Inferring Brain Signals Synchronicity From a Sample of EEG Readings

References

• Bastos, A. M., and Schoffelen, J. (2016), “A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretation Pitfalls,” Frontiers in System Neuroscience, 9, 175.
   PubMed Web of Science ®Google Scholar
• Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., and Robbins, K. A. (2015), “The PREP Pipeline: Standardized Preprocessing for Large-Scale EEG Analysis,” Frontiers in Neuroinformatics, 9, 16, available at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4471356/.
   PubMed Web of Science ®Google Scholar
• Broyd, S., Demanuele, C., Debener, S., Helps, K., James, C., and Sonuga-Barke, E. J. (2008), “Default-Mode Brain Dysfunction in Mental Disorders: A Systematic Review,” Nature Biobehavioral Review, 33, 279–296. DOI:10.1016/j.neubiorev.2008.09.002.
   PubMed Web of Science ®Google Scholar
• Burnette, C. P., Henderson, H. A., Inge, A. P., Zahka, N. E., Schwartz, C. B., and Mundy, P. C. (2011), “Anterior EEG Asymmetry and the Modifier Model of Autism,” Journal of Autism and Developmental Disorders, 41, 1113–1124.
   PubMed Web of Science ®Google Scholar
• Celeux, G., Hurn, M., and Robert, C. P. (2000), “Computational and Inferential Difficulties With Mixture Posterior Distributions,” Journal of the American Statistical Association, 95, 957–970. DOI:10.1080/01621459.2000.10474285.
   Web of Science ®Google Scholar
• Craddock, R. C., Jbadi, S., Yan, C. G., Vogelstein, J. T., Castellanos, F. X., Di Martino, A., Kelly, C., Heberlein, K., Colcombe, S., and Milham, M. P. (2013), “Imaging Human Connectomes at the Macroscale,” Nature Methods, 10, 524–539. DOI:10.1038/nmeth.2482.
   PubMed Web of Science ®Google Scholar
• Dahl, D. (2006), “Model-Based Clustering for Expression Data Via a Dirichlet Process Mixture Model,” in Bayesian Inference for Gene Expression and Proteomics, eds. M. Vannucci, K.-A. Do, and P. Müller, Cambridge: Cambridge University Press, pp. 201–215.
   Google Scholar
• Dahlhaus, R. (1997), “Fitting Time Series Models to Nonstationary Processes,” The Annals of Statistics, 25, 1–37. DOI:10.1214/aos/1034276620.
   Web of Science ®Google Scholar
• Di Martino, A., Fair, D. A., Satterthwaite, T. D., Castellanos, F. X., Thomason, M. E., Craddock, R. C., Luna, B., Leventhal, B. L., Zuo, X.N., and Milham, M. P. (2014), “Unraveling the Miswired Connectome: A Developmental Perspective,” Neuron, 17, 1335–1353. DOI:10.1016/j.neuron.2014.08.050.
   Web of Science ®Google Scholar
• Euan, C., Ombao, H., and Ortega, J. (2015), “Spectral Synchronicity in Brain Signals,” arXiv preprint arXiv:1507.05018.
   Google Scholar
• Fell, J., and Axamcher, N. (2011), “The Role of Phase Synchronization in Memory Processes,” Nature Reviews Neuroscience, 12, 105–118. DOI:10.1038/nrn2979.
   PubMed Web of Science ®Google Scholar
• Gelman, A., and Hill, J. (2007), Data Analysis Using Regression and Multilevel/Hierarchical Models, New York: Cambridge University Press.
   Google Scholar
• Hasenstab, K., Scheffler, A., Telesca, D., Sugar, C. A., DiStefano, C., and Şentürk, D. (2016a), “A Multi-Dimensional Functional Principal Components Analysis of EEG Data,” Technical Report, UCLA-Biostatistics.
   Google Scholar
• Hasenstab, K., Sugar, C. A., Telesca, D., Jeste, S., and Şentürk, D. (2016b), “Robust Functional Clustering of ERP Data With Application to a Study of Implicit Learning in Autism,” Biostatistics, 3, 484–498. DOI:10.1093/biostatistics/kxw002.
   Web of Science ®Google Scholar
• Hasenstab, K., Sugar, C., Jeste, S., Telesca, D., McEvoy, K., and Senturk, D. (2015), “Identifying Longitudinal Trends Within EEG Experiments,” Biometrics, 71, 1090–1100. DOI:10.1111/biom.12347.
   PubMed Web of Science ®Google Scholar
• Jasra, A., Holmes, C., and Stephens, D. (2005), “Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling,” Statistical Science, 20, 50–67. DOI:10.1214/088342305000000016.
   Web of Science ®Google Scholar
• Laufs, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., Salek-Haddadi, A., and Kleinschmidt, A. (2003), “Electroencephalographic Signatures of Attentional and Cognitive Default Modes in Spontaneous Brain Activity Fluctuations at Rest,” Proceedings of the National Academy of Sciences of the United States of America, 100, 11053–11058. DOI:10.1073/pnas.1831638100.
   PubMed Web of Science ®Google Scholar
• Lock, E. F., and Dunson, D. B. (2013), “Bayesian Consensus Clustering,” Bioinformatics, 29, 2610–2616. DOI:10.1093/bioinformatics/btt425.
   PubMed Web of Science ®Google Scholar
• Müller, P., Quintana, F., and Rosner, G. L. (2011), “A Product Partition Model With Regression on Covariates,” Journal of Computational and Graphical Statistics, 20, 260–278. DOI:10.1198/jcgs.2011.09066.
   PubMed Web of Science ®Google Scholar
• Narayan, M., and Allen, G. I. (2015), “Population Inference for Node Level Differences in Multi-Subject Functional Connectivity,” in IEEE International Workshop on Pattern Recognition in Neuroimaging.
   Google Scholar
• Ng, A. Y., Jordan, M. I., and Weiss, Y. (2001), “On Spectral Clustering: Analysis and an Algorithm,” in Advances in Neural Information Processing Systems, Cambridge: MIT Press, pp. 849–856.
   Google Scholar
• Nguyen, N., and Caruana, R. (2007), “Consensus Clusterings,” in Proceedings of the 7th IEEE International Conference on Data Mining, ed. I. C. Society, pp. 607–612.
   Google Scholar
• Ombao, H. C., Raz, J. A., Strawderman, R. L., and von Sachs, R. (2001), “A Simple Generalized Cross-Validation Method of Span Selection for Periodogram Smoothing,” Biometrika, 88, 1186–1192. DOI:10.1093/biomet/88.4.1186.
   Web of Science ®Google Scholar
• Parr, J. R., Le Couteur, A., Baird, G., Rutter, M., Pickles, A., Fombonne, E., Bailey, A. J., and International Molecular Genetic Study of Autism Consortium (IMGSAC) Members, (2011), “Early Developmental Regression in Autism Spectrum Disorder: Evidence From an International Multiplex Sample,” Journal of Autism and Developmental Disorders, 41, 332–340. DOI:10.1007/s10803-010-1055-2.
   PubMed Web of Science ®Google Scholar
• Rosen, O., Wood, S., and Stoffer, D. S. (2012), “AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series,” Journal of the American Statistical Association, 107, 1575–1589. DOI:10.1080/01621459.2012.716340.
   Web of Science ®Google Scholar
• Schwarz, G. (1978), “Estimating the Dimension of a Model,” The Annals of Statistics, 6, 461–464. DOI:10.1214/aos/1176344136.
   Web of Science ®Google Scholar
• Shou, H., Eloyan, A., Nebel, M. B., Mejia, A., Pekar, J. J., Mostofsky, S., Caffo, B., Lindquist, M. A., and Crainiceanu, C. (2014), “Shrinkage Prediction of Seed-Voxel Brain Connectivity Using Resting State fMRI,” Neuroimage, 15, 938–944. DOI:10.1016/j.neuroimage.2014.05.043.
   Web of Science ®Google Scholar
• Steele, R. J., and Raftery, A. E. (2010), “Performance of Bayesian Model Selection Criteria for Gaussian Mixture Models,” in Frontiers of Statistical Decision Making and Bayesian Analysis, eds. M.-H. Chen, P. Müller, D. K. Dey, and D. Sun, Berlin: Springer, pp. 113–130.
   Google Scholar
• Stroganova, T. A., Nygren, G., Tsetlin, M. M., Posikera, I. N., Gillberg, C., Elam, M., and Orekhova, E. V. (2007), “Abnormal EEG Lateralization in Boys With Autism,” Clinical Neurophysiology, 118, 1842–1854.
   PubMed Web of Science ®Google Scholar
• Teplan, M. (2002), “Fundamentals of EEG Measurements,” Measurement Science Review, 2, 1–11.
   Google Scholar
• Wang, J., Barstein, J., Ethridge, L. E., Mosconi, M. W., Takarae, Y., and Sweeney, J. A. (2013), “Resting State EEG Abnormalities in Autism Spectrum Disorders,” Journal of Neurodevelopmental Disorders, 5, 1.
   PubMed Web of Science ®Google Scholar