ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)


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Pitsik E. N. Recurrence quantification analysis provides the link between age-related decline in motor brain response and complexity of the baseline EEG. Izvestiya VUZ. Applied Nonlinear Dynamics, 2021, vol. 29, iss. 3, pp. 386-397. DOI: 10.18500/0869-6632-2021-29-3-386-397

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Recurrence quantification analysis provides the link between age-related decline in motor brain response and complexity of the baseline EEG

Autors: 
Pitsik Elena N, Innopolis University
Abstract: 

The goal of the present study is to investigate the effect of healthy aging on the neuronal mechanisms supporting human brain activity during motor task performance. Such biomarkers of the age-related changes can be detected using mathematical methods of time-series analysis and complexity analysis. Methods. In the present paper, recurrence quantification analysis (RQA) measures are employed to explore the complexity of the pre-movement EEG in young and elderly adult groups. To evaluate the neural brain response during motor execution, we applied the traditional methods of time-frequency analysis. Results. The proposed approach demonstrated that (i) the RQA measures show a significant increase of complexity in elderly adults; (ii) increased pre-movement EEG complexity comes with the reduced motor-related brain response in the α/µ-band (p < 0.01), estimated via the traditional methods of time-frequency analysis. It allows to conclude that the increased pre-movement EEG complexity indicates the weak neuronal plasticity degenerated under the factor of age. Conclusion. The complexity of the pre-movement α/μ-band neuronal oscillations could be considered as a relevant measure for the detection of age-related cognitive or motor impairments. Besides, applied RQA method demonstrated a good ability to assess the complexity features of pre-stimulus EEG and provided a clear interpretation of age-related changes in electrical activity of the brain cortex.

Acknowledgments: 
This work has been supported by Russian Foundation for Basic Research (Grant 19-52-55001) and the Council on Grants of the President of the Russian Federation (Grant NSh-2594.2020.2). The author thanks Dr. N. Frolov and Prof. A. Hramov for useful discussions within the framework of this study
Reference: 
  1. Adeli H, Ghosh-Dastidar S. Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology. CRC Press; 2010. 423 p. DOI: 10.1201/9781439815328.
  2. Musaeus C, Engedal K, Høgh P, Jelic V, Mørup M, Naik M, Oeksengaard A, Snaedal J, Wahlund L, Waldemar G, Andersen B. EEG theta power is an early marker of cognitive decline in dementia due to Alzheimer’s disease. Journal of Alzheimer’s Disease. 2018;64(4):1359–1371. DOI: 10.3233/JAD-180300.
  3. Smailovic U, Jelic V. Neurophysiological markers of Alzheimer’s disease: Quantitative EEG approach. Neurology and Therapy. 2019;8(2):37–55. DOI: 10.1007/s40120-019-00169-0.
  4. Liu J, He T, Zheng C, Huang Y. Measuring EEG complexity for studying the state of mental load. Journal of Biomedical Engineering. 1997;14(1):33–37.
  5. Cao Z, Lin C. Inherent fuzzy entropy for the improvement of EEG complexity evaluation. IEEE Transactions on Fuzzy Systems. 2018;26(2):1032–1035. DOI: 10.1109/TFUZZ.2017.2666789.
  6. Acharya U, Bhat S, Faust O, Adeli H, Chua E, Lim W, Koh J. Nonlinear dynamics measures for automated EEG-based sleep stage detection. European Neurology. 2015;74(5–6):268–287. DOI: 10.1159/000441975.
  7. Billeci L, Varanini M. Characterizing electrocardiographic changes during pre-seizure periods through temporal and spectral features. In: 2017 Computing in Cardiology (CinC). Rennes, France: IEEE; 2017. P. 1–4. DOI: 10.22489/CinC.2017.098-282.
  8. Gao X, Yan X, Gao P, Gao X, Zhang S. Automatic detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks. Artificial Intelligence in Medicine. 2020;102:101711. DOI: 10.1016/j.artmed.2019.101711.
  9. Smits F, Porcaro C, Cottone C, Cancelli A, Rossini P, Tecchio F. Electroencephalographic fractal dimension in healthy ageing and Alzheimer’s disease. PLOS One. 2016;11(2):e0149587. DOI: 10.1371/journal.pone.0149587.
  10. Ishii R, Canuet L, Aoki Y, Hata M, Iwase M, Ikeda S, Nishida K, Ikeda M. Healthy and pathological brain aging: From the perspective of oscillations, functional connectivity, and signal complexity. Neuropsychobiology. 2017;75(4):151–161. DOI: 10.1159/000486870.
  11. Barry R, Clarke A, Johnstone S, Brown C. EEG differences in children between eyes[1]closed and eyes-open resting conditions. Clinical Neurophysiology. 2009;120(10):1806–1811. DOI: 10.1016/j.clinph.2009.08.006.
  12. Frolov N, Pitsik E, Maksimenko V, Grubov V, Kiselev A, Wang Z, Hramov A. Age-related slowing down in the motor initiation in elderly adults. Plos One. 2020;15(9):e0233942. DOI: 10.1371/journal.pone.0233942.
  13. Kesic S, Spasi´c S. Application of Higuchi’s fractal dimension from basic to clinical neurophysiology: A review. Computer Methods and Programs in Biomedicine. 2016;133:55–70. DOI: 10.1016/j.cmpb.2016.05.014.
  14. Richman J, Moorman J. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology Heart and Circulatory Physiology. 2000;278(6):H2039– H2049. DOI: 10.1152/ajpheart.2000.278.6.H2039.
  15. Mizuno T, Takahashi T, Cho R, Kikuchi M, Murata T, Takahashi K, Wada Y. Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy. Clinical Neurophysiology. 2010;121(9):1438–1446. DOI: 10.1016/j.clinph.2010.03.025.
  16. Simons S, Abasolo D, Escudero J. Classification of Alzheimer’s disease from quadratic sample entropy of electroencephalogram. Healthcare Technology Letters. 2015;2(3):70–73. DOI: 10.1049/htl.2014.0106.
  17. Pavlov A, Pitsik E, Frolov N, Badarin A, Pavlova O, Hramov A. Age-related distinctions in EEG signals during execution of motor tasks characterized in terms of long-range correlations. Sensors (Basel). 2020;20(20):5843. DOI: 10.3390/s20205843.
  18. Peng C, Buldyrev S, Havlin S, Simons M, Stanley H, Goldberger A. Mosaic organization of DNA nucleotides. Physical Review E. 1994;49(2):1685–1689. DOI: 10.1103/PhysRevE.49.1685.
  19. Frolov N, Grubov V, Maksimenko V, Luttjohann A, Makarov V, Pavlov A, Sitnikova E, Pisarchik A, Kurths J, Hramov A. Statistical properties and predictability of extreme epileptic events. Scientific Reports. 2019;9(1):7243. DOI: 10.1038/s41598-019-43619-3.
  20. Webber Jr C, Marwan N. Recurrence Quantification Analysis: Theory and Best Practices. Understanding Complex Systems. Springer International Publishing Switzerland; 2015. DOI: 10.1007/978-3-319-07155-8.
  21. Trauth M, Asrat A, Duesing W, Foerster V, Kraemer K, Marwan N, Maslin M, Schaebitz F. Classifying past climate change in the Chew Bahir basin, southern Ethiopia, using recurrence quantification analysis. Climate Dynamics. 2019;53(5–6):2557–2572. DOI: 10.1007/s00382-019- 04641-3.
  22. Panagoulia D, Vlahogianni E. Recurrence quantification analysis of extremes of maximum and minimum temperature patterns for different climate scenarios in the Mesochora catchment in Central-Western Greece. Atmospheric Research. 2018;205:33–47. DOI: 10.1016/j.atmosres.2018.02.004.
  23. Hou Y, Aldrich C, Lepkova K, Kinsella B. Detection of under deposit corrosion in a CO2 environment by using electrochemical noise and recurrence quantification analysis. Electrochimica Acta. 2018;274:160–169. DOI: 10.1016/j.electacta.2018.04.037.
  24. Stender M, Oberst S, Tiedemann M, Hoffmann N. Complex machine dynamics: systematic recurrence quantification analysis of disk brake vibration data. Nonlinear Dynamics. 2019;97(4):2483–2497. DOI: 10.1007/s11071-019-05143-x.
  25. Mitra V, Sarma B, Sarma A, Janaki M, Sekar Iyengar A, Marwan N, Kurths J. Investigation of complexity dynamics in a DC glow discharge magnetized plasma using recurrence quantification analysis. Physics of Plasmas. 2016;23(6):062312. DOI: 10.1063/1.4953903.
  26. Allen L, Likens A, McNamara D. Recurrence quantification analysis: A technique for the dynamical analysis of student writing. In: Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. Marco Island, United States: AAAI Press; 2017. P. 240–245.
  27. Bosl W, Tager-Flusberg H, Nelson C. EEG analytics for early detection of autism spectrum disorder: A data-driven approach. Scientific Reports. 2018;8(1):6828. DOI: 10.1038/s41598-018-24318-x.
  28. Carrubba S, Minagar A, Chesson Jr A, Frilot 2nd C, Marino A. Increased determinism in brain electrical activity occurs in association with multiple sclerosis. Neurological Research. 2012;34(3):286–290. DOI: 10.1179/1743132812Y.0000000010.
  29. Dick O, Svyatogor I, Reznikova T, Fedoryaka D, Nozdrachev A. Analysis of EEG patterns in subjects with panic attacks. Human Physiology. 2020;46(2):163–174. DOI: 10.1134/S0362119720010065.
  30. Fan M, Tootooni M, Sivasubramony R, Miskovic V, Rao P, Chou C. Acute stress detection using recurrence quantification analysis of electroencephalogram (EEG) signals. In: Brain Informatics and Health. BIH 2016. vol. 9919 of Lecture Notes in Computer Science. Omaha, NE, USA: Springer, Cham; 2016. p. 252–261. DOI: 10.1007/978-3-319-47103-7_25.
  31. Pitsik E, Frolov N, Kraemer K, Grubov V, Maksimenko V, Kurths J, Hramov A. Motor execution reduces EEG signals complexity: Recurrence quantification analysis study. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2020;30(2):023111. DOI: 10.1063/1.5136246.
  32. Michely J, Volz L, Hoffstaedter F, Tittgemeyer M, Eickhoff S, Fink G, Grefkes C. Network connectivity of motor control in the ageing brain. NeuroImage: Clinical. 2018;18:443–455. DOI: 10.1016/j.nicl.2018.02.001.
  33. Burianova H, Marstaller L, Rich A, Williams M, Savage G, Ryan M, Sowman P. Motor neuroplasticity: A MEG-fMRI study of motor imagery and execution in healthy ageing. Neuropsychologia. 2020;146:107539. DOI: 10.1016/j.neuropsychologia.2020.107539.
  34. Mitchell T, Starrs F, Soucy J, Thiel A, Paquette C. Impaired sensorimotor processing during complex gait precedes behavioral changes in middle-aged adults. The Journals of Gerontology: Series A. 2019;74(12):1861–1869. DOI: 10.1093/gerona/gly210.
  35. Quandt F, Bonstrup M, Schulz R, Timmermann J, Zimerman M, Nolte G, Hummel F. Spectral variability in the aged brain during fine motor control. Frontiers in Aging Neuroscience. 2016;8:305. DOI: 10.3389/fnagi.2016.00305.
  36. Nuwer M, Comi G, Emerson R, Fuglsang-Frederiksen A, Guerit J, Hinrichs H, Ikeda A, Luccas F, Rappelsburger P. IFCN standards for digital recording of clinical EEG. Electroencephalography and Clinical Neurophysiology. 1998;106(3):259–261. DOI: 10.1016/s0013-4694(97)00106-5.
  37. Hyvarinen A, Oja E. Independent component analysis: algorithms and applications. Neural Networks. 2000;13(4–5):411–430. DOI: 10.1016/S0893-6080(00)00026-5.
  38. Gramfort A, Luessi M, Larson E, Engemann D, Strohmeier D, Brodbeck C, Goj R, Jas M, Brooks T, Parkkonen L, Ham al ainen M. MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience. 2013;7:267. DOI: 10.3389/fnins.2013.00267.
  39. Frolov N, Pitsik E, Grubov V, Kiselev A, Maksimenko V, Hramov A. EEG dataset for the analysis of age-related changes in motor-related cortical activity during a series of fine motor tasks performance [Electronic resource]; 2020. Available from: https://figshare.com/articles/EEG_dataset_for_the_analysis_of_age-related_ changes_in_motor-related_cortical_activity_during_a_series_of_fine_motor_ tasks_performance/12301181/1. DOI: 10.6084/m9.figshare.12301181.
  40. Takens F. Detecting strange attractors in turbulence. In: Dynamical Systems and Turbulence, Warwick 1980. vol. 898 of Lecture Notes in Mathematics. Springer, Berlin, Heidelberg; 1981. p. 366–381. DOI: 10.1007/BFb0091924.
  41. Roulston M. Estimating the errors on measured entropy and mutual information. Physica D: Nonlinear Phenomena. 1999;125(3–4):285–294. DOI: 10.1016/S0167-2789(98)00269-3.
  42. Kennel M, Brown R, Abarbanel H. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A. 1992;45(6):3403–3411. DOI: 10.1103/PhysRevA.45.3403.
  43. Marwan N, Carmen Romano M, Thiel M, Kurths J. Recurrence plots for the analysis of complex systems. Physics Reports. 2007;438(5–6):237–329. DOI: 10.1016/j.physrep.2006.11.001.
  44. Kraemer K, Donner R, Heitzig J, Marwan N. Recurrence threshold selection for obtaining robust recurrence characteristics in different embedding dimensions. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2018;28(8):085720. DOI: 10.1063/1.5024914.
  45. Maris E, Oostenveld R. Nonparametric statistical testing of EEG-and MEG-data. Journal of Neuroscience Methods. 2007;164(1):177–190. DOI: 10.1016/j.jneumeth.2007.03.024.
  46. Donges J, Heitzig J, Beronov B, Wiedermann M, Runge J, Feng Q, Tupikina L, Stolbova V, Donner R, Marwan N, Dijkstra H, Kurths J. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2015;25(11):113101. DOI: 10.1063/1.4934554.
  47. Pfurtscheller G, Brunner C, Schlogl A, Lopes Da Silva F. Mu rhythm (de)synchronization and ¨ EEG single-trial classification of different motor imagery tasks. NeuroImage. 2006;31(1):153–159. DOI: 10.1016/j.neuroimage.2005.12.003.
  48. Gao L, Wang J, Chen L. Event-related desynchronization and synchronization quantification in motor-related EEG by Kolmogorov entropy. Journal of Neural Engineering. 2013;10(3):036023. DOI: 10.1088/1741-2560/10/3/036023.
  49. Maksimenko V, Pavlov A, Runnova A, Nedaivozov V, Grubov V, Koronovskii A, Pchelintseva S, Pitsik E, Pisarchik A, Hramov A. Nonlinear analysis of brain activity, associated with motor action and motor imaginary in untrained subjects. Nonlinear Dynamics. 2018;91(4):2803–2817. DOI: 10.1007/s11071-018-4047-y.
  50. Maksimenko V, Kurkin S, Pitsik E, Musatov V, Runnova A, Efremova T, Hramov A, Pisarchik A. Artificial neural network classification of motor-related EEG: An increase in classification accuracy by reducing signal complexity. Complexity. 2018;2018:1–10. DOI: 10.1155/2018/9385947.
  51. Chholak P, Niso G, Maksimenko V, Kurkin S, Frolov N, Pitsik E, Hramov A, Pisarchik A. Visual and kinesthetic modes affect motor imagery classification in untrained subjects. Scientific Reports. 2019;9(1):9838. DOI: 10.1038/s41598-019-46310-9.
  52. Hramov A, Koronovskii A, Makarov V, Pavlov A, Sitnikova E. Wavelets in Neuroscience. Springer Series in Synergetics. Springer-Verlag Berlin Heidelberg; 2015. DOI: 10.1007/978-3-662-43850-3.
  53. Pavlov A, Hramov A, Koronovskii A, Sitnikova E, Makarov V, Ovchinnikov A. Wavelet analysis in neurodynamics. Physics Uspekhi. 2012;55(9):845–875. DOI: 10.3367/UFNe.0182.201209a.0905.
  54. Sun J, Wang B, Niu Y, Tan Y, Fan C, Zhang N, Xue J, Wei J, Xiang J. Complexity analysis of EEG, MEG, and fMRI in mild cognitive impairment and Alzheimer’s disease: A review. Entropy (Basel). 2020;22(2):239. DOI: 10.3390/e22020239.
  55. Jia Y, Gu H, Luo Q. Sample entropy reveals an age-related reduction in the complexity of dynamic brain. Scientific Reports. 2017;7(1):7990. DOI: 10.1038/s41598-017-08565-y.
  56. Li X, Zhu Z, Zhao W, Sun Y, Wen D, Xie Y, Liu X, Niu H, Han Y. Decreased resting-state brain signal complexity in patients with mild cognitive impairment and Alzheimer’s disease: a multi-scale entropy analysis. Biomedical Optics Express. 2018;9(4):1916–1929. DOI: 10.1364/BOE.9.001916.
  57. Ruiz-Gomez S, Gomez C, Poza J, Martınez-Zarzuela M, Tola-Arribas M, Cano M, Hornero R. Measuring alterations of spontaneous EEG neural coupling in Alzheimer’s disease and mild cognitive impairment by means of cross-entropy metrics. Frontiers in Neuroinformatics. 2018;12:76. DOI: 10.3389/fninf.2018.00076.
  58. Mattay V, Fera F, Tessitore A, Hariri A, Das S, Callicott J, Weinberger D. Neurophysiological correlates of age-related changes in human motor function. Neurology. 2002;58(4):630–635. DOI: 10.1212/wnl.58.4.630.
  59. Sailer A, Dichgans J, Gerloff C. The influence of normal aging on the cortical processing of a simple motor task. Neurology. 2000;55(7):979–985. DOI: 10.1212/wnl.55.7.979.
  60. Lariviere S, Xifra-Porxas A, Kassinopoulos M, Niso G, Baillet S, Mitsis G, Boudrias M. Functional and effective reorganization of the aging brain during unimanual and bimanual hand movements. Human Brain Mapping. 2019;40(10):3027–3040. DOI: 10.1002/hbm.24578.
  61. Guttmann C, Jolesz F, Kikinis R, Killiany R, Moss M, Sandor T, Albert M. White matter changes with normal aging. Neurology. 1998;50(4):972–978. DOI: 10.1212/WNL.50.4.972.
  62. Webb C, Rodrigue K, Hoagey D, Foster C, Kennedy K. Contributions of white matter connectivity and BOLD modulation to cognitive aging: A lifespan structure-function association study. Cerebral Cortex. 2020;30(3):1649–1661. DOI: 10.1093/cercor/bhz193.
Received: 
30.10.2020
Accepted: 
21.01.2021
Published: 
31.05.2021