Dynamic Default Mode Network across Different Brain States

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Dynamic Default Mode Network across Different Brain States

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Title: Dynamic Default Mode Network across Different Brain States
Author: Lin, Pan; Yang, Yong; Gao, Junfeng; De Pisapia, Nicola; Ge, Sheng; Wang, Xiang; Zuo, Chun S.; Jonathan Levitt, James; Niu, Chen

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Citation: Lin, Pan, Yong Yang, Junfeng Gao, Nicola De Pisapia, Sheng Ge, Xiang Wang, Chun S. Zuo, James Jonathan Levitt, and Chen Niu. 2017. “Dynamic Default Mode Network across Different Brain States.” Scientific Reports 7 (1): 46088. doi:10.1038/srep46088. http://dx.doi.org/10.1038/srep46088.
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Abstract: The default mode network (DMN) is a complex dynamic network that is critical for understanding cognitive function. However, whether dynamic topological reconfiguration of the DMN occurs across different brain states, and whether this potential reorganization is associated with prior learning or experience is unclear. To better understand the temporally changing topology of the DMN, we investigated both nodal and global dynamic DMN-topology metrics across different brain states. We found that DMN topology changes over time and those different patterns are associated with different brain states. Further, the nodal and global topological organization can be rebuilt by different brain states. These results indicate that the post-task, resting-state topology of the brain network is dynamically altered as a function of immediately prior cognitive experience, and that these modulated networks are assembled in the subsequent state. Together, these findings suggest that the changing topology of the DMN may play an important role in characterizing brain states.
Published Version: doi:10.1038/srep46088
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5382672/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:32630501
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