Method and computing system for modelling a primate 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.

1 . A computing system for standardized modelling of a primate brain, the computing system comprising:
 a plurality of dynamical models of the brain;   an interface for including at least one neuroimaging modality input; and,   a decision engine for comparing results of the dynamical model to the at least one neuroimaging modality input.   
     
     
         2 . The system of  claim 1  wherein the dynamical model comprises a plurality of nodes,
 wherein at least a first subset of the plurality of nodes is connected to a second subset of the plurality of nodes based on an anatomical founded connectivity structure and 
 wherein at least one of the plurality of nodes includes a representation of a local dynamic model and a parameter set of the local dynamic model. 
 
     
     
         3 . The system of  claim 1  wherein the dynamical model is configured to incorporate an anatomically founded connectivity structure and information from the neuroimaging modality input. 
     
     
         4 . The system of  claim 1  wherein the decision engine comprises a forward transformation model for mapping activity of the plurality of nodes into a space corresponding to the neuroimaging modality input. 
     
     
         5 . The system of  claim 1  wherein the decision engine comprises an entity for fitting at least one parameter set of the at least one of the plurality of nodes by comparing an output of the dynamical model of the brain and the imaging modality input in a space corresponding to the neuroimaging modality input. 
     
     
         6 . The system of  claim 2  wherein the decision engine comprises an inverse model for transformation of the imaging modality input into a source space corresponding to the plurality of nodes. 
     
     
         7 . The system of  claim 5  wherein the entity for fitting the at least one parameter set is configured to simulate an output of at least a first mapping subset of the plurality of nodes by a mapped neuroimaging modality input, which is transformed into source space, and to fit the parameter set of at least one of the plurality of nodes, the at least one node not belonging to the first mapping subset. 
     
     
         8 . The system of  claim 5 , wherein the entity for fitting the at least one parameter set of the at least one of the plurality of nodes is configured to fit the at least one parameter set based on at least one time segment of the neuroimaging modality input. 
     
     
         9 . The system of  claim 1  including neuroimaging modality input from at least two different neuroimaging methods. 
     
     
         10 . A computing system for modelling a human brain, the computing system comprising:
 a database including a plurality of datasets, each dataset including:
 at least a dynamical model of the brain including at least one node including a representation of a local dynamic model and a parameter set of the local dynamic model; 
 a set of values for the parameter set of the local dynamic model; and 
 a neurodataset of a neuroimaging modality input, wherein the set of values represents a fit between the dataset and the parameter set in a predetermined space. 
   
     
     
         11 . The computing system of  claim 10  wherein the computing system further comprises a search entity configured to receive input of a property of a neurodataset as a search request and to forward an output of the plurality of datasets, the output including one or more parameter sets and the corresponding representation of a local dynamic model, the neurodataset corresponding to the one or more parameter sets matching the search request. 
     
     
         12 . The computing system of  claim 11  wherein the search request includes signal properties of the neurodatasets and the search entity is configured to analyze the neurodatasets of the database using signal processing methods to match neurodatasets to the signal properties of the search request. 
     
     
         13 . The computing system of any of  claim 10  wherein the neurodatasets include at least one time snippet or epoch of experimental data. 
     
     
         14 . The computing system of any of  claim 10  wherein a subunit of the dynamical model includes a plurality of nodes, the plurality of nodes connected to each other based on an anatomically founded connectivity structure and each node includes a representation of a local dynamic model and a parameter set of the local dynamic model. 
     
     
         15 . The computing system of  claim 14  wherein at least one dataset of the database further includes information of at least one of the plurality of nodes, the parameter set of which is fit to the neurodataset. 
     
     
         16 . The computing system of any of  claim 10  wherein the predetermined space includes at least one of a space corresponding to the neuroimaging modality input and a source space corresponding to the at least one node. 
     
     
         17 . A computing system for distributing and receiving data on primate brain modelling, the computing system comprising:
 a back-end system configured to connect to a web interface and configured for accessing neuroimaging modality input data, the back-end system including a simulation component with a simulation core and a simulation controller, wherein the simulation core is configured to receive a set of parameters and configured to retrieve neuroimaging modality input data; and the simulation controller is configured to control a work-flow of the simulation core; and   a storage system configured for storing:
 at least one neurodataset of neuroimaging modality input data; 
 a plurality of dynamical models, each dynamical model including a plurality of nodes; 
 anatomical connectivity data; 
 a plurality of local dynamic models representing a node; 
 at least one forward transformation model for mapping an activity of the plurality of nodes into a space corresponding to the neuroimaging modality input; 
 at least one inverse transformation model for mapping the imaging modality input into a source space corresponding to the plurality of nodes; and 
 a decision engine including at least one entity for fitting at least one node of the plurality of nodes to the at least one neurodataset. 
   
     
     
         18 . A computing system for standardized modelling of a primate brain by a dynamical model, which comprises a plurality of nodes,
 wherein at least a first subset of the plurality of nodes is connected to a second subset of the plurality of nodes based on an anatomical founded connectivity structure and/or functional connectivity data and   wherein at least one of the plurality of nodes includes a representation of a local dynamic model and a parameter set of the local dynamic model.