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基于模糊近似熵的抑郁症患者静息态功能磁共振成像信号复杂度分析

杨孝敬 杨阳 李淮周 钟宁

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基于模糊近似熵的抑郁症患者静息态功能磁共振成像信号复杂度分析

杨孝敬, 杨阳, 李淮周, 钟宁

Analysis of resting state functional magnetic resonance imaging signal complexity of adult major depressive disorder based on fuzzy approximate entropy

Yang Xiao-Jing, Yang Yang, Li Huai-Zhou, Zhong Ning
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  • 提出采用模糊近似熵的方法对功能磁共振成像(functional magnetic resonance imaging,fMRI)复杂度量化分析,并与样本熵进行比较.采用的22个成年抑郁症患者中,11位男性,年龄在1865岁之间.我们期望测量的静息态fMRI信号复杂度与Goldberger/Lipsitz模型一致,越健康、越稳健其生理表现的复杂度越大,且复杂度随年龄的增大而降低.全脑平均模糊近似熵与年龄之间差异性显著(r=-0.512,p 0.001).相比之下,样本熵与年龄之间差异性不显著(r=-0.102,p=0.482).模糊近似熵同样与年龄相关脑区(额叶、顶叶、边缘系统、颞叶、小脑顶叶)之间差异性显著(p0.05),样本熵与年龄相关脑区之间差异性不显著性.这些结果与Goldberger/Lipsitz模型一致,说明采用模糊近似熵分析fMRI数据复杂度是一个有效的新方法.
    Major depressive disorder (MDD) is a kind of mental disease which has characteristics of the low mood,sense of worthless,less interest in the surrounding things,sadness or hopeless,slow thinking,intelligence,language,action,etc. The aim of this research is to find the differences between entropy values and ages,genders of MDD patients,MDD patients and healthy controls.Twenty-two MDD patients (male 11;age 18-65) and their matched healthy controls in gender,age,and education are examined by analyzing (blood oxygenation level dependent-functional magnetic resonance imaging,BOLD-fMRI) signals from nonlinear complexity perspective.As the BOLD-fMRI signals have limited time resolution,so they are very difficult to quantify the complexities of fMRI signals.We extract the corresponding signals from the fMRI signals.The complexities of the age,gender,MDD patients and healthy controls can be predicted by the proposed approach.However,information redundancy and other issues may exist in non-linear dynamic signals. These issues will cause an increase in computational complexity or a decrease in computational accuracy.To solve the above problems,we propose a method of fuzzy approximate entropy (fApEn),and compare it with sample entropy (SampEn).The addition and subtraction under different emotional stimuli as a multi-task are used to coordinate brain sense with motion control.The 12-channel fMRI signals are obtained involving the BOLD signals on resting signals (about 24 s).The methods of the fApEn and SampEn are proposed to deal with the BOLD-fMRI signals in the different ages and genders,and those between MDD patients and healthy controls from the differences between fApEn and SampEn of different genders,main effect and interaction effect analysis of fApEn and SampEn measures, regression curve between entropy and age of the whole sample,correlations of fApEn and SampEn with age,fApEn-age correlation and magnitude in gray matter and white matter,multiple regression analysis of fApEn with age for the whole sample,also the receiver operating characteristic analyses of fApEn and SampEn,the relationship between fAPEn and N aspects.The results show that 1) the complexities of the resting state fMRI signals measured are consistent with those from the Goldberger/Lipsitz model:the more the health,the greater the complexity is;2) the mean whole brain fApEn demonstrates significant negative correlation (r=-0.512,P0.001) with age,SampEn produces a non-significant negative correlation (r=-0.102,p=0.412),and fApEn also demonstrates a significant (P0.05) negative correlation with age-region (frontal,parietal,limbic,temporal and cerebellum parietal lobes),there is non-significant region between the SampEn maps and age;3) the fuzzy approximate entropy values of major depressive disorder patients are lower than those of healthy controls during resting.These results support the Goldberger/Lipsitz model,and the results also show that the fApEn is a new effective method to analyze the complexity of BOLD-fMRI signals.
      通信作者: 杨孝敬, yangxj84@163.com
    • 基金项目: 国家重点基础研究发展计划(批准号:2014CB744600)和国家自然科学基金(批准号:61272345,61105118)资助的课题.
      Corresponding author: Yang Xiao-Jing, yangxj84@163.com
    • Funds: Project supported by the National Basic Research Program of China(Grant No. 2014CB744600) and the National Natural Science Foundation of China(Grant Nos. 61272345, 61105118).
    [1]

    Lipsitz L A 2004 Sci. Aging Knowl. Environ. 16 7

    [2]

    Sokunbi M O, Staff R T, Waiter G D, Ahearn T S, Fox H C, Deary I J 2011 IEEE Trans. Biomed. Eng. 58 3206

    [3]

    Pritchard W S, Duke D W, Coburn K L, Moore N C, Tucker K A, Jann M W 1994 Electroenceph. Clin. Neurophysiol. 91 118

    [4]

    Wolf A, Swift J B, Swinney H L, Vastano J A 1985 Physica D 16 285

    [5]

    Eckmann J P, Ruelle D 1992 Physica D 56 185

    [6]

    Grassberger P, Procaccia I 1983 Phys. Rev. Lett. 50 346

    [7]

    Bertolaccini M, Bussolati C, Padovini G 1978 IEEE Trans. Biomed. Eng. 25 159

    [8]

    Pesin Y B 1977 Russ. Math. Surv. 32 55

    [9]

    Kaplan J, Yorke J 1979 Chaotic Behavior of Multidimensional Difference Equations (Berlin Heidelberg:Springer) 17204

    [10]

    Kolmogorov A N 1958 Doki. Akad. Nauk. 119 861

    [11]

    Pincus S 1995 Chaos 5 110

    [12]

    Pincus S M 2001 Ann. NY. Acad. Sci. 954 245

    [13]

    Pincus S M 1991 Proc. Natl. Acad. Sci. USA 88 2297

    [14]

    Wang Z, Li Y, Childress A R, Detre J A 2014 PLoS ONE 9 e89948

    [15]

    Xie H B, Guo J Y, Zheng Y P 2010 Ann. Biomed. Eng. 38 1483

    [16]

    Li Q, Wang T Y, Leng Y G, He G Y, He H L 2007 Acta Phys. Sin. 56 6803(in Chinese)[李强, 王太勇, 冷永刚, 何改云, 何慧龙2007物理学报56 6803]

    [17]

    Bosl W, Tierney A, Tager-Flusberg H, Nelson C 2011 BMC Med. 9 18

    [18]

    Catarino A, Churches O, Baron-Cohen S, Andrade A, Ring H 2011 Clin. Neurophysiol. 122 2375

    [19]

    Ahmadlou M, Adeli H, Adeli A 2010 J. Clin. Neurophysiol. 27 328

    [20]

    Liu D Z, Wang J, Li J, Li Y, Xu W M, Zhao X 2014 Acta Phys. Sin. 63 198703(in Chinese)[刘大钊, 王俊, 李锦, 李瑜, 许文敏, 赵筱2014物理学报63 198703]

    [21]

    Wang K M, Zhong N, Zhou H Y 2014 Acta Phys. Sin. 63 178701(in Chinese)[王凯明, 钟宁, 周海燕2014物理学报63 178701]

    [22]

    Gomez C, Abasolo D, Poza J, Fernandez A, Hornero R 2010 Conf. Proc. IEEE Eng. Med. Biol. Soc. 75 2379

    [23]

    Richman J S, Moorman J R 2000 Am. J. Physiol.-Heart Circul Physiol. 278 H2039

    [24]

    Abasolo D, Hornero R, Espino P, álvarez D, Poza J 2006 Physiol. Meas. 27 241

    [25]

    Gomez C, Poza J, Garcia M, Fernandez, Hornero R 2011 Regularity Analysis of Spontaneous MEG Activity in Attention-Deficit/Hyperactivity Disorder (IEEE:Proceedings of the 33rd Annual International Conference of the IEEE EMBS) p1765

    [26]

    Sokunbi M O 2014 Front. Neuroinform. 8 69

    [27]

    Sokunbi M O, Gradin V B, Waiter G D, Cameron G G, Ahearn T S, Murray A D, Steele D J, Staff R T 2014 PLoS ONE 9 e95146

    [28]

    Yang A C, Huang C C, Yeh H L, Liu M E, Hong C J, Tu P C 2013 Neurobiol. Aging 34 428

    [29]

    Sokunbi M O, Fung W, Sawlani V, Choppin S, Linden D E J, Thome J 2013 Neuroimaging 214 341

    [30]

    Chen W, Wang Z, Xie H, Yu W 2007 IEEE Trans. Neural Syst. Rehabil. Eng. 15 266

    [31]

    Sun R, Song R, Tong K Y 2014 IEEE Trans. Neural Syst. Rehab. Eng. 22 1013

    [32]

    Kumar Y, Dewal M L, Anand R S 2014 Neurocomputing 133 271

    [33]

    Logothetis N K, Wandell B A 2004 Annu. Rev. Physiol. 66 735

    [34]

    Gawryluk J R, Mazerolle E L, D'Arcy R C N 2014 Front. Neurosci. 8 239

    [35]

    Goldberger A L 1996 Lancet 347 1312

    [36]

    Goldberger A L 1997 Perspect. Biol. Med. 40 543

    [37]

    Goldberger A L, Peng C, Lipsitz L A 2002 Neurobiol. Aging 23 23

    [38]

    Deary I J, Corley J, Gow A J, Harris SE, Houlihan L M, Marioni R E 2009 Br. Med. Bull. 92 135

    [39]

    Yao Y, Lu W L, Xu B, Li C B, Lin C P, Waxman D 2013 Sci. Rep. 3 2853

    [40]

    Anokhin A P, Birbaumer N, Lutzenberger W, Nikolaev A, Vogel F 1996 Electroencephalogr. Clin. Neurophysiol. 99 63

    [41]

    Zadeh L A 1965 Inform. Control 8 338

    [42]

    Xiong G, Zhang L, Liu H, Zou H, Guo W 2010 J. Zhejiang University-Sci. A(Appl. Phys. Eng.) 11 270

    [43]

    Fernández A, Hornero R, Gómez C, Turrero A, Gil-Gregorio P, Matias-Santos J, Ortiz T 2010 Alzheimer Dis. Assoc. Disord. 24 182

    [44]

    Fernandez A, Zuluaga P, Abasolo D, Gomez C, Serra A, Mendez M A 2012 Clin. Neurophysiol. 123 2154

    [45]

    Liu C Y, Krishnan A P, Yan L, Smith R X, Kilroy E, Alger J R, Ringman J M, Wang D J 2013 J. Magn. Reson. Imaging 38 36

    [46]

    Thomas B P, Liu P, Park D C, van Osch M J, Lu H 2014 J. Cereb. Blood Flow Metab. 34 242

    [47]

    Lu H, Xu F, Rodrigue K M, Kennedy K M, Cheng Y, Flicker B 2011 Cereb. Cortex 21 1426

    [48]

    Samanez-Larkin G R, D'Esposito M 2008 Soc. Cogn. Affect. Neurosci. 3 290

    [49]

    Tsvetanov K A, Henson R N A, Tyler L K, Davis S W, Shafto M A, Taylor J R 2015 Hum. Brain Mapp. 36 2248

    [50]

    Liu C, Zheng D, Li P, Zhao L, Liu C, Murray A 2013 Proceedings of the IEEE Computing in Cardiology Conference(CinC) Zaragoza, Spain, September 22-25, 2013p39

    [51]

    Zweig M H, Campbell G 1993 Clin. Chem. 39 561

    [52]

    Pincus S M, Goldberger A L 1994 Am. J. Physiol.-Heart Circul Physiol. 266 H1643

  • [1]

    Lipsitz L A 2004 Sci. Aging Knowl. Environ. 16 7

    [2]

    Sokunbi M O, Staff R T, Waiter G D, Ahearn T S, Fox H C, Deary I J 2011 IEEE Trans. Biomed. Eng. 58 3206

    [3]

    Pritchard W S, Duke D W, Coburn K L, Moore N C, Tucker K A, Jann M W 1994 Electroenceph. Clin. Neurophysiol. 91 118

    [4]

    Wolf A, Swift J B, Swinney H L, Vastano J A 1985 Physica D 16 285

    [5]

    Eckmann J P, Ruelle D 1992 Physica D 56 185

    [6]

    Grassberger P, Procaccia I 1983 Phys. Rev. Lett. 50 346

    [7]

    Bertolaccini M, Bussolati C, Padovini G 1978 IEEE Trans. Biomed. Eng. 25 159

    [8]

    Pesin Y B 1977 Russ. Math. Surv. 32 55

    [9]

    Kaplan J, Yorke J 1979 Chaotic Behavior of Multidimensional Difference Equations (Berlin Heidelberg:Springer) 17204

    [10]

    Kolmogorov A N 1958 Doki. Akad. Nauk. 119 861

    [11]

    Pincus S 1995 Chaos 5 110

    [12]

    Pincus S M 2001 Ann. NY. Acad. Sci. 954 245

    [13]

    Pincus S M 1991 Proc. Natl. Acad. Sci. USA 88 2297

    [14]

    Wang Z, Li Y, Childress A R, Detre J A 2014 PLoS ONE 9 e89948

    [15]

    Xie H B, Guo J Y, Zheng Y P 2010 Ann. Biomed. Eng. 38 1483

    [16]

    Li Q, Wang T Y, Leng Y G, He G Y, He H L 2007 Acta Phys. Sin. 56 6803(in Chinese)[李强, 王太勇, 冷永刚, 何改云, 何慧龙2007物理学报56 6803]

    [17]

    Bosl W, Tierney A, Tager-Flusberg H, Nelson C 2011 BMC Med. 9 18

    [18]

    Catarino A, Churches O, Baron-Cohen S, Andrade A, Ring H 2011 Clin. Neurophysiol. 122 2375

    [19]

    Ahmadlou M, Adeli H, Adeli A 2010 J. Clin. Neurophysiol. 27 328

    [20]

    Liu D Z, Wang J, Li J, Li Y, Xu W M, Zhao X 2014 Acta Phys. Sin. 63 198703(in Chinese)[刘大钊, 王俊, 李锦, 李瑜, 许文敏, 赵筱2014物理学报63 198703]

    [21]

    Wang K M, Zhong N, Zhou H Y 2014 Acta Phys. Sin. 63 178701(in Chinese)[王凯明, 钟宁, 周海燕2014物理学报63 178701]

    [22]

    Gomez C, Abasolo D, Poza J, Fernandez A, Hornero R 2010 Conf. Proc. IEEE Eng. Med. Biol. Soc. 75 2379

    [23]

    Richman J S, Moorman J R 2000 Am. J. Physiol.-Heart Circul Physiol. 278 H2039

    [24]

    Abasolo D, Hornero R, Espino P, álvarez D, Poza J 2006 Physiol. Meas. 27 241

    [25]

    Gomez C, Poza J, Garcia M, Fernandez, Hornero R 2011 Regularity Analysis of Spontaneous MEG Activity in Attention-Deficit/Hyperactivity Disorder (IEEE:Proceedings of the 33rd Annual International Conference of the IEEE EMBS) p1765

    [26]

    Sokunbi M O 2014 Front. Neuroinform. 8 69

    [27]

    Sokunbi M O, Gradin V B, Waiter G D, Cameron G G, Ahearn T S, Murray A D, Steele D J, Staff R T 2014 PLoS ONE 9 e95146

    [28]

    Yang A C, Huang C C, Yeh H L, Liu M E, Hong C J, Tu P C 2013 Neurobiol. Aging 34 428

    [29]

    Sokunbi M O, Fung W, Sawlani V, Choppin S, Linden D E J, Thome J 2013 Neuroimaging 214 341

    [30]

    Chen W, Wang Z, Xie H, Yu W 2007 IEEE Trans. Neural Syst. Rehabil. Eng. 15 266

    [31]

    Sun R, Song R, Tong K Y 2014 IEEE Trans. Neural Syst. Rehab. Eng. 22 1013

    [32]

    Kumar Y, Dewal M L, Anand R S 2014 Neurocomputing 133 271

    [33]

    Logothetis N K, Wandell B A 2004 Annu. Rev. Physiol. 66 735

    [34]

    Gawryluk J R, Mazerolle E L, D'Arcy R C N 2014 Front. Neurosci. 8 239

    [35]

    Goldberger A L 1996 Lancet 347 1312

    [36]

    Goldberger A L 1997 Perspect. Biol. Med. 40 543

    [37]

    Goldberger A L, Peng C, Lipsitz L A 2002 Neurobiol. Aging 23 23

    [38]

    Deary I J, Corley J, Gow A J, Harris SE, Houlihan L M, Marioni R E 2009 Br. Med. Bull. 92 135

    [39]

    Yao Y, Lu W L, Xu B, Li C B, Lin C P, Waxman D 2013 Sci. Rep. 3 2853

    [40]

    Anokhin A P, Birbaumer N, Lutzenberger W, Nikolaev A, Vogel F 1996 Electroencephalogr. Clin. Neurophysiol. 99 63

    [41]

    Zadeh L A 1965 Inform. Control 8 338

    [42]

    Xiong G, Zhang L, Liu H, Zou H, Guo W 2010 J. Zhejiang University-Sci. A(Appl. Phys. Eng.) 11 270

    [43]

    Fernández A, Hornero R, Gómez C, Turrero A, Gil-Gregorio P, Matias-Santos J, Ortiz T 2010 Alzheimer Dis. Assoc. Disord. 24 182

    [44]

    Fernandez A, Zuluaga P, Abasolo D, Gomez C, Serra A, Mendez M A 2012 Clin. Neurophysiol. 123 2154

    [45]

    Liu C Y, Krishnan A P, Yan L, Smith R X, Kilroy E, Alger J R, Ringman J M, Wang D J 2013 J. Magn. Reson. Imaging 38 36

    [46]

    Thomas B P, Liu P, Park D C, van Osch M J, Lu H 2014 J. Cereb. Blood Flow Metab. 34 242

    [47]

    Lu H, Xu F, Rodrigue K M, Kennedy K M, Cheng Y, Flicker B 2011 Cereb. Cortex 21 1426

    [48]

    Samanez-Larkin G R, D'Esposito M 2008 Soc. Cogn. Affect. Neurosci. 3 290

    [49]

    Tsvetanov K A, Henson R N A, Tyler L K, Davis S W, Shafto M A, Taylor J R 2015 Hum. Brain Mapp. 36 2248

    [50]

    Liu C, Zheng D, Li P, Zhao L, Liu C, Murray A 2013 Proceedings of the IEEE Computing in Cardiology Conference(CinC) Zaragoza, Spain, September 22-25, 2013p39

    [51]

    Zweig M H, Campbell G 1993 Clin. Chem. 39 561

    [52]

    Pincus S M, Goldberger A L 1994 Am. J. Physiol.-Heart Circul Physiol. 266 H1643

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出版历程
  • 收稿日期:  2016-06-26
  • 修回日期:  2016-07-23
  • 刊出日期:  2016-11-05

基于模糊近似熵的抑郁症患者静息态功能磁共振成像信号复杂度分析

  • 1. 北京工业大学 国际WIC研究院, 北京 100124;
  • 2. 前桥工业大学生命科学与信息工程系, 前桥 371-0816;
  • 3. 首都医科大学安定医院, 北京 100124
  • 通信作者: 杨孝敬, yangxj84@163.com
    基金项目: 国家重点基础研究发展计划(批准号:2014CB744600)和国家自然科学基金(批准号:61272345,61105118)资助的课题.

摘要: 提出采用模糊近似熵的方法对功能磁共振成像(functional magnetic resonance imaging,fMRI)复杂度量化分析,并与样本熵进行比较.采用的22个成年抑郁症患者中,11位男性,年龄在1865岁之间.我们期望测量的静息态fMRI信号复杂度与Goldberger/Lipsitz模型一致,越健康、越稳健其生理表现的复杂度越大,且复杂度随年龄的增大而降低.全脑平均模糊近似熵与年龄之间差异性显著(r=-0.512,p 0.001).相比之下,样本熵与年龄之间差异性不显著(r=-0.102,p=0.482).模糊近似熵同样与年龄相关脑区(额叶、顶叶、边缘系统、颞叶、小脑顶叶)之间差异性显著(p0.05),样本熵与年龄相关脑区之间差异性不显著性.这些结果与Goldberger/Lipsitz模型一致,说明采用模糊近似熵分析fMRI数据复杂度是一个有效的新方法.

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