학술논문

Complexity analysis of resting state fMRI signals in depressive patients
Document Type
Conference
Source
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE. :3190-3193 Jul, 2017
Subject
Bioengineering
Complexity theory
Time series analysis
Entropy
Magnetic resonance imaging
Mental disorders
Standards
Hospitals
Language
ISSN
1557-170X
1558-4615
Abstract
Analysis of brain signal complexity reveals the intrinsic network dynamics and is widely utilized in the investigation of mechanisms in mental disorders. In this study, the complexity of resting-state functional magnetic resonance imaging (fMRI) signals was explored in patients with depression using multiscale entropy (MSE). Thirty-five patients diagnosed with depression and 22 age- and gender-matched healthy controls were considered. The MSE profiles in five brain networks of the two participant groups were evaluated and analyzed. The results showed that depressive patients exhibited higher complexity in the left frontoparietal network than that seen in healthy controls, which is known to be critical for executive control functions. Through this study, the efficacy of MSE in identifying and understanding the mental disorders was also demonstrated.