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, Volume 77, Issue 17, pp 22689–22704 | Cite as

Research on node properties of resting-state brain functional networks by using node activity and ALFF

  • Zhuqing Jiao
  • Kai Ma
  • Huan Wang
  • Ling Zou
  • Yudong Zhang
Article

Abstract

Human brain functional networks have some attractive topological properties in anatomical space, whereas relatively few literatures to discuss the local properties of brain networks. In this paper, a method for judging nodes properties of resting-state brain functional networks is proposed based on node activity and Amplitude of Low Frequency Fluctuation (ALFF). We utilized it to research the active degree of brain regions. Firstly, functional Magnetic Resonance Imaging (fMRI) data are employed to construct the resting-state brain functional network, and calculate node degree, clustering coefficient and average distance. Then, by comparing the differences in the above indexes between stroke patients and normal subjects, we further analyzed the distribution of active degree in various brain regions and their connection states through node activity of brain functional networks. Finally, the ALFF values of normal subjects and patients are measured respectively in contrast experiment, and the activity of the related nodes was compared and judged. The node activities of some brain regions in stroke patients are lower than that of normal subjects and even zero, and the ALFF values of the normal are generally higher than those of the stroke patients. The experimental results verify the feasibility of node activity in judging active degree of various brain regions from physiological significance of ALFF in resting-state brain functional networks.

Keywords

Brain functional networks Resting-state Functional magnetic resonance imaging (fMRI) Node activity Complex networks 

Notes

Acknowledgements

The authors would like to thank the reviewers and the editors for their valuable comments and suggestions on improving this paper. This work is supported by the National Natural Science Foundation of China (Nos. 51307010 and 61602250) and the University Natural Science Research Program of Jiangsu Province (No. 17KJB510003).

Authors’ contributions

ZJ and KM conceived of the study and contributed to the manuscript. HW contributed to the resting-state functional networks analysis. LZ and YZ contributed to the fMRI experimental design and analysis. All authors read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

We have no conflicts of interest to disclose with regard to the subject matter of this paper.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Information Science and EngineeringChangzhou UniversityChangzhouChina
  2. 2.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  3. 3.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina

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