Browse > Article
http://dx.doi.org/10.5351/KJAS.2022.35.1.161

Statistical analysis issues for neuroimaging MEG data  

Kim, Jaehee (Department of Statistics, Duksung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.35, no.1, 2022 , pp. 161-175 More about this Journal
Abstract
Oscillatory magnetic fields produced in the brain due to neuronal activity can be measured by the sensor. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution, which gives information about the brain's functional activity. Potential utilization of high spatial resolution in MEG is likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in some diseases under resting state or task state. This review is a comprehensive report to introduce statistical models from MEG data including graphical network modelling. It is also meaningful to note that statisticians should play an important role in the brain science field.
Keywords
brain network; brain imaging magnetoencephalography (MEG) data; connectivity;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Greenblatt RE, Pflieger ME, Ossadtchi AE (2012). Connectivity measures applied to human brain electrophysiological data, Journal of Neuroscience Methods, 207, 1-16.   DOI
2 Gupta D (2011). Space-time network connectivity and cortical activations preceding spike wave discharges in human absence epilepsy: a MEG study, Medical & Biological Engineering & Computing , 49, 555-565.   DOI
3 Ikeda S and Toyama K (2000). Independent component analysis for noisy data-MEG data analysis, Neural Networks, 13, 1063-1074.   DOI
4 Kloppel S, Stonnington CM, Barnes J, et al. (2008). Accuracy of dementia diagnosis: A direct comparison between radiologists and a computerized method, Brain, 131, 2969-2974.   DOI
5 Shappell H, Caffo BS, Pekar JJ, and Lindquist MA (2019). Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models, NeuroImage, 191, 243-257   DOI
6 Sun FT, Miller LM, and Esposito MD (2004). Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data, NeuroImage, 21, 647-658.   DOI
7 Vaiana M and Muldoon SF (2018). Multilayer brain networks, Journal of Nonlinear Science, 30, 1-23.   DOI
8 Whittaker J (1990). Graphical Models in Applied Multivariate Statistics, Wiley, New York.
9 Zhang J and Su L (2015). Temporal autocorrelation-based beamforming with MEG neuroimaging Data, Journal of the American Statistical Association, 110, 1375-1388.   DOI
10 Foley E, Cerquiglini A, Cavanna A, Nakubulwa MA, Furlong PL, Witton C, and Seri S (2014). Magnetoencephalography in the study of epilepsy and consciousness, Epilepsy and Behavior 30, 38-42.   DOI
11 Chella F, Marzetti L, Stenroos M, Parkkonen L, Ilmoniemi RJ, Romani GL, and Pizzella V (2019). The impact of improved MEG-MRI co-registration on MEG connectivity analysis, Neuroimage, 197, 354-367.   DOI
12 Muthukumaraswamy SD (2013). High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations, Frontiers in Human Neuroscience, 7.
13 Lopez-Sanz D, Serrano N, and Maestu F (2018). The Role of magnetoencephalography in the early stages of alzheimer's disease, Frontiers in Neuroscience, 12.
14 Luckhoo H, Hale JR, Stokes MG, Nobre AC, and Morris PG (2012). Inferring task-related networks using independent component analysis in magnetoencephalography, NeuroImage, 62, 530-541.   DOI
15 Mandal PK, Banerjee A, Tripathi M, and Sharma A (2018). A comprehensive review of MEG studies for brain functionality in healthy aging and alzheimer's disease(AD), Frontiers in Computational Neuroscience, 12.
16 Maestu F, Garcia-Segura J, Ortiz T, et al. (2005). Evidence of biochemical and biomagnetic interactions in Alzheimer's disease: an MEG and MR spectroscopy study, Dementia and Geriatric Cognitive Disorders, 20, 145-152.   DOI
17 Monti MM (2011). Statistical analysis of fMRI time-series: a critical review of the GLM approach, Frontiers in Human Neuroscience, 18.
18 Niso G, Rogers C, Moreau JT, et al. (2016). OMEGA: The Open MEG Archive, NeuroImage, 124, 1182-1187.   DOI
19 Pantazis D and Adler A (2021). MEG source localization via deep learning, Sensors (Basel), 21.   DOI
20 Friston KJ (2011). Functional and effecive connectivity: a Review, Brain Connectivity, 1, 13-36.   DOI
21 Pourahmadi M (2013). High-dimensional covariance estimation, Wiley, New York.
22 Rajesh KK, Donna LM, Lauren EL, Mark RP, Heather MW, Rishi D, and Christi PH (2011). Probing the brain in autism using fMRI and diffusion tensor imaging, Journal of Visualized Experiments, 55, 3178.
23 Robinson LF, Atlas LY, Wager TD (2015). Dynamic functional connectivity using state-based dynamic community structure: method and application to opioid analgesia, Neuroimage, 108, 274-291.   DOI
24 Li K, Guo L, Niea J, Li G, and Liub T (2009). Review of methods for functional brain connectivity detection using fMRI, Computerized Medical Imaging and Graphics, 33, 131-139.   DOI
25 Lindquist MA, Xu Y, Nebel MB, and Caffo BS (2014). Evaluating dynamic bivariate correlations in resting-state fMRI:A comparison study and a new approach, NeuroImage, 101, 531-546.   DOI
26 Ramgopal S, Thome-Souza S, Jackson M, et al. (2014). Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy, Epilepsy Behavior, 37, 291-307.   DOI
27 Rubinov M and Sporns O (2010). Complex network measures of brain connectivity:Uses and interpretations. NeuroImage, 52, 1059-1069.   DOI
28 Sekihara K and Nagarajan SS (2010). Adaptive Spatial Filters for Electro-magnetic Brain Imaging, Springer-Verlag, Berlin.
29 Solo V, Poline JB, Lindquist MA, Simpson SL, Bowman DF, Chung MK, and Cassidy B (2018). Connectivity in fMRI: Blind Spots and Breakthroughs, IEEE Transactions on Medical Imaging, 37, 1537-1550.   DOI
30 Babiloni F, Mattia D, Babiloni C, et al. (2004). Multimodal integration of EEG, MEG and fMRI data for the solution of the neuroimage puzzle, Magnetic Resonance Imaging, 22, 1471-1476.   DOI
31 Vidaurre D, Smith SM, and Woolrich MW (2017). Brain network dynamics are hierarchically organized in time, PNAS 114, 48, https://doi.org/10.1073/pnas.1705120114   DOI
32 Sui J, Adali T, Yu Q, and Calhouna VD (2012). A review of multivariate methods for multimodal fusion of brain imaging data, Journal of Neuroscience Methods 15, 68-81.
33 Taylor JR, Williams N, Cusack R, et al. (2017). The Cambridge Centre for ageing and neuroscience (Cam-CAN) data repository: structural and functional MRI, MEG, and cognitive data from across-sectional adult lifespan sample, Neuroimage, 144, 262-269.   DOI
34 Tovar-Spinoza ZS, Ochi A, Rutka JT, Go C, and Otsubo H (2008). The role of magnetoencephalography in epilepsy surgery, Neurosurgical Focus, 25.
35 Wu W, Nagarajan S, and Chen Z (2016). Bayesian Machine Learning: EEGMEG signal processing measurements, IEEE Signal Processing Magazine, 33, 14-36.
36 Bassett DS and Sporns O (2017). Network neuroscience, Nature Neuroscience, 20, 353-364.   DOI
37 Bowman FD, Zhang L, Derado G, and Chen S (2012). Determining functional connectivity using fMRI data with diffusion-based anatomical weighting, NeuroImage, 62, 1769-1779.   DOI
38 Brookes MJ, Woolrich M, Luckhoo H et al. (2011b). Investigating the electrophysiological basis of resting state networks using magnetoencephalography. In Proceedings of the National Academy of Sciences of the United States of America, 16.
39 Chen SY, He ZP, Han XY, et al. (2019). How big data and high-performance computing drive brain science, Genomics Proteomics & Bioinformatics, 17.
40 Cabral J, Luckhoo H, Woolrich M, et al. (2014). Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations, NeuroImage, 90, 423-435.   DOI
41 Zalesky A, Fornito A, and Bulmore E (2012). On the use of correlation as a measure of network connectivity, NeuroImage, 60, 2096-2106.   DOI
42 Zhang S, Cao C, Quinn A, et al. (2021). Dynamic analysis on simultaneous iEEG-MEG data via hidden Markov model, NeuroImage, 233, 117923.   DOI
43 Zubarev I, Zetter R, Halme HL, Parkkonen LL (2019). Adaptive neural network classifier for decoding MEG signals, Neuroimage, 197, 425-434.   DOI
44 Cassidy B, Rae C, and Solo V (2014), Brain activity: Connectivity, sparsity, and mutual information, IEEE Transactions on Medical Imaging, 34, 846-860.   DOI
45 Bianconi G (2019). Multilayer Networks, Oxford, London.
46 Bolstad A, Veen BDV, and Nowak R (2011). Causal network inference via group sparse regularization, IEEE Transactions on Signal Processing, 59, 2628-2641.   DOI
47 Brookes MJ, Hale JR, Zumer JM et al. (2011a). Measuring functional connectivity using MEG: methodology and comparison with fcMRI, NeuroImage, 56, 1082-1104.   DOI
48 Brookes MJ, Tewarie PK, Hunt BAE, et al. (2016). A multi-layer network approach to MEG connectivity analysis, Neuroimage, 132, 425-438.   DOI
49 Bulmore E (2016). Fundamentals of Brain Network Analysis, Academic Press, London.
50 Cribben I, Haraldsdottir R, Atlas LY, Wager TD, and Lindquist MA (2012). Dynamic connectivity regression: Determining state-related changes in brain connectivity, NeuroImage, 61, 907-920.   DOI
51 Dash D, Ferrari P, and Wang J (2020). Decoding imagined and spoken phrases from non-invasive neural (MEG) signals, Frontiers in Neuroscience, 07.
52 Florin E and Baillet S (2015). The brain's resting-state activity is shaped by synchronized cross-frequency coupling of neural oscillations, NeuroImage, 111, 26-35.   DOI
53 Fornito A, Zalesky A, Pantelis C, and Bullmore ET (2012). Schizophrenia, neuroimaging and connectomics, NeuroImage, 62, 2296-2314.   DOI
54 Friston KJ, Frith CD, Liddle PF, and Frackowiak RSJ (1993). Functional connectivity: the principal-component analysis of large (PET) data sets, Journal of Cerebral Blood Flow & Metabolism 13 5-14.   DOI
55 Brillinger DR (2001). Time Series: Data Analysis and Theory, SIAM, New York.
56 Kim J, Jeong W, and Chung CK (2021). Dynamic functional connectivity change-point detection with random matrix theory inference, Frontiers in Neuroscience, https://doi.org/10.3389/fnins.2021.565029   DOI
57 Warnick R, Guindani M, Erhardt E, Allen E, Calhoun V, and Vannucci M (2018). A bayesian approach for estimating dynamic functional network connectivity in fmri data, Journal of the American Statistical Association, 113, 134-151.   DOI
58 Fornito A, Zalesky A, and Bullmore E (2016). Fundamentals of brain network analysis, Elsevier Academic Press.
59 Iwasaki M, Nakasato N, Shamoto H, et al. (2002). Surgical implications of neuromagnetic spike localization in temporal lobe epilepsy, Epilepsia, 43, 415-424.   DOI
60 Georopoulos AP and Karageorgiou E (2008). Neurostatistics: Applications, challenges and expectations, Statistics in Medicine, 27, 407-417.   DOI
61 Kolaczyk ED (2009). Statistical Analysis of Network Data, Springer, New York.
62 Dharmaprani D, Nguyen HK, Lewis TW, DeLosAngeles D, Willoughby JO, and Pope KJ (2016). A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 825-828.
63 Friston KJ (2005). Models of brain function in neuroimaging, Annual Review of Psychology, 56, 57-87   DOI
64 Golmohammadi M, Torbati AH, Diego SL, Obeid I, and Picone J (2019). Automatic analysis of EEGs using big data and hybrid deep learning architectures, Frontiers in Human Neuroscience, 13.
65 Hastie T, Tibshirani R, and Wainwright M (2015). Statistical Learning with Sparsity: The Lasso and Generalization, CRC Press, New York.
66 Jirsa VK (2004). Connectivity and dynamics of neural information processing, Neuroinformatics, 4, 183-204.   DOI
67 Kostas D, Pang EW, and Rudzicz F (2019). Machine learning for MEG during speech tasks, Scientific Reports, 9.
68 Lee H, Kang H, Chung MK, Kim B, and Lee DS (2012). Persistent brain network homology from the perspective of dendrogram, IEEE Trans Med Imaging, 31, 2267-2277.   DOI