Virtual Epileptic Patient (VEP): Data-driven probabilistic personalized brain modeling in drug-resistant epilepsy

Wang, Huifang; Woodman, Michael; Triebkorn, Paul; Lemaréchal, Jean-Didier; Jha, Jayant; Dollomaja, Borana; Vattikonda, Anirudh N.; Sip, Viktor; Villalon, Samuel Médina; Hashemi, Meysam; Guye, Maxime; Scholly, Julia; Bartolomei, Fabricē; Jirsa, Viktor K.

doi:10.1101/2022.01.19.22269404

Cited by 5 publications

(14 citation statements)

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“…The French clinical trial (EPINOV NCT03643016) is now recruiting 350 patients from 11 epilepsy centers to test the capacity of the Virtual Epileptic Patient algorithms to improve clinical outcomes. 45 However, most network neuroscience findings in epilepsy face multiple systemic barriers ( Table 1 ) that obstruct the road to clinical translation. To address these barriers, we must examine the stages prior to randomized controlled trials and/or clinical adoption.…”

Section: Barriers and Next Stepsmentioning

confidence: 99%

Integrating Network Neuroscience Into Epilepsy Care: Progress, Barriers, and Next Steps

et al. 2022

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Drug resistant epilepsy is a disorder involving widespread brain network alterations. Recently, many groups have reported neuroimaging and electrophysiology network analysis techniques to aid medical management, support presurgical planning, and understand postsurgical seizure persistence. While these approaches may supplement standard tests to improve care, they are not yet used clinically or influencing medical or surgical decisions. When will this change? Which approaches have shown the most promise? What are the barriers to translating them into clinical use? How do we facilitate this transition? In this review, we will discuss progress, barriers, and next steps regarding the integration of brain network analysis into the medical and presurgical pipeline.

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Section: Barriers and Next Stepsmentioning

confidence: 99%

Integrating Network Neuroscience Into Epilepsy Care: Progress, Barriers, and Next Steps

et al. 2022

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“…The Virtual Brain platform (Sanz Leon et al, 2013) was used to construct a large-scale brain network model that is capable of generating timeseries datasets with the basic features of epileptic brain network activity as observed in SEEG. Structurally, the simulated brain is made of several brain areas (nodes) according to the parcellation of the Virtual Epileptic Patient atlas (Wang et al, 2021) which includes 162 regions of interest (ROI) that represent 73 cortical and 8 subcortical structures per hemisphere. Connectivity between the nodes of the network was prescribed by a sample structural connectome obtained from tractography analysis of diffusion weighted imaging (Wang et al, 2021).…”

Section: The Brain Network Modelmentioning

confidence: 99%

“…Structurally, the simulated brain is made of several brain areas (nodes) according to the parcellation of the Virtual Epileptic Patient atlas (Wang et al, 2021) which includes 162 regions of interest (ROI) that represent 73 cortical and 8 subcortical structures per hemisphere. Connectivity between the nodes of the network was prescribed by a sample structural connectome obtained from tractography analysis of diffusion weighted imaging (Wang et al, 2021). The activity of each node was modeled using the Epileptor (Jirsa et al, 2014) model which is a phenomenological model devised to capture the canonical dynamical features of ictal dynamics.…”

Section: The Brain Network Modelmentioning

confidence: 99%

“…The described selection algorithm was applied to real ictal SEEG data from 21 retrospective patients with drug-resistant focal epilepsy, whose pre-operative evaluation was done at La Timone hospital, Marseille (Wang et al, 2021). Signals were acquired by SEEG invasive recordings from multiple depth electrodes, with 10-18 contacts of 2 mm length and 1.5 or 5 mm spacing, collected on a 256 channel Deltamed/Natus system, without digital filters.…”

Section: Clinical Seeg Datamentioning

confidence: 99%

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In pursuit of the epileptogenic zone in focal epilepsy: A dynamical network biomarker approach

Runfola

Sheheitli

Bartolomei

et al. 2022

Preprint

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The success of resective surgery for drug-resistant epilepsy patients hinges on the correct identification of the epileptogenic zone (EZ) consisting of the subnetwork of brain regions that underlies seizure genesis in focal epilepsy. The dynamic network biomarker (DNB) method is a dynamical systems-based network analysis approach for identifying subnetworks that are the first to exhibit the transition as a complex system undergoes a bifurcation. The approach was devised and validated in the context of complex disease onset where the dynamics is known to be nonlinear and high-dimensional. We here adapt and implement the DNB approach for the identification of the EZ from the analysis of SEEG data. The method is first successfully tested on simulated data generated with a large-scale brain network model of epilepsy using The Virtual Brain neuroinformatic platform and then applied to clinical SEEG data from focal epilepsy patients. The results are compared with those obtained by expert clinicians that designate the EZ using the Epileptogenicity Index (EI) method. High average precision values are obtained and posit the presented approach as a promising candidate tool for the pursuit of EZ in focal epilepsy.Author SummaryWe present a novel SEEG signal analysis tool for the identification of EZ regions in patients with drug-resistant focal epilepsy. The proposed method adapts and implements the dynamic network biomarker approach which builds on dynamical systems theory for complex networked systems. The method is first successfully tested on synthetic seizure data generated with The Virtual Brain modeling framework and then applied to retrospective patients’ clinical SEEG data. High precision values are obtained when the DNB subnetwork is compared with that designated as EZ by expert clinicians using empirical signal analysis measures and indicate that the DNB approach is a promising tool for the identification of EZ regions through SEEG signal analysis.

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“…A new method using large scale virtual brain models for the estimation of EZN has been proposed (Jirsa et al, 2017;Proix, Bartolomei, Guye, & Jirsa, 2017; H. E. Wang et al, 2022). The virtual epileptic patient (VEP) is a multimodal probabilistic modeling framework, based on Virtual Brain technology.…”

Section: Introductionmentioning

confidence: 99%

Brain sodium MRI-derived priors support the estimation of epileptogenic zones using personalized model-based methods in Epilepsy

AZILINON

Wang

Scholly

et al. 2022

Preprint

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Epilepsy patients with drug-resistant epilepsy are eligible for surgery aiming to remove the cerebral regions that generate seizures, the so-called epileptogenic zone network (EZN). Thus, the accurate delineation of the EZN is crucial. Personalized virtual brain models derived from patient-specific anatomical and functional data are used in Virtual Epileptic Patient (VEP) to estimate the EZN via optimization methods from Bayesian inference. The Bayesian inference approach used in previous VEP integrates priors, based on the features of stereotactic-electroencephalography (SEEG) seizures recordings. Here, we propose new priors, based on quantitative 23Na-MRI. The 23Na-MRI data were acquired at 7T and provided several features characterizing the sodium signal decay. The hypothesis is that the sodium features are biomarkers of neuronal excitability related to the EZN and will add additional information to VEP estimation. In this paper, we first proposed the mapping from 23Na-MRI features to predict the EZN via a machine learning approach. Then, exploiting these predictions as priors in the VEP pipeline, we demonstrated that 23Na-MRI prior based VEP estimation of the EZN improved the results in terms of balanced accuracy and as good as SEEG priors in terms of the weighted harmonic mean of the precision and recall.

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Virtual Epileptic Patient (VEP): Data-driven probabilistic personalized brain modeling in drug-resistant epilepsy

Cited by 5 publications

References 55 publications

Integrating Network Neuroscience Into Epilepsy Care: Progress, Barriers, and Next Steps

Integrating Network Neuroscience Into Epilepsy Care: Progress, Barriers, and Next Steps

In pursuit of the epileptogenic zone in focal epilepsy: A dynamical network biomarker approach

Brain sodium MRI-derived priors support the estimation of epileptogenic zones using personalized model-based methods in Epilepsy

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