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International audience; 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.

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.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 showed a precision of 64% and a recall of 44% for EZVEP 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.

One-third of 50 million epilepsy patients worldwide suffer from drug resistant epilepsy and are candidates for surgery. Precise estimates of the epileptogenic zone networks (EZNs) are crucial for planning intervention strategies. Here, we present the Virtual Epileptic Patient (VEP), a multimodal probabilistic modeling framework for personalized end-to-end analysis of brain imaging data of drug resistant epilepsy patients. The VEP uses data-driven, personalized virtual brain models derived from patient-specific anatomical (such as T1-MRI, DW-MRI, and CT scan) and functional data (such as stereo-EEG). It employs Markov Chain Monte Carlo (MCMC) and optimization methods from Bayesian inference to estimate a patient’s EZN while considering robustness, convergence, sensor sensitivity, and identifiability diagnostics. We describe both high-resolution neural field simulations and a low-resolution neural mass model inversion. The VEP workflow was evaluated retrospectively with 53 epilepsy patients and is now being used in an ongoing clinical trial (EPINOV).

SummaryDuring the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO;N= 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.

Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread., Author summary The electrical activity of the brain during an epileptic seizure can be observed with intracranial EEG, that is electrodes implanted in the patient’s brain. However, due to the practical constraints only selected brain regions can be implanted, which brings a risk that the abnormal electrical activity in some non-implanted regions is hidden from the observers. In this work we introduce a method to infer what is happening in the unobserved parts based on the incomplete observations of the epileptic seizure. The method relies on the assumption that the seizure spreads along the white-matter structural connections, and finds the explanation of the whole-brain seizure spread consistent with the data. The structural connectome can be estimated from diffusion-weighted imaging for an individual patient, therefore this way the patient-specific structural connectome is utilized to better analyze the patients’ seizure recordings.

Background Several automated parcellation atlases of the human brain have been developed over the past decades, based on various criteria, and have been applied in basic and clinical research. New method : Here we present the Virtual Epileptic Patient (VEP) atlas that offers a new automated brain region parcellation and labeling, which has been developed for the specific use in the domains of epileptology and functional neurosurgery and is able to apply at individual patient’s level. Results It comprises 162 brain regions, including 73 cortical and 8 subcortical regions per hemisphere. We demonstrate the successful application of the VEP atlas in a cohort of 50 retrospective patients. The structural organization is complemented by the functional variation of stereotactic intracerebral EEG (SEEG) signal data features establishing brain region-specific 3d-maps. Comparison with existing methods The VEP atlas integrates both anatomical and functional definitions in the same atlas, adapted to applications for epilepsy patients and individualizable. Conclusion The covariation of structural and functional organization is the basis for current efforts of patient-specific large-scale brain network modeling exploiting virtual brain technologies for the identification of the epileptogenic regions in an ongoing prospective clinical trial EPINOV.

Objective In focal epilepsies, the accurate delineation of the epileptogenic network is a fundamental step before surgery. For years, the relationship between the interictal epileptic spikes (defining the “irritative zone”, IZ) and the sites of seizure initiation (SOZ) has been a matter of debate. Methods Our goal was to investigate from intracerebral recordings (stereoelectroencephalography, SEEG) the distribution of interictal epileptic spikes (based on a spike frequency index, SI) and the topography of the SOZ (based on the Epileptogenicity Index, EI) in patients having focal neocortical epilepsies. Thirty-one patients were studied. A total of 539 brain regions were quantified in term of both spike generation (SI) and seizure initiation (EI). Results We found a 56% (18/32) rate of agreement between maximal EI and maximal SI values. When considering separately patients with focal cortical dysplasia (FCD), the proportion of patients with good concordance was ∼75% (15/20), whereas it was only 33% (4/12) in the non FCD group. Conclusions Our results show that a significant part of patients have some dissociation between regions showing pronounced spiking activity and those showing high epileptogenicity. e is clinically important. Significance For patients with these dissociations, other markers than spiking frequency remain to be investigated. In the FCD group, the good concordance between SI and EI confirms that the mapping of the irritative zone is clinically important.

Visual Abstract, Connectome-based modeling of large-scale brain network dynamics enables causal in silico interrogation of the brain’s structure-function relationship, necessitating the close integration of diverse neuroinformatics fields. Here we extend the open-source simulation software The Virtual Brain (TVB) to whole mouse brain network modeling based on individual diffusion magnetic resonance imaging (dMRI)-based or tracer-based detailed mouse connectomes. We provide practical examples on how to use The Virtual Mouse Brain (TVMB) to simulate brain activity, such as seizure propagation and the switching behavior of the resting state dynamics in health and disease. TVMB enables theoretically driven experimental planning and ways to test predictions in the numerous strains of mice available to study brain function in normal and pathological conditions.

The brain at rest exhibits a spatio-temporally rich dynamics which adheres to systematic behaviours that persist in task paradigms but appear altered in disease. Despite this hypothesis, many rest state paradigms do not act directly upon the rest state and therefore cannot confirm hypotheses about its mechanisms. To address this challenge, we combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to study brain’s relaxation toward rest following a transient perturbation. Specifically, TMS targeted either the medial prefrontal cortex (MPFC), i.e. part of the Default Mode Network (DMN) or the superior parietal lobule (SPL), involved in the Dorsal Attention Network. TMS was triggered by a given brain state, namely an increase in occipital alpha rhythm power. Following the initial TMS-Evoked Potential, TMS at MPFC enhances the induced occipital alpha rhythm, called Event Related Synchronisation, with a longer transient lifetime than TMS at SPL and a higher amplitude. Our findings show a strong coupling between MPFC and the occipital alpha power. Although the rest state is organized around a core of resting state networks, the DMN functionally takes a special role among these resting state networks.

International audience; Ictal MEG recordings constitute rare data. The objective of this study was to evaluate ictal magnetic source localization (MSI), using two algorithms: linearly constrained minimum variance (LCMV), a beamforming technique and equivalent current dipole (ECD). Ictal MSI was studied in six patients. Three of them were undergoing post-operative re-evaluation. For all patients, results were validated by the stereoelectroencephalographic (SEEG) definition of the epileptogenic zone (EZ). EZ was quantified using the epileptogenicity index (EI) method, which accounts for both the propensity of a brain area to generate rapid discharges and the time for this area to become involved in the seizure. EI values range from 0 (no epileptogenicity) to 1 (maximal epileptogenicity). Levels of concordance between ictal MSI and EZ were determined as follows: A: ictal MSI localized the site whose value EI = 1, B: MSI localized a part of the EZ (not corresponding to the maximal value of EI = 1), C: a region could be identified on ictal MSI but not on SEEG, D: a region could be identified on SEEG but not on MSI, E: different regions were localized on MSI and SEEG. Ictal MEG pattern consisted of rhythmic activities between 10 and 20 Hz for all patients. For LCMV (first maxima), levels of concordance were A (two cases), B (two cases) and E (two cases). For ECD fitted on each time point separately (location characterized by the best goodness-of-fit value), levels of concordance were A (one case), B (one case), D (three cases) and E (one case). For ECD calculated for the whole time window, levels of concordance were A (two cases) and D (four cases). Source localization methods performed on rhythmic patterns can localize the EZ as validated by SEEG. In terms of concordance, LCMV was superior to ECD. In some cases, LCMV allows extraction of several maxima that could reflect ictal dynamics. In a medial temporal lobe epilepsy case, ictal MSI indicated an area of delayed propagation and was non-contributory to the presurgical assessment.

International audience; BACKGROUND: The importance of digital signal processing in clinical neurophysiology is growing steadily, involving clinical researchers and methodologists. There is a need for crossing the gap between these communities by providing efficient delivery of newly designed algorithms to end users. We have developed such a tool which both visualizes and processes data and, additionally, acts as a software development platform.NEW METHOD: AnyWave was designed to run on all common operating systems. It provides access to a variety of data formats and it employs high fidelity visualization techniques. It also allows using external tools as plug-ins, which can be developed in languages including C++, MATLAB and Python. RESULTS In the current version, plug-ins allow computation of connectivity graphs (non-linear correlation h2) and time-frequency representation (Morlet wavelets). The software is freely available under the LGPL3 license.COMPARISON WITH EXISTING METHODS: AnyWave is designed as an open, highly extensible solution, with an architecture that permits rapid delivery of new techniques to end users.CONCLUSIONS: We have developed AnyWave software as an efficient neurophysiological data visualizer able to integrate state of the art techniques. AnyWave offers an interface well suited to the needs of clinical research and an architecture designed for integrating new tools. We expect this software to strengthen the collaboration between clinical neurophysiologists and researchers in biomedical engineering and signal processing.

TheVirtualBrain (TVB) is a neuroinformatics Python package representing theconvergence of clinical, systems, and theoretical neuroscience in the analysis,visualization and modeling of neural and neuroimaging dynamics. TVB iscomposed of a flexible simulator for neural dynamics measured across scalesfrom local populations to large-scale dynamics measured byelectroencephalography (EEG), magnetoencephalography (MEG) and functionalmagnetic resonance imaging (fMRI), and core analytic and visualizationfunctions, all accessible through a web browser user interface. A datatypesystem modeling neuroscientific data ties together these pieces with persistentdata storage, based on a combination of SQL & HDF5. These datatypes combinewith adapters allowing TVB to integrate other algorithms or computationalsystems. TVB provides infrastructure for multiple projects and multiple users,possibly participating under multiple roles. For example, a clinician mightimport patient data to identify several potential lesion points in thepatient's connectome. A modeler, working on the same project, tests thesepoints for viability through whole brain simulation, based on the patient'sconnectome, and subsequent analysis of dynamical features. TVB also drivesresearch forward: the simulator itself represents the culmination of severalsimulation frameworks in the modeling literature. The availability of thenumerical methods, set of neural mass models and forward solutions allows forthe construction of a wide range of brain-scale simulation scenarios. Thispaper briefly outlines the history and motivation for TVB, describing theframework and simulator, giving usage examples in the web UI and Pythonscripting.

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

Low-dimensional attractive manifolds with flows prescribing the evolution of state variables are commonly used to capture the lawful behavior of behavioral and cognitive variables. Neural network dynamics underlie many of the mechanistic explanations of function and demonstrate the existence of such low-dimensional attractive manifolds. In this study, we focus on exploring the network mechanisms due to asymmetric couplings giving rise to the emergence of arbitrary flows in low dimensional spaces. Here we use a spiking neural network model, specifically the theta neuron model and simple synaptic dynamics, to show how a qualitatively identical set of basic behaviors arises from different combinations of couplings with broken symmetry, in fluctuations of both firing rate and spike timing. We further demonstrate how such network dynamics can be combined to create more complex processes. These results suggest that 1) asymmetric coupling is not always a variance to be averaged over, 2) different networks may produce the same dynamics by different dynamical routes and 3) complex dynamics may be formed by simpler dynamics through a combination of couplings.

In the past few decades, behavioral and cognitive science have demonstrated that many human behaviors can be captured by low-dimensional observations and models, even though the neuromuscular systems possess orders of magnitude more potential degrees of freedom than are found in a specific behavior. We suggest that this difference, due to a separation in the time scales of the dynamics guiding neural processes and the overall behavioral expression, is a key point in understanding the implementation of cognitive processes in general. In this paper we use Structured Flows on Manifolds (SFM) to understand the organization of behavioral dynamics possessing this property. Next, we discuss how this form of behavioral dynamics can be distributed across a network, such as those recruited in the brain for particular cognitive functions. Finally, we provide an example of an SFM style functional architecture of handwriting, motivated by studies in human movement sciences, that demonstrates hierarchical sequencing of behavioral processes.

In the past few decades, behavioral and cognitive science have demonstrated that many human behaviors can be captured by low-dimensional observations and models, even though the neuromuscular systems possess orders of magnitude more potential degrees of freedom than are found in a specific behavior. We suggest that this difference, due to a separation in the time scales of the dynamics guiding neural processes and the overall behavioral expression, is a key point in understanding the implementation of cognitive processes in general. In this paper we use Structured Flows on Manifolds (SFM) to understand the organization of behavioral dynamics possessing this property. Next, we discuss how this form of behavioral dynamics can be distributed across a network, such as those recruited in the brain for particular cognitive functions. Finally, we provide an example of an SFM style functional architecture of handwriting, motivated by studies in human movement sciences, that demonstrates hierarchical sequencing of behavioral processes.

Gamma oscillations can synchronize with near zero phase lag over multiple cortical regions and between hemispheres, and between two distal sites in hippocampal slices. How synchronization can take place over long distances in a stable manner is considered an open question. The phase resetting curve (PRC) keeps track of how much an input advances or delays the next spike, depending upon where in the cycle it is received. We use PRCs under the assumption of pulsatile coupling to derive existence and stability criteria for 1:1 phase-locking that arises via bidirectional pulse coupling of two limit cycle oscillators with a conduction delay of any duration for any 1:1 firing pattern. The coupling can be strong as long as the effect of one input dissipates before the next input is received. We show the form that the generic synchronous and anti-phase solutions take in a system of two identical, identically pulse-coupled oscillators with identical delays. The stability criterion has a simple form that depends only on the slopes of the PRCs at the phases at which inputs are received and on the number of cycles required to complete the delayed feedback loop. The number of cycles required to complete the delayed feedback loop depends upon both the value of the delay and the firing pattern. We successfully tested the predictions of our methods on networks of model neurons. The criteria can easily be extended to include the effect of an input on the cycle after the one in which it is received.

The genesis of both theory of neural field modeling and forward solutions for neuroimaging modalities and empirical methods to obtain connectivity datasets necessary to simulate the human brain dynamics has lead to the development of a whole-brain simulator, built within a freely available neuroinformatics platform called the Virtual Brain (TVB) [1-3]. TVB and the research that led to it, has opened up several possibilities as well as further research questions. On the one hand, such simulations reproduce aspects of human resting state activity, and on the other, it has been observed that certain neural pathologies manifest changes in the resting state, therefore suggesting that the Virtual Brain presents an opportunity to model the dynamics of a brain, on a case by case basis, enabled by non-invasive tractography methods such as diffusion tensor or spectrum imaging. Other use cases are also possible, e.g. in the case of stroke patients to model the effects of resulting lesions on resting state dynamics. However, obstacles remain: First, the relative effects of the parameters on the network model stability and its associated parameter dispersion in TVB are not well understood, and second, because of realism of finite conduction velocities in the brain connectivity in TVB, the models remain computationally expensive. In the work presented here, we address both obstacles in computational terms by introducing and implementing strategies to improve the performance of the simulator in TVB, based on generating customized code per simulation [4]. These improvements are drastic when access to recent graphics processing units (GPUs) is available and represent orders of magnitude increase in the ability of users to explore both parameter spaces or qualitative dynamical regimes, and therefore significantly amplify the impact of whole-brain modeling approaches.

TheVirtualBrain (TVB) is the first integrative neuroinformatics platform for the modeling of full brain network dynamics. TVB simulator, written in Python, enables the systematic model-based inference of neurophysiological mechanisms on different brain scales that underlie the generation of macroscopic commonly used neuroimaging signals (EEG, MEG, fMRI). In the framework of TVB we build full brain network models by incorporating biologically realistic large scale couplings of neural populations. The couplings within the network are captured by the space-time structure of the connectivity matrix, which defines the connection strengths and time delays via signal transmission between all network nodes. Researchers from computational, theoretical and clinical neuroscience can benefit of this tool. The web interface allows users without programming knowledge to access a powerful computing platform as well as visualization and analysis tools. Users with strong programming skills benefit of all the advantages provided by the Python programming through a shell interface.

Focal drug resistant epilepsy is a neurological disorder characterized by seizures caused by abnormal activity originating in one or more regions together called as epileptogenic zone. Treatment for such patients involves surgical resection of affected regions. Epileptogenic zone is typically identified using stereotactic EEG recordings from the electrodes implanted into the patient’s brain. Identifying the epileptogenic zone is a challenging problem due to the spatial sparsity of electrode implantation. We propose a probabilistic hierarchical model of seizure propagation patterns, based on a phenomenological model of seizure dynamics called Epileptor. Using Bayesian inference, the Epileptor model is optimized to build patient specific virtual models that best fit to the log power of intracranial recordings. First, accuracy of the model predictions and identifiability of the model are investigated using synthetic data. Then, model predictions are evaluated against a retrospective patient cohort of 25 patients with varying surgical outcomes. In the patients who are seizure free after surgery, model predictions showed good match with the clinical hypothesis. In patients where surgery failed to achieve seizure freedom model predictions showed a strong mismatch. Our results demonstrate that proposed probabilistic model could be a valuable tool to aid the clinicians in identifying the seizure focus., Vattikonda et al. combine a conceptual mathematical model of seizure generation with personalized information about brain connectivity to tackle the challenge of predictive brain surgery. The validated model was used to successfully determine the outcome of surgery in retrospective patient data for removal of epileptogenic brain tissue.

The so-called rest state of the human brain has revealed a rich repertoire of dynamic patterns as viewed through neuroimaging modalities [1]. Much of this richness is thought to come from inherent multistable structure of cortical activity, yet only recently have theoretical studies on the origin of these dynamics begun to dissect the relations between corticocortical connectivity, local neural dynamics and the global, whole brain dynamics [2,3]. In the case of realistic connectivities, the delays due to finite conduction delays between regions presents a major roadblock for analytic approaches, and simulation becomes the main workhorse for understanding cortical dynamics. In this work, we contribute to the theory of such rest state cortical activity by exploring the variability of spatiotemporal pattern formation in brain networks as a function of neural mass model dynamics, connectivity and intersubject variability. We find that the neural mass model chosen for network modeling can reshape the stability structure of cortical dynamics. Additionally, it is shown that the fine structure of the stability of the dynamics depends on the joint distribution of connection weights and delays. Lastly we also show that manipulations of the connectivity delays can reshape the functional connectivity on the longer time scale of several seconds or minutes. In general, we attempt to layout the first steps towards an atlas on the modeled cortical dynamics of the human brain.