Dynamic complexity analysis of individual brain functional networks over long-term time scales
NI Huangjing1, QIN Jiaolong2*
(1 Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu, China;2 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China)
Abstract:
The dynamic complexity of human brain functional networks on a long-term time scale from day to month to year are investigated. Based on the unique longitudinal resting-state functional magnetic resonance imaging dataset scanned on a single subject for nearly 100 times within 18 months, permutation entropy is adopted to quantitatively analyze the dynamic complexity of whole brain functional networks. The results show that the complexity of all functional networks will fluctuate dynamically with the scanning time, but their fluctuation ranges are relatively limited. Meanwhile,it can be found that the performances of different functional networks are different. The cerebellar and subcortical networks demonstrate the highest complexity, while the fronto-parietal network shows the lowest.
KeyWords:
brain functional network; long-term time scale; dynamic; complexity