Dynamic Default Mode Network across Different Brain States

被引：0
作者：
Pan Lin
Yong Yang
Junfeng Gao
Nicola De Pisapia
Sheng Ge
Xiang Wang
Chun S. Zuo
James Jonathan Levitt
Chen Niu
机构：
[1] College of Biomedical Engineering,Department of Psychiatry
[2] South-Central University for Nationalities,Clinical Neuroscience Division, Department of Psychiatry, Brockton Division
[3] School of Information Technology,Department of Psychiatry
[4] Jiangxi University of Finance and Economics,Department of Medical Imaging
[5] Key Laboratory of Biomedical Information Engineering of Education Ministry,undefined
[6] Institute of Biomedical Engineering,undefined
[7] Xi’an Jiaotong University,undefined
[8] Center for Mind/Brain Sciences,undefined
[9] University of Trento,undefined
[10] Key Laboratory of Child Development and Leaning Science of Ministry of Education,undefined
[11] Research Center for Learning Science,undefined
[12] Southeast University,undefined
[13] Medical Psychological Institute of Second Xiangya Hospital,undefined
[14] Central South University,undefined
[15] Brain Imaging Center,undefined
[16] McLean Hospital,undefined
[17] Harvard Medical School,undefined
[18] Laboratory of Neuroscience,undefined
[19] VA,undefined
[20] Boston Healthcare System,undefined
[21] and Harvard Medical School,undefined
[22] Psychiatry Neuroimaging Laboratory,undefined
[23] Brigham & Women’s Hospital,undefined
[24] Harvard Medical School,undefined
[25] First Affiliated Hospital of Xi’an Jiaotong University College of Medicine,undefined
来源：
Scientific Reports | / 7卷
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摘要：
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.
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