ПРИМЕНЕНИЕ ЭЭГ В ОЦЕНКЕ ФУНКЦИОНАЛЬНЫХ ИЗМЕНЕНИЙ ГОЛОВНОГО МОЗГА ПРИ КОГНИТИВНЫХ НАРУШЕНИЯХ
Обзорная статья
Дата публикации: декабря 26, 2023
Ключевые слова
коннективность
нейрональные сети
болезнь Альцгеймера
нейрональные сети
болезнь Альцгеймера
Как цитировать
Кожокару А. Б., Ажигова А. М., Ларкина Е. С. ПРИМЕНЕНИЕ ЭЭГ В ОЦЕНКЕ ФУНКЦИОНАЛЬНЫХ ИЗМЕНЕНИЙ ГОЛОВНОГО МОЗГА ПРИ КОГНИТИВНЫХ НАРУШЕНИЯХ // Кремлевская медицина. Клинический вестник. 2023. Т. № 4. С. 78-83.
Аннотация
Деменция при болезни Альцгеймера и других неврологических заболеваниях занимают третье место среди всех случаев нетрудоспособности людей старшего возраста во всем мире. Болезнь Альцгеймера и другие типы деменции относятся к так называемым синдромам «дисконнекции». ЭЭГ является доступным методом с высоким временным разрешением, параметры которого демонстрируют высокую диагностическую ценность в диагностике болезни Альцгеймера. В настоящем обзоре рассмотрены методы оценки «дисконнекции» и функциональные изменения при болезни Альцгеймера, обещающие альтернативу дорогостоящим и сложным в применении биомаркерам, принятым в качестве диагностических на сегодняшний день. Совершенствование методов ранней неинвазивной диагностики заболевания способствует правильному диагнозу на доклинических и ранних этапах заболевания, что облегчит включение пациентов в клинические исследования профилактических мер и патогенетической терапии заболевания. Приведенные в статье данные свидетельствуют о высокой специфичности и чувствительности, предложенных ЭЭГ-маркеров для диагностики БА и их пригодность в использовании для ранней неинвазивной диагностики заболевания, способствующей более раннему началу терапии и более широкому включению пациентов в клинические исследования, при дальнейшем исследовании данных маркеров.Литература
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56. Sanz-Arigita E.J. et al. Loss of 'small-world' networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity // PLoS One. – 2010. – V. 5(11). - e13788. doi: 10.1371/journal.pone.0013788.
57. Vecchio F. et al. Human brain networks in cognitive decline: a graph theoretical analysis of cortical connectivity from EEG data // J Alzheimers Dis. – 2014. – V. 41(1). – P. 113-127. doi:10.3233/JAD-132087
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60. Vecchio F. et al. "Small World" architecture in brain connectivity and hippocampal volume in Alzheimer's disease: a study via graph theory from EEG data // Brain Imaging BehaV. – 2017. – V. 11(2). – P. 473-485.
61. Engels M.M. et al. Declining functional connectivity and changing hub locations in Alzheimer's disease: an EEG study // BMC Neurol. – 2015. – V. 15. – P. 145.
2. Chui H.C., Ramirez-Gomez L. Clinical and imaging features of mixed Alzheimer and vascular pathologies // Alzheimers Res Ther. – 2015. – V. 7. – P. 21–21.
3. D'Amelio M., Rossini P.M. Brain excitability and connectivity of neuronal assemblies in Alzheimer's disease: from animal models to human findings // Prog Neurobiol. – 2012. – V. 99(1). – P. 42-60. doi: 10.1016/j.pneurobio.2012.07.001
4. Arendt T. Synaptic degeneration in Alzheimer’s disease // Acta Neuropathol. 2009. – V. 118. – P. 167–179.
5. Delbeuck X., Van der Linden M., Collette F. Alzheimer’s disease as a disconnection syndrome? // Neuropsychol. ReV. – 2003. – V. 13. – P. 79–92.
6. Rossini Р.M. et al. Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts // Clin. Neurophysiol. – 2020. – V. 131. – P. 1287–1310.
7. C. P World Alzheimer report 2018. London: Alzheimer’s Disease International, 2018.
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9. Al-Nuaimi A.H. et al. Robust EEG Based Biomarkers to Detect Alzheimer’s Disease // Brain Sci. – 2021. – V. 11. – P. 1026. doi: 10.3390/brainsci11081026
10. Stam C.J. et al. Small-world networks and functional connectivity in Alzheimer’s disease // Cereb Cortex. – 2007. – V. 17. – P. 92–99.
11. Signorino M. et al. EEG spectral analysis in vascular and Alzheimer dementia // Electroencephalogr. Clin. Neurophysiol. – 1995. – V. 94. – P. 313–325.
12. Al-Nuaimi A.H.H. et al. Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimer’s Disease // Complexity. - 2018. – P. 1–12.
13. Nunez P. Toward a quantitative description of large-scale neocortical dynamic function and EEG // Behavioral and Brain Sciences. – 2000. – V. 23(3). – P. 371-398. doi:10.1017/S0140525X00003253
14. Nunez P. L. et al. EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales // Electroencephalography and clinical neurophysiology. – 1997. – V. 103(5). – P. 499–515. doi: 10.1016/s0013-4694(97)00066-7
15. Stam C. J., van Dijk B. W. Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets // Physica D: Nonlinear Phenomena. – 2002. – V. 163(3-4). – P. 236–251.
16. Nolte G. et al. Identifying true brain interaction from EEG data using the imaginary part of coherency // Clin Neurophysiol. - 2004a. – V. 115. – P. 2292–307. doi: 10.1016/j.clinph.2004.04.029
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18. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems // Nature reviews. Neuroscience. – 2009. - V. 10(3). – P. 186–198. doi: 10.1038/nrn2575
19. Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis // Brain Res. ReV. – 1999. – V. 29. – P. 169–195.
20. Pfurtscheller G. Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest // Electroencephalogr Clin Neurophysiol. – 1992. – V. 83. – P. 62–69.
21. Pfurtscheller G., Lopes da Silva F.H. Event-related EEG/MEG synchronization and desynchronization: basic principles // Clin Neurophysiol. - 1999. – V. 110. – P. 1842–1857.
22. Klimesch W. EEG-alpha rhythms and memory processes // Int J Psychophysiol. – 1997. – V. 26. – P. 319–340.
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24. Rossini P.M. et al. Clinical neurophysiology of aging brain: from normal aging to neurodegeneration // Prog Neurobiol. – 2007. – V. 83. – P. 375–400.
25. Honaga E. et al. Post-movement beta rebound abnormality as indicator of mirror neuron system dysfunction in autistic spectrum disorder: an MEG study // Neurosci Lett. – 2010. – V. 478. – P. 141–145.
26. Kurimoto R. et al: Induced oscillatory responses during the Sternberg’s visual memory task in patients with Alzheimer’s disease and mild cognitive impairment // Neuroimage. – 2012. – V. 59. – P. 4132–4140.
27. Klass D.W., Brenner R.P. Electroencephalography of the elderly // J Clin Neurophysiol. – 1995. – V. 12. – P. 116–131.
28. Babiloni C. et al. Sources of cortical rhythms change as a function of cognitive impairment in pathological aging: a multicenter study // Clin Neurophysiol. – 2006. – V. 117. – P. 252–268.
29. Finnigan S., Robertson I.H. Resting EEG theta power correlates with cognitive performance in healthy older adults // Psychophysiology. – 2011. – V. 48. – P. 1083–1087.
30. Hartikainen P., Soininen H., Partanen J. Aging and spectral analysis of EEG in normal subjects: a link to memoryand CSF AChE // Acta Neurol Scand. – 1992. – V. 86. – P. 148–155.
31. Tononi G., Edelman G.M., Sporns O. Complexity and coherency: Integrating information in the brain // Trends Cogn. Sci. – 1998. – V. 2. – P. 474–484.
32. Smits F. et al. Electroencephalographic fractal dimension in healthy ageing and Alzheimer’s disease // PLoS ONE. – 2016. – V. 11. - e0149587.
33. Fernández A. et al. MEG delta mapping along the healthy aging-Alzheimer's disease continuum: diagnostic implications // Journal of Alzheimer's disease: JAD. – 2013. - V. 35(3). – P. 495–507.
34. Fernández A. et al. Correlations of hippocampal atrophy and focal low-frequency magnetic activity in Alzheimer disease: volumetric MR imaging-magnetoencephalographic study // AJNR Am J Neuroradiol. – 2003. – V. 24. – P. 481–487.
35. Babiloni C. et al. Classification of single normal and Alzheimer’s disease individuals from cortical sources of resting state EEG rhythms // Front Neurosci. – 2016. – V. 10. – P. 47.
36. Besthorn C et al. Parameters of EEG dimensional complexity in Alzheimer’s disease // Electroencephalogr. Clin. Neurophysiol. – 1995. – V. 95. – P. 84–89.
37. Jeong J. EEG dynamics in patients with Alzheimer’s disease // Clin. Neurophysiol. – 2004. – V. 115. – P. 1490–1505.
38. McBride J.C. et al. Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer’s disease // Comput. Methods Programs Biomed. – 2014. – V. 114. – P. 153–163.
39. Abásolo D. et al. Analysis of EEG background activity in Alzheimer’s disease patients with Lempel–Ziv complexity and central tendency measure // Medical Engineering & Physics. – 2006. – V. 28(4). – P. 315–322. doi: 10.1016/j.medengphy.2005.07.004
40. Laptinskaya D. et al. Global EEG coherence as a marker for cognition in older adults at risk for dementia // Psychophysiology. – 2020. – V. 57. - e13515.
41. Leuchter A.F. et al. Changes in brain functional connectivity in Alzheimer-type and multi-infarct dementia // Brain. – 1992. – V. 115. – P. 1543–1561
42. Hogan M. J. et al. Memory-related EEG power and coherence reductions in mild Alzheimer’s disease // International Journal Of Psychophysiology. – 2003. – V. 49(2). – P. 147-163.
43. Uhlhaas Р.J., Singer W. Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology // Neuron. – 2006. – V. 52(1). – P. 155-168. doi: 10.1016/j.neuron.2006.09.020
44. Brunovsky M. et al. Objective Assessment of the Degree of Dementia by Means of EEG // Neuropsychobiology. – 2003. – V. 48(1). – P. 19–26. doi: 10.1159/000071824
45. Canuet L. et al. Network disruption and cerebrospinal fluid amyloid-beta and phospho-tau levels in mild cognitive impairment // J Neurosci. – 2015. – V. 35. – P. 10325–10330.
46. Rossini P.M. et al. Conversion from mild cognitive impairment to Alzheimer’s disease is predicted by sources and coherence of brain electroencephalography rhythms // Neuroscience. – 2006. – V. 143. – P. 793–803.
47. Hata M. et al. Cerebrospinal fluid biomarkers of Alzheimer’s disease correlate with electroencephalography parameters assessed by exact low-resolution electromagnetic tomography (eLORETA) // Clin EEG Neurosci. – 2017. – V. 48. – P. 338–347.
48. Ferreira D. et al. Meta-review of CSF core biomarkers in Alzheimer’s disease: The state-of-the-art after the new revised diagnostic criteria // Front. Aging Neurosci. – 2014. – V. 6. – P. 47.
49. Dubois B. et al. Advancing research diagnostic criteria for Alzheimer’s disease: The IWG-2 criteria // Lancet Neurol. – 2014. – V. 13. – P. 614–629.
50. Vickers J.C. et al. The cause of neuronal degeneration in Alzheimer’s disease // Prog. Neurobiol. – 2000. – V. 60. – P. 139–165
51. He Y., Evans A. Graph theoretical modeling of brain connectivity // Curr Opin Neurol. – 2010. – V. 23. – P. 341–350
52. Vecchio F. et al. Human brain networks in physiological aging: a graph theoretical analysis of cortical connectivity from EEG data // J Alzheimers Dis. – 2014. – V. 41. – P. 1239–1249.
53. Miraglia F. et al. EEG characteristics in “eyes-open” versus “eyes-closed” conditions: small-world network architecture in healthy aging and age-related brain degeneration // Clin Neurophysiol. – 2016. – V. 127. – P. 1261–1268.
54. Stam C.J. et al. Small-world networks and functional connectivity in Alzheimer’s disease // Cereb Cortex. - 2007. – V. 17. – P. 92–9.
55. Wang R.F. et al. Decreased coherence and functional connectivity of electroencephalograph in Alzheimer’s disease // Chaos. - 2014. – V. 24. - 033136.
56. Sanz-Arigita E.J. et al. Loss of 'small-world' networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity // PLoS One. – 2010. – V. 5(11). - e13788. doi: 10.1371/journal.pone.0013788.
57. Vecchio F. et al. Human brain networks in cognitive decline: a graph theoretical analysis of cortical connectivity from EEG data // J Alzheimers Dis. – 2014. – V. 41(1). – P. 113-127. doi:10.3233/JAD-132087
58. de Haan W. et al. Functional neural network analysis in frontotemporal dementia and Alzheimer’s disease using EEG and graph theory // BMC Neurosci. – 2009. – V. 10. – P. 101.
59. Yu M.C. et al. Different functional connectivity and network topology in behavioral variant of frontotemporal dementia and Alzheimer’s disease: an EEG study // Neurobiol Aging. - 2016. – V. 42. – P. 150–62.
60. Vecchio F. et al. "Small World" architecture in brain connectivity and hippocampal volume in Alzheimer's disease: a study via graph theory from EEG data // Brain Imaging BehaV. – 2017. – V. 11(2). – P. 473-485.
61. Engels M.M. et al. Declining functional connectivity and changing hub locations in Alzheimer's disease: an EEG study // BMC Neurol. – 2015. – V. 15. – P. 145.