Controversies on the network theory of epilepsy : Debates held during the ICTALS 2019 conference

Acknowledgements We would like to acknowledge the contributions of the discussants to the exposition and discussion of the six debate topics. The discussants for debates 1-6 were Fabrice Wendling, Mark Cook, Mark Richardson, Thorsten Rings, Klaus Lehnertz and Piotr Suffczynski, respectively. Funding for ICTALS 2019 was received from the following foundations and industry partners: UCB S.A. (Belgium), American Epilepsy Society (AES), Epilepsy Innovation Institute (Ei2) and Epilepsy Foundation of America (EFA), NeuraLynx (Bozeman, MT, USA) and LivaNova (London, UK). The contribution of HZ was supported by award R01NS109062 from the National Institutes of Health, MG by the EPSRC via grants EP/P021417/1 and EP/N014391/1 and a Wellcome Trust Institutional Strategic Support Award (WT105618MA), and PJ by awards from the Ministry of Health of the Czech Republic AZV 17-28427A and the Czech Science Foundation 20-25298S. The opinions expressed in this article do not necessarily reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government.Peer reviewedPostprin

Modeling brain dynamics after tumor resection using The Virtual Brain

Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, simulation of the activity of neural populations connected according to the white matter fibers, producing personalized brain network models, has been introduced as a promising tool for this purpose. The Virtual Brain provides a robust open source framework to implement these models. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first "virtual neurosurgery", mimicking patient's actual surgery by removing white matter fibers in the resection mask and simulating again neural activity on this new connectome. We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. Concerning the virtual neurosurgery analyses, use of the pre-surgery model implemented on the virtually resected structural connectome resulted in improved similarity with post-surgical empirical functional connectivity in some patients, but negligible improvement in others. These findings reveal interesting avenues for increasing interactions between computational neuroscience and neuro-oncology, as well as important limitations that warrant further investigation

Desafíos epistémicos de los gemelos digitales y los cerebros virtuales: perspectivas desde la neuroética fundamental

In this article, we present and analyse the concept of Digital Twin (DT) linked to distinct types of objects (artefacts, natural, inanimate or living) and examine the challenges involved in creating them from a fundamental neuroethics approach that emphasises conceptual analyses. We begin by providing a brief description of DTs and their initial devel-opment as models of artefacts and physical inanimate objects, identifying core challenges in building these tools and noting their intended benefits. Next, we describe attempts to build DTs of model living entities, such as hearts, highlighting the novel challenges raised by this shift from DTs of inanimate objects to DTs of living objects. Against that background, we give an account of contemporary research aiming to develop DTs of the human brain by building “virtual brains”, e.g. the simulation engine The Virtual Brain (TVB) as it is carried out in the European Human Brain Project. Since the brain is structurally and functionally the most complex organ in the human body, and our integrated knowledge of its functional architecture remains limited in spite of recent neuroscientific advances, the attempts to create virtual copies of the human brain are correspondingly challenging. We suggest that a clear scientific theoretical structure, conceptual clarity and transparency regarding the methods and goals of this technological development are necessary prerequisites in order to make the project of constructing virtual brains a theoretically promising and socially beneficial scientific, technological and philosophical enterprise.En este artículo, presentamos y analizamos el concepto de gemelo digital vinculado a distintos tipos de objetos (artefactos, objetos naturales, animados e inanimados) y examinamos los desafíos que presenta su creación utilizando la perspectiva de la neuroética fundamental que enfatiza el análisis conceptual. Comenzamos con una breve descrip-ción de los gemelos digitales y de su desarrollo inicial como modelos de artefactos y objetos físicos no animados, identificando los desafíos centrales que presenta su construcción y destacando sus beneficios. Luego describimos intentos de construir gemelos digitales de entes vivos, como el corazón, identificando los desafíos novedosos que se plantean en este caso. A continuación describimos estudios contemporáneos que tienen como objeto desarrollar gemelos digitales del cerebro humano por medio de la construcción de “cerebros virtuales”, tal como se lleva a cabo en el Human Brain Project europeo por medio del motor de simulación The Virtual Brain (TVB). Si consideramos que el cerebro es el órgano mas complejo del cuerpo humano, tanto estructural como funcionalmente, y teniendo en cuenta que nuestro conocimiento integral de su arquitectura funcional sigue siendo limitado, los intentos de crear copias virtuales del cerebro humanos constituyen un reto significativo. Sugerimos que una estructura científicamente clara y una transparencia conceptual sobre los métodos y fines de este desarrollo tecnológico son requisitos necesarios para lograr que el proyecto de construir cerebros virtuales se convierta en una iniciativa teóricamente prometedora, así como científica, social y filosóficamente beneficiosa.Filosofí

Simuladores cerebrales: revisión de modelos micro- y macroescala

Las simulaciones de redes cerebrales pretenden comprender las funciones del cerebro tanto en condiciones normales como patológicas. A tal fin, existen en la actualidad múltiples simuladores y paquetes software pertenecientes al ámbito de la neurociencia computacional. El objetivo final de los modelos computacionales consiste en tratar de explicar la relación entre la estructura, la función y la dinámica cerebrales. Los modelos multiescala trabajan con datos biológicos de distinto tipo y granularidad, en un rango que va desde modelos de neuronas, sinapsis y microcircuitos -microescala- hasta modelos a macroescala con cerebros virtuales. En el presente trabajo se pretende realizar una revisión de los modelos microescala y macroescala, resaltando sus principales características y funcionalidades y su orientación para investigación. Finalmente, se trabajará experimentalmente con un modelo macroescala, para el cual se elaborará una guía de usuario.Grado en Ingeniería Biomédic

Functional network correlates of language and semiology in epilepsy

Epilepsy surgery is appropriate for 2-3% of all epilepsy diagnoses. The goal of the presurgical workup is to delineate the seizure network and to identify the risks associated with surgery. While interpretation of functional MRI and results in EEG-fMRI studies have largely focused on anatomical parameters, the focus of this thesis was to investigate canonical intrinsic connectivity networks in language function and seizure semiology. Epilepsy surgery aims to remove brain areas that generate seizures. Language dysfunction is frequently observed after anterior temporal lobe resection (ATLR), and the presurgical workup seeks to identify the risks associated with surgical outcome. The principal aim of experimental studies was to elaborate understanding of language function as expressed in the recruitment of relevant connectivity networks and to evaluate whether it has value in the prediction of language decline after anterior temporal lobe resection. Using cognitive fMRI, we assessed brain areas defined by parameters of anatomy and canonical intrinsic connectivity networks (ICN) that are involved in language function, specifically word retrieval as expressed in naming and fluency. fMRI data was quantified by lateralisation indices and by ICN_atlas metrics in a priori defined ICN and anatomical regions of interest. Reliability of language ICN recruitment was studied in 59 patients and 30 healthy controls who were included in our language experiments. New and established language fMRI paradigms were employed on a three Tesla scanner, while intellectual ability, language performance and emotional status were established for all subjects with standard psychometric assessment. Patients who had surgery were reinvestigated at an early postoperative stage of four months after anterior temporal lobe resection. A major part of the work sought to elucidate the association between fMRI patterns and disease characteristics including features of anxiety and depression, and prediction of postoperative language outcome. We studied the efficiency of reorganisation of language function associated with disease features prior to and following surgery. A further aim of experimental work was to use EEG-fMRI data to investigate the relationship between canonical intrinsic connectivity networks and seizure semiology, potentially providing an avenue for characterising the seizure network in the presurgical workup. The association of clinical signs with the EEG-fMRI informed activation patterns were studied using the data from eighteen patients’ whose seizures and simultaneous EEG-fMRI activations were reported in a previous study. The accuracy of ICN_atlas was validated and the ICN construct upheld in the language maps of TLE patients. The ICN construct was not evident in ictal fMRI maps and simulated ICN_atlas data. Intrinsic connectivity network recruitment was stable between sessions in controls. Amodal linguistic processing and the relevance of temporal intrinsic connectivity networks for naming and that of frontal intrinsic connectivity networks for word retrieval in the context of fluency was evident in intrinsic connectivity networks regions. The relevance of intrinsic connectivity networks in the study of language was further reiterated by significant association between some disease features and language performance, and disease features and activation in intrinsic connectivity networks. However, the anterior temporal lobe (ATL) showed significantly greater activation compared to intrinsic connectivity networks – a result which indicated that ATL functional language networks are better studied in the context of the anatomically demarked ATL, rather than its functionally connected intrinsic connectivity networks. Activation in temporal lobe networks served as a predictor for naming and fluency impairment after ATLR and an increasing likelihood of significant decline with greater magnitude of left lateralisation. Impairment of awareness served as a significant classifying feature of clinical expression and was significantly associated with the inhibition of normal brain functions. Canonical intrinsic connectivity networks including the default mode network were recruited along an anterior-posterior anatomical axis and were not significantly associated with clinical signs

Controlling seizure propagation in large-scale brain networks.

Information transmission in the human brain is a fundamentally dynamic network process. In partial epilepsy, this process is perturbed and highly synchronous seizures originate in a local network, the so-called epileptogenic zone (EZ), before recruiting other close or distant brain regions. We studied patient-specific brain network models of 15 drug-resistant epilepsy patients with implanted stereotactic electroencephalography (SEEG) electrodes. Each personalized brain model was derived from structural data of magnetic resonance imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes equipped with region specific neural mass models capable of demonstrating a range of epileptiform discharges. Each patient's virtual brain was further personalized through the integration of the clinically hypothesized EZ. Subsequent simulations and connectivity modulations were performed and uncovered a finite repertoire of seizure propagation patterns. Across patients, we found that (i) patient-specific network connectivity is predictive for the subsequent seizure propagation pattern; (ii) seizure propagation is characterized by a systematic sequence of brain states; (iii) propagation can be controlled by an optimal intervention on the connectivity matrix; (iv) the degree of invasiveness can be significantly reduced via the proposed seizure control as compared to traditional resective surgery. To stop seizures, neurosurgeons typically resect the EZ completely. We showed that stability analysis of the network dynamics, employing structural and dynamical information, estimates reliably the spatiotemporal properties of seizure propagation. This suggests novel less invasive paradigms of surgical interventions to treat and manage partial epilepsy