We develop a method for estimating brain networks from fMRI datasets that have not all been measured using the same set of brain regions. Some of the coarse scale regions have been split in smaller subregions. The proposed penalized estimation procedure selects undirected graphical models with similar structures that combine information from several subjects and several coarseness scales. Both within scale edges and between scale edges that identify possible connections between a large region and its subregions are estimated.status: publishe
© 2016 NIPS Foundation - All Rights Reserved. Functional brain networks are well described and estim...
Thesis (Ph.D.)--University of Washington, 2019The main purpose of this thesis is to develop statisti...
Methods based on the use of multivariate autoregressive modelling (MVAR) have proved to be an accura...
We develop a method for estimating brain networks from fMRI datasets that have not all been measured...
We develop a method for estimating brain networks from fMRI datasets that have not all been measured...
We have developed a method for estimating brain networks from fMRI datasets that have not all been m...
We develop a method for estimating brain networks from fMRI datasets that have not all been measured...
We develop a method for estimating brain networks from fMRI datasets that have not all been measured...
We develop a new method to estimate simultaneously multiple graphs and apply it to fMRI data. The me...
A new method is proposed to simultaneously estimate graphical models from data obtained at different...
International audienceFunctional brain networks are well described and estimated from data with Gaus...
International audienceFunctional brain networks are well described and estimated from data with Gaus...
International audienceFunctional brain networks are well described and estimated from data with Gaus...
International audienceFunctional brain networks are well described and estimated from data with Gaus...
International audienceFunctional brain networks are well described and estimated from data with Gaus...
© 2016 NIPS Foundation - All Rights Reserved. Functional brain networks are well described and estim...
Thesis (Ph.D.)--University of Washington, 2019The main purpose of this thesis is to develop statisti...
Methods based on the use of multivariate autoregressive modelling (MVAR) have proved to be an accura...
We develop a method for estimating brain networks from fMRI datasets that have not all been measured...
We develop a method for estimating brain networks from fMRI datasets that have not all been measured...
We have developed a method for estimating brain networks from fMRI datasets that have not all been m...
We develop a method for estimating brain networks from fMRI datasets that have not all been measured...
We develop a method for estimating brain networks from fMRI datasets that have not all been measured...
We develop a new method to estimate simultaneously multiple graphs and apply it to fMRI data. The me...
A new method is proposed to simultaneously estimate graphical models from data obtained at different...
International audienceFunctional brain networks are well described and estimated from data with Gaus...
International audienceFunctional brain networks are well described and estimated from data with Gaus...
International audienceFunctional brain networks are well described and estimated from data with Gaus...
International audienceFunctional brain networks are well described and estimated from data with Gaus...
International audienceFunctional brain networks are well described and estimated from data with Gaus...
© 2016 NIPS Foundation - All Rights Reserved. Functional brain networks are well described and estim...
Thesis (Ph.D.)--University of Washington, 2019The main purpose of this thesis is to develop statisti...
Methods based on the use of multivariate autoregressive modelling (MVAR) have proved to be an accura...