Search

International audience; Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. Recently, studies focused on the role of regional variance of model parameters. Such analyses however necessarily depend on the properties of preselected neural mass model. We introduce a method to infer from the functional data both the neural mass model representing the regional dynamics and the region-and subject-specific parameters while respecting the known network structure. We apply the method to human resting-state fMRI. We find that the underlying dynamics can be described as noisy fluctuations around a single fixed point. The method reliably discovers three regional parameters with clear and distinct role in the dynamics, one of which is strongly correlated with the first principal component of the gene expression spatial map. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications for understanding the brain dynamics during aging or neurodegeneration.

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.

Whole-brain network modeling of epilepsy is a data-driven approach that combines personalized anatomical information with dynamical models of abnormal brain activity to generate spatio-temporal seizure patterns as observed in brain imaging signals. 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 inference algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas in the brain, ideally including the uncertainty. In this detailed study, we present simulation-based inference for the virtual epileptic patient (SBI-VEP) model, which only requires forward simulations, enabling us to amortize posterior inference on parameters from low-dimensional data features representing whole-brain epileptic patterns. We use state-of-the-art deep learning algorithms for conditional density estimation to retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. This approach enables us to readily predict seizure dynamics from new input data. We show that the SBI-VEP is able to accurately estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones in the brain from the sparse observations of intracranial EEG signals. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for reliable prediction of neurological disorders from neuroimaging modalities, which can be crucial for planning intervention strategies.

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).

Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. In recent years a special focus was placed on the role of regional variance of model parameters for the emergent activity. Such analyses however depend on the properties of the employed neural mass model, which is often obtained through a series of major simplifications or analogies. Here we propose a data-driven approach where the neural mass model needs not to be specified. Building on the recent progresses in identification of dynamical systems with neural networks, we propose a method to infer from the functional data both the neural mass model representing the regional dynamics as well as the region- and subject-specific parameters, while respecting the known network structure. We demonstrate on two synthetic data sets that our method is able to recover the original model parameters, and that the trained generative model produces dynamics resembling the training data both on the regional level and on the whole-brain level. We further apply the method to resting-state fMRI data from Human Connectome Project. We find that to achieve best fit, the model needs two dimensional state space and three regional parameters, one of which is strongly correlated with the map of genetic expression in human brain. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications in understanding the changes of whole-brain dynamics during aging or in neurodegenerative diseases.

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.

A dry deposition model suitable for use in the moment method has been developed. Contributions from five main processes driving the deposition - Brownian diffusion, interception, impaction, turbulent impaction, and sedimentation - are included in the model. The deposition model was employed in the moment method solver implemented in the OpenFOAM framework. Applicability of the developed expression and the moment method solver was tested on two example problems of particle dispersion in the presence of a vegetation on small scales: a flow through a tree patch in 2D and a flow through a hedgerow in 3D. Comparison with the sectional method showed that the moment method using the developed deposition model is able to reproduce the shape of the particle size distribution well. The relative difference in terms of the third moment of the distribution was below 10\% in both tested cases, and decreased away from the vegetation. Main source of this difference is a known overprediction of the impaction efficiency. When tested on the 3D test case, the moment method achieved approximately eightfold acceleration compared to the sectional method using 41 bins.

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.

Atmospheric particulate matter (PM) is a well known risk to human health. Vehicular traffic is one of the major sources of particulates in an urban setting.We study a problem of road dust dispersion. Using CFD solver based on RANS equations, we investigate the effect of a vegetation barrier on the concentration of airborne PM induced by road traffic. Simplified 2D model of a porous obstacle adjacent to a road source of two classes of particles serves as an idealization of a real-world situation.Filtering efficiency of the barrier is investigated under varying atmospheric conditions. Our model indicate that the efficiency decreases for increasing wind speed. Effect of atmospheric stratification on~the~air quality behind the barrier is shown to be highly dependent on the wind speed.

Objectives Contemporary interpretations of the Epileptogenic Zone (EZ) are rooted in static attributions of pathological properties to brain tissue, resulting in complex interpretations of the dynamic discharge properties. We wish to critically consider alternative EZ interpretations based on traveling discharge patterns. Methods Most commonly observed patterns in intracranial electroencephalography (iEEG) recordings comprise fast oscillations (8–30 Hz) with gradually increasing amplitude. The activity of the folded sheet is observed through the local field potential recorded on the contacts of a depth electrode. We hypothesize that the gradual amplitude increase on the recorded signals is caused by the physical translation of the neural field and the subsequent spatial averaging of the source activity. We use The Virtual Brain platform to simulate the spread of the epileptic seizure on a piece of a cortical surface located in the vicinity of the intracranial electrode of interest. Results Simulated iEEG signals exhibit the typical feature of the seizure onset pattern – slowly increasing amplitude of the oscillations. The comparison of the recorded and simulated signals in terms of the timing of the apparent seizure onset and the duration of the period of amplitude increase show also quantitative consistency, demonstrating the plausibility of the mechanism on the realistic spatial and temporal scales. Conclusion We show that that field effects of traveling neural activity along the folded cortex may mimic onset patterns at distant iEEG electrodes, in particular those with increasing oscillatory amplitude. Notably, these effects can be observed in contacts distant from the true EZ, and under certain conditions even in absence of highly epileptogenic tissue. These results underscore the importance of the computational modeling in clinical neuroscience and suggest more dynamic interpretations of the concept of Epileptogenic Zone.

Selection of invited and contributed lectures from the ENUMATH 2015 conference organised by the Institute of Applied Mathematics (IAM), Middle East Technical University, Ankara, Turkey, from September 14 to 18, 201; International audience; In this study we deal with a problem of particulate matter dispersion modelling in a presence of a vegetation. We present a method to evaluate the efficiency of the barrier and to optimize its parameters. We use a CFD solver based on the RANS equations to model the air flow in a simplified 2D domain containing a vegetation block adjacent to a road, which serves as a source of the pollutant. Modelled physics captures the processes of a gravitational settling of the particles, dry deposition of the particles on the vegetation, turbulence generation by the road traffic and effect of the vegetation on the air flow. To optimize the effectivity of the barrier we employ a gradient based optimization process. The results show that the optimized variant relies mainly on the effect of increased turbulent diffusion by a sparse vegetation and less on the dry deposition of the pollutant on the vegetation.