@inproceedings{5e4662fa2e8d43c4a056da30a2305720,
title = "Complexity analysis of resting state fMRI signals in depressive patients",
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.",
author = "Ho, {Pei Shan} and Chemin Lin and Chen, {Guan Yen} and Liu, {Ho Ling} and Chih-Mao Huang and Lee, {Tatia Mei Chun} and Lee, {Shwu Hua} and Wu, {Shun Chi}",
year = "2017",
month = sep,
day = "13",
doi = "10.1109/EMBC.2017.8037535",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3190--3193",
booktitle = "2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society",
note = "null ; Conference date: 11-07-2017 Through 15-07-2017",
}