Complexity analysis of resting state fMRI signals in depressive patients

Pei Shan Ho; Chemin Lin; Guan Yen Chen; Ho Ling Liu; Chih Mao Huang; Tatia Mei Chun Lee; Shwu Hua Lee; Shun Chi Wu

doi:10.1109/EMBC.2017.8037535

Complexity analysis of resting state fMRI signals in depressive patients

Pei Shan Ho
, Chemin Lin
, Guan Yen Chen
, Ho Ling Liu
, Chih Mao Huang
, Tatia Mei Chun Lee
, Shwu Hua Lee
, Shun Chi Wu

National Tsing Hua University
Chang Gung Memorial Hospital
University of Texas MD Anderson Cancer Center
National Yang Ming Chiao Tung University
The University of Hong Kong

研究成果: 圖書/報告稿件的類型 › 會議稿件 › 同行評審

9 引文斯高帕斯（Scopus）

摘要

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.

原文	英語
主出版物標題	2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
主出版物子標題	Smarter Technology for a Healthier World, EMBC 2017 - Proceedings
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	3190-3193
頁數	4
ISBN（電子）	9781509028092
DOIs	https://doi.org/10.1109/EMBC.2017.8037535
出版狀態	已出版 - 13 09 2017
對外發佈	是
事件	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, 韓國持續時間: 11 07 2017 → 15 07 2017

出版系列

名字	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN（列印）	1557-170X

Conference

Conference	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
國家/地區	韓國
城市	Jeju Island
期間	11/07/17 → 15/07/17

文獻附註

Publisher Copyright:
© 2017 IEEE.

存取文件

10.1109/EMBC.2017.8037535

其它文件與鏈接

Link to publication in Scopus

引用此

Ho, P. S., Lin, C., Chen, G. Y., Liu, H. L., Huang, C. M., Lee, T. M. C., Lee, S. H., & Wu, S. C. (2017). Complexity analysis of resting state fMRI signals in depressive patients. 於 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings (頁 3190-3193). 文章 8037535 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2017.8037535

Ho, Pei Shan ; Lin, Chemin ; Chen, Guan Yen 等. / Complexity analysis of resting state fMRI signals in depressive patients. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 頁 3190-3193 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{7b58f45ba33f46d69533ed35784cd3ab,

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 Huang, \{Chih Mao\} and Lee, \{Tatia Mei Chun\} and Lee, \{Shwu Hua\} and Wu, \{Shun Chi\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 ; Conference date: 11-07-2017 Through 15-07-2017",

year = "2017",

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doi = "10.1109/EMBC.2017.8037535",

language = "英语",

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",

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address = "美国",

}

Ho, PS, Lin, C, Chen, GY, Liu, HL, Huang, CM, Lee, TMC, Lee, SH & Wu, SC 2017, Complexity analysis of resting state fMRI signals in depressive patients. 於 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings., 8037535, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., 頁 3190-3193, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, Jeju Island, 韓國, 11/07/17. https://doi.org/10.1109/EMBC.2017.8037535

Complexity analysis of resting state fMRI signals in depressive patients. / Ho, Pei Shan; Lin, Chemin; Chen, Guan Yen 等.
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3190-3193 8037535 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

研究成果: 圖書/報告稿件的類型 › 會議稿件 › 同行評審

TY - GEN

T1 - Complexity analysis of resting state fMRI signals in depressive patients

AU - Ho, Pei Shan

AU - Lin, Chemin

AU - Chen, Guan Yen

AU - Liu, Ho Ling

AU - Huang, Chih Mao

AU - Lee, Tatia Mei Chun

AU - Lee, Shwu Hua

AU - Wu, Shun Chi

PY - 2017/9/13

Y1 - 2017/9/13

N2 - 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.

AB - 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.

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T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

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BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017

Y2 - 11 July 2017 through 15 July 2017

ER -

Ho PS, Lin C, Chen GY, Liu HL, Huang CM, Lee TMC 等. Complexity analysis of resting state fMRI signals in depressive patients. 於 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3190-3193. 8037535. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2017.8037535

Complexity analysis of resting state fMRI signals in depressive patients

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