1
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Ye C, Zhang Y, Ran C, Ma T. Recent Progress in Brain Network Models for Medical Applications: A Review. HEALTH DATA SCIENCE 2024; 4:0157. [PMID: 38979037 PMCID: PMC11227951 DOI: 10.34133/hds.0157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 05/28/2024] [Indexed: 07/10/2024]
Abstract
Importance: Pathological perturbations of the brain often spread via connectome to fundamentally alter functional consequences. By integrating multimodal neuroimaging data with mathematical neural mass modeling, brain network models (BNMs) enable to quantitatively characterize aberrant network dynamics underlying multiple neurological and psychiatric disorders. We delved into the advancements of BNM-based medical applications, discussed the prevalent challenges within this field, and provided possible solutions and future directions. Highlights: This paper reviewed the theoretical foundations and current medical applications of computational BNMs. Composed of neural mass models, the BNM framework allows to investigate large-scale brain dynamics behind brain diseases by linking the simulated functional signals to the empirical neurophysiological data, and has shown promise in exploring neuropathological mechanisms, elucidating therapeutic effects, and predicting disease outcome. Despite that several limitations existed, one promising trend of this research field is to precisely guide clinical neuromodulation treatment based on individual BNM simulation. Conclusion: BNM carries the potential to help understand the mechanism underlying how neuropathology affects brain network dynamics, further contributing to decision-making in clinical diagnosis and treatment. Several constraints must be addressed and surmounted to pave the way for its utilization in the clinic.
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Affiliation(s)
- Chenfei Ye
- International Research Institute for Artificial Intelligence,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Yixuan Zhang
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Chen Ran
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Ting Ma
- International Research Institute for Artificial Intelligence,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
- Peng Cheng Laboratory, Shenzhen, China
- Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology,
Harbin Institute of Technology at Shenzhen, China
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2
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Wang SH, Arnulfo G, Nobili L, Myrov V, Ferrari P, Ciuciu P, Palva S, Palva JM. Neuronal synchrony and critical bistability: Mechanistic biomarkers for localizing the epileptogenic network. Epilepsia 2024; 65:2041-2053. [PMID: 38687176 DOI: 10.1111/epi.17996] [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: 11/26/2023] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 05/02/2024]
Abstract
OBJECTIVE Postsurgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30% to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). We aimed to advance a novel approach to better characterize epileptogenicity and investigate whether the EZ encompasses a broader epileptogenic network (EpiNet) beyond the seizure zone (SZ) that exhibits seizure activity. METHODS We first used computational modeling to test putative complex systems-driven and systems neuroscience-driven mechanistic biomarkers for epileptogenicity. We then used these biomarkers to extract features from resting-state stereoelectroencephalograms recorded from DRE patients and trained supervised classifiers to localize the SZ against gold standard clinical localization. To further explore the prevalence of pathological features in an extended brain network outside of the clinically identified SZ, we also used unsupervised classification. RESULTS Supervised SZ classification trained on individual features achieved accuracies of .6-.7 area under the receiver operating characteristic curve (AUC). Combining all criticality and synchrony features further improved the AUC to .85. Unsupervised classification discovered an EpiNet-like cluster of brain regions, in which 51% of brain regions were outside of the SZ. Brain regions in the EpiNet-like cluster engaged in interareal hypersynchrony and locally exhibited high-amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure risk regime revealed by our computational modeling. SIGNIFICANCE The finding that combining biomarkers improves SZ localization accuracy indicates that the novel mechanistic biomarkers for epileptogenicity employed here yield synergistic information. On the other hand, the discovery of SZ-like brain dynamics outside of the clinically defined SZ provides empirical evidence of an extended pathophysiological EpiNet.
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Affiliation(s)
- Sheng H Wang
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Le Commissariat à l'énergie atomique et aux énergies alternatives, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
- Models and Inference for Neuroimaging Data, Inria, Palaiseau, France
| | - Gabriele Arnulfo
- Department of Informatics, Bioengineering, Robotics, and System Engineering, University of Genoa, Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, Istituto di Ricovero e Cura a Carattere Scientifico Istituto Giannina Gaslini, Member of the European Reference Network EpiCARE, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, and Maternal and Children's Sciences, University of Genoa, Genoa, Italy
| | - Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Paul Ferrari
- Jack H. Miller Magnetoencephalography Center, Helen DeVos Childrens Hospital, Grand Rapids, Michigan, USA
- Department of Pediatrics and Human Development, Michigan State University, East Lansing, Michigan, USA
| | - Philippe Ciuciu
- Le Commissariat à l'énergie atomique et aux énergies alternatives, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
- Models and Inference for Neuroimaging Data, Inria, Palaiseau, France
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Division of Psychology, Values, Ideologies and Social Contexts of Education, Faculty of Education and Psychology, University of Oulu, Oulu, Finland
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
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3
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Penas DR, Hashemi M, Jirsa VK, Banga JR. Parameter estimation in a whole-brain network model of epilepsy: Comparison of parallel global optimization solvers. PLoS Comput Biol 2024; 20:e1011642. [PMID: 38990984 PMCID: PMC11265693 DOI: 10.1371/journal.pcbi.1011642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 07/23/2024] [Accepted: 06/18/2024] [Indexed: 07/13/2024] Open
Abstract
The Virtual Epileptic Patient (VEP) refers to a computer-based representation of a patient with epilepsy that combines personalized anatomical data with dynamical models of abnormal brain activities. It is capable of generating spatio-temporal seizure patterns that resemble those recorded with invasive methods such as stereoelectro EEG data, allowing for the evaluation of clinical hypotheses before planning surgery. This study highlights the effectiveness of calibrating VEP models using a global optimization approach. The approach utilizes SaCeSS, a cooperative metaheuristic algorithm capable of parallel computation, to yield high-quality solutions without requiring excessive computational time. Through extensive benchmarking on synthetic data, our proposal successfully solved a set of different configurations of VEP models, demonstrating better scalability and superior performance against other parallel solvers. These results were further enhanced using a Bayesian optimization framework for hyperparameter tuning, with significant gains in terms of both accuracy and computational cost. Additionally, we added a scalable uncertainty quantification phase after model calibration, and used it to assess the variability in estimated parameters across different problems. Overall, this study has the potential to improve the estimation of pathological brain areas in drug-resistant epilepsy, thereby to inform the clinical decision-making process.
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Affiliation(s)
- David R. Penas
- Computational Biology Lab, MBG-CSIC (Spanish National Research Council), Pontevedra, Spain
| | - Meysam Hashemi
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Viktor K. Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julio R. Banga
- Computational Biology Lab, MBG-CSIC (Spanish National Research Council), Pontevedra, Spain
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4
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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.
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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
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5
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Harrington EG, Kissack P, Terry JR, Woldman W, Junges L. Treatment effects in epilepsy: a mathematical framework for understanding response over time. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1308501. [PMID: 38988793 PMCID: PMC11233745 DOI: 10.3389/fnetp.2024.1308501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 05/30/2024] [Indexed: 07/12/2024]
Abstract
Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom.
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Affiliation(s)
- Elanor G. Harrington
- School of Mathematics, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Peter Kissack
- School of Mathematics, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - John R. Terry
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
- Neuronostics Ltd, Engine Shed, Station Approach, Bristol, United Kingdom
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
- Neuronostics Ltd, Engine Shed, Station Approach, Bristol, United Kingdom
| | - Leandro Junges
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
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6
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Moguilner S, Herzog R, Perl YS, Medel V, Cruzat J, Coronel C, Kringelbach M, Deco G, Ibáñez A, Tagliazucchi E. Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition. Alzheimers Res Ther 2024; 16:79. [PMID: 38605416 PMCID: PMC11008050 DOI: 10.1186/s13195-024-01449-0] [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: 11/06/2023] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND The hypothesis of decreased neural inhibition in dementia has been sparsely studied in functional magnetic resonance imaging (fMRI) data across patients with different dementia subtypes, and the role of social and demographic heterogeneities on this hypothesis remains to be addressed. METHODS We inferred regional inhibition by fitting a biophysical whole-brain model (dynamic mean field model with realistic inter-areal connectivity) to fMRI data from 414 participants, including patients with Alzheimer's disease, behavioral variant frontotemporal dementia, and controls. We then investigated the effect of disease condition, and demographic and clinical variables on the local inhibitory feedback, a variable related to the maintenance of balanced neural excitation/inhibition. RESULTS Decreased local inhibitory feedback was inferred from the biophysical modeling results in dementia patients, specific to brain areas presenting neurodegeneration. This loss of local inhibition correlated positively with years with disease, and showed differences regarding the gender and geographical origin of the patients. The model correctly reproduced known disease-related changes in functional connectivity. CONCLUSIONS Results suggest a critical link between abnormal neural and circuit-level excitability levels, the loss of grey matter observed in dementia, and the reorganization of functional connectivity, while highlighting the sensitivity of the underlying biophysical mechanism to demographic and clinical heterogeneities in the patient population.
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Affiliation(s)
- Sebastian Moguilner
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), 1207 1651 4th St, 3rd Floor, San Francisco, CA, 94143, USA
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284, B1644BID, Buenos Aires, VIC, Argentina
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 25 Shattuck St, Boston, MA, 02115, USA
- Trinity College Dublin, Lloyd Building Trinity College Dublin, Dublin, D02 PN40, Ireland
| | - Rubén Herzog
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile
| | - Yonatan Sanz Perl
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284, B1644BID, Buenos Aires, VIC, Argentina
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, 1425, Argentina
- Institute of Applied and Interdisciplinary Physics and Department of Physics, University of Buenos Aires, Pabellón 1, Ciudad Universitaria, CABA, 1428, Argentina
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Plaça de La Mercè, 10-12, Barcelona, 08002, Spain
| | - Vicente Medel
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington 287, Valparaíso, 2381850, Chile
| | - Josefina Cruzat
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile
| | - Carlos Coronel
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile
| | - Morten Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, St.Cross Rd, Oxford, OX1 3JA, UK
- Department of Psychiatry, University of Oxford, Warneford Hospital, Warneford Ln, Headington, Oxford, OX3 7JX, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Blvd. 82, Aarhus, 8200, Denmark
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Plaça de La Mercè, 10-12, Barcelona, 08002, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, Leipzig, 04103, Germany
- Institució Catalana de Recerca I Estudis Avancats (ICREA), Passeig de Lluís Companys, 23, Barcelona, 08010, Spain
- Turner Institute for Brain and Mental Health, Monash University, 770 Blackburn Rd,, Clayton, VIC, 3168, Australia
| | - Agustín Ibáñez
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile.
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), 1207 1651 4th St, 3rd Floor, San Francisco, CA, 94143, USA.
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284, B1644BID, Buenos Aires, VIC, Argentina.
- Trinity College Institute of Neuroscience, Trinity College Dublin, 152 - 160 Pearse St, Dublin, D02 R590, Ireland.
- Trinity College Dublin, Lloyd Building Trinity College Dublin, Dublin, D02 PN40, Ireland.
| | - Enzo Tagliazucchi
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Av. Diag. Las Torres 2640, Santiago Región Metropolitana, Peñalolén, 7941169, Chile.
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284, B1644BID, Buenos Aires, VIC, Argentina.
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, 1425, Argentina.
- Institute of Applied and Interdisciplinary Physics and Department of Physics, University of Buenos Aires, Pabellón 1, Ciudad Universitaria, CABA, 1428, Argentina.
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Saggio ML, Jirsa V. Bifurcations and bursting in the Epileptor. PLoS Comput Biol 2024; 20:e1011903. [PMID: 38446814 PMCID: PMC10947678 DOI: 10.1371/journal.pcbi.1011903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/18/2024] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
The Epileptor is a phenomenological model for seizure activity that is used in a personalized large-scale brain modeling framework, the Virtual Epileptic Patient, with the aim of improving surgery outcomes for drug-resistant epileptic patients. Transitions between interictal and ictal states are modeled as bifurcations, enabling the definition of seizure classes in terms of onset/offset bifurcations. This establishes a taxonomy of seizures grounded in their essential underlying dynamics and the Epileptor replicates the activity of the most common class, as observed in patients with focal epilepsy, which is characterized by square-wave bursting properties. The Epileptor also encodes an additional mechanism to account for interictal spikes and spike and wave discharges. Here we use insights from a more generic model for square-wave bursting, based on the Unfolding Theory approach, to guide the bifurcation analysis of the Epileptor and gain a deeper understanding of the model and the role of its parameters. We show how the Epileptor's parameters can be modified to produce activities for other seizures classes of the taxonomy, as observed in patients, so that the large-scale brain models could be further personalized. Some of these classes have already been described in the literature in the Epileptor, others, predicted by the generic model, are new. Finally, we unveil how the interaction with the additional mechanism for spike and wave discharges alters the bifurcation structure of the main burster.
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Affiliation(s)
- Maria Luisa Saggio
- Institut de Neurosciences des Systemes INS UMR1106, AMU, INSERM, Marseille, France
| | - Viktor Jirsa
- Institut de Neurosciences des Systemes INS UMR1106, AMU, INSERM, Marseille, France
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8
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Brogin JAF, Faber J, Reyes-Garcia SZ, Cavalheiro EA, Bueno DD. Epileptic seizure suppression: A computational approach for identification and control using real data. PLoS One 2024; 19:e0298762. [PMID: 38416729 PMCID: PMC10901337 DOI: 10.1371/journal.pone.0298762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 01/31/2024] [Indexed: 03/01/2024] Open
Abstract
Epilepsy affects millions of people worldwide every year and remains an open subject for research. Current development on this field has focused on obtaining computational models to better understand its triggering mechanisms, attain realistic descriptions and study seizure suppression. Controllers have been successfully applied to mitigate epileptiform activity in dynamic models written in state-space notation, whose applicability is, however, restricted to signatures that are accurately described by them. Alternatively, autoregressive modeling (AR), a typical data-driven tool related to system identification (SI), can be directly applied to signals to generate more realistic models, and since it is inherently convertible into state-space representation, it can thus be used for the artificial reconstruction and attenuation of seizures as well. Considering this, the first objective of this work is to propose an SI approach using AR models to describe real epileptiform activity. The second objective is to provide a strategy for reconstructing and mitigating such activity artificially, considering non-hybrid and hybrid controllers - designed from ictal and interictal events, respectively. The results show that AR models of relatively low order represent epileptiform activities fairly well and both controllers are effective in attenuating the undesired activity while simultaneously driving the signal to an interictal condition. These findings may lead to customized models based on each signal, brain region or patient, from which it is possible to better define shape, frequency and duration of external stimuli that are necessary to attenuate seizures.
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Affiliation(s)
- João A. F. Brogin
- Department of Mechanical Engineering, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
| | - Jean Faber
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Selvin Z. Reyes-Garcia
- Departamento de Ciencias Morfológicas, Facultad de Ciencias Médicas, Universidad Nacional Autónoma de Honduras, Tegucigalpa, Honduras
| | - Esper A. Cavalheiro
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Douglas D. Bueno
- Department of Mathematics, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
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9
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Withers CP, Diamond JM, Yang B, Snyder K, Abdollahi S, Sarlls J, Chapeton JI, Theodore WH, Zaghloul KA, Inati SK. Identifying sources of human interictal discharges with travelling wave and white matter propagation. Brain 2023; 146:5168-5181. [PMID: 37527460 PMCID: PMC11046055 DOI: 10.1093/brain/awad259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/30/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023] Open
Abstract
Interictal epileptiform discharges have been shown to propagate from focal epileptogenic sources as travelling waves or through more rapid white matter conduction. We hypothesize that both modes of propagation are necessary to explain interictal discharge timing delays. We propose a method that, for the first time, incorporates both propagation modes to identify unique potential sources of interictal activity. We retrospectively analysed 38 focal epilepsy patients who underwent intracranial EEG recordings and diffusion-weighted imaging for epilepsy surgery evaluation. Interictal discharges were detected and localized to the most likely source based on relative delays in time of arrival across electrodes, incorporating travelling waves and white matter propagation. We assessed the influence of white matter propagation on distance of spread, timing and clinical interpretation of interictal activity. To evaluate accuracy, we compared our source localization results to earliest spiking regions to predict seizure outcomes. White matter propagation helps to explain the timing delays observed in interictal discharge sequences, underlying rapid and distant propagation. Sources identified based on differences in time of receipt of interictal discharges are often distinct from the leading electrode location. Receipt of activity propagating rapidly via white matter can occur earlier than more local activity propagating via slower cortical travelling waves. In our cohort, our source localization approach was more accurate in predicting seizure outcomes than the leading electrode location. Inclusion of white matter in addition to travelling wave propagation in our model of discharge spread did not improve overall accuracy but allowed for identification of unique and at times distant potential sources of activity, particularly in patients with persistent postoperative seizures. Since distant white matter propagation can occur more rapidly than local travelling wave propagation, combined modes of propagation within an interictal discharge sequence can decouple the commonly assumed relationship between spike timing and distance from the source. Our findings thus highlight the clinical importance of recognizing the presence of dual modes of propagation during interictal discharges, as this may be a cause of clinical mislocalization.
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Affiliation(s)
- C Price Withers
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Joshua M Diamond
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Braden Yang
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kathryn Snyder
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Shervin Abdollahi
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Joelle Sarlls
- NIH MRI Research Facility, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Julio I Chapeton
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - William H Theodore
- Clinical Epilepsy Section, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sara K Inati
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
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10
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Wang SH, Siebenhühner F, Arnulfo G, Myrov V, Nobili L, Breakspear M, Palva S, Palva JM. Critical-like Brain Dynamics in a Continuum from Second- to First-Order Phase Transition. J Neurosci 2023; 43:7642-7656. [PMID: 37816599 PMCID: PMC10634584 DOI: 10.1523/jneurosci.1889-22.2023] [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: 10/05/2022] [Revised: 06/07/2023] [Accepted: 09/25/2023] [Indexed: 10/12/2023] Open
Abstract
The classic brain criticality hypothesis postulates that the brain benefits from operating near a continuous second-order phase transition. Slow feedback regulation of neuronal activity could, however, lead to a discontinuous first-order transition and thereby bistable activity. Observations of bistability in awake brain activity have nonetheless remained scarce and its functional significance unclear. Moreover, there is no empirical evidence to support the hypothesis that the human brain could flexibly operate near either a first- or second-order phase transition despite such a continuum being common in models. Here, using computational modeling, we found bistable synchronization dynamics to emerge through elevated positive feedback and occur exclusively in a regimen of critical-like dynamics. We then assessed bistability in vivo with resting-state MEG in healthy adults (7 females, 11 males) and stereo-electroencephalography in epilepsy patients (28 females, 36 males). This analysis revealed that a large fraction of the neocortices exhibited varying degrees of bistability in neuronal oscillations from 3 to 200 Hz. In line with our modeling results, the neuronal bistability was positively correlated with classic assessment of brain criticality across narrow-band frequencies. Excessive bistability was predictive of epileptic pathophysiology in the patients, whereas moderate bistability was positively correlated with task performance in the healthy subjects. These empirical findings thus reveal the human brain as a one-of-a-kind complex system that exhibits critical-like dynamics in a continuum between continuous and discontinuous phase transitions.SIGNIFICANCE STATEMENT In the model, while synchrony per se was controlled by connectivity, increasing positive local feedback led to gradually emerging bistable synchrony with scale-free dynamics, suggesting a continuum between second- and first-order phase transitions in synchrony dynamics inside a critical-like regimen. In resting-state MEG and SEEG, bistability of ongoing neuronal oscillations was pervasive across brain areas and frequency bands and was observed only with concurring critical-like dynamics as the modeling predicted. As evidence for functional relevance, moderate bistability was positively correlated with executive functioning in the healthy subjects, and excessive bistability was associated with epileptic pathophysiology. These findings show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.
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Affiliation(s)
- Sheng H Wang
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Doctoral Programme Brain & Mind, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Felix Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, 16136 Genoa, Italy
| | - Vladislav Myrov
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Lino Nobili
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Children's Sciences, University of Genoa, 16136 Genoa, Italy
- Child Neuropsychiatry Unit, Istituto Di Ricovero e Cura a Carattere Scientifico Istituto Giannina Gaslini, 16147 Genoa, Italy
- Centre of Epilepsy Surgery "C. Munari," Department of Neuroscience, Niguarda Hospital, 20162 Milan, Italy
| | - Michael Breakspear
- College of Engineering, Science and Environment, College of Health and Medicine, University of Newcastle, Callaghan, 2308 Australia
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
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11
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Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. EBioMedicine 2023; 97:104848. [PMID: 37898096 PMCID: PMC10630610 DOI: 10.1016/j.ebiom.2023.104848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 10/30/2023] Open
Abstract
BACKGROUND When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. METHODS We retrospectively investigated data from 43 patients (42% female) with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. FINDINGS Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p = 0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. INTERPRETATION Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. FUNDING This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
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Affiliation(s)
- Jonathan J Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia; Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Division of Neurology, Department of Medicine, Queen's University, Kingston, Canada
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
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12
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Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. ARXIV 2023:arXiv:2304.03192v3. [PMID: 37064531 PMCID: PMC10104180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Background When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. Methods We retrospectively investigated data from 43 patients with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. Findings Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p=0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. Interpretation Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. Funding This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
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Affiliation(s)
- Jonathan J. Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H. Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A. Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W. McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B. Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
- Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C. Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P. Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Division of Neurology, Department of Medicine, Queen’s University, Kingston, Canada
| | - John S. Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N. Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
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13
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Sinha N, Duncan JS, Diehl B, Chowdhury FA, de Tisi J, Miserocchi A, McEvoy AW, Davis KA, Vos SB, Winston GP, Wang Y, Taylor PN. Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery. Neurology 2023; 101:e1293-e1306. [PMID: 37652703 PMCID: PMC10558161 DOI: 10.1212/wnl.0000000000207661] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 06/02/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Surgery is an effective treatment for drug-resistant epilepsy, which modifies the brain's structure and networks to regulate seizure activity. Our objective was to examine the relationship between brain structure and function to determine the extent to which this relationship affects the success of the surgery in controlling seizures. We hypothesized that a stronger association between brain structure and function would lead to improved seizure control after surgery. METHODS We constructed functional and structural brain networks in patients with drug-resistant focal epilepsy by using presurgery functional data from intracranial EEG (iEEG) recordings, presurgery and postsurgery structural data from T1-weighted MRI, and presurgery diffusion-weighted MRI. We quantified the relationship (coupling) between structural and functional connectivity by using the Spearman rank correlation and analyzed this structure-function coupling at 2 spatial scales: (1) global iEEG network level and (2) individual iEEG electrode contacts using virtual surgeries. We retrospectively predicted postoperative seizure freedom by incorporating the structure-function connectivity coupling metrics and routine clinical variables into a cross-validated predictive model. RESULTS We conducted a retrospective analysis on data from 39 patients who met our inclusion criteria. Brain areas implanted with iEEG electrodes had stronger structure-function coupling in seizure-free patients compared with those with seizure recurrence (p = 0.002, d = 0.76, area under the receiver operating characteristic curve [AUC] = 0.78 [95% CI 0.62-0.93]). Virtual surgeries on brain areas that resulted in stronger structure-function coupling of the remaining network were associated with seizure-free outcomes (p = 0.007, d = 0.96, AUC = 0.73 [95% CI 0.58-0.89]). The combination of global and local structure-function coupling measures accurately predicted seizure outcomes with a cross-validated AUC of 0.81 (95% CI 0.67-0.94). These measures were complementary to other clinical variables and, when included for prediction, resulted in a cross-validated AUC of 0.91 (95% CI 0.82-1.0), accuracy of 92%, sensitivity of 93%, and specificity of 91%. DISCUSSION Our study showed that the strength of structure-function connectivity coupling may play a crucial role in determining the success of epilepsy surgery. By quantitatively incorporating structure-function coupling measures and standard-of-care clinical variables into presurgical evaluations, we may be able to better localize epileptogenic tissue and select patients for epilepsy surgery. CLASSIFICATION OF EVIDENCE This is a Class IV retrospective case series showing that structure-function mapping may help determine the outcome from surgical resection for treatment-resistant focal epilepsy.
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Affiliation(s)
- Nishant Sinha
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada.
| | - John S Duncan
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Beate Diehl
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Fahmida A Chowdhury
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Jane de Tisi
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Anna Miserocchi
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Andrew William McEvoy
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Kathryn A Davis
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Sjoerd B Vos
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Gavin P Winston
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Yujiang Wang
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Peter Neal Taylor
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
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14
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Dallmer-Zerbe I, Jiruska P, Hlinka J. Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy. Epilepsia 2023; 64:2221-2238. [PMID: 37340565 DOI: 10.1111/epi.17690] [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: 03/09/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 06/22/2023]
Abstract
Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long-term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
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15
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Geerts H, Bergeler S, Lytton WW, van der Graaf PH. Computational neurosciences and quantitative systems pharmacology: a powerful combination for supporting drug development in neurodegenerative diseases. J Pharmacokinet Pharmacodyn 2023:10.1007/s10928-023-09876-6. [PMID: 37505397 DOI: 10.1007/s10928-023-09876-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023]
Abstract
Successful clinical development of new therapeutic interventions is notoriously difficult, especially in neurodegenerative diseases, where predictive biomarkers are scarce and functional improvement is often based on patient's perception, captured by structured interviews. As a consequence, mechanistic modeling of the processes relevant to therapeutic interventions in CNS disorders has been lagging behind other disease indications, probably because of the perceived complexity of the brain. However in this report, we develop the argument that a combination of Computational Neurosciences and Quantitative Systems Pharmacology (QSP) modeling of molecular pathways is a powerful simulation tool to enhance the probability of successful drug development for neurodegenerative diseases. Computational Neurosciences aims to predict action potential dynamics and neuronal circuit activation that are ultimately linked to behavioral changes and clinically relevant functional outcomes. These processes can not only be affected by the disease state, but also by common genotype variants on neurotransmitter-related proteins and the psycho-active medications often prescribed in these patient populations. Quantitative Systems Pharmacology (QSP) modeling of molecular pathways allows to simulate key pathological drivers of dementia, such as protein aggregation and neuroinflammatory responses. They often impact neurotransmitter homeostasis and voltage-gated ion-channels or lead to mitochondrial dysfunction, ultimately leading to changes in action potential dynamics and clinical readouts. Combining these two modeling approaches can lead to better actionable understanding of the many non-linear pharmacodynamic processes active in the human diseased brain. Practical applications include a rational selection of the optimal doses in combination therapies, identification of subjects more likely to respond to treatment, a more balanced stratification of treatment arms in terms of comedications, disease status and common genotype variants and re-analysis of small clinical trials to uncover a possible clinical signal. Ultimately this will lead to a higher success rate of bringing new therapeutics to the right patient populations.
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Affiliation(s)
| | | | - William W Lytton
- Downstate Health Science University, State University of New York, Brooklyn, USA
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16
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Guo Z, Zhang J, Hu W, Wang X, Zhao B, Zhang K, Zhang C. Does seizure propagate within or across intrinsic brain networks? An intracranial EEG study. Neurobiol Dis 2023; 184:106220. [PMID: 37406713 DOI: 10.1016/j.nbd.2023.106220] [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: 04/28/2023] [Revised: 06/27/2023] [Accepted: 07/01/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Understanding the spatiotemporal propagation profiles of seizures is crucial for the preoperative assessment of epilepsy patients. The present study aimed to investigate whether seizures exhibit propagation patterns that align with intrinsic networks (INs). METHODS A quantitative analysis was conducted to examine ictal fast activity (IFA). The Epileptogenicity Index (EI) was employed to assess the epileptogenicity, spectral features, and temporal characteristics of IFA. Intra-network and inter-network comparisons were made regarding the IFA-related metrics. Additionally, the metrics were correlated with Euclidean distance. Network connection maps were generated to visualize seizures originating from different INs, allowing for comparisons between distinct groups. RESULTS Data for 81 seizures in 43 subjects were captured using stereoelectroencephalography implantation. Three metrics were compared: EI, time involvement (TI), and energy ratio index (ERI). Intra-network channels exhibited higher EI, earlier involvement of IFA, and stronger high-frequency energy. These findings were further validated through subgroup analyses stratified by neuropathology, seizure type, and seizure origination lobe. Correlation analyses revealed a negative association between distance and both EI and ERI, while distance exhibited a positive correlation with TI. Seizures originating from different INs exhibited varying propagation characteristics. CONCLUSIONS The study findings highlight the dominant role of intra-network dynamics over inter-network during seizure propagation. These results contribute to our understanding of seizure dynamics and their relationship with INs.
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Affiliation(s)
- Zhihao Guo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
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17
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Johnson GW, Doss DJ, Morgan VL, Paulo DL, Cai LY, Shless JS, Negi AS, Gummadavelli A, Kang H, Reddy SB, Naftel RP, Bick SK, Williams Roberson S, Dawant BM, Wallace MT, Englot DJ. The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence. Brain 2023; 146:2828-2845. [PMID: 36722219 PMCID: PMC10316780 DOI: 10.1093/brain/awad016] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/24/2022] [Accepted: 01/08/2023] [Indexed: 02/02/2023] Open
Abstract
Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.
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Affiliation(s)
- Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Derek J Doss
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Danika L Paulo
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Jared S Shless
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Aarushi S Negi
- Department of Neuroscience, Vanderbilt University, Nashville, TN 37232, USA
| | - Abhijeet Gummadavelli
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA
| | - Shilpa B Reddy
- Department of Pediatrics, Vanderbilt Children’s Hospital, Nashville, TN 37232, USA
| | - Robert P Naftel
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Sarah K Bick
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Benoit M Dawant
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
| | - Mark T Wallace
- Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Psychology, Vanderbilt University, Nashville, TN 37232, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
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18
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Sotomayor GA, Grayden DB, Nesic D. Observers for Phenomenological Models of Epileptic Seizures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083352 DOI: 10.1109/embc40787.2023.10341198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Progress towards effective treatment of epileptic seizures has seen much improvement in the past decade. In particular, the emergence of phenomenological models of epileptic seizures specifically designed to capture the electrical seizure dynamics in the Epileptor model is inspiring new approaches to predicting and controlling seizures. These new models present in various forms and contain important but unmeasurable variables that control the occurrence of seizures. These models have been used mostly as nodes in large networks to study the complex brain behaviour of seizures. In order to use this model for the purposes of seizure forecasting or to control seizures through deep brain stimulation, the states of the model will need to be estimated. Although devices such as EEG electrodes can be related to some of the states of the model, most remain unmeasured and would require an observer (as defined in control theory) for their estimation. Additionally, we would like to consider the case for large nodes of systems where the number of electrodes is far smaller than the number of nodes being estimated. In this paper, we provide methods towards obtaining the full states of these phenomenological models using nonlinear observers. In particular, we explore the effectiveness of the Extended Kalman Filter for small networks of nodes of a smoothed sixth order Epileptor model. We show that observer design is possible for this family of systems and identify the difficulties in doing so.Clinical relevance-The methods presented here can be applied with an individual epileptic patient's EEG to reveal previously hidden biomarkers of epilepsy for seizure forecasting.
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19
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Ye H, He C, Hu W, Xiong K, Hu L, Chen C, Xu S, Xu C, Wang Y, Ding Y, Wu Y, Zhang K, Wang S, Wang S. Pre-ictal fluctuation of EEG functional connectivity discriminates seizure phenotypes in mesial temporal lobe epilepsy. Clin Neurophysiol 2023; 151:107-115. [PMID: 37245497 DOI: 10.1016/j.clinph.2023.05.004] [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: 12/23/2022] [Revised: 04/29/2023] [Accepted: 05/10/2023] [Indexed: 05/30/2023]
Abstract
OBJECTIVE We explored whether quantifiable differences between clinical seizures (CSs) and subclinical seizures (SCSs) occur in the pre-ictal state. METHODS We analyzed pre-ictal stereo-electroencephalography (SEEG) retrospectively across mesial temporal lobe epilepsy patients with recorded CSs and SCSs. Power spectral density and functional connectivity (FC) were quantified within and between the seizure onset zone (SOZ) and the early propagation zone (PZ), respectively. To evaluate the fluctuation of neural connectivity, FC variability was computed. Measures were further verified by a logistic regression model to evaluate their classification potentiality through the area under the receiver-operating-characteristics curve (AUC). RESULTS Fifty-four pre-ictal SEEG epochs (27 CSs and 27 SCSs) were selected among 14 patients. Within the SOZ, pre-ictal FC variability of CSs was larger than SCSs in 1-45 Hz during 30 seconds before seizure onset. Pre-ictal FC variability between the SOZ and PZ was larger in SCSs than CSs in 55-80 Hz within 1 minute before onset. Using these two variables, the logistic regression model achieved an AUC of 0.79 when classifying CSs and SCSs. CONCLUSIONS Pre-ictal FC variability within/between epileptic zones, not signal power or FC value, distinguished SCSs from CSs. SIGNIFICANCE Pre-ictal epileptic network stability possibly marks seizure phenotypes, contributing insights into ictogenesis and potentially helping seizure prediction.
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Affiliation(s)
- Hongyi Ye
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chenmin He
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kai Xiong
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
| | - Lingli Hu
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Cong Chen
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Sha Xu
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Cenglin Xu
- Department of Pharmacology, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Zhejiang Province Key Laboratory of Neurobiology, Basic Medical College, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yi Wang
- Department of Pharmacology, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Zhejiang Province Key Laboratory of Neurobiology, Basic Medical College, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yao Ding
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yingcai Wu
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shan Wang
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
| | - Shuang Wang
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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20
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Cuesta P, Bruña R, Shah E, Laohathai C, Garcia-Tarodo S, Funke M, Von Allmen G, Maestú F. An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application. Brain Commun 2023; 5:fcad168. [PMID: 37274829 PMCID: PMC10236945 DOI: 10.1093/braincomms/fcad168] [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: 06/05/2022] [Revised: 01/24/2023] [Accepted: 05/23/2023] [Indexed: 06/07/2023] Open
Abstract
Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30-70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient's magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient's clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography-MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery.
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Affiliation(s)
- Pablo Cuesta
- Correspondence to: Pablo Cuesta Pza. Ramón y Cajal, s/n. Ciudad Universitaria 28040 Madrid, Spain E-mail:
| | - Ricardo Bruña
- Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, 28040, Spain
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, 28040, Spain
| | - Ekta Shah
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | | | - Stephanie Garcia-Tarodo
- Département de la femme, de l'enfant et de l'adolescent, Hôpital des Enfants - Hôpitaux Universitaires de Genève, Geneva, 1211 Genève 14, Switzerland
| | - Michael Funke
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Gretchen Von Allmen
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, 28040, Spain
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, 28040, Spain
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, 28040, Spain
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21
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Moosavi SA, Truccolo W. Linear feedback control of spreading dynamics in stochastic nonlinear network models: epileptic seizures. INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING : [PROCEEDINGS]. INTERNATIONAL IEEE EMBS CONFERENCE ON NEURAL ENGINEERING 2023; 2023:10.1109/ner52421.2023.10123896. [PMID: 37593369 PMCID: PMC10433950 DOI: 10.1109/ner52421.2023.10123896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
The development of models and approaches for controlling the spreading dynamics of epileptic seizures is an essential step towards new therapies for people with pharmacologically resistant epilepsy. Beyond resective neurosurgery, in which epileptogenic zones (EZs) are the target of surgery, closed-loop control based on intracranial electrical stimulation, applied at the very early stage of seizure evolution, has been the main alternative, e.g. the RNS system from NeuroPace (Mountain View, CA). In this approach the electrical stimulation is delivered to target brain areas after detection of seizure initiation in the EZ. Here, we examined, on model simulations, some of the closed-loop control aspects of the problem. Seizure dynamics and spread are typically modeled with highly nonlinear dynamics on complex brain networks. Despite the nonlinearity and complexity, currently available optimal feedback control approaches are mostly based on linear approximations. Alternative machine learning control approaches might require amounts of data beyond what is commonly available in the intended application. We thus examined how standard linear feedback control approaches perform when applied to nonlinear models of neural dynamics of seizure generation and spread. In particular, we considered patient-specific epileptor network models for seizure onset and spread. The models incorporate network connectivity derived from (diffusion MRI) white-matter tractography, have been shown to capture the qualitative dynamics of epileptic seizures and can be fit to patient data. For control, we considered simple linear quadratic Gaussian (LQG) regulators. The LQG control was based on a discrete-time state-space model derived from the linearization of the patient-specific epileptor network model around a stable fixed point in the regime of preictal dynamics. We show in simulations that LQG regulators acting on the EZ node during the initial seizure period tend to be unstable. The LQG solution for the control law in this case leads to global feedback to the EZ-node actuator. However, if the LQG solution is constrained to depend on only local feedback originating from the EZ node itself, the controller is stable. In this case, we demonstrate that localized LQG can easily terminate the seizure at the early stage and prevent spread. In the context of optimal feedback control based on linear approximations, our results point to the need for investigating in more detail feedback localization and additional relevant control targets beyond epileptogenic zones.
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Affiliation(s)
- S A Moosavi
- Department of Neuroscience, Brown University, Providence RI, USA
| | - W Truccolo
- Department of Neuroscience, Brown University, Providence RI, USA
- Carney Institute for Brain Science, Brown University, Providence RI, USA
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22
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Dollomaja B, Makhalova J, Wang H, Bartolomei F, Jirsa V, Bernard C. Personalized whole brain modeling of status epilepticus. Epilepsy Behav 2023; 142:109175. [PMID: 37003103 DOI: 10.1016/j.yebeh.2023.109175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 02/10/2023] [Accepted: 03/08/2023] [Indexed: 04/03/2023]
Abstract
How status epilepticus (SE) is generated and propagates in the brain is not known. As for seizures, a patient-specific approach is necessary, and the analysis should be performed at the whole brain level. Personalized brain models can be used to study seizure genesis and propagation at the whole brain scale in The Virtual Brain (TVB), using the Epileptor mathematical construct. Building on the fact that SE is part of the repertoire of activities that the Epileptor can generate, we present the first attempt to model SE at the whole brain scale in TVB, using data from a patient who experienced SE during presurgical evaluation. Simulations reproduced the patterns found with SEEG recordings. We find that if, as expected, the pattern of SE propagation correlates with the properties of the patient's structural connectome, SE propagation also depends upon the global state of the network, i.e., that SE propagation is an emergent property. We conclude that individual brain virtualization can be used to study SE genesis and propagation. This type of theoretical approach may be used to design novel interventional approaches to stop SE. This paper was presented at the 8th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures held in September 2022.
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Affiliation(s)
- Borana Dollomaja
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Makhalova
- APHM, Timone Hospital, Epileptology Departement, Marseille, France; Aix Marseille Univ, CNRS, CRMBM, Marseille, France; APHM, Timone Hospital, CEMEREM, Marseille, France
| | - Huifang Wang
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; APHM, Timone Hospital, Epileptology Departement, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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23
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Jirsa V, Wang H, Triebkorn P, Hashemi M, Jha J, Gonzalez-Martinez J, Guye M, Makhalova J, Bartolomei F. Personalised virtual brain models in epilepsy. Lancet Neurol 2023; 22:443-454. [PMID: 36972720 DOI: 10.1016/s1474-4422(23)00008-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 12/20/2022] [Accepted: 01/04/2023] [Indexed: 03/29/2023]
Abstract
Individuals with drug-resistant focal epilepsy are candidates for surgical treatment as a curative option. Before surgery can take place, the patient must have a presurgical evaluation to establish whether and how surgical treatment might stop their seizures without causing neurological deficits. Virtual brains are a new digital modelling technology that map the brain network of a person with epilepsy, using data derived from MRI. This technique produces a computer simulation of seizures and brain imaging signals, such as those that would be recorded with intracranial EEG. When combined with machine learning, virtual brains can be used to estimate the extent and organisation of the epileptogenic zone (ie, the brain regions related to seizure generation and the spatiotemporal dynamics during seizure onset). Virtual brains could, in the future, be used for clinical decision making, to improve precision in localisation of seizure activity, and for surgical planning, but at the moment these models have some limitations, such as low spatial resolution. As evidence accumulates in support of the predictive power of personalised virtual brain models, and as methods are tested in clinical trials, virtual brains might inform clinical practice in the near future.
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Affiliation(s)
- Viktor Jirsa
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France.
| | - Huifang Wang
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Paul Triebkorn
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Meysam Hashemi
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Jayant Jha
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | | | - Maxime Guye
- Centre National de la Recherche Scientifique, Center for Magnetic Resonance in Biology and Medicine, Aix Marseille Université, Marseille, France; Centre d'Exploration Métabolique par Résonance Magnétique, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
| | - Julia Makhalova
- Centre National de la Recherche Scientifique, Center for Magnetic Resonance in Biology and Medicine, Aix Marseille Université, Marseille, France; Centre d'Exploration Métabolique par Résonance Magnétique, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France; Epileptology and Clinical Neurophysiology Department, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
| | - Fabrice Bartolomei
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France; Epileptology and Clinical Neurophysiology Department, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
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24
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Hashemi M, Vattikonda AN, Jha J, Sip V, Woodman MM, Bartolomei F, Jirsa VK. Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators. Neural Netw 2023; 163:178-194. [PMID: 37060871 DOI: 10.1016/j.neunet.2023.03.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 03/24/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders. However, the calculation of likelihood function at whole-brain scale is often intractable. Thus, likelihood-free algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas, ideally including the uncertainty. In this study, we introduce the simulation-based inference for the virtual epileptic patient model (SBI-VEP), enabling us to amortize the approximate posterior of the generative process from a low-dimensional representation of whole-brain epileptic patterns. The state-of-the-art deep learning algorithms for conditional density estimation are used to readily retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. We show that the SBI-VEP is able to efficiently estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones from sparse intracranial electroencephalography recordings. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for fast and reliable inference on brain disorders from neuroimaging modalities.
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25
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Moosavi SA, Truccolo W. Criticality in probabilistic models of spreading dynamics in brain networks: Epileptic seizures. PLoS Comput Biol 2023; 19:e1010852. [PMID: 36749796 PMCID: PMC9904505 DOI: 10.1371/journal.pcbi.1010852] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 01/05/2023] [Indexed: 02/08/2023] Open
Abstract
The spread of seizures across brain networks is the main impairing factor, often leading to loss-of-consciousness, in people with epilepsy. Despite advances in recording and modeling brain activity, uncovering the nature of seizure spreading dynamics remains an important challenge to understanding and treating pharmacologically resistant epilepsy. To address this challenge, we introduce a new probabilistic model that captures the spreading dynamics in patient-specific complex networks. Network connectivity and interaction time delays between brain areas were estimated from white-matter tractography. The model's computational tractability allows it to play an important complementary role to more detailed models of seizure dynamics. We illustrate model fitting and predictive performance in the context of patient-specific Epileptor networks. We derive the phase diagram of spread size (order parameter) as a function of brain excitability and global connectivity strength, for different patient-specific networks. Phase diagrams allow the prediction of whether a seizure will spread depending on excitability and connectivity strength. In addition, model simulations predict the temporal order of seizure spread across network nodes. Furthermore, we show that the order parameter can exhibit both discontinuous and continuous (critical) phase transitions as neural excitability and connectivity strength are varied. Existence of a critical point, where response functions and fluctuations in spread size show power-law divergence with respect to control parameters, is supported by mean-field approximations and finite-size scaling analyses. Notably, the critical point separates two distinct regimes of spreading dynamics characterized by unimodal and bimodal spread-size distributions. Our study sheds new light on the nature of phase transitions and fluctuations in seizure spreading dynamics. We expect it to play an important role in the development of closed-loop stimulation approaches for preventing seizure spread in pharmacologically resistant epilepsy. Our findings may also be of interest to related models of spreading dynamics in epidemiology, biology, finance, and statistical physics.
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Affiliation(s)
- S Amin Moosavi
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
| | - Wilson Truccolo
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
- Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
- * E-mail:
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26
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Wang HE, Woodman M, Triebkorn P, Lemarechal JD, Jha J, Dollomaja B, Vattikonda AN, Sip V, Medina Villalon S, Hashemi M, Guye M, Makhalova J, Bartolomei F, Jirsa V. Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy. Sci Transl Med 2023; 15:eabp8982. [PMID: 36696482 DOI: 10.1126/scitranslmed.abp8982] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Precise estimates of epileptogenic zone networks (EZNs) are crucial for planning intervention strategies to treat drug-resistant focal epilepsy. Here, we present the virtual epileptic patient (VEP), a workflow that uses personalized brain models and machine learning methods to estimate EZNs and to aid surgical strategies. The structural scaffold of the patient-specific whole-brain network model is constructed from anatomical T1 and diffusion-weighted magnetic resonance imaging. Each network node is equipped with a mathematical dynamical model to simulate seizure activity. Bayesian inference methods sample and optimize key parameters of the personalized model using functional stereoelectroencephalography recordings of patients' seizures. These key parameters together with their personalized model determine a given patient's EZN. Personalized models were further used to predict the outcome of surgical intervention using virtual surgeries. We evaluated the VEP workflow retrospectively using 53 patients with drug-resistant focal epilepsy. VEPs reproduced the clinically defined EZNs with a precision of 0.6, where the physical distance between epileptogenic regions identified by VEP and the clinically defined EZNs was small. Compared with the resected brain regions of 25 patients who underwent surgery, VEP showed lower false discovery rates in seizure-free patients (mean, 0.028) than in non-seizure-free patients (mean, 0.407). VEP is now being evaluated in an ongoing clinical trial (EPINOV) with an expected 356 prospective patients with epilepsy.
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Affiliation(s)
- Huifang E Wang
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Marmaduke Woodman
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Paul Triebkorn
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Jean-Didier Lemarechal
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Centre MEG-EEG and Experimental Neurosurgery team, Paris F-75013, France
| | - Jayant Jha
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Borana Dollomaja
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Anirudh Nihalani Vattikonda
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Viktor Sip
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Samuel Medina Villalon
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France
| | - Meysam Hashemi
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Maxime Guye
- Aix-Marseille Université, CNRS, CRMBM, Marseille 13005, France.,APHM, Timone University Hospital, CEMEREM, Marseille 13005, France
| | - Julia Makhalova
- APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France.,Aix-Marseille Université, CNRS, CRMBM, Marseille 13005, France.,APHM, Timone University Hospital, CEMEREM, Marseille 13005, France
| | - Fabrice Bartolomei
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France
| | - Viktor Jirsa
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
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27
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Lagarde S, Bénar CG, Wendling F, Bartolomei F. Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG. Brain Connect 2022; 12:850-869. [PMID: 35972755 PMCID: PMC9807250 DOI: 10.1089/brain.2021.0190] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Introduction: Focal epilepsies are diseases of neuronal excitability affecting macroscopic networks of cortical and subcortical neural structures. These networks ("epileptogenic networks") can generate pathological electrophysiological activities during seizures, and also between seizures (interictal period). Many works attempt to describe these networks by using quantification methods, particularly based on the estimation of statistical relationships between signals produced by brain regions, namely functional connectivity (FC). Results: FC has been shown to be greatly altered during seizures and in the immediate peri-ictal period. An increasing number of studies have shown that FC is also altered during the interictal period depending on the degree of epileptogenicity of the structures. Furthermore, connectivity values could be correlated with other clinical variables including surgical outcome. Significance: This leads to a conceptual change and to consider epileptic areas as both hyperexcitable and abnormally connected. These data open the door to the use of interictal FC as a marker of epileptogenicity and as a complementary tool for predicting the effect of surgery. Aim: In this article, we review the available data concerning interictal FC estimated from intracranial electroencephalograhy (EEG) in focal epilepsies and discuss it in the light of data obtained from other modalities (EEG imaging) and modeling studies.
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Affiliation(s)
- Stanislas Lagarde
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, Marseille, France.,Address correspondence to: Stanislas Lagarde, Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, 264 Rue Saint-Pierre, 13005 Marseille, France
| | | | | | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, Marseille, France
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28
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Kobeleva X, Varoquaux G, Dagher A, Adhikari M, Grefkes C, Gilson M. Advancing brain network models to reconcile functional neuroimaging and clinical research. Neuroimage Clin 2022; 36:103262. [PMID: 36451365 PMCID: PMC9723311 DOI: 10.1016/j.nicl.2022.103262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/26/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022]
Abstract
Functional magnetic resonance imaging (fMRI) captures information on brain function beyond the anatomical alterations that are traditionally visually examined by neuroradiologists. However, the fMRI signals are complex in addition to being noisy, so fMRI still faces limitations for clinical applications. Here we review methods that have been proposed as potential solutions so far, namely statistical, biophysical and decoding models, with their strengths and weaknesses. We especially evaluate the ability of these models to directly predict clinical variables from their parameters (predictability) and to extract clinically relevant information regarding biological mechanisms and relevant features for classification and prediction (interpretability). We then provide guidelines for useful applications and pitfalls of such fMRI-based models in a clinical research context, looking beyond the current state of the art. In particular, we argue that the clinical relevance of fMRI calls for a new generation of models for fMRI data, which combine the strengths of both biophysical and decoding models. This leads to reliable and biologically meaningful model parameters, which thus fulfills the need for simultaneous interpretability and predictability. In our view, this synergy is fundamental for the discovery of new pharmacological and interventional targets, as well as the use of models as biomarkers in neurology and psychiatry.
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Affiliation(s)
- Xenia Kobeleva
- Department of Neurology, University of Bonn, Bonn, Germany; German Center for Neurodegenerative Diseases (DZNE) Bonn, Bonn, Germany
| | | | - Alain Dagher
- Montreal Neurological Institute, McGill University, Montréal, Canada
| | - Mohit Adhikari
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
| | - Christian Grefkes
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Institute of Neuroscience and Medicine (INM-1, INM-3), Research Centre Juelich, Juelich, Germany; Department of Neurology, Goethe University Frankfurt, Frankfurt, Germany
| | - Matthieu Gilson
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany; Center for Brain and Cognition, Department of Information and Telecommunication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France.
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29
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Tobochnik S, Bateman LM, Akman CI, Anbarasan D, Bazil CW, Bell M, Choi H, Feldstein NA, Kent PF, McBrian D, McKhann GM, Mendiratta A, Pack AM, Sands TT, Sheth SA, Srinivasan S, Schevon CA. Tracking Multisite Seizure Propagation Using Ictal High-Gamma Activity. J Clin Neurophysiol 2022; 39:592-601. [PMID: 34812578 PMCID: PMC8611231 DOI: 10.1097/wnp.0000000000000833] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 12/28/2020] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Spatial patterns of long-range seizure propagation in epileptic networks have not been well characterized. Here, we use ictal high-gamma activity (HGA) as a proxy of intense neuronal population firing to map the spatial evolution of seizure recruitment. METHODS Ictal HGA (80-150 Hz) was analyzed in 13 patients with 72 seizures recorded by stereotactic depth electrodes, using previously validated methods. Distinct spatial clusters of channels with the ictal high-gamma signature were identified, and seizure hubs were defined as stereotypically recruited nonoverlapping clusters. Clusters correlated with asynchronous seizure terminations to provide supportive evidence for independent seizure activity at these sites. The spatial overlap between seizure hubs and interictal ripples was compared. RESULTS Ictal HGA was detected in 71% of seizures and 10% of implanted contacts, enabling tracking of contiguous and noncontiguous seizure recruitment. Multiple seizure hubs were identified in 54% of cases, including 43% of patients thought preoperatively to have unifocal epilepsy. Noncontiguous recruitment was associated with asynchronous seizure termination (odds ratio = 19.7; p = 0.029). Interictal ripples demonstrated greater spatial overlap with ictal HGA in cases with single seizure hubs compared with those with multiple hubs (100% vs. 66% per patient; p = 0.03). CONCLUSIONS Ictal HGA may serve as a useful adjunctive biomarker to distinguish contiguous seizure spread from propagation to remote seizure sites. High-gamma sites were found to cluster in stereotyped seizure hubs rather than being broadly distributed. Multiple hubs were common even in cases that were considered unifocal.
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Affiliation(s)
- Steven Tobochnik
- Brigham and Women’s Hospital, Department of Neurology, Boston, MA
| | - Lisa M. Bateman
- Columbia University Medical Center, Department of Neurology, New York, NY
| | - Cigdem I. Akman
- Columbia University Medical Center, Division of Child Neurology, New York, NY
| | | | - Carl W. Bazil
- Columbia University Medical Center, Department of Neurology, New York, NY
| | - Michelle Bell
- Columbia University Medical Center, Department of Neurology, New York, NY
| | - Hyunmi Choi
- Columbia University Medical Center, Department of Neurology, New York, NY
| | - Neil A. Feldstein
- Columbia University Medical Center, Department of Neurological Surgery, New York, NY
| | - Paul F. Kent
- Columbia University Medical Center, Department of Neurology, New York, NY
| | - Danielle McBrian
- Columbia University Medical Center, Division of Child Neurology, New York, NY
| | - Guy M. McKhann
- Columbia University Medical Center, Department of Neurological Surgery, New York, NY
| | - Anil Mendiratta
- Columbia University Medical Center, Department of Neurology, New York, NY
| | - Alison M. Pack
- Columbia University Medical Center, Department of Neurology, New York, NY
| | - Tristan T. Sands
- Columbia University Medical Center, Division of Child Neurology, New York, NY
| | - Sameer A. Sheth
- Baylor College of Medicine, Department of Neurosurgery, Houston, TX
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30
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Madan Mohan V, Banerjee A. A perturbative approach to study information communication in brain networks. Netw Neurosci 2022; 6:1275-1295. [PMID: 38800461 PMCID: PMC11117119 DOI: 10.1162/netn_a_00260] [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: 02/21/2022] [Accepted: 06/15/2022] [Indexed: 05/29/2024] Open
Abstract
How communication among neuronal ensembles shapes functional brain dynamics is a question of fundamental importance to neuroscience. Communication in the brain can be viewed as a product of the interaction of node activities with the structural network over which these activities flow. The study of these interactions is, however, restricted by the difficulties in describing the complex dynamics of the brain. There is thus a need to develop methods to study these network-dynamical interactions and how they impact information flow, without having to ascertain dynamics a priori or resort to restrictive analytical approaches. Here, we adapt a recently established network analysis method based on perturbations, it to a neuroscientific setting to study how information flow in the brain can raise from properties of underlying structure. For proof-of-concept, we apply the approach on in silico whole-brain models. We expound on the functional implications of the distributions of metrics that capture network-dynamical interactions, termed net influence and flow. We also study the network-dynamical interactions at the level of resting-state networks. An attractive feature of this method is its simplicity, which allows a direct translation to an experimental or clinical setting, such as for identifying targets for stimulation studies or therapeutic interventions.
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31
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Brogin JAF, Faber J, Bueno DD. Estimating the Parameters of the Epileptor Model for Epileptic Seizure Suppression. Neuroinformatics 2022; 20:919-941. [PMID: 35303252 DOI: 10.1007/s12021-022-09583-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/10/2022] [Indexed: 12/31/2022]
Abstract
Epilepsy is one of the most common brain disorders worldwide, affecting millions of people every year. Given the partially successful existing treatments for epileptiform activity suppression, dynamic mathematical models have been proposed with the purpose of better understanding the factors that might trigger an epileptic seizure and how to mitigate it, among which Epileptor stands out, due to its relative simplicity and consistency with experimental observations. Recent studies using this model have provided evidence that establishing a feedback-based control approach is possible. However, for this strategy to work properly, Epileptor's parameters, which describe the dynamic characteristics of a seizure, must be known beforehand. Therefore, this work proposes a methodology for estimating such parameters based on a successive optimization technique. The results show that it is feasible to approximate their values as they converge to reference values based on different initial conditions, which are modeled by an uncertainty factor or noise addition. Also, interictal (healthy) and ictal (ongoing seizure) conditions, as well as time resolution, must be taken into account for an appropriate estimation. At last, integrating such a parameter estimation approach with observers and controllers for purposes of seizure suppression is carried out, which might provide an interesting alternative for seizure suppression in practice in the future.
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Affiliation(s)
- João Angelo Ferres Brogin
- Department of Mechanical Engineering, São Paulo State University (UNESP), 56 Brasil Avenue, Ilha Solteira, 15385-000, São Paulo, Brazil.
| | - Jean Faber
- Department of Neurology and Neurosurgery, Federal University of São Paulo, 667 Pedro de Toledo Street, São Paulo, 04039-032, São Paulo, Brazil
| | - Douglas D Bueno
- Department of Mathematics, São Paulo State University (UNESP), 56 Brasil Avenue, Ilha Solteira, 15385-000, São Paulo, Brazil
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32
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Gong Y, Xu C, Wang S, Wang Y, Chen Z. Computerized application for epilepsy in China: Does the era of artificial intelligence comes? Acta Neurol Scand 2022; 146:732-742. [PMID: 36156212 DOI: 10.1111/ane.13711] [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: 07/31/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 12/01/2022]
Abstract
Epilepsy, one of the most common neurological diseases in China, is notorious for its spontaneous, unprovoked and recurrent seizures. The etiology of epilepsy varies among individual patients, including congenital gene mutation, traumatic injury, infections, etc. This heterogeneity partly hampered the accurate diagnosis and choice of appropriate treatments. Encouragingly, great achievements have been achieved in computational science, making it become a key player in medical fields gradually and bringing new hope for rapid and accurate diagnosis as well as targeted therapies in epilepsy. Here, we historically review the advances of computerized applications in epilepsy-especially those tremendous findings achieved in China-for different purposes including seizure prediction, localization of epileptogenic zone, post-surgical prognosis, etc. Special attentions are paid to the great progress based on artificial intelligence (AI), which is more "sensitive", "smart" and "in-depth" than human capacities. At last, we give a comprehensive discussion about the disadvantages and limitations of current computerized applications for epilepsy and propose some future directions as further stepping stones to embrace "the era of AI" in epilepsy.
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Affiliation(s)
- Yiwei Gong
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuang Wang
- Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yi Wang
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
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33
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Jha J, Hashemi M, Vattikonda AN, Wang H, Jirsa V. Fully Bayesian estimation of virtual brain parameters with self-tuning Hamiltonian Monte Carlo. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac9037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Abstract
Virtual brain models are data-driven patient-specific brain models integrating individual brain imaging data with neural mass modeling in a single computational framework, capable of autonomously generating brain activity and its associated brain imaging signals. Along the example of epilepsy, we develop an efficient and accurate Bayesian methodology estimating the parameters linked to the extent of the epileptogenic zone. State-of-the-art advances in Bayesian inference using Hamiltonian Monte Carlo (HMC) algorithms have remained elusive for large-scale differential-equations based models due to their slow convergence. We propose appropriate priors and a novel reparameterization to facilitate efficient exploration of the posterior distribution in terms of computational time and convergence diagnostics. The methodology is illustrated for in-silico dataset and then, applied to infer the personalized model parameters based on the empirical stereotactic electroencephalography (SEEG) recordings of retrospective patients. This improved methodology may pave the way to render HMC methods sufficiently easy and efficient to use, thus applicable in personalized medicine.
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34
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Scholly J, Gras A, Guye M, Bilger M, Valenti Hirsch MP, Hirsch E, Timofeev A, Vidailhet P, Bénar CG, Bartolomei F. Connectivity Alterations in Emotional and Cognitive Networks During a Manic State Induced by Direct Electrical Stimulation. Brain Topogr 2022; 35:627-635. [PMID: 36071370 DOI: 10.1007/s10548-022-00913-0] [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: 11/19/2021] [Accepted: 08/27/2022] [Indexed: 11/28/2022]
Abstract
Mania is characterized by affective and cognitive alterations, with heightened external and self-awareness that are opposite to the alteration of awareness during epileptic seizures. Electrical stimulations carried out routinely during stereotactic intracerebral EEG (SEEG) recordings for presurgical evaluation of epilepsy may represent a unique opportunity to study the pathophysiology of such complex emotional-behavioral phenomenon, particularly difficult to reproduce in experimental setting. We investigated SEEG signals-based functional connectivity between different brain regions involved in emotions and in consciousness processing during a manic state induced by electrical stimulation in a patient with drug-resistant focal epilepsy. The stimulation inducing manic state and an asymptomatic stimulation of the same site, as well as a seizure with alteration of awareness (AOA) were analyzed. Functional connectivity analysis was performed by measuring interdependencies (nonlinear regression analysis based on the h2 coefficient) between broadband SEEG signals and within typical sub-bands, before and after stimulation, or before and during the seizure with AOA, respectively. Stimulation of the right lateral prefrontal cortex induced a manic state lasting several hours. Its onset was associated with significant increase of broadband-signal functional coupling between the right hemispheric limbic nodes, the temporal pole and the claustrum, whereas significant decorrelation between the right lateral prefrontal and the anterior cingulate cortex was observed in theta-band. In contrast, ictal alteration of awareness was associated with increased broadband and sub-bands synchronization within and between the internal and external awareness networks, including the anterior and middle cingulate, the mesial and lateral prefrontal, the inferior parietal and the temporopolar cortex. Our data suggest the existence of network- and frequency-specific functional connectivity patterns during manic state. A transient desynchronization of theta activity between the external and internal awareness network hubs is likely to increase awareness, with potential therapeutic effect.
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Affiliation(s)
- Julia Scholly
- Service d'Epileptologie et de Rythmololgie Cérébrale, Hôpital Timone, AP-HM, Marseille, France. .,Aix Marseille Univ, CNRS, CRMBM, Marseille, France. .,Service d'Epileptologie et Rythmologie Cérébrale, Hôpital Timone, AP-HM, 264 Rue St Pierre, 13005, Marseille, France.
| | - Adrien Gras
- Consultation-Liaison Psychiatry Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Maxime Guye
- Service d'Epileptologie et de Rythmololgie Cérébrale, Hôpital Timone, AP-HM, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Mathias Bilger
- Medical and Surgical Epilepsy Unit, University Hospital of Strasbourg, Strasbourg, France
| | | | - Edouard Hirsch
- Medical and Surgical Epilepsy Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Alexander Timofeev
- Medical and Surgical Epilepsy Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Pierre Vidailhet
- Consultation-Liaison Psychiatry Unit, University Hospital of Strasbourg, Strasbourg, France
| | - Christian G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Fabrice Bartolomei
- Service d'Epileptologie et de Rythmololgie Cérébrale, Hôpital Timone, AP-HM, Marseille, France.,Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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35
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Moosavi SA, Jirsa VK, Truccolo W. Critical dynamics in the spread of focal epileptic seizures: Network connectivity, neural excitability and phase transitions. PLoS One 2022; 17:e0272902. [PMID: 35998146 PMCID: PMC9397939 DOI: 10.1371/journal.pone.0272902] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 07/29/2022] [Indexed: 11/24/2022] Open
Abstract
Focal epileptic seizures can remain localized or, alternatively, spread across brain areas, often resulting in impairment of cognitive function and loss of consciousness. Understanding the factors that promote spread is important for developing better therapeutic approaches. Here, we show that: (1) seizure spread undergoes “critical” phase transitions in models (epileptor-networks) that capture the neural dynamics of spontaneous seizures while incorporating patient-specific brain network connectivity, axonal delays and identified epileptogenic zones (EZs). We define a collective variable for the spreading dynamics as the spread size, i.e. the number of areas or nodes in the network to which a seizure has spread. Global connectivity strength and excitability in the surrounding non-epileptic areas work as phase-transition control parameters for this collective variable. (2) Phase diagrams are predicted by stability analysis of the network dynamics. (3) In addition, the components of the Jacobian’s leading eigenvector, which tend to reflect the connectivity strength and path lengths from the EZ to surrounding areas, predict the temporal order of network-node recruitment into seizure. (4) However, stochastic fluctuations in spread size in a near-criticality region make predictability more challenging. Overall, our findings support the view that within-patient seizure-spread variability can be characterized by phase-transition dynamics under transient variations in network connectivity strength and excitability across brain areas. Furthermore, they point to the potential use and limitations of model-based prediction of seizure spread in closed-loop interventions for seizure control.
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Affiliation(s)
- S. Amin Moosavi
- Department of Neuroscience, Brown University, Providence, RI, United States of America
| | - Viktor K. Jirsa
- Aix Marseille University, INSERM, INS, Institut de Neurosciences de Système, Marseille, France
| | - Wilson Truccolo
- Department of Neuroscience, Brown University, Providence, RI, United States of America
- Carney Institute for Brain Science, Brown University, Providence, RI, United States of America
- * E-mail:
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36
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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.
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Affiliation(s)
- Jonathan J Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - 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.
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37
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Petkoski S, Jirsa VK. Normalizing the brain connectome for communication through synchronization. Netw Neurosci 2022; 6:722-744. [PMID: 36607179 PMCID: PMC9810372 DOI: 10.1162/netn_a_00231] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/10/2022] [Indexed: 01/10/2023] Open
Abstract
Networks in neuroscience determine how brain function unfolds, and their perturbations lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave perspective of information processing based on synchronization. We extend traditional graph theory to a dual, particle-wave, perspective, integrate time delays due to finite transmission speeds, and derive a normalization of the connectome. When applied to the database of the Human Connectome Project, it explains the emergence of frequency-specific network cores including the visual and default mode networks. These findings are robust across human subjects (N = 100) and are a fundamental network property within the wave picture. The normalized connectome comprises the particle view in the limit of infinite transmission speeds and opens the applicability of graph theory to a wide range of novel network phenomena, including physiological and pathological brain rhythms. These two perspectives are orthogonal, but not incommensurable, when understood within the novel, here-proposed, generalized framework of structural connectivity.
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Affiliation(s)
- Spase Petkoski
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France,* Corresponding Authors: ;
| | - Viktor K. Jirsa
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France,* Corresponding Authors: ;
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38
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Wang C, Chen S, Huang L, Yu L. Prediction and control of focal seizure spread: Random walk with restart on heterogeneous brain networks. Phys Rev E 2022; 105:064412. [PMID: 35854502 DOI: 10.1103/physreve.105.064412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 05/25/2022] [Indexed: 06/15/2023]
Abstract
Whole-brain models offer a promising method of predicting seizure spread, which is critical for successful surgical treatment of focal epilepsy. Existing methods are largely based on structural connectome, which ignores the effects of heterogeneity within the regional excitability of brains. In this study we used a whole-brain model to show that heterogeneity in nodal excitability had a significant impact on seizure propagation in the networks and compromised the prediction accuracy with structural connections. We then addressed this problem with an algorithm based on random walk with restart on graphs. We demonstrated that by establishing a relationship between the restarting probability and the excitability for each node, this algorithm could significantly improve the seizure spread prediction accuracy in heterogeneous networks and was more robust against the extent of heterogeneity. We also strategized surgical seizure control as a process to identify and remove the key nodes (connections) responsible for the early spread of seizures from the focal region. Compared to strategies based on structural connections, virtual surgery with a strategy based on a modified random walk with extended restart generated outcomes with a high success rate while maintaining low damage to the brain by removing fewer anatomical connections. These findings may have potential applications in developing personalized surgery strategies for epilepsy.
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Affiliation(s)
- Chen Wang
- School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
| | - Sida Chen
- School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
| | - Liang Huang
- School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
- Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Lianchun Yu
- School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China
- Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, Lanzhou University, Lanzhou, Gansu 730000, China
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39
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Makhalova J, Medina Villalon S, Wang H, Giusiano B, Woodman M, Bénar C, Guye M, Jirsa V, Bartolomei F. Virtual Epileptic Patient brain modeling: relationships with seizure onset and surgical outcome. Epilepsia 2022; 63:1942-1955. [PMID: 35604575 PMCID: PMC9543509 DOI: 10.1111/epi.17310] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/29/2022]
Abstract
Objective The virtual epileptic patient (VEP) is a large‐scale brain modeling method based on virtual brain technology, using stereoelectroencephalography (SEEG), anatomical data (magnetic resonance imaging [MRI] and connectivity), and a computational neuronal model to provide computer simulations of a patient's seizures. VEP has potential interest in the presurgical evaluation of drug‐resistant epilepsy by identifying regions most likely to generate seizures. We aimed to assess the performance of the VEP approach in estimating the epileptogenic zone and in predicting surgical outcome. Methods VEP modeling was retrospectively applied in a cohort of 53 patients with pharmacoresistant epilepsy and available SEEG, T1‐weighted MRI, and diffusion‐weighted MRI. Precision recall was used to compare the regions identified as epileptogenic by VEP (EZVEP) to the epileptogenic zone defined by clinical analysis incorporating the Epileptogenicity Index (EI) method (EZC). In 28 operated patients, we compared the VEP results and clinical analysis with surgical outcome. Results VEP showed a precision of 64% and a recall of 44% for EZVEP detection compared to EZC. There was a better concordance of VEP predictions with clinical results, with higher precision (77%) in seizure‐free compared to non‐seizure‐free patients. Although the completeness of resection was significantly correlated with surgical outcome for both EZC and EZVEP, there was a significantly higher number of regions defined as epileptogenic exclusively by VEP that remained nonresected in non‐seizure‐free patients. Significance VEP is the first computational model that estimates the extent and organization of the epileptogenic zone network. It is characterized by good precision in detecting epileptogenic regions as defined by a combination of visual analysis and EI. The potential impact of VEP on improving surgical prognosis remains to be exploited. Analysis of factors limiting the performance of the actual model is crucial for its further development.
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Affiliation(s)
- Julia Makhalova
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Timone Hospital, CEMEREM, Marseille, France
| | - Samuel Medina Villalon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France.,Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Huifang Wang
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Bernard Giusiano
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,APHM, Public Health Department, Marseille, France
| | - Marmaduke Woodman
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Christian Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Maxime Guye
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Timone Hospital, CEMEREM, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France.,Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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40
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Batulin D, Lagzi F, Vezzani A, Jedlicka P, Triesch J. A mathematical model of neuroimmune interactions in epileptogenesis for discovering treatment strategies. iScience 2022; 25:104343. [PMID: 35601918 PMCID: PMC9121278 DOI: 10.1016/j.isci.2022.104343] [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: 08/19/2021] [Revised: 12/22/2021] [Accepted: 04/25/2022] [Indexed: 11/16/2022] Open
Abstract
The development of epilepsy (epileptogenesis) involves a complex interplay of neuronal and immune processes. Here, we present a first-of-its-kind mathematical model to better understand the relationships among these processes. Our model describes the interaction between neuroinflammation, blood-brain barrier disruption, neuronal loss, circuit remodeling, and seizures. Formulated as a system of nonlinear differential equations, the model reproduces the available data from three animal models. The model successfully describes characteristic features of epileptogenesis such as its paradoxically long timescales (up to decades) despite short and transient injuries or the existence of qualitatively different outcomes for varying injury intensity. In line with the concept of degeneracy, our simulations reveal multiple routes toward epilepsy with neuronal loss as a sufficient but non-necessary component. Finally, we show that our model allows for in silico predictions of therapeutic strategies, revealing injury-specific therapeutic targets and optimal time windows for intervention. A dynamical systems model describes the development of epilepsy after different injuries Simulation results are in agreement with data from three animal models Model shows degeneracy: multiple distinct but linked mechanisms cause epileptogenesis Framework permits studying the effects of therapeutic interventions in silico
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Affiliation(s)
- Danylo Batulin
- Frankfurt Institute for Advanced Studies, Frankfurt 60438, Germany
- Faculty of Computer Science and Mathematics, Goethe University, Frankfurt 60486, Germany
- CePTER – Center for Personalized Translational Epilepsy Research, Frankfurt, Germany
- Corresponding author
| | - Fereshteh Lagzi
- Frankfurt Institute for Advanced Studies, Frankfurt 60438, Germany
- CePTER – Center for Personalized Translational Epilepsy Research, Frankfurt, Germany
- Center for Computational Neuroscience and Swartz Center for Theoretical Neuroscience, University of Washington, Seattle 98195, USA
- Department of Physiology and Biophysics, University of Washington, Seattle 98195, USA
| | - Annamaria Vezzani
- Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano 20156, Italy
| | - Peter Jedlicka
- Frankfurt Institute for Advanced Studies, Frankfurt 60438, Germany
- CePTER – Center for Personalized Translational Epilepsy Research, Frankfurt, Germany
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus-Liebig-University, Giessen 35390, Germany
- Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, Frankfurt 60528, Germany
- Corresponding author
| | - Jochen Triesch
- Frankfurt Institute for Advanced Studies, Frankfurt 60438, Germany
- Faculty of Computer Science and Mathematics, Goethe University, Frankfurt 60486, Germany
- CePTER – Center for Personalized Translational Epilepsy Research, Frankfurt, Germany
- Faculty of Physics, Goethe University, Frankfurt 60438, Germany
- Corresponding author
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41
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Taylor PN, Papasavvas CA, Owen TW, Schroeder GM, Hutchings FE, Chowdhury FA, Diehl B, Duncan JS, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Wang Y. Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue. Brain 2022; 145:939-949. [PMID: 35075485 PMCID: PMC9050535 DOI: 10.1093/brain/awab380] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/19/2021] [Accepted: 09/03/2021] [Indexed: 11/14/2022] Open
Abstract
The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localizing epileptogenic tissue. However, this identification may be challenging during non-seizure (interictal) periods, especially if abnormalities are subtle compared to the repertoire of possible healthy brain dynamics. Here, we investigate if such interictal abnormalities become more salient by quantitatively accounting for the range of healthy brain dynamics in a location-specific manner. To this end, we constructed a normative map of brain dynamics, in terms of relative band power, from interictal intracranial recordings from 234 participants (21 598 electrode contacts). We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions. We proposed that if the most abnormal regions were spared by surgery, then patients would be more likely to experience continued seizures postoperatively. We first confirmed that the spatial variations of band power in the normative map across brain regions were consistent with healthy variations reported in the literature. Second, when accounting for the normative variations, regions that were spared by surgery were more abnormal than those resected only in patients with persistent postoperative seizures (t = -3.6, P = 0.0003), confirming our hypothesis. Third, we found that this effect discriminated patient outcomes (area under curve 0.75 P = 0.0003). Normative mapping is a well-established practice in neuroscientific research. Our study suggests that this approach is feasible to detect interictal abnormalities in intracranial EEG, and of potential clinical value to identify pathological tissue in epilepsy. Finally, we make our normative intracranial map publicly available to facilitate future investigations in epilepsy and beyond.
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Affiliation(s)
- Peter N Taylor
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Christoforos A Papasavvas
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Thomas W Owen
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Gabrielle M Schroeder
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Frances E Hutchings
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Beate Diehl
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - John S Duncan
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Andrew W McEvoy
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Sjoerd B Vos
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Matthew C Walker
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Yujiang Wang
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
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42
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Pérez-Cervera A, Lindner B, Thomas PJ. Quantitative comparison of the mean-return-time phase and the stochastic asymptotic phase for noisy oscillators. BIOLOGICAL CYBERNETICS 2022; 116:219-234. [PMID: 35320405 PMCID: PMC9068686 DOI: 10.1007/s00422-022-00929-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 02/16/2022] [Indexed: 05/10/2023]
Abstract
Seminal work by A. Winfree and J. Guckenheimer showed that a deterministic phase variable can be defined either in terms of Poincaré sections or in terms of the asymptotic (long-time) behaviour of trajectories approaching a stable limit cycle. However, this equivalence between the deterministic notions of phase is broken in the presence of noise. Different notions of phase reduction for a stochastic oscillator can be defined either in terms of mean-return-time sections or as the argument of the slowest decaying complex eigenfunction of the Kolmogorov backwards operator. Although both notions of phase enjoy a solid theoretical foundation, their relationship remains unexplored. Here, we quantitatively compare both notions of stochastic phase. We derive an expression relating both notions of phase and use it to discuss differences (and similarities) between both definitions of stochastic phase for (i) a spiral sink motivated by stochastic models for electroencephalograms, (ii) noisy limit-cycle systems-neuroscience models, and (iii) a stochastic heteroclinic oscillator inspired by a simple motor-control system.
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Affiliation(s)
- Alberto Pérez-Cervera
- National Research University Higher School of Economics, Moscow, Russia
- Instituto de Matemática Interdisciplinar, Universidad Complutense de Madrid, Madrid, Spain
| | - Benjamin Lindner
- Bernstein Center for Computational Neuroscience Berlin, Institute of Physics, Humboldt University, Berlin, Germany
| | - Peter J. Thomas
- Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH USA
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43
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Cao M, Vogrin SJ, Peterson ADH, Woods W, Cook MJ, Plummer C. Dynamical Network Models From EEG and MEG for Epilepsy Surgery—A Quantitative Approach. Front Neurol 2022; 13:837893. [PMID: 35422755 PMCID: PMC9001937 DOI: 10.3389/fneur.2022.837893] [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: 12/17/2021] [Accepted: 03/01/2022] [Indexed: 11/16/2022] Open
Abstract
There is an urgent need for more informative quantitative techniques that non-invasively and objectively assess strategies for epilepsy surgery. Invasive intracranial electroencephalography (iEEG) remains the clinical gold standard to investigate the nature of the epileptogenic zone (EZ) before surgical resection. However, there are major limitations of iEEG, such as the limited spatial sampling and the degree of subjectivity inherent in the analysis and clinical interpretation of iEEG data. Recent advances in network analysis and dynamical network modeling provide a novel aspect toward a more objective assessment of the EZ. The advantage of such approaches is that they are data-driven and require less or no human input. Multiple studies have demonstrated success using these approaches when applied to iEEG data in characterizing the EZ and predicting surgical outcomes. However, the limitations of iEEG recordings equally apply to these studies—limited spatial sampling and the implicit assumption that iEEG electrodes, whether strip, grid, depth or stereo EEG (sEEG) arrays, are placed in the correct location. Therefore, it is of interest to clinicians and scientists to see whether the same analysis and modeling techniques can be applied to whole-brain, non-invasive neuroimaging data (from MRI-based techniques) and neurophysiological data (from MEG and scalp EEG recordings), thus removing the limitation of spatial sampling, while safely and objectively characterizing the EZ. This review aims to summarize current state of the art non-invasive methods that inform epilepsy surgery using network analysis and dynamical network models. We also present perspectives on future directions and clinical applications of these promising approaches.
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Affiliation(s)
- Miao Cao
- Center for MRI Research, Peking University, Beijing, China
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Simon J. Vogrin
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Andre D. H. Peterson
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - William Woods
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Mark J. Cook
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Chris Plummer
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
- *Correspondence: Chris Plummer
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44
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Revell AY, Silva AB, Arnold TC, Stein JM, Das SR, Shinohara RT, Bassett DS, Litt B, Davis KA. A framework For brain atlases: Lessons from seizure dynamics. Neuroimage 2022; 254:118986. [PMID: 35339683 PMCID: PMC9342687 DOI: 10.1016/j.neuroimage.2022.118986] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/13/2022] [Accepted: 02/07/2022] [Indexed: 01/03/2023] Open
Abstract
Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain’s function from its underlying structure. We show how network topology, structure–function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.
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Affiliation(s)
- Andrew Y Revell
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Alexander B Silva
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Medical Scientist Training Program, University of California, San Francisco, CA 94143, USA
| | - T Campbell Arnold
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joel M Stein
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R Das
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Endeavor, Perelman school of Medicine, University of Pennsylvania, PA 19104, USA
| | - Dani S Bassett
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA; Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Brian Litt
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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45
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Millán AP, van Straaten ECW, Stam CJ, Nissen IA, Idema S, Baayen JC, Van Mieghem P, Hillebrand A. Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings. Sci Rep 2022; 12:4086. [PMID: 35260657 PMCID: PMC8904850 DOI: 10.1038/s41598-022-07730-2] [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: 09/12/2021] [Accepted: 02/24/2022] [Indexed: 11/08/2022] Open
Abstract
Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on MEG brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome. We have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual brain networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a 90% reduction in seizure propagation. The individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area. Epidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resections in silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome. MEG-based networks can provide a good approximation of structural connectivity for computational models of seizure propagation, and facilitate their clinical use.
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Affiliation(s)
- Ana P Millán
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ida A Nissen
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sander Idema
- Department of Neurosurgery, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Johannes C Baayen
- Department of Neurosurgery, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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46
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Scheid BH, Bernabei JM, Khambhati AN, Mouchtaris S, Jeschke J, Bassett DS, Becker D, Davis KA, Lucas T, Doyle W, Chang EF, Friedman D, Rao VR, Litt B. Intracranial electroencephalographic biomarker predicts effective responsive neurostimulation for epilepsy prior to treatment. Epilepsia 2022; 63:652-662. [PMID: 34997577 PMCID: PMC9887634 DOI: 10.1111/epi.17163] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 11/22/2021] [Accepted: 12/27/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Despite the overall success of responsive neurostimulation (RNS) therapy for drug-resistant focal epilepsy, clinical outcomes in individuals vary significantly and are hard to predict. Biomarkers that indicate the clinical efficacy of RNS-ideally before device implantation-are critically needed, but challenges include the intrinsic heterogeneity of the RNS patient population and variability in clinical management across epilepsy centers. The aim of this study is to use a multicenter dataset to evaluate a candidate biomarker from intracranial electroencephalographic (iEEG) recordings that predicts clinical outcome with subsequent RNS therapy. METHODS We assembled a federated dataset of iEEG recordings, collected prior to RNS implantation, from a retrospective cohort of 30 patients across three major epilepsy centers. Using ictal iEEG recordings, each center independently calculated network synchronizability, a candidate biomarker indicating the susceptibility of epileptic brain networks to RNS therapy. RESULTS Ictal measures of synchronizability in the high-γ band (95-105 Hz) significantly distinguish between good and poor RNS responders after at least 3 years of therapy under the current RNS therapy guidelines (area under the curve = .83). Additionally, ictal high-γ synchronizability is inversely associated with the degree of therapeutic response. SIGNIFICANCE This study provides a proof-of-concept roadmap for collaborative biomarker evaluation in federated data, where practical considerations impede full data sharing across centers. Our results suggest that network synchronizability can help predict therapeutic response to RNS therapy. With further validation, this biomarker could facilitate patient selection and help avert a costly, invasive intervention in patients who are unlikely to benefit.
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Affiliation(s)
- Brittany H. Scheid
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John M. Bernabei
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ankit N. Khambhati
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA,Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Sofia Mouchtaris
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jay Jeschke
- Comprehensive Epilepsy Center, NYU Langone Health, New York, New York, USA
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Danielle Becker
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kathryn A. Davis
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Timothy Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Werner Doyle
- Department of Neurosurgery, NYU Langone, New York, New York, USA
| | - Edward F. Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA,Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Daniel Friedman
- Comprehensive Epilepsy Center, NYU Langone Health, New York, New York, USA,Department of Neurology, NYU Langone, New York, New York, USA
| | - Vikram R. Rao
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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47
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Linking Brain Structure, Activity, and Cognitive Function through Computation. eNeuro 2022; 9:ENEURO.0316-21.2022. [PMID: 35217544 PMCID: PMC8925650 DOI: 10.1523/eneuro.0316-21.2022] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 01/19/2023] Open
Abstract
Understanding the human brain is a “Grand Challenge” for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.
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48
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Cao M, Galvis D, Vogrin SJ, Woods WP, Vogrin S, Wang F, Woldman W, Terry JR, Peterson A, Plummer C, Cook MJ. Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery. Nat Commun 2022; 13:994. [PMID: 35194035 PMCID: PMC8863890 DOI: 10.1038/s41467-022-28640-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 01/28/2022] [Indexed: 12/17/2022] Open
Abstract
Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery. Dynamical network models developed from physiological evidence offer insights into rapidly evolving brain networks in the epileptic seizure. A limitation of previous studies in this field is the dependence on invasive cortical recordings with constrained spatial sampling of brain regions that might be involved in seizure dynamics. Here, we propose virtual intracranial electroencephalography (ViEEG), which combines non-invasive ictal magnetoencephalographic imaging (MEG), dynamical network models and a virtual resection technique. In this proof-of-concept study, we show that ViEEG signals reconstructed from MEG alone preserve critical temporospatial characteristics for dynamical approaches to identify brain areas involved in seizure generation. We show the non-invasive ViEEG approach may have some advantage over intracranial electroencephalography (iEEG). Future work may be designed to test the potential of the virtual iEEG approach for use in surgical management of epilepsy.
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Affiliation(s)
- Miao Cao
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia
| | - Daniel Galvis
- Translational Research Exchange at Exeter, University of Exeter, Exeter, UK.,Living Systems Institute, University of Exeter, Exeter, UK.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK.,Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Simon J Vogrin
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia.,Faculty of Health, Art and Design, Swinburne University of Technology, Melbourne, Australia
| | - William P Woods
- Faculty of Health, Art and Design, Swinburne University of Technology, Melbourne, Australia
| | - Sara Vogrin
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Department of Medicine Western Health, The University of Melbourne, Melbourne, Australia
| | - Fan Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,CAS Centre for Excellence in Brain Science and Intelligence Technology, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wessel Woldman
- Translational Research Exchange at Exeter, University of Exeter, Exeter, UK.,Living Systems Institute, University of Exeter, Exeter, UK.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK.,Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - John R Terry
- Translational Research Exchange at Exeter, University of Exeter, Exeter, UK.,Living Systems Institute, University of Exeter, Exeter, UK.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK.,Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Andre Peterson
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Chris Plummer
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia. .,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia. .,Faculty of Health, Art and Design, Swinburne University of Technology, Melbourne, Australia.
| | - Mark J Cook
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
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49
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Brain simulation as a cloud service: The Virtual Brain on EBRAINS. Neuroimage 2022; 251:118973. [DOI: 10.1016/j.neuroimage.2022.118973] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 12/18/2022] Open
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50
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van Albada SJ, Morales-Gregorio A, Dickscheid T, Goulas A, Bakker R, Bludau S, Palm G, Hilgetag CC, Diesmann M. Bringing Anatomical Information into Neuronal Network Models. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:201-234. [DOI: 10.1007/978-3-030-89439-9_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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