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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

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.

Original language	English
Title of host publication	2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Smarter Technology for a Healthier World, EMBC 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3190-3193
Number of pages	4
ISBN (Electronic)	9781509028092
DOIs	https://doi.org/10.1109/EMBC.2017.8037535
State	Published - 13 09 2017
Externally published	Yes
Event	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: 11 07 2017 → 15 07 2017

Publication series

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

Conference

Conference	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	11/07/17 → 15/07/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

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10.1109/EMBC.2017.8037535

Cite this

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. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings (pp. 3190-3193). Article 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 et al. / 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. pp. 3190-3193 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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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\}",

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year = "2017",

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doi = "10.1109/EMBC.2017.8037535",

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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. in 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., pp. 3190-3193, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, Jeju Island, Korea, Republic of, 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 et al.
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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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Ho PS, Lin C, Chen GY, Liu HL, Huang CM, Lee TMC et al. Complexity analysis of resting state fMRI signals in depressive patients. In 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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