Chemin Lin1, Shwu-Hua Lee2, Chih-Mao Huang3, Guan-Yen Chen4, Pei-Shan Ho4, Ho-Ling Liu5, Yao-Liang Chen6, Tatia Mei-Chun Lee7, Shun-Chi Wu8. 1. Department of Psychiatry, Keelung Chang Gung Memorial Hospital, Keelung City, Taiwan; College of Medicine, Chang Gung University, Taoyuan County, Taiwan; Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Keelung, Taiwan. 2. College of Medicine, Chang Gung University, Taoyuan County, Taiwan; Department of Psychiatry, Linkou Chang Gung Memorial Hospital, Taoyuan County, Taiwan. 3. Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan. 4. Department of Engineering and System Science, National Tsing Hua University, Hsinchu, Taiwan. 5. Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA. 6. Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Keelung, Taiwan. 7. Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong; Laboratory of Cognitive Affective Neuroscience, The University of Hong Kong, Hong Kong; State Key Laboratory of Brain and Cognitive Science, The University of Hong Kong, Hong Kong; Institute of Clinical Neuropsychology, The University of Hong Kong, Hong Kong. Electronic address: tmclee@hku.hk. 8. Department of Engineering and System Science, National Tsing Hua University, Hsinchu, Taiwan. Electronic address: shunchi.wu@mx.nthu.edu.tw.
Abstract
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.
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.
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