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Static and dynamic posterior cingulate cortex nodal topology of default mode network predicts attention task performance

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Abstract

Characterization of the default mode network (DMN) as a complex network of functionally interacting dynamic systems has received great interest for the study of DMN neural mechanisms. In particular, understanding the relationship of intrinsic resting-state DMN brain network with cognitive behaviors is an important issue in healthy cognition and mental disorders. However, it is still unclear how DMN functional connectivity links to cognitive behaviors during resting-state. In this study, we hypothesize that static and dynamic DMN nodal topology is associated with upcoming cognitive task performance. We used graph theory analysis in order to understand better the relationship between the DMN functional connectivity and cognitive behavior during resting-state and task performance. Nodal degree of the DMN was calculated as a metric of network topology. We found that the static and dynamic posterior cingulate cortex (PCC) nodal degree within the DMN was associated with task performance (Reaction Time). Our results show that the core node PCC nodal degree within the DMN was significantly correlated with reaction time, which suggests that the PCC plays a key role in supporting cognitive function.

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Acknowledgments

Government of the Provincia Autonoma di Trento, Italy, Project PAT Post-doc 2006; Fondazione Cassa di Risparmio di Trento e Rovereto; and University of Trento, Italy. This work was supported by the National Natural Science Foundation of China (61473221, 61462031, 61262034, 31271061), by Doctoral Fund of Ministry of Education of China (20120201120071), by the Young Scientist Foundation of Jiangxi Province (20122BCB23017), by the Project of the Education Department of Jiangxi Province (KJLD14031), by the Fundamental Research Funds for the Central Universities of China, by the Program for New Century Excellent Talents in University of China (NCET-12-0557 to XW).

Competing interests

Pan Lin, Yong Yang, Jorge Jovicich, Nicola De Pisapia, Xiang Wang, Chun S. Zuo, and James Jonathan Levitt declare that they have no conflicts of interest.

Informed consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, and the applicable revisions at the time of the investigation. Informed consent was obtained from all patients for being included in the study.

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Authors and Affiliations

  1. Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Pan Lin

  2. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, People’s Republic of China

    Yong Yang

  3. Center for Mind/Brain Sciences, University of Trento, Mattarello, 38100, Italy

    Pan Lin, Jorge Jovicich & Nicola De Pisapia

  4. Department of Cognitive and Education Sciences, University of Trento, Rovereto, 38068, Italy

    Jorge Jovicich

  5. Medical Psychological Institute of Second Xiangya Hospital, Central South University, Changsha, China

    Xiang Wang

  6. Brain Imaging Center, McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA

    Pan Lin & Chun S. Zuo

  7. Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, VA, Boston Healthcare System, Brockton Division, Harvard Medical School, Boston, MA, 02301, USA

    James Jonathan Levitt

  8. Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, 02215, USA

    James Jonathan Levitt

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  1. Pan Lin
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Lin, P., Yang, Y., Jovicich, J. et al. Static and dynamic posterior cingulate cortex nodal topology of default mode network predicts attention task performance. Brain Imaging and Behavior 10, 212–225 (2016). https://doi.org/10.1007/s11682-015-9384-6

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