본문 바로가기
KERD
논문KCI 등재

Computational electroencephalography analysis for characterizing brain networks

1

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류신경과학

초록

Electroencephalography (EEG) produces time-series data of neural oscillations in the brain, and is one of the most commonly used methods for investigating both normal brain functions and brain disorders. Quantitative EEG analysis enables identification of frequencies and brain activity that are activated or impaired. With studies on the structural and functional networks of the brain, the concept of the brain as a complex network has been fundamental to understand normal brain functions and the pathophysiology of various neurological disorders. Functional connectivity is a measure of neural synchrony in the brain network that refers to the statistical interdependency between neural oscillations over time. In this review, we first discuss the basic methods of EEG analysis, including preprocessing, spectral analysis, and functional-connectivity and graph-theory measures. We then review previous EEG studies of brain network characterization in several neurological disorders, including epilepsy, Alzheimer’s disease, dementia with Lewy bodies, and idiopathic rapid eye movement sleep behavior disorder. Identifying the EEG-based network characteristics might improve the understanding of disease processes and aid the development of novel therapeutic approaches for various neurological disorders.

Abstract

Electroencephalography (EEG) produces time-series data of neural oscillations in the brain, and is one of the most commonly used methods for investigating both normal brain functions and brain disorders. Quantitative EEG analysis enables identification of frequencies and brain activity that are activated or impaired. With studies on the structural and functional networks of the brain, the concept of the brain as a complex network has been fundamental to understand normal brain functions and the pathophysiology of various neurological disorders. Functional connectivity is a measure of neural synchrony in the brain network that refers to the statistical interdependency between neural oscillations over time. In this review, we first discuss the basic methods of EEG analysis, including preprocessing, spectral analysis, and functional-connectivity and graph-theory measures. We then review previous EEG studies of brain network characterization in several neurological disorders, including epilepsy, Alzheimer’s disease, dementia with Lewy bodies, and idiopathic rapid eye movement sleep behavior disorder. Identifying the EEG-based network characteristics might improve the understanding of disease processes and aid the development of novel therapeutic approaches for various neurological disorders.

참고문헌 (45)

  1. 홍승봉, "뇌파의 전기생리학", 대한신경과학회지, 21(3), pp.225~238 (2003), 대한신경과학회
  2. Buzsáki G, "The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes", Nat Rev Neurosci, 13, pp.407~420 (2012)
  3. Buzsáki G, "Neuronal oscillations in cortical networks", Science, 304, pp.1926~1929 (2004)
  4. Jensen O, "Magnetoencephalography: from signals to dynamic cortical networks", pp.1~46 (2019), Springer International Publishing
  5. Jung KY, "Art and application of EEG analysis:from basic to clinical research", pp.124~145 (2017), Daehan Medical Book Publishing
  6. Stam CJ, "The organization of physiological brain networks", Clin Neurophysiol, 123, pp.1067~1087 (2012)
  7. Stam CJ, "Modern network science of neurological disorders", Nat Rev Neurosci, 15, pp.683~695 (2014)
  8. Delorme A, "EEGLAB : an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis", J Neurosci Methods, 134, pp.9~21 (2004)
  9. Makeig S, "Independent component analysis of electroencephalographic data", Advances in Neural Information Processing Systems 9 (NIPS 1996), pp.145~151 (1996), MIT Press
  10. Jung TP, "Removing electroencephalographic artifacts by blind source separation", Psychophysiology, 37, pp.163~178 (2000)
  11. Crespo-Garcia M, "Muscle artifact removal from human sleep EEG by using independent component analysis", Ann Biomed Eng, 36, pp.467~475 (2008)
  12. Polat K, "Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform", Appl Math Comput, 187, pp.1017~1026 (2007)
  13. Mormann F, "Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients", Physica D : Nonlinear Phenomena, 144, pp.358~369 (2000)
  14. Lachaux JP, "Measuring phase synchrony in brain signals", Hum Brain Mapp, 8, pp.194~208 (1999)
  15. Doesburg SM, "Asynchrony from synchrony : long-range gamma-band neural synchrony accompanies perception of audiovisual speech asynchrony", Exp Brain Res, 185, pp.11~20 (2008)
  16. Vinck M, "An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias", Neuroimage, 55, pp.1548~1565 (2011)
  17. Bullmore E, "Complex brain networks : graph theoretical analysis of structural and functional systems", Nat Rev Neurosci, 10, pp.186~198 (2009)
  18. Stam CJ, "Graph theoretical analysis of complex networks in the brain", Nonlinear Biomed Phys, 1, pp.3~ (2007)
  19. Fisher RS, "ILAE official report : a practical clinical definition of epilepsy", Epilepsia, 55, pp.475~482 (2014)
  20. Kramer MA, "Epilepsy as a disorder of cortical network organization", Neuroscientist, 18, pp.360~372 (2012)
  21. Bartolomei F, "Pre-ictal synchronicity in limbic networks of mesial temporal lobe epilepsy", Epilepsy Res, 61, pp.89~104 (2004)
  22. Bettus G, "Enhanced EEG functional connectivity in mesial temporal lobe epilepsy", Epilepsy Res, 81, pp.58~68 (2008)
  23. Bernhardt B, "Imaging structural and functional brain networks in temporal lobe epilepsy", Front Hum Neurosci, 7, pp.624~ (2013)
  24. Bonilha L, "Presurgical connectome and postsurgical seizure control in temporal lobe epilepsy", Neurology, 81, pp.1704~1710 (2013)
  25. Lagarde S, "Interictal stereotactic-EEG functional connectivity in refractory focal epilepsies", Brain, 141, pp.2966~2980 (2018)
  26. Horstmann MT, "State dependent properties of epileptic brain networks : comparative graph-theoretical analyses of simultaneously recorded EEG and MEG", Clin Neurophysiol, 121, pp.172~185 (2010)
  27. Quraan MA, "Altered resting state brain dynamics in temporal lobe epilepsy can be observed in spectral power, functional connectivity and graph theory metrics", PLoS One, 8, pp.e68609~ (2013)
  28. Wilke C, "Graph analysis of epileptogenic networks in human partial epilepsy", Epilepsia, 52, pp.84~93 (2011)
  29. Reitz C, "Epidemiology of Alzheimer disease", Nat Rev Neurol, 7, pp.137~152 (2011)
  30. Jeong J, "EEG dynamics in patients with Alzheimer’s disease", Clin Neurophysiol, 115, pp.1490~1505 (2004)
  31. Stam CJ, "EEG synchronization in mild cognitive impairment and Alzheimer’s disease", Acta Neurol Scand, 108, pp.90~96 (2003)
  32. Stam CJ, "Disturbed fluctuations of resting state EEG synchronization in Alzheimer’s disease", Clin Neurophysiol, 116, pp.708~715 (2005)
  33. Stam CJ, "Smallworld networks and functional connectivity in Alzheimer’s disease", Cereb Cortex, 17, pp.92~99 (2007)
  34. Stam CJ, "Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease", Brain, 132(Pt 1), pp.213~224 (2009)
  35. McKeith IG, "Diagnosis and management of dementia with Lewy bodies : Fourth consensus report of the DLB Consortium", Neurology, 89, pp.88~100 (2017)
  36. Briel RC, "EEG findings in dementia with Lewy bodies and Alzheimer’s disease", J Neurol Neurosurg Psychiatry, 66, pp.401~403 (1999)
  37. Bonanni L, "EEG comparisons in early Alzheimer’s disease, dementia with Lewy bodies and Parkinson’s disease with dementia patients with a 2-year follow-up", Brain, 131(Pt 3), pp.690~705 (2008)
  38. van Dellen E, "Loss of EEG network efficiency is related to cognitive impairment in dementia with lewy bodies", Mov Disord, 30, pp.1785~1793 (2015)
  39. McCann H, "α-Synucleinopathy phenotypes", Parkinsonism Relat Disord, 20(Suppl 1), pp.S62~S67 (2014)
  40. 유수연, "렘수면 행동 장애", Journal of sleep medicine, 15(1), pp.1~7 (2018), 대한수면연구학회[DOI]
  41. Postuma RB, "Risk and predictors of dementia and parkinsonism in idiopathic REM sleep behaviour disorder : a multicentre study", Brain, 142, pp.744~759 (2019)
  42. Fantini ML, "Slowing of electroencephalogram in rapid eye movement sleep behavior disorder", Ann Neurol, 53, pp.774~780 (2003)
  43. Rodrigues Brazète J, "Electroencephalogram slowing predicts neurodegeneration in rapid eye movement sleep behavior disorder", Neurobiol Aging, 37, pp.74~81 (2016)
  44. Sunwoo JS, "Altered functional connectivity in idiopathic rapid eye movement sleep behavior disorder : a resting-state EEG study", Sleep (2017)[DOI]
  45. Olde Dubbelink KT, "Resting-state functional connectivity as a marker of disease progression in Parkinson’s disease : a longitudinal MEG study", Neuroimage Clin, 2, pp.612~619 (2013)

관련 논문AI추천

이 간행물의 다른 논문 (12건)