Mixed scale joint graphical lasso.
We have developed 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.
KU Leuven, ORSTAT and Leuven Statistics Research Center, Naamsestraat 69, 3000 Leuven, Belgium, gerda.claeskens@kuleuven.be.
Eugen
E
Pircalabelu
KU Leuven, ORSTAT and Leuven Statistics Research Center, Naamsestraat 69, 3000 Leuven, Belgium, gerda.claeskens@kuleuven.be.
Gerda
G
Claeskens
University of Amsterdam, Department of Psychological Methods,Weesperplein 4,1018 Amsterdam, The Netherlands.
Lourens J
LJ
Waldorp
D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D009415:Nerve Net / Q000502:physiology*
Fused and group lasso; Joint graphical lasso; Mixed scale data; Sparsistency; l1 penalization