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- aggregation classification "A1".
- aggregation creator B814790.
- aggregation creator B814791.
- aggregation creator B814792.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2013".
- aggregation format "application/pdf".
- aggregation hasFormat 4131493.bibtex.
- aggregation hasFormat 4131493.csv.
- aggregation hasFormat 4131493.dc.
- aggregation hasFormat 4131493.didl.
- aggregation hasFormat 4131493.doc.
- aggregation hasFormat 4131493.json.
- aggregation hasFormat 4131493.mets.
- aggregation hasFormat 4131493.mods.
- aggregation hasFormat 4131493.rdf.
- aggregation hasFormat 4131493.ris.
- aggregation hasFormat 4131493.txt.
- aggregation hasFormat 4131493.xls.
- aggregation hasFormat 4131493.yaml.
- aggregation isPartOf urn:issn:1932-6203.
- aggregation language "eng".
- aggregation rights "I have retained and own the full copyright for this publication".
- aggregation subject "Medicine and Health Sciences".
- aggregation title "Mapping the voxel-wise effective connectome in resting state fMRI".
- aggregation abstract "A network approach to brain and dynamics opens new perspectives towards understanding of its function. The functional connectivity from functional MRI recordings in humans is widely explored at large scale, and recently also at the voxel level. The networks of dynamical directed connections are far less investigated, in particular at the voxel level. To reconstruct full brain effective connectivity network and study its topological organization, we present a novel approach to multivariate Granger causality which integrates information theory and the architecture of the dynamical network to efficiently select a limited number of variables. The proposed method aggregates conditional information sets according to community organization, allowing to perform Granger causality analysis avoiding redundancy and overfitting even for high-dimensional and short datasets, such as time series from individual voxels in fMRI. We for the first time depicted the voxel-wise hubs of incoming and outgoing information, called Granger causality density (GCD), as a complement to previous repertoire of functional and anatomical connectomes. Analogies with these networks have been presented in most part of default mode network; while differences suggested differences in the specific measure of centrality. Our findings could open the way to a new description of global organization and information influence of brain function. With this approach is thus feasible to study the architecture of directed networks at the voxel level and individuating hubs by investigation of degree, betweenness and clustering coefficient.".
- aggregation authorList BK1187627.
- aggregation issue "9".
- aggregation volume "8".
- aggregation aggregates 4131779.
- aggregation isDescribedBy 4131493.
- aggregation similarTo journal.pone.0073670.
- aggregation similarTo LU-4131493.