Browsing by Author "Wu, Shun-Chi"

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AGE-RELATED AND INDIVIDUAL DIFFERENCES IN ENTROPY-BASED COMPLEXITY OF HEALTHY AGING BRAIN
(2019-09-01)
Huang, Chih-Mao
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Fang, Ya-Wen
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Chen, Guan-Yen
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Tzeng, Ovid J. L.
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Huang, Hsu-Wen
Complexity analysis of resting state fMRI signals in depressive patients
(2017-01-01)
Ho, Pei-Shan
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Lin, Chemin
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Chen, Guan-Yen
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Liu, Ho-Ling
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Huang, Chih-Mao
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.
Increased brain entropy of resting-state fMRI mediates the relationship between depression severity and mental health-related quality of life in late-life depressed elderly
(2019-05-01)
Lin, Chemin
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Lee, Shwu-Hua
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Huang, Chih-Mao
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Chen, Guan-Yen
;
Ho, Pei-Shan
Background: Entropy analysis is a computational method used to quantify the complexity in a system, and loss of brain complexity is hypothesized to be related to mental disorders. Here, we applied entropy analysis to the resting-state functional magnetic resonance imaging (rs-fMRI) signal in subjects with late-life depression (LLD), an illness combined with emotion dysregulation and aging effect. Methods: A total of 35 unremitted depressed elderly and 22 control subjects were recruited. Multiscale entropy (MSE) analysis was performed in the entire brain, 90 automated anatomical labeling-parcellated ROIs, and five resting networks in each study participant. Limitations: Due to ethical concerns, all the participants were under medication during the study. Results: Regionally, subjects with LLD showed decreased entropy only in the right posterior cingulate gyrus but had universally increased entropy in affective processing (putamen and thalamus), sensory, motor, and temporal nodes across different time scales. We also found higher entropy in the left frontoparietal network (FPN), which partially mediated the negative correlation between depression severity and mental components of the quality of life, reflecting the possible neural compensation during depression treatment. Conclusion: MSE provides a novel and complementary approach in rs-fMRI analysis. The temporal-spatial complexity in the resting brain may provide the adaptive variability beneficial for the elderly with depression.