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|Title:||Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex||Authors:||Yeo, B.T.T.
Human Connectome Project
|Issue Date:||Feb-2014||Citation:||Yeo, B.T.T., Krienen, F.M., Chee, M.W.L., Buckner, R.L. (2014-02). Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex. NeuroImage 88 : 212-227. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neuroimage.2013.10.046||Abstract:||The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP. © 2013 Elsevier Inc.||Source Title:||NeuroImage||URI:||http://scholarbank.nus.edu.sg/handle/10635/110061||ISSN:||10538119||DOI:||10.1016/j.neuroimage.2013.10.046|
|Appears in Collections:||Staff Publications|
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