Research on node properties of resting-state brain functional networks by using node activity and ALFF
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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.
KeywordsBrain functional networks Resting-state Functional magnetic resonance imaging (fMRI) Node activity Complex networks
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).
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.
- 8.Jiao ZQ, Ma K, Wang H, Zou L, Xiang JB (2017) Functional connectivity analysis of brain default mode networks using Hamiltonian path. CNS & Neurological Disorders - drug. Targets 16(1):44–50Google Scholar
- 11.Li W, Li YP, Zhu WZ, Chen X (2012) Changes in brain network after stroke. Chin J Biomed Eng 31(3):344–348Google Scholar
- 14.Liu Y, Zhang XY, Cui JS, Wu C, Aghajan H, Zha HB (2010) Visual analysis of child-adult interactive behaviors in video sequences. 16th Int Conf Virtual Syst Multimed 2010:26–33Google Scholar
- 15.Liu Y, Cui JS, Zhao HJ, Zha HB (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. 21st Int Conf Pattern Recog 2012:898–901Google Scholar
- 17.Liu Y, Nie LQ, Han L, Zhang L, Rosenblum DS (2016) Action2Activity: recognizing complex activities from sensor data. Int Conf Artif Intell 2016:1617–1623Google Scholar
- 18.Liu L, Cheng L, Liu Y, Jia YP (2016) Rosenblum DS (2016) recognizing complex activities by a probabilistic interval-based model. Proc Thirtieth AAAI Conf Artif Intell:1266–1272Google Scholar
- 19.Luo YM, Li BL, Liu J (2015) Amplitude of low-frequency fluctuations in happiness: a resting-state fMRI study. Chinese. Journal 60(2):170–178Google Scholar
- 24.Smith R, Baxter LC, Thaye JF, Lane RD (2016) Disentangling introspective and exteroceptive attentional control from emotional appraisal in depression using fMRI: a preliminary study. Psychiatry Res Neuroimaging 248(2):431–455Google Scholar
- 27.Wang SH, SD D, Atangana A, Liu AJ, ZY L (2016) Application of stationary wavelet entropy in pathological brain detection. Multimed Tools Appl:1–14. https://doi.org/10.1007/s11042-016-3401-7
- 36.Zhang YD, Dong ZC, Phillips P, Wang SH, Ji GL, Yang JQ, Yuan TF (2015) Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning. Front Comput Neurosci 9:66Google Scholar
- 39.Zhang YD, Yang JQ, Yang JF, Liu AJ, Sun P (2016) A novel compressed sensing method for magnetic resonance imaging: exponential wavelet iterative shrinkage-thresholding algorithm with random shift. Int J Biomed Imaging 2016(3):1–10Google Scholar