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• W3005726610 abstract "Coordinated brain activity reflects underlying cognitive processes and can be modeled as a network of inter-regional functional connections. The most costly connections in the network are long-distance correlations that, in the absence of underlying structural connections, are maintained by sustained energetic inputs. Here, we present a spatial modeling approach that amplifies contributions made by long-distance functional connections to whole-brain network architecture, while simultaneously suppressing contributions made by short-range connections. We use this method to characterize the long-distance architecture of functional networks and to identify aspects of community and hub structure that are driven by long-distance correlations and that, we argue, are of greater functional significance. We find that based only on patterns of long-distance connectivity, primary sensory cortices occupy increasingly central positions and appear more “hub-like”. Additionally, we show that the community structure of long-distance connections spans multiple topological levels and differs from the community structure detected in networks that include both short-range and long-distance connections. In summary, these findings highlight the complex relationship between the brain’s physical layout and its functional architecture. The results presented here inform future analyses of community structure and network hubs in health, across development, and in the case of neuropsychiatric disorders." @default.
• W3005726610 created "2020-02-24" @default.
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• W3005726610 date "2020-05-01" @default.
• W3005726610 modified "2023-10-16" @default.
• W3005726610 title "Space-independent community and hub structure of functional brain networks" @default.
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• W3005726610 doi "https://doi.org/10.1016/j.neuroimage.2020.116612" @default.
• W3005726610 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7104557" @default.
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