Abstract：In our research, we proposed a new method to measure functional magnetic resonance imaging (fMRI) signal complexity adopt fuzzy approximate entropy (fApEn) and compare it with sample entropy (SampEn). Here we collect resting state fMRI data of 22 major depressive disorder (MDD) (11 males; age: 18-65). We expect the complexity of the resting state fMRI signals measured to be consistent with the Goldberger/Lipsitz model for robustness where healthier (younger) and more robust systems exhibit more complexity in their physiological output and system complexity decrease with age. The mean whole brain fApEn demonstrated significant negative correlation (r = -0.512, p<0.001) with age. In comparison, SampEn produced a non-significant negative correlation (r = -0.102, p = 0.412). fApEn also demonstrated a significant (p < 0.05) negative correlation with age regionally (frontal, parietal, limbic, temporal and cerebellum parietal lobes). There was no significant correlation regionally between the SampEn maps and age. These results support the Goldberger/Lipsitz model for robustness and have shown that fApEn is potentially a sensitive new method for the complexity analysis of fMRI data.