search for




 

MEG 복잡계 네트워크 분석에 대한 통계적 고찰
Review of complex network analysis for MEG
Korean J Appl Stat 2023;36(5):361-380
Published online October 31, 2023
© 2023 The Korean Statistical Society.

신선한a, 김재희1,a
Sunhan Shina, Jaehee Kim1,a

a덕성여자대학교, 정보통계학과

aDepartment of Statistics, Duksung Women’s University
1Department of Statistics, Duksung Women’s University, 419 Samyang-ro, 144 Gil 33, Dobong-Gu, Seoul 01369, Korea. E-mail: jaehee@duksung.ac.kr
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A4A5028907) and Basic Research (No. 2021R1F1A1054968).
Received December 9, 2022; Revised December 31, 2022; Accepted January 2, 2023.
Abstract
Magnetoencephalography (MEG)는 뉴론 활동에 신경 세포들간 전류 흐름에 의해 유도된 자기장을 측정하는 비침습 뇌영상 기술이다. 기능적 뇌활동은 뇌영역간 또는 뉴런들의 연결로 기능적 연결로 수행된다. MEG 데이터는 상관성, 시공간성을 가지며 다중 다층적 동적 네트워크인 특징을 갖는다. 이러한 복잡성 때문에 MEG 네트워크에 대한 연구는 아직 많지 않은 편이다. 본 연구에서는 MEG 네트워크 모형과 분석법을 소개하고 실제 MEG 데이터 분석에 활용되어 해석된 경우를 요약하고 앞으로 MEG 네트워크 모형 개발 연구의 필요성을 설명하고자 한다. 그러므로 통계적 네트워크 분석이 뇌과학에서 신경학적 질병을 포함하여 뇌기능에 대한 이해에 중요한 역할을 할 수 있음을 알리고자 한다.
Magnetoencephalography (MEG) is a technique to record oscillatory magnetic fields coming from ongoing neuronal activity. Functional brain activities performing cognitive or physiological tasks are performed on structural connections between neurons or brain regions. MEG data can be characterized as highly correlated, spatio-temporal, multidimensional, multilayered dynamic networks. Due to its complex structure, many studies on MEG network have not yet been conducted. In this study, we will explain the concept, necessity, and possible approaches of MEG network analysis. We reviewed the characteristics of MEG data. Network measures and potential network models in MEG and clinical studies are also reviewed.
주요어 : 복잡계 네트워크, MEG, 네트워크 모형, MEG 네트워크 분석
Keywords : complex network, MEG, network model, MEG network analysis
References
  1. Achard S, Salvador R, Whitcher B, Suckling J, and Bullmore E (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs, Journal of Neuroscience, 26, 63-72.
    Pubmed KoreaMed CrossRef
  2. Albert R and Barabasi AL (2002). Statistical mechanics of complex networks, Reviews of Modern Physics, 74, 47-97.
    CrossRef
  3. Azondekon R, Harper ZJ, and Welzig CM (2018). Combined MEG and fMRI exponential random graph modeling for inferring functional brain connectivity,
  4. Barabasi AL and Albert R (1999). Emergence of scaling in random networks, Science, 286, 509-512.
    Pubmed CrossRef
  5. Bassett DS, Meyer-Lindenberg A, Achard S, Duke T, and Bullmore E (2006). Adaptive reconfiguration of fractal small-world human brain functional networks, Proceedings of the National Academy of Sciences, 103, 19518-19523.
    Pubmed KoreaMed CrossRef
  6. Bassett DS and Gazzaniga MS (2011). Understanding complexity in the human brain,Trends in Cognitive Sciences, 15, 200-209.
    Pubmed KoreaMed CrossRef
  7. Blomsma N, B de Rooy, Gerritse F et al. (2022). Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology, and disorder specificity, Network Neuroscience, 6, 301-319.
    Pubmed KoreaMed CrossRef
  8. Boersma M, Smit DJA, Boomsma DI, Eco JC De Geus, Henriette ADW, and Stam CJ (2013). Growing trees in child brains: Graph theoretical analysis of electroencephalography-derived minimum spanning tree in 5-and 7-year-old children reflects brain maturation, Brain Connectivity, 3, 50-60.
    Pubmed CrossRef
  9. Brookes MJ, Tewarie PK, Hunt BAE, Robson SE, Gascoyne LE, Liddle EB, Liddle PF, and Morris PG (2016). A multi-layer network approach to MEG connectivity analysis, NeuroImage,132, 425-438.
    Pubmed KoreaMed CrossRef
  10. Brush SG (1967). History of the Lenz-Ising model, Reviews of Modern Physics, 39, 883-893.
    CrossRef
  11. Bullmore E and Sporns O (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems, Nature Reviews Neuroscience, 10, 186-198.
    Pubmed CrossRef
  12. Caldarelli G (2007). Scale-Free Networks: Ccomplex Webs in Nature and Technology, Oxford University Press, Oxford.
    KoreaMed CrossRef
  13. Cao M, Shu N, Cao Q, Wang Y, and He Y (2014). Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder, Molecular Neurobiology, 50, 1111-1123.
    Pubmed CrossRef
  14. Chattun MR, Zhang S, Chen Y, Wang Q, Amdanee N, Tian S, Lu Q, and Yao Z (2020). Caudothalamic dysfunction in drug-free suicidally depressed patients: An MEG study, European Archives of Psychiatry and Clinical Neuroscience, 270, 217-227.
    Pubmed CrossRef
  15. de Almeida ML, Mendes GA, Madras Viswanathan G, and da Silva LR (2013). Scale-free homophilic network, The European Physical Journal B, 86 1-6.
    CrossRef
  16. De Haan W, Pijnenburg YA, Strijers RL, van der Made Y, van der Flier WM, Scheltens P, and Stam CJ (2009). Functional neural network analysis in frontotemporal dementia and Alzheimer’s disease using EEG and graph theory, BMC Neuroscience, 10, 1-12.
    Pubmed KoreaMed CrossRef
  17. Dorogovtsev SN and Mendes JFF (2002). Evolution of networks, Advances in Physics, 51, 1079-1187.
    CrossRef
  18. Dorogovtesv SN and Mendes JFF (2005). Complex Systems and Interdisciplinary Science, World Scientific.
  19. Erdős P and Rényi A (1959). On random graphs I, Publicationes Mathematicae Debrecen, 6, 290-297.
    CrossRef
  20. Euler L (1741). Solutio problematis ad geometriam situs pertinentis, Commentarii academiae scientiarum Petrop olitanae, 128-140.
  21. Ewald A, Marzetti L, Zappasodi F, Meinecke FC, and Nolte G (2012). Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space, Neuroimage, 60, 476-488.
    Pubmed CrossRef
  22. Gomez S, Diaz-Guilera A, Gomez-Gardenes J, Perez-Vicente CJ, Moreno Y, and Arenas A (2013). Diffusion dynamics on multiplex networks, Physical Review Letters, 110, 028701.
    Pubmed CrossRef
  23. Gupta D, Ossenblok P, and van Luijtelaar G (2011). Space-Time network connectivity and cortical activations preceding spike wave discharges in human absence epilepsy: A MEG study, Medical and Biological Engineering and Computing, 49, 555-565.
    Pubmed CrossRef
  24. Hammoud Z and Kramer F (2020). Multilayer networks: Aspects, implementations, and application in biomedicin e, Big Data Analytics, 5, 1-18.
    CrossRef
  25. Hasegawa C, Takahashi T, Ikeda T et al. (2021). Effects of familiarity on child brain networks when listening to a storybook reading: A magneto-encephalographic study, NeuroImage, 241, 118389.
    Pubmed CrossRef
  26. Hedley WT, Brantefors P, and Fransson P (2017). From static to temporal network theory: Applications to functional brain connectivity, Network Neuroscience, 1, 69-99.
    Pubmed KoreaMed CrossRef
  27. Hironaga N, Takei Y, Mitsudo T, Kimura T, and Hirano Y (2020). Prospects for future methodological development and application of magnetoencephalography devices in psychiatry, Frontiers in Psychiatry, 11, 863.
    Pubmed KoreaMed CrossRef
  28. Kim J (2022). Statistical analysis issues for neuroimaging MEG data, The Korean Journal of Applied Statistics, 35, 161-175.
  29. Krivitsky PN and Handcock MS (2014). A separable model for dynamic networks, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76, 29-46.
    Pubmed KoreaMed CrossRef
  30. Kozma R and Puljic M (2015). Random graph theory and neuropercolation for modeling brain oscillations at criticality, Current Opinion in Neurobiology, 31, 181-188.
    Pubmed CrossRef
  31. Kulik SD, Derks J, Numan T et al. (2019). P14. 53 Deconstructing pathologically increased MEG network clustering in glioma patients, Neuro-Oncology, 21, iii79.
    KoreaMed CrossRef
  32. Lambiotte R, Delvenne JC, and Barahona M (2008). Laplacian dynamics and multiscale modular structure in networks,
  33. Lee KH, Xue L, and Hunter DR (2020). Model-based clustering of time-evolving networks through temporal exponential-family random graph models, Journal of Multivariate Analysis, 175, 104540.
    Pubmed KoreaMed CrossRef
  34. Lehmann BCL (2019). Inferring differences between networks using Bayesian exponential random graph models (Doctoral dissertation), University of Cambridge, Cambridge.
  35. Lehmann BCL, Henson RN, Geerligs L, and White SR (2021). Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models, NeuroImage, 225, 117480.
    Pubmed KoreaMed CrossRef
  36. Leifeld P, Cranmer SJ, and Desmarais BA (2018). Temporal exponential random graph models with btergm: Estimation and bootstrap confidence intervals, Journal of Statistical Software, 83, 1-36.
    CrossRef
  37. Liu Z, Zhang Y, Bai L et al. (2012). Investigation of the effective connectivity of resting state networks in Alzheimer’s disease: A functional MRI study combining independent components analysis and multivariate Granger causality analysis, NMR in Biomedicine, 25, 1311-1320.
    Pubmed CrossRef
  38. Liu S, Perra N, Karsai M, and Vespignani A (2014). Controlling contagion processes in activity driven networks, Physical Review Letters, 112, 118702.
    Pubmed CrossRef
  39. López ME, Engels MMA, van Straaten ECW et al. (2017). MEG beamformer-based reconstructions of functional networks in mild cognitive impairment, Frontiers in Aging Neuroscience, 9, 107.
    Pubmed KoreaMed CrossRef
  40. Mata ASD (2020). Complex networks: A mini-review, Brazilian Journal of Physics, 50, 658-672.
    KoreaMed CrossRef
  41. Mandal PK, Banerjee A, Tripathi M, and Sharma A (2018). A comprehensive review of magnetoencephalography (MEG) studies for brain functionality in healthy aging and Alzheimer’s disease (AD), Frontiers in Computational Neuroscience, 12, 60.
    Pubmed KoreaMed CrossRef
  42. Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, and De Peralta RG (2004). EEG source imaging, Clinical Neurophysiology, 115, 2195-2222.
    Pubmed CrossRef
  43. Micheloyannis S, Pachou E, Stam CJ, Vourkas M, Erimaki S, and Tsirka V (2006). Using graph theoretical analysis of multi channel EEG to evaluate the neural eciency hypothesis, Neuroscience Letters, 402, 273-277.
    Pubmed CrossRef
  44. Mucha PJ, Richardson T, Macon K, Porter MA, and Onnela JP (2010). Community structure in time-dependent, multiscale, and multiplex networks, Science, 328, 876-878.
    Pubmed CrossRef
  45. Newman M (2018). Networks (2nd ed), Oxford University Press, Oxford.
    KoreaMed CrossRef
  46. Nissen IA, Stam CJ, Reijneveld JC et al. (2017). Identifying the epileptogenic zone in interictal resting-state MEG source-space networks, Epilepsia, 58, 137-148.
    Pubmed CrossRef
  47. Nissen IA, Stam CJ, Van Straaten EC et al. (2018). Localization of the epileptogenic zone using interictal MEG and machine learning in a large cohort of drug-resistant epilepsy patients, Frontiers in Neurology, 9, 647.
    Pubmed KoreaMed CrossRef
  48. Nugent AC, Ballard ED, Gilbert JR, Tewarie PK, Brookes MJ, and Zarate Jr CA (2020). Multilayer MEG functional connectivity as a potential marker for suicidal thoughts in major depressive disorder, NeuroImage: Clinical, 28, 102378.
    Pubmed KoreaMed CrossRef
  49. O’Neill GC, Tewarie PK, Colclough GL, Gascoyne LE, Hunt BAE, Morris PG, Woolrich MW, and Brookes MJ (2017). Measurement of dynamic task related functional networks using MEG, NeuroImage, 146, 667-678.
    Pubmed KoreaMed CrossRef
  50. Pan RK and Saramäki J (2011). Path lengths, correlations, and centrality in temporal networks, Physical Review E, 84, 016105.
    Pubmed CrossRef
  51. Paraskevopoulos E, Kuchenbuch A, Herholz SC, and Pantev C (2012). Statistical learning effects in musicians and non-musicians: An MEG study, Neuropsychologia, 50, 341-349.
    Pubmed CrossRef
  52. Paraskevopoulos E, Dobel C, Wollbrink A, Salvari V, Bamidis PD, and Pantev C (2019). Maladaptive alterations of resting state cortical network in Tinnitus: A directed functional connectivity analysis of a larger MEG data set, Scientific Reports, 9, 1-11.
    Pubmed KoreaMed CrossRef
  53. Partamian H, Tabbal J, Hassan M, and Karameh F (2022). Analysis of task-related MEG functional brain networks using dynamic mode decomposition, Journal of Neural Engineering, 20, 016011,
    Available from: bioRxiv
    Pubmed CrossRef
  54. Pasquale DF, Penna SD, Snyder AZ et al. (2010). Temporal dynamics of spontaneous MEG activity in brain networks, Proceedings of the National Academy of Sciences, 107, 6040-6045.
    Pubmed KoreaMed CrossRef
  55. Perra N, Gonçalves B, Pastor-Satorras R, and Vespignani A (2012). Activity driven modeling of time varying networks, Scientific Reports, 2, 1-7.
    Pubmed KoreaMed CrossRef
  56. Pourmotabbed H, Wheless JW, and Babajani-Feremi A (2020). Lateralization of epilepsy using intra-hemispheric brain networks based on resting-state MEG data, Human Brain Mapping, 41, 2964-2979.
    Pubmed KoreaMed CrossRef
  57. Ramaraju S,Wang Y, Sinha N, McEvoy AW, Miserocchi A, de Tisi J, and Duncan JS (2020). Removal of interictal MEG-derived network hubs is associated with postoperative seizure freedom, Frontiers in Neurology, 11, 563847.
    Pubmed KoreaMed CrossRef
  58. Rowland JA, Stapleton-Kotloski JR, Dobbins DL, Rogers E, Godwin DW, and Taber KH (2018). Increased small-world network topology following deployment-acquired traumatic brain injury associated with the development of post-traumatic stress disorder, Brain Connectivity, 8, 205-211.
    Pubmed KoreaMed CrossRef
  59. Soares DJB, Tsallis C, Mariz AM, and Silva da LR (2005). Preferential attachment growth model and nonextensive statistical mechanics, Europhysics Letters, 70, 70.
    CrossRef
  60. Song C, Wang D, and Barabasi AL (2012). Joint scaling theory of human dynamics and network science,
    Available from: arXiv:1209.1411v1
  61. Stam CJ (2004). Functional connectivity patterns of human magnetoencephalographic recordings: A ‘small-world’network?, Neuroscience Letters, 355, 25-28.
    Pubmed CrossRef
  62. Stam CJ, Jones BF, Manshanden I et al. (2006). Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer’s disease, Neuroimage, 32, 1335-1344.
    Pubmed CrossRef
  63. Stam CJ, Tewarie P, Van Dellen E, Van Straaten ECW, Hillebrand A, and Van Mieghem P (2014). The trees and the forest: Characterization of complex brain networks with minimum spanning trees, International Journal of Psychophysiology, 92, 129-138.
    Pubmed CrossRef
  64. Supekar K, Menon V, Rubin D, Musen M, and Greicius MD (2008). Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease, PLoS Computational Biology, 4, e1000100.
    Pubmed KoreaMed CrossRef
  65. Tewarie P, Hillebrand A, van Dellen E et al. (2014). Structural degree predicts functional network connectivity: A multimodal resting-tate fMRI and MEG study, NeuroImage, 97, 296-307.
    Pubmed CrossRef
  66. Tewarie P, Hillebrand A, Schoonheim MM, van Dijk BW, Geurts JJG, Barkhof F, Polman CH, and Stam CJ (2014). Functional brain network analysis using minimum spanning trees in multiple sclerosis: An MEG source-space study, Neuroimage, 88, 308-318.
    Pubmed CrossRef
  67. Tewarie P, van Dellen E, Hillebrand A, and Stam CJ (2015). The minimum spanning tree: An unbiased method for brain network analysis, Neuroimage, 104, 177-188.
    Pubmed CrossRef
  68. Tewarie P, Schoonheim MM, Schouten DI et al. (2015). Functional brain networks: Linking thalamic atrophy to clinical disability in multiple sclerosis, a multimodal fMRI and MEG study, Human Brain Mapping, 36, 603-618.
    Pubmed KoreaMed CrossRef
  69. Thompson WH, Brantefors P, and Fransson P (2017). From static to temporal network theory: Applications to functional brain connectivity, Network Neuroscience, 1, 69-99.
    Pubmed KoreaMed CrossRef
  70. Tsai ML,Wang CC, Lee FC, Peng SJ, Chang H, and Tseng SH (2022). Resting-State EEG functional connectivity in children with rolandic spikes with or without clinical seizures, Biomedicines, 10, 1553.
    Pubmed KoreaMed CrossRef
  71. van Dellen E, Douw L, Hillebrand A et al. (2012). MEG network differences between low-and high-grade glioma related to epilepsy and cognition, PloS One, 7, e50122.
    Pubmed KoreaMed CrossRef
  72. van Dellen E, Douw L, Hillebrand A, Hamer PC, Baayen JC, Heimans JJ, and Stam CJ (2014). Epilepsy surgery outcome and functional network alterations in longitudinal MEG: A minimum spanning tree analysis, NeuroImage, 86, 354-363.
    Pubmed CrossRef
  73. van Dellen E, Sommer IE, BohlkenMMet al. (2018). Minimum spanning tree analysis of the human connectome, Human Brain Mapping, 39, 2455-2471.
    Pubmed KoreaMed CrossRef
  74. Volkovich Y, Scellato S, Mascolo C, Laniado D, and Kaltenbrunner A (2017). The impact of geographic distance on online social interactions, Information Systems Frontiers, 20, 1203-1218.
    CrossRef
  75. Watts DJ and Strogatz SH (1998). Collective dynamics of ‘small-world’ networks, Nature, 393, 440-42.
    Pubmed CrossRef
  76. Waxman BM (1988). Routing of multipoint connections, IEEE Journal on Selected Areas in Communications, 6, 1617-1622.
    CrossRef
  77. Wang B, Niu Y, Miao L et al. (2017). Decreased complexity in Alzheimer’s disease: Resting-state fMRI evidence of brain entropy mapping, Frontiers in Aging Neuroscience, 9, 378.
    Pubmed KoreaMed CrossRef
  78. Wilke C, Worrell G, and He B (2011). Graph analysis of epileptogenic networks in human partial epilepsy, Epilepsia, 52, 84-93.
    Pubmed KoreaMed CrossRef
  79. Xu Y, Belyi A, Bojic I, and Ratti C (2017). How friends share urban space: An exploratory spatiotemporal analysis using mobile phone data, Transactions in GIS, 21, 468-487.
    CrossRef
  80. Zalesky A, Fornito A, Seal ML, Cocchi L, Westin C-F, Bullmore ET, Egan GF, and Pantelis C (2011). Disrupted axonal fiber connectivity in schizophrenia, Biological Psychiatry, 69, 80-89.
    Pubmed KoreaMed CrossRef
  81. Zhang X, Lei X, Wu T, and Jiang T (2014). A review of EEG and MEG for brainnetome research, Cognitive Neurodynamics, 8, 87-98.
    Pubmed KoreaMed CrossRef
  82. Zhu Y, Liu J, Ye C, Mathiak K, Astikainen P, Ristaniemi T, and Cong F (2020). Discovering dynamic task-modulated functional networks with specific spectral modes using MEG, NeuroImage, 218, 116924.
    Pubmed CrossRef
February 2024, 37 (1)