Abstract: In one aspect the application relates to a computing system for providing data for modelling a human brain comprises a database including a plurality of datasets (or allow access to a plurality of datasets), each dataset including at least a dynamical model of the brain including at least one node and a neurodataset of a neuroimaging modality input. The at least one node include a representation of a local dynamic model and a parameter set of the local dynamic model.

Abstract: The method for inferring epileptogenicity of a brain region not observed as recruited or not observed as not recruited, in a seizure activity of an epileptic patient brain, includes: providing a computerized model modelling various regions of a primate brain and connectivity between the regions; providing the computerized model with a model able to reproduce an epileptic seizure dynamic in the primate brain; providing structural data of the epileptic patient brain and personalizing the computerized model using the structural data to obtain a virtual epileptic patient (VEP) brain model; translating a state-space representation of the VEP brain model into a probabilistic programming language (PPL) using probabilistic state transitions to obtain a probabilistic virtual epileptic patient brain model (BVEP); and acquiring electro- or magneto-encephalographic data of the patient brain and fitting the probabilistic VEP brain model against the data to infer the epileptogenicity of the brain region that is not observe

Abstract: The method of optimizing an intracranial implantation scheme of a set of electroencephalographic electrodes for identification of an epileptogenic zone in an epileptic patient’s brain includes: - providing a model of the epileptogenic zone and a model of the propagation of an epileptic discharge from the epileptic zone to a propagation zone, and loading the models in a computerized platform personalized according to the patient’s brain; - providing at least one hypothesis of the localization of the epileptogenic zone in the patient brain; - for the at least hypothesis of the localization of the epileptogenic zone, simulating, in the personalized computerized patient’s brain, epileptic seizures, and determining, for the hypothesis and epileptic seizures, a network of propagation zones; - for a plurality of intracranial implantation schemes of the electroencephalographic electrodes, obtaining, using the personalized computerized patient’s brain, simulated electroencephalographic signals activity to be measured

Abstract: In one aspect the application relates to a computing system for providing data for modelling a human brain comprises a database including a plurality of datasets (or allow access to a plurality of datasets), each dataset including at least a dynamical model of the brain including at least one node and a neurodataset of a neuroimaging modality input. The at least one node include a representation of a local dynamic model and a parameter set of the local dynamic model.

Abstract: The method for determining an onset time and an excitability of a brain region that is not observed as recruited or not recruited in a seizure activity of an epileptic patient brain includes: providing a dynamical model of a propagation of an epileptic seizure in the brain networks; providing a statistical model which defines the probability of generating sets of observations of the state of the brain networks by said dynamical model; training the dynamical model of the propagation of an epileptic seizure using the statistical model and the data set of observations of the training cohort; and inverting the trained dynamical model and inferring the onset time and excitability of a third region from the onset time that is observed for the first and second regions using the statistical model.

Abstract: The method of identifying a potentially surgically operable target zone in an epileptic patient's brain includes: providing a computerized platform modelling various zones of a primate brain and connectivity between said zones; providing a model of an epileptogenic zone and a model of the propagation of an epileptic discharge from an epileptic zone to a propagation zone; obtaining a patient's personalized computerized platform; deriving the potential target zones based on modularity analysis; evaluating the target zones' effectiveness by simulating epileptic seizures propagation in the personalized patient's computerized platform; evaluating the target zones' safety by simulating spatiotemporal brain activation patterns in a defined state condition and comparing the simulated spatiotemporal brain activation patterns obtained before removal of the target zone with the spatiotemporal brain activation patterns obtained after removal of the target zone; identifying the target zones which satisfy both effectivenes

Abstract: The invention relates to modulating epileptogenicity in a brain of an epileptic patient. The method according to the invention comprises the steps of: providing a virtual brain; providing a model of an epileptogenic and of a propagation zones and loading said models in the virtual brain to create a virtual epileptic brain; acquiring data of the brain of the epileptic patient; identifying, in said data, a location of at least one possible epileptogenic zone; fitting the virtual epileptic brain against the data acquired from the epileptic patient and parametrizing said at least one possible epileptogenic zone in the virtual epileptic brain as an epileptogenic zone; and simulating, within the virtual epileptic brain, the effect of a network modulation mimicking a clinical intervention of the brain of the patient.

Abstract: The method of modulating epileptogenicity in a brain of an epileptic patient includes: providing a virtual brain; providing a model of an epileptogenic and of a propagation zones and loading the model in the virtual brain to create a virtual epileptic brain; acquiring data of the brain of the epileptic patient; identifying, in the data, a location of at least one possible epileptogenic zone; fitting the virtual epileptic brain against the data acquired from the epileptic patient and parametrizing the at least one possible epileptogenic zone in the virtual epileptic brain as an epileptogenic zone; and simulating, within the virtual epileptic brain, the effect of a network modulation mimicking a clinical intervention of the brain of the patient.

Abstract: The invention relates to modulating epileptogenicity in a brain of an epileptic patient. The method according to the invention comprises the steps of: providing a virtual brain; providing a model of an epileptogenic and of a propagation zones and loading said models in the virtual brain to create a virtual epileptic brain; acquiring data of the brain of the epileptic patient; identifying, in said data, a location of at least one possible epileptogenic zone; fitting the virtual epileptic brain against the data acquired from the epileptic patient and parametrizing said at least one possible epileptogenic zone in the virtual epileptic brain as an epileptogenic zone; and simulating, within the virtual epileptic brain, the effect of a network modulation mimicking a clinical intervention of the brain of the patient.

Abstract: In one aspect the application relates to a computing system for providing data for modelling a human brain comprises a database including a plurality of datasets (or allow access to a plurality of datasets), each dataset including at least a dynamical model of the brain including at least one node and a neurodataset of a neuroimaging modality input. The at least one node include a representation of a local dynamic model and a parameter set of the local dynamic model.

Abstract: The invention relates to a method for estimating a location of an epileptogenic zone of a mammalian brain, a system for performing a method for estimating a location of an epileptogenic zone of a mammalian brain as well as a computer-readable medium including a set of instructions for estimating a location of an epileptogenic zone of a mammalian brain.

Abstract: In one aspect the application relates to a computing system for providing data for modelling a human brain comprises a database including a plurality of datasets (or allow access to a plurality of datasets), each dataset including at least a dynamical model of the brain including at least one node and a neurodataset of a neuroimaging modality input. The at least one node include a representation of a local dynamic model and a parameter set of the local dynamic model.