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Dynamic Bayesian Networks for multimodal brain imaging data

Grant number: 16/02621-0
Support type:Scholarships abroad - Research Internship - Doctorate (Direct)
Effective date (Start): May 01, 2016
Effective date (End): October 31, 2016
Field of knowledge:Engineering - Electrical Engineering
Principal Investigator:Carlos Dias Maciel
Grantee:Fernando Pasquini Santos
Supervisor abroad: Tamer S. Ibrahim
Home Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Local de pesquisa : University of Pittsburgh (Pitt), United States  
Associated to the scholarship:12/24272-7 - Structure learning of non-stationary dynamic Bayesian networks, BP.DD


Modeling effective connectivity in the brain is always a task limited by the size of the model intended and data required for training. In recent years, however, advances have been achieved with MRI equipment able to obtain images with better signal to noise ratio, tissue contrast and spatial/spectral resolution, such as the 7 tesla MRI equipment at the University of Pittsburgh. Furthermore, many effective connectivity studies started to employ multimodal techniques, integrating knowledge and data from many different types of images; as for example, the use of DTI helping fMRI analysis. Therefore, in the present project, Dynamic Bayesian Networks (DBNs) with higher dimensions will be learned given these more abundant data and multimodal techniques obtained at the radiology facility at the University of Pittsburgh, which will on their turn provide new findings about the human brain.