In this paper, we proposed two novel non-linear analysis methods including cross-sample entropy of ordinal pattern and inner composition alignment (IOTA) of ordinal pattern to construct brain network based on functional magnetic resonance imaging. Group-level statistical comparisons were performed to investigate the differences of brain networks. The results showed that the network related to hippocampus, amygdala and posterior cingulate cortexin in mild cognitive impairment (MCI) participants significantly differ from in normal controls. Our results suggest that both the non-linear methods can be applied to estimate the characteristics of brain network in MCI.
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