The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Review of complex network analysis for MEG

MEG 복잡계 네트워크 분석에 대한 통계적 고찰

  • Sunhan Shin (Department of Statistics, Duksung Women's University) ;
  • Jaehee Kim (Department of Statistics, Duksung Women's University)
  • 신선한 (덕성여자대학교, 정보통계학과) ;
  • 김재희 (덕성여자대학교, 정보통계학과)
  • Received : 2022.12.09
  • Accepted : 2023.01.02
  • Published : 2023.10.31
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Abstract

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.

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.

Keywords

Acknowledgement

이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 연구 기초연구실 (No. 2021R1A4A5028907) 지원과 기본연구 (No. 2021R1F1A1054968) 지원을 받아 수행한 연구 과제입니다.

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.  https://doi.org/10.1523/JNEUROSCI.3874-05.2006
  2. Albert R and Barabasi AL (2002). Statistical mechanics of complex networks, Reviews of Modern Physics, 74, 47-97.  https://doi.org/10.1103/RevModPhys.74.47
  3. Azondekon R, Harper ZJ, and Welzig CM (2018). Combined MEG and fMRI exponential random graph modeling for inferring functional brain connectivity, Available from: arXiv preprint arXiv:0812.177 
  4. Barabasi AL and Albert R (1999). Emergence of scaling in random networks, Science, 286, 509-512.  https://doi.org/10.1126/science.286.5439.509
  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.  https://doi.org/10.1073/pnas.0606005103
  6. Bassett DS and Gazzaniga MS (2011). Understanding complexity in the human brain,Trends in Cognitive Sciences, 15, 200-209.  https://doi.org/10.1016/j.tics.2011.03.006
  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.  https://doi.org/10.1162/netn_a_00245
  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.  https://doi.org/10.1089/brain.2012.0106
  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.  https://doi.org/10.1016/j.neuroimage.2016.02.045
  10. Brush SG (1967). History of the Lenz-Ising model, Reviews of Modern Physics, 39, 883-893. https://doi.org/10.1103/RevModPhys.39.883
  11. Bullmore E and Sporns O (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems, Nature Reviews Neuroscience, 10, 186-198.  https://doi.org/10.1038/nrn2575
  12. Caldarelli G (2007). Scale-Free Networks: Ccomplex Webs in Nature and Technology, Oxford University Press, Oxford. 
  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.  https://doi.org/10.1007/s12035-014-8685-x
  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.  https://doi.org/10.1007/s00406-018-0968-1
  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.  https://doi.org/10.1140/epjb/e2012-30793-6
  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.  https://doi.org/10.1186/1471-2202-10-1
  17. Dorogovtsev SN and Mendes JFF (2002). Evolution of networks, Advances in Physics, 51, 1079-1187.  https://doi.org/10.1080/00018730110112519
  18. Dorogovtesv SN and Mendes JFF (2005). Complex Systems and Interdisciplinary Science, World Scientific. 
  19. Erdos P and Renyi A (1959). On random graphs I, Publicationes Mathematicae Debrecen, 6, 290-297.  https://doi.org/10.5486/PMD.1959.6.3-4.12
  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.  https://doi.org/10.1016/j.neuroimage.2011.11.084
  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. 
  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.  https://doi.org/10.1007/s11517-011-0778-3
  24. Hammoud Z and Kramer F (2020). Multilayer networks: Aspects, implementations, and application in biomedicin e, Big Data Analytics, 5, 1-18.  https://doi.org/10.1186/s41044-020-00046-0
  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. 
  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.  https://doi.org/10.1162/NETN_a_00011
  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. 
  28. Kim J (2022). Statistical analysis issues for neuroimaging MEG data, The Korean Journal of Applied Statistics, 35, 161-175.  https://doi.org/10.5351/KJAS.2022.35.1.161
  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.  https://doi.org/10.1111/rssb.12014
  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.  https://doi.org/10.1016/j.conb.2014.11.005
  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. 
  32. Lambiotte R, Delvenne JC, and Barahona M (2008). Laplacian dynamics and multiscale modular structure in networks, Available from: arXiv preprint arXiv:0812.177 
  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. 
  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. 
  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.  https://doi.org/10.18637/jss.v083.i06
  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.  https://doi.org/10.1002/nbm.2803
  38. Liu S, Perra N, Karsai M, and Vespignani A (2014). Controlling contagion processes in activity driven networks, Physical Review Letters, 112, 118702. 
  39. Lopez 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. 
  40. Mata ASD (2020). Complex networks: A mini-review, Brazilian Journal of Physics, 50, 658-672.  https://doi.org/10.1007/s13538-020-00772-9
  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. 
  42. Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, and De Peralta RG (2004). EEG source imaging, Clinical Neurophysiology, 115, 2195-2222.  https://doi.org/10.1016/j.clinph.2004.06.001
  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 efficiency hypothesis, Neuroscience Letters, 402, 273-277.  https://doi.org/10.1016/j.neulet.2006.04.006
  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.  https://doi.org/10.1126/science.1184819
  45. Newman M (2018). Networks (2nd ed), Oxford University Press, Oxford. 
  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.  https://doi.org/10.1111/epi.13622
  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. 
  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. 
  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.  https://doi.org/10.1016/j.neuroimage.2016.08.061
  50. Pan RK and Saramaki J (2011). Path lengths, correlations, and centrality in temporal networks, Physical Review E, 84, 016105. 
  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.  https://doi.org/10.1016/j.neuropsychologia.2011.12.007
  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.  https://doi.org/10.1038/s41598-018-37186-2
  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 
  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.  https://doi.org/10.1073/pnas.0913863107
  55. Perra N, Gonc,alves B, Pastor-Satorras R, and Vespignani A (2012). Activity driven modeling of time varying networks, Scientific Reports, 2, 1-7.  https://doi.org/10.1038/srep00469
  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.  https://doi.org/10.1002/hbm.24990
  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. 
  58. Rowland JA, Stapleton-Kotloski JR, Dobbins DL, Rogers E, Godwin DW, and Taber KH (2018). Increased smallworld network topology following deployment-acquired traumatic brain injury associated with the development of post-traumatic stress disorder, Brain Connectivity, 8, 205-211.  https://doi.org/10.1089/brain.2017.0556
  59. Soares DJB, Tsallis C, Mariz AM, and Silva da LR (2005). Preferential attachment growth model and nonextensive statistical mechanics, Europhysics Letters, 70, 70. 
  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.  https://doi.org/10.1016/j.neulet.2003.10.063
  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.  https://doi.org/10.1016/j.neuroimage.2006.05.033
  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.  https://doi.org/10.1016/j.ijpsycho.2014.04.001
  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. 
  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.  https://doi.org/10.1016/j.neuroimage.2014.04.038
  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.  https://doi.org/10.1016/j.neuroimage.2013.10.022
  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.  https://doi.org/10.1016/j.neuroimage.2014.10.015
  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.  https://doi.org/10.1002/hbm.22650
  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.  https://doi.org/10.1162/NETN_a_00011
  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. 
  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. 
  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.  https://doi.org/10.1016/j.neuroimage.2013.10.010
  73. van Dellen E, Sommer IE, Bohlken MM et al. (2018). Minimum spanning tree analysis of the human connectome, Human Brain Mapping, 39, 2455-2471.  https://doi.org/10.1002/hbm.24014
  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.  https://doi.org/10.1007/s10796-017-9784-9
  75. Watts DJ and Strogatz SH (1998). Collective dynamics of 'small-world' networks, Nature, 393, 440-42.  https://doi.org/10.1038/30918
  76. Waxman BM (1988). Routing of multipoint connections, IEEE Journal on Selected Areas in Communications, 6, 1617-1622.  https://doi.org/10.1109/49.12889
  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. 
  78. Wilke C, Worrell G, and He B (2011). Graph analysis of epileptogenic networks in human partial epilepsy, Epilepsia, 52, 84-93.  https://doi.org/10.1111/j.1528-1167.2010.02785.x
  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.  https://doi.org/10.1111/tgis.12285
  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.  https://doi.org/10.1016/j.biopsych.2010.08.022
  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.  https://doi.org/10.1007/s11571-013-9274-9
  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.