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain
| | - Arturo Ugalde-Canitrot
- School of Medicine. Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800. Pozuelo de Alarcón 28223, Spain; Epilepsy Unit, Neurology and Clinical Neurophysiology Service, Hospital Universitario La Paz, Paseo de la Castellana, 261, Madrid 28046, Spain
| |
Collapse
|
25
|
Giansante G, Mazzoleni S, Zippo AG, Ponzoni L, Ghilardi A, Maiellano G, Lewerissa E, van Hugte E, Nadif Kasri N, Francolini M, Sala M, Murru L, Bassani S, Passafaro M. Neuronal network activity and connectivity are impaired in a conditional knockout mouse model with PCDH19 mosaic expression. Mol Psychiatry 2023:10.1038/s41380-023-02022-1. [PMID: 36997609 DOI: 10.1038/s41380-023-02022-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 02/24/2023] [Accepted: 02/28/2023] [Indexed: 04/01/2023]
Abstract
Mutations in PCDH19 gene, which encodes protocadherin-19 (PCDH19), cause Developmental and Epileptic Encephalopathy 9 (DEE9). Heterogeneous loss of PCDH19 expression in neurons is considered a key determinant of the disorder; however, how PCDH19 mosaic expression affects neuronal network activity and circuits is largely unclear. Here, we show that the hippocampus of Pcdh19 mosaic mice is characterized by structural and functional synaptic defects and by the presence of PCDH19-negative hyperexcitable neurons. Furthermore, global reduction of network firing rate and increased neuronal synchronization have been observed in different limbic system areas. Finally, network activity analysis in freely behaving mice revealed a decrease in excitatory/inhibitory ratio and functional hyperconnectivity within the limbic system of Pcdh19 mosaic mice. Altogether, these results indicate that altered PCDH19 expression profoundly affects circuit wiring and functioning, and provide new key to interpret DEE9 pathogenesis.
Collapse
Affiliation(s)
| | - Sara Mazzoleni
- Institute of Neuroscience, CNR, 20854, Vedano al Lambro, Italy
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, 20129, Milano, Italy
| | - Antonio G Zippo
- Institute of Neuroscience, CNR, 20854, Vedano al Lambro, Italy
- NeuroMI Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milano, Italy
| | - Luisa Ponzoni
- Institute of Neuroscience, CNR, 20854, Vedano al Lambro, Italy
| | - Anna Ghilardi
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, 20129, Milano, Italy
| | - Greta Maiellano
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, 20129, Milano, Italy
| | - Elly Lewerissa
- Radboud University Nijmegen Medical Centre, Donders Institute for Brain, Cognition, and Behaviour, Department of Human Genetics, Department of Human Genetics Cognitive Neuroscience, Nijmegen, Netherlands
| | - Eline van Hugte
- Radboud University Nijmegen Medical Centre, Donders Institute for Brain, Cognition, and Behaviour, Department of Human Genetics, Department of Human Genetics Cognitive Neuroscience, Nijmegen, Netherlands
| | - Nael Nadif Kasri
- Radboud University Nijmegen Medical Centre, Donders Institute for Brain, Cognition, and Behaviour, Department of Human Genetics, Department of Human Genetics Cognitive Neuroscience, Nijmegen, Netherlands
| | - Maura Francolini
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, 20129, Milano, Italy
| | | | - Luca Murru
- Institute of Neuroscience, CNR, 20854, Vedano al Lambro, Italy
- NeuroMI Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milano, Italy
| | - Silvia Bassani
- Institute of Neuroscience, CNR, 20854, Vedano al Lambro, Italy.
- NeuroMI Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milano, Italy.
| | - Maria Passafaro
- Institute of Neuroscience, CNR, 20854, Vedano al Lambro, Italy.
- NeuroMI Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milano, Italy.
| |
Collapse
|
26
|
Tan J, Wang Z, Tang Y, Tian Y. Alterations in Human Hippocampus Subregions across the Lifespan: Reflections on White Matter Structure and Functional Connectivity. Neural Plast 2023; 2023:7948140. [PMID: 37025422 PMCID: PMC10072963 DOI: 10.1155/2023/7948140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 03/08/2023] [Accepted: 03/17/2023] [Indexed: 03/31/2023] Open
Abstract
During growth and aging, the role of the hippocampus in memory depends on its interactions with related brain regions. Particularly, two subregions, anterior hippocampus (aHipp) and posterior hippocampus (pHipp), play different and critical roles in memory processing. However, age-related changes of hippocampus subregions on structure and function are still unclear. Here, we investigated age-related structural and functional characteristics of 106 participants (7-85 years old) in resting state based on fractional anisotropy (FA) and functional connectivity (FC) in aHipp and pHipp in the lifespan. The correlation between FA and FC was also explored to identify the coupling. Furthermore, the Wechsler Abbreviated Scale of Intelligence (WASI) was used to explore the relationship between cognitive ability and hippocampal changes. Results showed that there was functional separation and integration in aHipp and pHipp, and the number of functional connections in pHipp was more than that in aHipp across the lifespan. The age-related FC changes showed four different trends (U-shaped/inverted U-shaped/linear upward/linear downward). And around the age of 40 was a critical period for transformation. Then, FA analyses indicated that all effects of age on the hippocampal structures were nonlinear, and the white matter integrity of pHipp was higher than that of aHipp. In the functional-structural coupling, we found that the age-related FA of the right aHipp (aHipp.R) was negatively related to the FC. Finally, through the WASI, we found that the age-related FA of the left aHipp (aHipp.L) was positively correlated with verbal IQ (VERB) and vocabulary comprehension (VOCAB.T), the FA of aHipp.R was only positively correlated with VERB, and the FA of the left pHipp (pHipp.L) was only positively correlated with VOCAB.T. These FC and FA results supported that age-related normal memory changes were closely related to the hippocampus subregions. We also provided empirical evidence that memory ability was altered with the hippocampus, and its efficiency tended to decline after age 40.
Collapse
|
27
|
Shahabi H, Nair DR, Leahy RM. Multilayer brain networks can identify the epileptogenic zone and seizure dynamics. eLife 2023; 12:e68531. [PMID: 36929752 PMCID: PMC10065796 DOI: 10.7554/elife.68531] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 03/16/2023] [Indexed: 03/18/2023] Open
Abstract
Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80-200Hz) for the identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the continuity of neural dynamics, here we have modeled brain connectivity constructed from stereoelectroencephalography (SEEG) data during seizures using multilayer networks. After introducing a new measure of brain connectivity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus hierarchical clustering on the developed model to identify the EZ as a cluster of nodes with distinctive brain connectivity in the ictal period. Our algorithm could successfully predict electrodes inside the resected volume as EZ for 88% of participants, who all were seizure-free for at least 12 months after surgery. Our findings illustrated significant and unique desynchronization between EZ and the rest of the brain in the early to mid-seizure. We showed that aging and the duration of epilepsy intensify this desynchronization, which can be the outcome of abnormal neuroplasticity. Additionally, we illustrated that seizures evolve with various network topologies, confirming the existence of different epileptogenic networks in each patient. Our findings suggest not only the importance of early intervention in epilepsy but possible factors that correlate with disease severity. Moreover, by analyzing the propagation patterns of different seizures, we demonstrate the necessity of collecting sufficient data for identifying epileptogenic networks.
Collapse
Affiliation(s)
- Hossein Shahabi
- Signal and Image Processing Institute, University of Southern CaliforniaLos AngelesUnited States
| | - Dileep R Nair
- Epilepsy Center, Cleveland Clinic Neurological InstituteClevelandUnited States
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern CaliforniaLos AngelesUnited States
| |
Collapse
|
28
|
A new and potential application for network science in the field of life sciences: Comment on "Networks behind the morphology and structural design of living systems" by Gosak et al. Phys Life Rev 2023; 44:105-107. [PMID: 36603331 DOI: 10.1016/j.plrev.2022.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 12/20/2022] [Indexed: 12/29/2022]
|
29
|
Lehnertz K, Bröhl T, Wrede RV. Epileptic-network-based prediction and control of seizures in humans. Neurobiol Dis 2023; 181:106098. [PMID: 36997129 DOI: 10.1016/j.nbd.2023.106098] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/08/2023] [Accepted: 03/22/2023] [Indexed: 03/30/2023] Open
Abstract
Epilepsy is now conceptualized as a network disease. The epileptic brain network comprises structurally and functionally connected cortical and subcortical brain regions - spanning lobes and hemispheres -, whose connections and dynamics evolve in time. With this concept, focal and generalized seizures as well as other related pathophysiological phenomena are thought to emerge from, spread via, and be terminated by network vertices and edges that also generate and sustain normal, physiological brain dynamics. Research over the last years has advanced concepts and techniques to identify and characterize the evolving epileptic brain network and its constituents on various spatial and temporal scales. Network-based approaches further our understanding of how seizures emerge from the evolving epileptic brain network, and they provide both novel insights into pre-seizure dynamics and important clues for success or failure of measures for network-based seizure control and prevention. In this review, we summarize the current state of knowledge and address several important challenges that would need to be addressed to move network-based prediction and control of seizures closer to clinical translation.
Collapse
Affiliation(s)
- Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany; Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany.
| | - Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany
| |
Collapse
|
30
|
Ding Y, Guo K, Wang X, Chen M, Li X, Wu Y. Brain functional connectivity and network characteristics changes after vagus nerve stimulation in patients with refractory epilepsy. Transl Neurosci 2023; 14:20220308. [PMID: 37719745 PMCID: PMC10500639 DOI: 10.1515/tnsci-2022-0308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 08/05/2023] [Accepted: 08/17/2023] [Indexed: 09/19/2023] Open
Abstract
Objective This study aims to investigate the impact of vagus nerve stimulation (VNS) on the connectivity and small-world metrics of brain functional networks during seizure periods. Methods Ten refractory epilepsy patients underwent video encephalographic monitoring before and after VNS treatment. The 2-min electroencephalogram segment containing the ictal was selected for each participant, resulting in a total of 20 min of seizure data. The weighted phase lag index (wPLI) and small-world metrics were calculated for the whole frequency band and different frequency bands (delta, theta, alpha, beta, and gamma). Finally, the relevant metrics were statistically analyzed, and the false discovery rate was used to correct for differences after multiple comparisons. Results In the whole band, the wPLI was notably enhanced, and the network metrics, including degree (D), clustering coefficient (CC), and global efficiency (GE), increased, while characteristic path length (CPL) decreased (P < 0.01). In different frequency bands, the wPLI between the parieto-occipital and frontal regions was significantly strengthened in the delta and beta bands, while the wPLI within the frontal region and between the frontal and parieto-occipital regions were significantly reduced in the beta and gamma bands (P < 0.01). In the low-frequency band (<13 Hz), the small-world metrics demonstrated significantly increased CC, D, and GE, with a significantly decreased CPL, indicating a more efficient network organization. In contrast, in the gamma band, the GE decreased, and the CPL increased, suggesting a shift toward less efficient network organization. Conclusion VNS treatment can significantly change the wPLI and small-world metrics. These findings contribute to a deeper understanding of the impact of VNS therapy on brain networks and provide objective indicators for evaluating the efficacy of VNS.
Collapse
Affiliation(s)
- Yongqiang Ding
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kunlin Guo
- Henan Key Laboratory of Brain Science and Brain–Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Xinjun Wang
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mingming Chen
- Henan Key Laboratory of Brain Science and Brain–Computer Interface Technology, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Xinxiao Li
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuehui Wu
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| |
Collapse
|
31
|
Operto FF, Pastorino GMG, Viggiano A, Dell’Isola GB, Dini G, Verrotti A, Coppola G. Epilepsy and Cognitive Impairment in Childhood and Adolescence: A Mini-Review. Curr Neuropharmacol 2023; 21:1646-1665. [PMID: 35794776 PMCID: PMC10514538 DOI: 10.2174/1570159x20666220706102708] [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: 01/05/2022] [Revised: 02/28/2022] [Accepted: 04/26/2022] [Indexed: 11/22/2022] Open
Abstract
Managing epilepsy in people with an intellectual disability remains a therapeutic challenge and must take into account additional issues such as diagnostic difficulties and frequent drug resistance. Advances in genomic technologies improved our understanding of epilepsy and raised the possibility to develop patients-tailored treatments acting on the key molecular mechanisms involved in the development of the disease. In addition to conventional antiseizure medications (ASMs), ketogenic diet, hormone therapy and epilepsy surgery play an important role, especially in cases of drugresistance. This review aims to provide a comprehensive overview of the mainfactors influencing cognition in children and adolescents with epilepsy and the main therapeutic options available for the epilepsies associated with intellectual disability.
Collapse
Affiliation(s)
- Francesca Felicia Operto
- Child and Adolescent Neuropsychiatry Unit, Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, SA, Italy
| | - Grazia Maria Giovanna Pastorino
- Child and Adolescent Neuropsychiatry Unit, Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, SA, Italy
| | - Andrea Viggiano
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, SA, Italy
| | | | - Gianluca Dini
- Department of Pediatrics, University of Perugia, Giorgio Menghini Square, 06129 Perugia, Italy
| | - Alberto Verrotti
- Department of Pediatrics, University of Perugia, Giorgio Menghini Square, 06129 Perugia, Italy
| | - Giangennaro Coppola
- Child and Adolescent Neuropsychiatry Unit, Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, SA, Italy
| |
Collapse
|
32
|
Koo GE, Jeong HT, Youn YC, Han SH. Is Functional Connectivity after a First Unprovoked Seizure Different Based on Subsequent Seizures and Future Diagnosis of Epilepsy? J Epilepsy Res 2022; 12:62-67. [PMID: 36685746 PMCID: PMC9830024 DOI: 10.14581/jer.22011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/06/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
Background and Purpose There are no highly sensitive biomarkers for epilepsy to date. Recently, promising results regarding functional connectivity analysis have been obtained, which may improve epilepsy diagnosis even in the absence of visible abnormality in electroencephalography. We aimed to investigate the differences in functional connectivity after a first unprovoked seizure between patients diagnosed with epilepsy within 1 year due to subsequent seizures and those who were not. Methods We compared quantitative electroencephalography power spectra and functional connectivity between 12 patients who were diagnosed with epilepsy (two or more unprovoked seizures) within 1 year and 17 controls (those not diagnosed within 1 year) using iSyncBrain® (iMediSync Inc., Suwon, Korea; https://isyncbrain.com/). In the source-level analysis, the current distribution across the brain was assessed using the standardized low-resolution brain electromagnetic tomography technique, to compare relative power values in 68 regions of interest and connectivity (the imaginary part of coherency) between regions of interest. Results In the epilepsy group, quantitative electroencephalography showed lower alpha2 band power in left frontal, central, superior temporal, and parietal regions and higher beta2 power in both frontal, central, temporal, occipital, and left parietal regions compared with the control group. Additionally, epilepsy patients had significantly lower connectivity in alpha2 and beta2 bands than the controls. Conclusions Patients experiencing their first unprovoked seizure presented different brain function according to whether they have subsequent seizures and future epilepsy. Our results propose the potential clinical ability to diagnose epilepsy after the first unprovoked seizure in the absence of interictal epileptiform discharges.
Collapse
Affiliation(s)
- Ga Eun Koo
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
| | - Ho Tae Jeong
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
| | - Su-Hyun Han
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
| |
Collapse
|
33
|
Kanai S, Oguri M, Okanishi T, Miyamoto Y, Maeda M, Yazaki K, Matsuura R, Tozawa T, Sakuma S, Chiyonobu T, Hamano SI, Maegaki Y. Quantitative pretreatment EEG predicts efficacy of ACTH therapy in infantile epileptic spasms syndrome. Clin Neurophysiol 2022; 144:83-90. [PMID: 36327598 DOI: 10.1016/j.clinph.2022.10.004] [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: 07/13/2022] [Revised: 09/13/2022] [Accepted: 10/04/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE This study aimed to determine the correlation between outcomes following adrenocorticotrophic hormone (ACTH) therapy and measurements of relative power spectrum (rPS), weighted phase lag index (wPLI), and graph theoretical analysis on pretreatment electroencephalography (EEG) in infants with non-lesional infantile epileptic spasms syndrome (IESS). METHODS Twenty-eight patients with non-lesional IESS were enrolled. Outcomes were classified based on seizure recurrence following ACTH therapy: seizure-free (F, n = 21) and seizure-recurrence (R, n = 7) groups. The rPS, wPLI, clustering coefficient, and betweenness centrality were calculated on pretreatment EEG and were statistically analyzed to determine the correlation with outcomes following ACTH therapy. RESULTS The rPS value was significantly higher in the delta frequency band in group R than in group F (p < 0.001). The wPLI values were significantly higher in the delta, theta, and alpha frequency bands in group R than in group F (p = 0.007, <0.001, and <0.001, respectively). The clustering coefficient in the delta frequency band was significantly lower in group R than in group F (p < 0.001). CONCLUSIONS Our findings demonstrate the significant differences in power and functional connectivity between outcome groups. SIGNIFICANCE This study may contribute to an early prediction of ACTH therapy outcomes and thus help in the development of appropriate treatment strategies.
Collapse
Affiliation(s)
- Sotaro Kanai
- Division of Child Neurology, Institute of Neurological Sciences, Faculty of Medicine, Tottori University, 86 Nishi-cho, Yonago 683-8503, Japan.
| | - Masayoshi Oguri
- Department of Medical Technology, Kagawa Prefectural University of Health Sciences, 281-1 Mure-cho, Takamatsu 761-0123, Japan
| | - Tohru Okanishi
- Division of Child Neurology, Institute of Neurological Sciences, Faculty of Medicine, Tottori University, 86 Nishi-cho, Yonago 683-8503, Japan
| | - Yosuke Miyamoto
- Department of Pediatrics, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto 602-8566, Japan
| | - Masanori Maeda
- Department of Pediatrics, Wakayama Medical University, 811-1 Kimiidera, Wakayama 641-8509, Japan
| | - Kotaro Yazaki
- Department of Pediatrics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Ryuki Matsuura
- Division of Neurology, Saitama Children's Medical Center, 1-2 Shintoshin, Chuo-ku. Saitama 330-8777, Japan
| | - Takenori Tozawa
- Department of Pediatrics, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto 602-8566, Japan
| | - Satoru Sakuma
- Department of Pediatrics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
| | - Tomohiro Chiyonobu
- Department of Pediatrics, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto 602-8566, Japan
| | - Shin-Ichiro Hamano
- Division of Neurology, Saitama Children's Medical Center, 1-2 Shintoshin, Chuo-ku. Saitama 330-8777, Japan
| | - Yoshihiro Maegaki
- Division of Child Neurology, Institute of Neurological Sciences, Faculty of Medicine, Tottori University, 86 Nishi-cho, Yonago 683-8503, Japan
| |
Collapse
|
34
|
Sun Y, Song Y, Ren H, Zhu H, Wang Y, Li X, Yan W, Wang Y. Synchronization clusters located on epileptic onset zones in neocortical epilepsy. ACTA EPILEPTOLOGICA 2022. [DOI: 10.1186/s42494-022-00113-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Abstract
Background
Brain function is thought to rely on complex interactions of dynamic neural systems, which depend on the integrity of structural and functional networks. Focal epilepsy is considered to result from excessive focal synchronization in the network. Synchronization analysis of multichannel electrocorticography (ECoG) contributes to the understanding of and orientation of epilepsy. The aim of this study was to explore the synchronization in multichannel ECoG recordings from patients with neocortical epilepsy and characterize neural activity inside and outside the onset zone.
Methods
Four patients with neocortical epilepsy, who became seizure-free for more than 1 year after surgery guided by ECoG monitoring, were included in this study. ECoG data recorded during pre-surgical evaluation were analyzed. Synchronizations in phase and amplitude of different frequency bands between ECoG channels was analyzed using MATLAB. We generated 100 surrogate data from the original ECoG data using Amplitude Adjusted Fourier Transform to calculate the enhanced synchronization. The relationship between synchronization characteristics and seizure onset zone was analyzed.
Results
We found synchronization clusters in the 14–30 Hz and 30–80 Hz bands around the onset areas during both interictal and the beginning of ictal periods in all four patients.
Conclusions
The enhanced-synchronization clusters play a central role in epilepsy, and may activate the onset areas and contribute to the spreading of epileptiform activity.
Collapse
|
35
|
Piper RJ, Richardson RM, Worrell G, Carmichael DW, Baldeweg T, Litt B, Denison T, Tisdall MM. Towards network-guided neuromodulation for epilepsy. Brain 2022; 145:3347-3362. [PMID: 35771657 PMCID: PMC9586548 DOI: 10.1093/brain/awac234] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/30/2022] [Accepted: 06/16/2022] [Indexed: 11/30/2022] Open
Abstract
Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal-onset epilepsy; and there is emerging evidence for their efficacy in children and generalized-onset seizure disorders. The convergence of these advancing fields is driving an era of 'network-guided neuromodulation' for epilepsy. In this review, we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key 'propagation points' in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (i) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (ii) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that have revealed structural and functional networks associated with these propagation points - including scalp and invasive EEG, and diffusion and functional MRI. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neuromodulation for epilepsy to accelerate progress towards two translational goals: (i) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (ii) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizure propagation through mapping and modulation of each patients' individual epileptogenic networks.
Collapse
Affiliation(s)
- Rory J Piper
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | | | | | - Torsten Baldeweg
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Brian Litt
- Department of Neurology and Bioengineering, University of Pennsylvania, Philadelphia, USA
| | | | - Martin M Tisdall
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| |
Collapse
|
36
|
Liu Y, Li C. Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational model. Front Physiol 2022; 13:1015838. [PMCID: PMC9632660 DOI: 10.3389/fphys.2022.1015838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022] Open
Abstract
Neuromodulation has emerged as a promising technique for the treatment of epilepsy. The target for neuromodulation is critical for the effectiveness of seizure control. About 30% of patients with drug-resistant epilepsy (DRE) fail to achieve seizure freedom after surgical intervention. It is difficult to find effective brain targets for neuromodulation in these patients because brain regions are damaged during surgery. In this study, we propose a novel approach for localizing neuromodulatory targets, which uses intracranial EEG and multi-unit computational models to simulate the dynamic behavior of epileptic networks through external stimulation. First, we validate our method on a multivariate autoregressive model and compare nine different methods of constructing brain networks. Our results show that the directed transfer function with surrogate analysis achieves the best performance. Intracranial EEGs of 11 DRE patients are further analyzed. These patients all underwent surgery. In three seizure-free patients, the localized targets are concordant with the resected regions. For the eight patients without seizure-free outcome, the localized targets in three of them are outside the resected regions. Finally, we provide candidate targets for neuromodulation in these patients without seizure-free outcome based on virtual resected epileptic network. We demonstrate the ability of our approach to locate optimal targets for neuromodulation. We hope that our approach can provide a new tool for localizing patient-specific targets for neuromodulation therapy in DRE.
Collapse
|
37
|
Myren‐Svelstad S, Jamali A, Ophus SS, D'gama PP, Ostenrath AM, Mutlu AK, Hoffshagen HH, Hotz AL, Neuhauss SCF, Jurisch‐Yaksi N, Yaksi E. Elevated photic response is followed by a rapid decay and depressed state in ictogenic networks. Epilepsia 2022; 63:2543-2560. [PMID: 36222083 PMCID: PMC9804334 DOI: 10.1111/epi.17380] [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: 02/04/2022] [Revised: 07/27/2022] [Accepted: 07/27/2022] [Indexed: 01/05/2023]
Abstract
OBJECTIVE The switch between nonseizure and seizure states involves profound alterations in network excitability and synchrony. In this study, we aimed to identify and compare features of neural excitability and dynamics across multiple zebrafish seizure and epilepsy models. METHODS Inspired by video-electroencephalographic recordings in patients, we developed a framework to study spontaneous and photically evoked neural and locomotor activity in zebrafish larvae, by combining high-throughput behavioral tracking and whole-brain in vivo two-photon calcium imaging. RESULTS Our setup allowed us to dissect behavioral and physiological features that are divergent or convergent across multiple models. We observed that spontaneous locomotor and neural activity exhibit great diversity across models. Nonetheless, during photic stimulation, hyperexcitability and rapid response dynamics were well conserved across multiple models, highlighting the reliability of photically evoked activity for high-throughput assays. Intriguingly, in several models, we observed that the initial elevated photic response is often followed by rapid decay of neural activity and a prominent depressed state. Elevated photic response and following depressed state in seizure-prone networks are significantly reduced by the antiseizure medication valproic acid. Finally, rapid decay and depression of neural activity following photic stimulation temporally overlap with slow recruitment of astroglial calcium signals that are enhanced in seizure-prone networks. SIGNIFICANCE We argue that fast decay of neural activity and depressed states following photic response are likely due to homeostatic mechanisms triggered by excessive neural activity. An improved understanding of the interplay between elevated and depressed excitability states might suggest tailored epilepsy therapies.
Collapse
Affiliation(s)
- Sverre Myren‐Svelstad
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway,Department of Neuromedicine and Movement Science, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway,Department of Neurology and Clinical NeurophysiologySt Olav's University HospitalTrondheimNorway
| | - Ahmed Jamali
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway,Department of Neuromedicine and Movement Science, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway,Department of Neurology and Clinical NeurophysiologySt Olav's University HospitalTrondheimNorway
| | - Sunniva S. Ophus
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
| | - Percival P. D'gama
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway,Department of Clinical and Molecular Medicine, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
| | - Anna M. Ostenrath
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
| | - Aytac Kadir Mutlu
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
| | - Helene Homme Hoffshagen
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
| | - Adriana L. Hotz
- Department of Molecular Life SciencesUniversity of ZürichZürichSwitzerland
| | | | - Nathalie Jurisch‐Yaksi
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway,Department of Neurology and Clinical NeurophysiologySt Olav's University HospitalTrondheimNorway,Department of Clinical and Molecular Medicine, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway
| | - Emre Yaksi
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health SciencesNorwegian University of Science and TechnologyTrondheimNorway,Koç University Research Center for Translational Medicine, Department of NeurologyKoç University School of MedicineIstanbulTurkey
| |
Collapse
|
38
|
Massot-Tarrús A, Mirsattari SM. Roles of fMRI and Wada tests in the presurgical evaluation of language functions in temporal lobe epilepsy. Front Neurol 2022; 13:884730. [PMID: 36247757 PMCID: PMC9562037 DOI: 10.3389/fneur.2022.884730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 08/26/2022] [Indexed: 11/21/2022] Open
Abstract
Surgical treatment of pharmacoresistant temporal lobe epilepsy (TLE) carries risks for language function that can significantly affect the quality of life. Predicting the risks of decline in language functions before surgery is, consequently, just as important as predicting the chances of becoming seizure-free. The intracarotid amobarbital test, generally known as the Wada test (WT), has been traditionally used to determine language lateralization and to estimate their potential decline after surgery. However, the test is invasive and it does not localize the language functions. Therefore, other noninvasive methods have been proposed, of which functional magnetic resonance (fMRI) has the greatest potential. Functional MRI allows localization of language areas. It has good concordance with the WT for language lateralization, and it is of predictive value for postsurgical naming outcomes. Consequently, fMRI has progressively replaced WT for presurgical language evaluation. The objective of this manuscript is to review the most relevant aspects of language functions in TLE and the current role of fMRI and WT in the presurgical evaluation of language. First, we will provide context by revising the language network distribution and the effects of TLE on them. Then, we will assess the functional outcomes following various forms of TLE surgery and measures to reduce postoperative language decline. Finally, we will discuss the current indications for WT and fMRI and the potential usefulness of the resting-state fMRI technique.
Collapse
Affiliation(s)
| | - Seyed M. Mirsattari
- Department of Clinical Neurological Sciences, Western University, London, ON, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
- Department of Medical Imaging, Western University, London, ON, Canada
- Department of Psychology, Western University, London, ON, Canada
| |
Collapse
|
39
|
Conrad EC, Bernabei JM, Sinha N, Ghosn NJ, Stein JM, Shinohara RT, Litt B. Addressing spatial bias in intracranial EEG functional connectivity analyses for epilepsy surgical planning. J Neural Eng 2022; 19:056019. [PMID: 36084621 PMCID: PMC9590099 DOI: 10.1088/1741-2552/ac90ed] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/26/2022] [Accepted: 09/09/2022] [Indexed: 01/25/2023]
Abstract
Objective.To determine the effect of epilepsy on intracranial electroencephalography (EEG) functional connectivity, and the ability of functional connectivity to localize the seizure onset zone (SOZ), controlling for spatial biases.Approach.We analyzed intracranial EEG data from patients with drug-resistant epilepsy admitted for pre-surgical planning. We calculated intracranial EEG functional networks and determined whether changes in functional connectivity lateralized the SOZ using a spatial subsampling method to control for spatial bias. We developed a 'spatial null model' to localize the SOZ electrode using only spatial sampling information, ignoring EEG data. We compared the performance of this spatial null model against models incorporating EEG functional connectivity and interictal spike rates.Main results.About 110 patients were included in the study, although the number of patients differed across analyses. Controlling for spatial sampling, the average connectivity was lower in the SOZ region relative to the same anatomic region in the contralateral hemisphere. A model using intra-hemispheric connectivity accurately lateralized the SOZ (average accuracy 75.5%). A spatial null model incorporating spatial sampling information alone achieved moderate accuracy in classifying SOZ electrodes (mean AUC = 0.70, 95% CI 0.63-0.77). A model incorporating intracranial EEG functional connectivity and spike rate data further outperformed this spatial null model (AUC 0.78,p= 0.002 compared to spatial null model). However, a model incorporating functional connectivity without spike rate data did not significantly outperform the null model (AUC 0.72,p= 0.38).Significance.Intracranial EEG functional connectivity is reduced in the SOZ region, and interictal data predict SOZ electrode localization and laterality, however a predictive model incorporating functional connectivity without interictal spike rates did not significantly outperform a spatial null model. We propose constructing a spatial null model to provide an estimate of the pre-implant hypothesis of the SOZ, and to serve as a benchmark for further machine learning algorithms in order to avoid overestimating model performance because of electrode sampling alone.
Collapse
Affiliation(s)
- Erin C Conrad
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - John M Bernabei
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Nishant Sinha
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Nina J Ghosn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Joel M Stein
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States of America
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Brian Litt
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| |
Collapse
|
40
|
Buchhalter J, Neuray C, Cheng JY, D’Cruz O, Datta AN, Dlugos D, French J, Haubenberger D, Hulihan J, Klein P, Komorowski RW, Kramer L, Lothe A, Nabbout R, Perucca E, der Ark PV. EEG Parameters as Endpoints in Epilepsy Clinical Trials- An Expert Panel Opinion Paper. Epilepsy Res 2022; 187:107028. [DOI: 10.1016/j.eplepsyres.2022.107028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/29/2022] [Accepted: 09/26/2022] [Indexed: 11/30/2022]
|
41
|
Simula S, Daoud M, Ruffini G, Biagi MC, Bénar CG, Benquet P, Wendling F, Bartolomei F. Transcranial current stimulation in epilepsy: A systematic review of the fundamental and clinical aspects. Front Neurosci 2022; 16:909421. [PMID: 36090277 PMCID: PMC9453675 DOI: 10.3389/fnins.2022.909421] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose Transcranial electrical current stimulation (tES or tCS, as it is sometimes referred to) has been proposed as non-invasive therapy for pharmacoresistant epilepsy. This technique, which includes direct current (tDCS) and alternating current (tACS) stimulation involves the application of weak currents across the cortex to change cortical excitability. Although clinical trials have demonstrated the therapeutic efficacy of tES, its specific effects on epileptic brain activity are poorly understood. We sought to summarize the clinical and fundamental effects underlying the application of tES in epilepsy. Methods A systematic review was performed in accordance with the PRISMA guidelines. A database search was performed in PUBMED, MEDLINE, Web of Science and Cochrane CENTRAL for articles corresponding to the keywords “epilepsy AND (transcranial current stimulation OR transcranial electrical stimulation)”. Results A total of 56 studies were included in this review. Through these records, we show that tDCS and tACS epileptic patients are safe and clinically relevant techniques for epilepsy. Recent articles reported changes of functional connectivity in epileptic patients after tDCS. We argue that tDCS may act by affecting brain networks, rather than simply modifying local activity in the targeted area. To explain the mechanisms of tES, various cellular effects have been identified. Among them, reduced cell loss, mossy fiber sprouting, and hippocampal BDNF protein levels. Brain modeling and human studies highlight the influence of individual brain anatomy and physiology on the electric field distribution. Computational models may optimize the stimulation parameters and bring new therapeutic perspectives. Conclusion Both tDCS and tACS are promising techniques for epilepsy patients. Although the clinical effects of tDCS have been repeatedly assessed, only one clinical trial has involved a consistent number of epileptic patients and little knowledge is present about the clinical outcome of tACS. To fill this gap, multicenter studies on tES in epileptic patients are needed involving novel methods such as personalized stimulation protocols based on computational modeling. Furthermore, there is a need for more in vivo studies replicating the tES parameters applied in patients. Finally, there is a lack of clinical studies investigating changes in intracranial epileptiform discharges during tES application, which could clarify the nature of tES-related local and network dynamics in epilepsy.
Collapse
Affiliation(s)
- Sara Simula
- Aix Marseille Univ, INSERM, INS, Int Neurosci Syst, Marseille, France
| | - Maëva Daoud
- Aix Marseille Univ, INSERM, INS, Int Neurosci Syst, Marseille, France
| | | | | | | | | | | | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Int Neurosci Syst, Marseille, France
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- *Correspondence: Fabrice Bartolomei
| |
Collapse
|
42
|
Zhong L, Wan J, Wu J, He S, Zhong X, Huang Z, Li Z. Temporal and spatial dynamic propagation of electroencephalogram by combining power spectral and synchronization in childhood absence epilepsy. Front Neuroinform 2022; 16:962466. [PMID: 36059863 PMCID: PMC9433125 DOI: 10.3389/fninf.2022.962466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Objective During the transition from normal to seizure and then to termination, electroencephalography (EEG) signals have complex changes in time-frequency-spatial characteristics. The quantitative analysis of EEG characteristics and the exploration of their dynamic propagation in this paper would help to provide new biomarkers for distinguishing between pre-ictal and inter-ictal states and to better understand the seizure mechanisms. Methods Thirty-three children with absence epilepsy were investigated with EEG signals. Power spectral and synchronization were combined to provide the time-frequency-spatial characteristics of EEG and analyze the spatial distribution and propagation of EEG in the brain with topographic maps. To understand the mechanism of spatial-temporal evolution, we compared inter-ictal, pre-ictal, and ictal states in EEG power spectral and synchronization network and its rhythms in each frequency band. Results Power, frequency, and spatial synchronization are all enhanced during the absence seizures to jointly dominate the epilepsy process. We confirmed that a rapid diffusion at the onset accompanied by the frontal region predominance exists. The EEG power rapidly bursts in 2–4 Hz through the whole brain within a few seconds after the onset. This spatiotemporal evolution is associated with spatial diffusion and brain regions interaction, with a similar pattern, increasing first and then decreasing, in both the diffusion of the EEG power and the connectivity of the brain network during the childhood absence epilepsy (CAE) seizures. Compared with the inter-ictal group, we observed increases in power of delta and theta rhythms in the pre-ictal group (P < 0.05). Meanwhile, the synchronization of delta rhythm decreased while that of alpha rhythm enhanced. Conclusion The initiation and propagation of CAE seizures are related to the abnormal discharge diffusion and the synchronization network. During the seizures, brain activity is completely changed with the main component delta rhythm. Furthermore, this article demonstrated for the first time that alpha inhibition, which is consistent with the brain’s feedback regulation mechanism, is caused by the enhancement of the network connection. Temporal and spatial evolution of EEG is of great significance for the transmission mechanism, clinical diagnosis and automatic detection of absence epilepsy seizures.
Collapse
Affiliation(s)
- Lisha Zhong
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Jiangzhong Wan
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Jia Wu
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Suling He
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xuefei Zhong
- Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Zhiwei Huang
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
- Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, China
| | - Zhangyong Li
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- Research Center of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
- *Correspondence: Zhangyong Li,
| |
Collapse
|
43
|
McKavanagh A, Kreilkamp BAK, Chen Y, Denby C, Bracewell M, Das K, De Bezenac C, Marson AG, Taylor PN, Keller SS. Altered Structural Brain Networks in Refractory and Nonrefractory Idiopathic Generalized Epilepsy. Brain Connect 2022; 12:549-560. [PMID: 34348477 DOI: 10.1089/brain.2021.0035] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction: Idiopathic generalized epilepsy (IGE) is a collection of generalized nonlesional epileptic network disorders. Around 20-40% of patients with IGE are refractory to antiseizure medication, and mechanisms underlying refractoriness are poorly understood. Here, we characterize structural brain network alterations and determine whether network alterations differ between patients with refractory and nonrefractory IGE. Methods: Thirty-three patients with IGE (10 nonrefractory and 23 refractory) and 39 age- and sex-matched healthy controls were studied. Network nodes were segmented from T1-weighted images, while connections between these nodes (edges) were reconstructed from diffusion magnetic resonance imaging (MRI). Diffusion networks of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and streamline count (Count) were studied. Differences between all patients, refractory, nonrefractory, and control groups were computed using network-based statistics. Nodal volume differences between groups were computed using Cohen's d effect size calculation. Results: Patients had significantly decreased bihemispheric FA and Count networks and increased MD and RD networks compared with controls. Alterations in network architecture, with respect to controls, differed depending on treatment outcome, including predominant FA network alterations in refractory IGE and increased nodal volume in nonrefractory IGE. Diffusion MRI networks were not influenced by nodal volume. Discussion: Although a nonlesional disorder, patients with IGE have bihemispheric structural network alterations that may differ between patients with refractory and nonrefractory IGE. Given that distinct nodal volume and FA network alterations were observed between treatment outcome groups, a multifaceted network analysis may be useful for identifying imaging biomarkers of refractory IGE.
Collapse
Affiliation(s)
- Andrea McKavanagh
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Barbara A K Kreilkamp
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
- Department of Neurology, University Medicine Göttingen, Göttingen, Germany
| | - Yachin Chen
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Christine Denby
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Martyn Bracewell
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
- School of Medical Sciences, Bangor University, Bangor, United Kingdom
- School of Psychology, Bangor University, Bangor, United Kingdom
| | - Kumar Das
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Christophe De Bezenac
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Anthony G Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Peter N Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle, United Kingdom
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| |
Collapse
|
44
|
Horsley JJ, Schroeder GM, Thomas RH, de Tisi J, Vos SB, Winston GP, Duncan JS, Wang Y, Taylor PN. Volumetric and structural connectivity abnormalities co-localise in TLE. Neuroimage Clin 2022; 35:103105. [PMID: 35863179 PMCID: PMC9421455 DOI: 10.1016/j.nicl.2022.103105] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 05/17/2022] [Accepted: 06/29/2022] [Indexed: 12/02/2022]
Abstract
Patients with temporal lobe epilepsy (TLE) exhibit both volumetric and structural connectivity abnormalities relative to healthy controls. How these abnormalities inter-relate and their mechanisms are unclear. We computed grey matter volumetric changes and white matter structural connectivity abnormalities in 144 patients with unilateral TLE and 96 healthy controls. Regional volumes were calculated using T1-weighted MRI, while structural connectivity was derived using white matter fibre tractography from diffusion-weighted MRI. For each regional volume and each connection strength, we calculated the effect size between patient and control groups in a group-level analysis. We then applied hierarchical regression to investigate the relationship between volumetric and structural connectivity abnormalities in individuals. Additionally, we quantified whether abnormalities co-localised within individual patients by computing Dice similarity scores. In TLE, white matter connectivity abnormalities were greater when joining two grey matter regions with abnormal volumes. Similarly, grey matter volumetric abnormalities were greater when joined by abnormal white matter connections. The extent of volumetric and connectivity abnormalities related to epilepsy duration, but co-localisation did not. Co-localisation was primarily driven by neighbouring abnormalities in the ipsilateral hemisphere. Overall, volumetric and structural connectivity abnormalities were related in TLE. Our results suggest that shared mechanisms may underlie changes in both volume and connectivity alterations in patients with TLE.
Collapse
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
| | - Gabrielle M Schroeder
- 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
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, 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
| | - 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; 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; 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.
| |
Collapse
|
45
|
Jin C, Yang L, Qi S, Teng Y, Li C, Yao Y, Ruan X, Wei X. Structural Brain Network Abnormalities in Parkinson’s Disease With Freezing of Gait. Front Aging Neurosci 2022; 14:944925. [PMID: 35875794 PMCID: PMC9304752 DOI: 10.3389/fnagi.2022.944925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveDiffusion tensor imaging (DTI) studies have investigated white matter (WM) integrity abnormalities in Parkinson’s disease (PD). However, little is known about the topological changes in the brain network. This study aims to reveal these changes by comparing PD without freezing of gait (FOG) (PD FOG–), PD with FOG (PD FOG+), and healthy control (HC).Methods21 PD FOG+, 34 PD FOG-, and 23 HC were recruited, and DTI images were acquired. The graph theoretical analysis and network-based statistical method were used to calculate the topological parameters and assess connections.ResultsPD FOG+ showed a decreased normalized clustering coefficient, small-worldness, clustering coefficient, and increased local network efficiency compared with HCs. PD FOG+ showed decreased centrality, degree centrality, and nodal efficiency in the striatum, frontal gyrus, and supplementary motor area (SMA). PD FOG+ showed decreased connections in the frontal gyrus, cingulate gyrus, and caudate nucleus (CAU). The between centrality of the left SMA and left CAU was negatively correlated with FOG questionnaire scores.ConclusionThis study demonstrates that PD FOG+ exhibits disruption of global and local topological organization in structural brain networks, and the disrupted topological organization can be potential biomarkers in PD FOG+. These new findings may provide increasing insight into the pathophysiological mechanism of PD FOG+.
Collapse
Affiliation(s)
- Chaoyang Jin
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Lei Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
- *Correspondence: Shouliang Qi,
| | - Yueyang Teng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Chen Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Xiuhang Ruan
- Department of Radiology, School of Medicine, Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, China
| | - Xinhua Wei
- Department of Radiology, School of Medicine, Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, China
| |
Collapse
|
46
|
Elbadry Ahmed R, Tang H, Asemota A, Huang L, Boling W, Bannout F. Meningioma Related Epilepsy- Pathophysiology, Pre/postoperative Seizures Predicators and Treatment. Front Oncol 2022; 12:905976. [PMID: 35860576 PMCID: PMC9289540 DOI: 10.3389/fonc.2022.905976] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
Meningiomas are the most common primary brain tumors accounting for about 30% of all brain tumors. The vast majority of meningiomas are slow-growing and of benign histopathology rendering them curable by surgery alone. Symptomatic lesions depend on the location with signs of mass effect or neurological deficits. Seizures are the presenting symptoms in approximately 30% of cases, which negatively affect quality of life, limit independence, impair cognitive functioning, as well as increase the risk for psychiatric comorbidities including depression. Although surgical resection may offer seizure freedom in 60-90% of meningiomas, seizures persist after surgical resection in approximately 12-19% of patients. Anti-seizure medications (ASMs) are employed in management, however, are limited by adverse neurocognitive side-effects and inefficacy in some patients. The potential predictors of pre- and post-operative seizures in meningioma patients have been identified in the literature. Understanding various factors associated with seizure likelihood in meningioma patients can help guide more effective seizure control and allow for better determination of risk before and after surgery.
Collapse
Affiliation(s)
- Rasha Elbadry Ahmed
- Department of Neurosurgery, Loma Linda University Medical Center, Loma Linda, CA, United States
| | - Hailiang Tang
- Department of Neurosurgery, Huasha Hospital, Fudan University, Shanghai, China
| | - Anthony Asemota
- Department of Neurosurgery, Loma Linda University Medical Center, Loma Linda, CA, United States
| | - Lei Huang
- Department of Neurosurgery, Loma Linda University Medical Center, Loma Linda, CA, United States
- Department of Physiology and Pharmacology, Loma Linda University, Loma Linda, CA, United States
| | - Warren Boling
- Department of Neurosurgery, Loma Linda University Medical Center, Loma Linda, CA, United States
- *Correspondence: Warren Boling, ; Firas Bannout,
| | - Firas Bannout
- Department of Neurology, Loma Linda University Medical Center, Loma Linda, CA, United States
- *Correspondence: Warren Boling, ; Firas Bannout,
| |
Collapse
|
47
|
Nandakumar N, Hsu D, Ahmed R, Venkataraman A. DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization from Resting-State fMRI Connectivity. IEEE Trans Biomed Eng 2022; 70:216-227. [PMID: 35776823 PMCID: PMC9841829 DOI: 10.1109/tbme.2022.3187942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data. METHODS Our network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds. RESULTS We validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort. CONCLUSION Our results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy. SIGNIFICANCE While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.
Collapse
Affiliation(s)
- Naresh Nandakumar
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - David Hsu
- Department of Neurology, University of Wisconsin, USA
| | - Raheel Ahmed
- Department of Neurosurgery, University of Wisconsin, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
| |
Collapse
|
48
|
Stožer A, Šterk M, Paradiž Leitgeb E, Markovič R, Skelin Klemen M, Ellis CE, Križančić Bombek L, Dolenšek J, MacDonald PE, Gosak M. From Isles of Königsberg to Islets of Langerhans: Examining the Function of the Endocrine Pancreas Through Network Science. Front Endocrinol (Lausanne) 2022; 13:922640. [PMID: 35784543 PMCID: PMC9240343 DOI: 10.3389/fendo.2022.922640] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 05/16/2022] [Indexed: 12/12/2022] Open
Abstract
Islets of Langerhans are multicellular microorgans located in the pancreas that play a central role in whole-body energy homeostasis. Through secretion of insulin and other hormones they regulate postprandial storage and interprandial usage of energy-rich nutrients. In these clusters of hormone-secreting endocrine cells, intricate cell-cell communication is essential for proper function. Electrical coupling between the insulin-secreting beta cells through gap junctions composed of connexin36 is particularly important, as it provides the required, most important, basis for coordinated responses of the beta cell population. The increasing evidence that gap-junctional communication and its modulation are vital to well-regulated secretion of insulin has stimulated immense interest in how subpopulations of heterogeneous beta cells are functionally arranged throughout the islets and how they mediate intercellular signals. In the last decade, several novel techniques have been proposed to assess cooperation between cells in islets, including the prosperous combination of multicellular imaging and network science. In the present contribution, we review recent advances related to the application of complex network approaches to uncover the functional connectivity patterns among cells within the islets. We first provide an accessible introduction to the basic principles of network theory, enumerating the measures characterizing the intercellular interactions and quantifying the functional integration and segregation of a multicellular system. Then we describe methodological approaches to construct functional beta cell networks, point out possible pitfalls, and specify the functional implications of beta cell network examinations. We continue by highlighting the recent findings obtained through advanced multicellular imaging techniques supported by network-based analyses, giving special emphasis to the current developments in both mouse and human islets, as well as outlining challenges offered by the multilayer network formalism in exploring the collective activity of islet cell populations. Finally, we emphasize that the combination of these imaging techniques and network-based analyses does not only represent an innovative concept that can be used to describe and interpret the physiology of islets, but also provides fertile ground for delineating normal from pathological function and for quantifying the changes in islet communication networks associated with the development of diabetes mellitus.
Collapse
Affiliation(s)
- Andraž Stožer
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Marko Šterk
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
| | - Eva Paradiž Leitgeb
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Rene Markovič
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Institute of Mathematics and Physics, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Maša Skelin Klemen
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Cara E. Ellis
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | | | - Jurij Dolenšek
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
| | - Patrick E. MacDonald
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Marko Gosak
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
| |
Collapse
|
49
|
Hybrid machine learning method for a connectivity-based epilepsy diagnosis with resting-state EEG. Med Biol Eng Comput 2022; 60:1675-1689. [DOI: 10.1007/s11517-022-02560-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/25/2022] [Indexed: 10/18/2022]
|
50
|
Khalife MR, Scott RC, Hernan AE. Mechanisms for Cognitive Impairment in Epilepsy: Moving Beyond Seizures. Front Neurol 2022; 13:878991. [PMID: 35645970 PMCID: PMC9135108 DOI: 10.3389/fneur.2022.878991] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
There has been a major emphasis on defining the role of seizures in the causation of cognitive impairments like memory deficits in epilepsy. Here we focus on an alternative hypothesis behind these deficits, emphasizing the mechanisms of information processing underlying healthy cognition characterized as rate, temporal and population coding. We discuss the role of the underlying etiology of epilepsy in altering neural networks thereby leading to both the propensity for seizures and the associated cognitive impairments. In addition, we address potential treatments that can recover the network function in the context of a diseased brain, thereby improving both seizure and cognitive outcomes simultaneously. This review shows the importance of moving beyond seizures and approaching the deficits from a system-level perspective with the guidance of network neuroscience.
Collapse
Affiliation(s)
- Mohamed R. Khalife
- Division of Neuroscience, Nemours Children's Health, Wilmington, DE, United States
- Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
| | - Rod C. Scott
- Division of Neuroscience, Nemours Children's Health, Wilmington, DE, United States
- Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
- Institute of Child Health, Neurosciences Unit University College London, London, United Kingdom
| | - Amanda E. Hernan
- Division of Neuroscience, Nemours Children's Health, Wilmington, DE, United States
- Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
| |
Collapse
|