Complex Networks Analysis of the Elderly People with Mild Cognitive Impairment by Nonlinear Interdependence of EEG
Yan Yan1, Xiao Shasha1, Liu Meng2, Li Yunxia2*, Li Yingjie3,4*
1(Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China) 2(Department of Neurology, Tongji Hospital of Tongji University, Shanghai 200065, China) 3(College of International Education, Shanghai University, Shanghai 200444, China) 4(School of Life Science, Shanghai University, Shanghai 200444, China)
Abstract：The aim of this study was to preliminarily investigate brain functional networks of the elderly with mild cognitive impairment (MCI) and the normal elderly during emotion regulation. Fourteen MCI patients and eighteen healthy subjects participated in this experiment. The scalp electroencephalography (EEG) was recorded whenthe subjects performed the cognitive reappraisal task, including neutral/negative viewing task and negative reappraisal task. We applied nonlinear interdependence in different EEG bands to measure the connectivity between brain regions. Then the nonlinear interdependence index was used to construct the brain functional networks of subjects in both groups. The global efficiency and average local efficiency were used to analyze the efficiency of information transmission among brain regions. The result showed that the nonlinear interdependence index of EEG theta activity (F=6.805,P=0.014,η2=0.185) in MCI patients were significantly lower than that in the control group, as well as of alpha band under negative viewing task (t=2.437,P=0.021, Cohen's d=0.865). Under some specific thresholds (threshold was 0.070 in low gamma band, threshold was 0.075 in theta band, threshold were 0.115 and 0.125 in alpha band), the network efficiency of MCI patients was significantly lower than that in the control group. In addition, the reappraisal effect (F=5.549,P=0.008, η2=0.246) was found in the control group (threshold was 0.115), showing that the global efficiency of EEG alpha activity(P=0.018) under the negative viewing condition(0.713±0.042) in controls was higher than that under the condition of negative reappraisal(0.699±0.045). However, similar reappraisal effect was not found in MCI patients. We therefore concluded that MCI patients had the deficit in brain network during emotion regulation, which was manifested in low efficiency of brain information transmission. In addition, no significant Pearson's correlation was found between nonlinear interdependence, network efficiency and behavioral scores, cognitive scores. More research of nonlinear correlation is necessary in order to find more stable brain network features with MCI in emotion regulation.
闫彦, 肖莎莎, 刘梦, 李云霞, 李颖洁. 轻度认知障碍老年人脑电的非线性相互依赖脑网络分析[J]. 中国生物医学工程学报, 2021, 40(6): 662-673.
Yan Yan, Xiao Shasha, Liu Meng, Li Yunxia, Li Yingjie. Complex Networks Analysis of the Elderly People with Mild Cognitive Impairment by Nonlinear Interdependence of EEG. Chinese Journal of Biomedical Engineering, 2021, 40(6): 662-673.
[1] Pagani M, Nobili F, Morbelli S, et al. Early identification of MCI converting to AD: a FDG PET study[J]. Eur J Nucl Med Mol I, 2017, 44(12): 2042-2052. [2] Morawetz C, Riedel MC, Salo T, et al. Multiple large-scale neural networks underlying emotion regulation[J]. Neurosci Biobehav R, 2020, 116(7):382-395. [3] Damayanti NR, Ali NM. Mild cognitive impairment and technology for older adults: a review[J]. Smart Trends Computing Commun: Proc SmartCom, 2020, 182: 477-485. [4] Ochsner KN, Silvers JA, Buhle JT. Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion[J]. Ann NY Acad Sci, 2012, 1251(1): E1-E24. [5] Chen Taolin, Becker B, Camilleri J, et al. A domain-general brain network underlying emotional and cognitive interference processing: Evidence from coordinate-based and functional connectivity meta-analyses[J]. Brain Struct Funct, 2018, 223(8): 3813-3840. [6] Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems[J]. Nat Rev Neurosci, 2009, 10(3): 186-198. [7] 孙俊峰,洪祥飞,童善保.复杂脑网络研究进展——结构、功能、计算与应用[J].复杂系统与复杂性科学,2010,7(4):74-90. [8] 周群. 脑电信号同步: 方法及应用研究[D]. 成都:电子科技大学, 2009. [9] Varela FJ, Lachaux J, Rodriguez E, et al. The brainweb: phase synchronization and large-scale integration[J]. Nat Rev Neurosci, 2001, 2(4): 229-239. [10] Ieracitano C, Mammone N, La Foresta F, et al. Investigating the brain connectivity evolution in AD and MCIpatients through the EEG signals' wavelet coherence[M]//Multidisciplinary Approaches to Neural Computing. Berlin: Springer Cham, 2018, 69: 259-269. [11] Tao Huaying, Tian Xin. Coherence characteristics of gamma-band EEG during rest and cognitive task in MCI and AD[C]//The 27th Annual Conference Engineering in Medicine and Biology. Shanghai: IEEE, 2005: 2747-2750. [12] Liu CJ, Huang CF, Chou CY, et al. Age-and disease-related features of task-related brain oscillations by using mutual information[J]. Brain Behav, 2012, 2(6): 754-762. [13] Handayani N, Haryanto F, Khotimah SN, et al. Coherence and phase synchrony analyses of EEG signals in Mild Cognitive Impairment (MCI): a study of functional brain connectivity[J]. Pol J Med Phys Eng, 2018, 24(1): 1-9. [14] Le Van Quyen M, Martinerie J, Adam C, et al. Nonlinear analyses of interictal EEG map the brain interdependences in human focal epilepsy[J]. Physica D, 1999, 127(3-4): 250-266. [15] Bakhshayesh H, Fitzgibbon SP, Janani AS, et al. Detecting synchrony in EEG: a comparative study of functional connectivity measures[J]. Comput Biol Med, 2018, 105(12): 1-15. [16] Chen Dan, Li Xiaoli, Cui Dong, et al. Global synchronization measurement of multivariate neural signals with massively parallel nonlinear interdependence analysis[J]. IEEETrans Neur Sys Reh, 2013, 22(1): 33-43. [17] Miraglia F, Vecchio F, Bramanti P, et al. EEG characteristics in “eyes-open” versus “eyes-closed” conditions: small-world network architecture in healthy aging and age-related brain degeneration[J]. Clin Neurophysiol, 2016, 127(2): 1261-1268. [18] Lou Wutao, Shi Lin, Wang Defeng, et al. Decreased activity with increased background network efficiency in amnestic MCI during a visuospatial working memory task[J]. Hum Brain Mapp, 2015, 36(9): 3387-3403. [19] Surya D, Puthankattil SD. Complex network analysis of MCI-AD EEG signals under cognitive and resting state[J]. Brain Res, 2020, 1735(20): 146743-146752. [20] Berlot R, Metzler-Baddeley C, Ikram MA, et al. Global efficiency of structural networks mediates cognitive control in mild cognitive impairment[J]. Front Aging Neurosci, 2016, 8: 292-302. [21] Pereira JB, Aarsland D, Ginestet CE, et al. Aberrant cerebral network topology and mild cognitive impairment in early Parkinson's disease[J]. Hum Brain Mapp, 2015, 36(8): 2980-2995. [22] Lang PJ, Bradley MM, Cuthbert BN. International affective picture system (IAPS): technical manual and affective ratings[J]. NIMH Cent Study Emot Atten, 1997, 1: 39-58. [23] Kohn N, Eickhoff SB, Scheller M, et al. Neural network of cognitive emotion regulation—an ALE meta-analysis and MACM analysis[J]. Neuroimage, 2014, 87(11): 345-355. [24] Xu Peng, Xiong Xiuchun, Xue Qing, et al. Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference[J]. Physiol Meas, 2014, 35(7): 1279-1298. [25] Lo Peichen, Tian Wujuemiao, Liu Fangling. Macrostate and microstate of EEG spatio-temporal nonlinear dynamics in Zen meditation[J]. J Behav Brain Sci, 2017, 07(13):705-721. [26] Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations[J]. Neuroimage, 2010, 52(3): 1059-1069. [27] Garrison KA, Scheinost D, Finn ES, et al. The (in) stability of functional brain network measures across thresholds[J]. Neuroimage, 2015, 118(5): 651-661. [28] Kitzbichler MG, Henson RNA, Smith ML, et al. Cognitive effort drives workspace configuration of human brain functional networks[J]. J Neurosci, 2011, 31(22): 8259-8270. [29] Opitz PC, Rauch LC, Terry DP, et al. Prefrontal mediation of age differences in cognitive reappraisal[J]. Neurobiol Aging, 2012, 33(4): 645-655. [30] Meltzer EP. Emotion regulation in relation to cognitive functioning in the preclinical stages of dementia[D]. New York: City University of New York, 2016. [31] Foti D, Hajcak G. Deconstructing reappraisal: Descriptions preceding arousing pictures modulate the subsequent neural response[J]. J Cognitive Neurosci, 2008, 20(6): 977-988. [32] Bridwell DA, Cavanagh JF, Collins AGE, et al. Moving beyond ERP components: a selective review of approaches to integrate EEG and behavior[J]. Front Hum Neurosci, 2018, 12: 106-122. [33] Kramer MA, Chang FL, Cohen ME, et al. Synchronization measures of the scalp electroencephalogram can discriminate healthy from Alzheimer's subjects[J]. Int J Neural Syst, 2007, 17(2): 61-69. [34] Olofsson JK, Nordin S, Sequeira H, et al. Affective picture processing: an integrative review of ERP findings[J]. Biol Psychol, 2008, 77(3): 247-265. [35] Vaish A, Grossmann T, Woodward A. Not all emotions are created equal: the negativity bias in social-emotional development[J]. Psychol Bull, 2008, 134(3): 383-403. [36] Li Yingjie, Cao Dan, Wei Ling, et al. Abnormal functional connectivity of EEG gamma band in patients with depression during emotional face processing[J]. Clin Neurophysiol, 2015, 126(11): 2078-2089. [37] Zhu Li, Lotte F, Cui Gaochao, et al. Neural mechanisms of social emotion perception: an EEG hyper-scanning study[C]//The 17th International Conference on Cyberworlds (CW). Singapore City: IEEE, 2018: 199-206. [38] Costa T, Rognoni E, Galati D. EEG phase synchronization during emotional response to positive and negative film stimuli[J]. Neurosci Lett, 2006, 406(3): 159-164. [39] Engels MMA, Stam CJ, van der Flier WM, et al. Declining functional connectivity and changing hub locations inAlzheimer's disease: an EEG study[J]. BMC Neurol, 2015, 15(1): 1-8. [40] Hajcak G, Nieuwenhuis S. Reappraisal modulates the electrocortical response to unpleasant pictures[J]. Cogn, Affect Behav Ne, 2006, 6(4): 291-297. [41] Zhao Yifan, Hanna E, Bigg GR, et al. Tracking nonlinear correlation for complex dynamic systems using a windowed error reduction ratio method[J]. Complexity, 2017, 29(6): 1-14.
