期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 111, 期 2, 页码 833-838出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1315529111
关键词
connectome; graph theory; network theory; brain connectivity
资金
- J.S. McDonnell Foundation
- National Science Foundation/Integrative Graduate Education and Research Traineeship Training Program in the Dynamics of Brain-Body-Environment Systems at Indiana University
- Netherlands Organization for Scientific Research [VENI-451-12-001]
- Brain Center Rudolf Magnus
- Swiss National Science Foundation [320030-130090]
- Leenaards Foundation
- Austrian Fonds zur Forderung der Wissenschaftlichen Forschung Project Quantifying Socioeconomic Multiplex Networks in a Massive Multiplayer Online Game [KPP23378FW]
- Austrian Science Fund (FWF) [P 23378] Funding Source: researchfish
- Swiss National Science Foundation (SNF) [320030_130090] Funding Source: Swiss National Science Foundation (SNF)
6 The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures-search information and path transitivity-which account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways.
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