Modeling the Evolution of Dynamic Brain Processes During an Associative Learning Experiment

References

• Arieli, A., Sterkin, A., Grinvald, A., and Aertsen, A. (1996), “Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses,” Science, 273, 1868–1453.
   PubMed Web of Science ®Google Scholar
• Bédard, P., and Sanes, J. (2009), “On a Basal Ganglia Role in Learning and Rehearsing Visual-motor Associations,” NeuroImage, 47, 1701–1710.
   PubMed Web of Science ®Google Scholar
• Brillinger, D. (1973), “The Analysis of Time Series Collected in an Experimental Design,” in Multivariate Analysis-III, ed. P. Krishnaiah, New York: Academic, pp. 241–256.
   Google Scholar
• Brillinger, D. (2001), Time Series: Data Analysis and Theory, Philadelphia, PA: Society for Industrial and Applied Mathematics.
   Google Scholar
• Courtemanche, R., Fujii, N., and Graybiel, A. (2003), “Synchronous, Focally Modulated β-Band Oscillations Characterize Local Field Potential Activity in the Striatum of Awake Behaving Monkeys,” The Journal of Neuroscience, 23, 11741–11752.
   PubMed Web of Science ®Google Scholar
• Czanner, G., Eden, U., Wirth, S., Yanike, M., Suzuki, W., and Brown, E. (2008), “Analysis of Between-Trial and Within-Trial Neural Spiking Dynamics,” Journal of Neurophysiology, 99, 2672–2693.
   PubMed Web of Science ®Google Scholar
• Dahlhaus, R. (1997), “Fitting Time Series Models to Nonstationary Processes,” The Annals of Statistics, 25, 1–37.
   Web of Science ®Google Scholar
• Dahlhaus, R. (2000), “A Likelihood Approximation for Locally Stationary Processes,” The Annals of Statistics, 28, 1762–1794.
   Web of Science ®Google Scholar
• DeCoteau, W., Thorn, C., Gibson, D., Courtemanche, R., Mitra, P., Kuobta, Y., and Graybiel, A. (2007), “Learning-Related Coordination of Striatal and Hippocampal Theta Rhythms During Acquisition of a Procedural Maze Task,” PNAS, 104, 5644–5649.
   PubMed Web of Science ®Google Scholar
• Dette, H., and Neumeyer, N. (2001), “Nonparametric Analysis of Covariance,” Annals of Statistics, 29, 1361–1400.
   Web of Science ®Google Scholar
• Diggle, P. J., and Wasel, I. A. (1997), “Spectral Analysis of Replicated Biomedical Time Series,” Journal of the Royal Statistical Society, Series C, 46, 31–71.
   Google Scholar
• Duann, J.-R., Jung, T.-P., Kuo, W.-J., Yeh, T.-C., Makeig, S., Hsieh, J.-C., and Sejnowski, T. (2002), “Single-Trial Variability in Event-Related Bold Signals,” NeuroImage, 15, 823–835.
   PubMed Web of Science ®Google Scholar
• Freyermuth, J.-M., Ombao, H., and von Sachs, R. (2010), “Tree-Structured Wavelet Estimation in a Mixed Effects Model for Spectra of Replicated Time Series,” Journal of the American Statistical Association, 105, 634–646.
   Web of Science ®Google Scholar
• Gasser, T., Möcks, J., and Verleger, R. (1983), “Selavco: A Method to Deal With Trial-to-Trial Variability of Evoked Potentials,” Electron, 55, 717–723.
   Google Scholar
• Guo, W., and Dai, M. (2006), “Multivariate Time-Dependent Spectral Analysis Using Cholesky Decomposition,” Statistica Sinica, 16, 825–845.
   Web of Science ®Google Scholar
• Guo, W., Dai, M., Ombao, H., and von Sachs, R. (2003), “Smoothing Spline Anova for Time-Dependent Spectral Analysis,” Journal of the American Statistical Association, 98, 643–652.
   Web of Science ®Google Scholar
• Hadj-Bouziane, F., and Boussaoud, D. (2003), “Neuronal Activity in the Monkey Striatum During Conditional Visuomotor Learning,” Experimental Brain Research, 153, 190–196.
   PubMed Web of Science ®Google Scholar
• Härdle, W., and Mammen, E. (1993), “Comparing Nonparametric Versus Parametric Regression Fits,” Annals of Statistics, 21, 1926–1947.
   Web of Science ®Google Scholar
• Hastie, T., and Tibshirani, R. (1990), Generalized Additive Models, London: Chapman and Hall.
   Google Scholar
• Nason, G., von Sachs, R., and Kroisandt, G. (2000), “Wavelet Processes and Adaptive Estimation of the Evolutionary Wavelet Spectrum,” Journal of the Royal Statistical Society, Series B, 62, 271–292.
   Google Scholar
• O’Keefe, J. (1993), “Hippocampus, Theta, and Spatial Memory,” Current Opinion in Neurobiology, 3, 917–924.
   PubMedGoogle Scholar
• Ombao, H., Raz, J., von Sachs, R., and Guo, W. (2002), “The Slex Model of a Non-Stationary Random Process,” Annals of the Institute of Statistical Mathematics, 54, 171–200.
   Web of Science ®Google Scholar
• Ombao, H., Raz, J., von Sachs, R., and Malow, B. (2001), “Automatic Statistical Analysis of Bivariate Nonstationary Time Series,” Journal of the American Statistical Association, 96, 543–560.
   Web of Science ®Google Scholar
• Ombao, H., and van Bellegem, S. (2008), “Coherence Analysis: A Linear Filtering Point of View,” IEEE Transactions on Signal Processing, 56, 2259–2266.
   Web of Science ®Google Scholar
• Park, T., Eckley, I., and Ombao, H. (2014), “Estimating Time-Evolving Partial Coherence Between Signals via Multivariate Locally Stationary Wavelet Processes,” IEEE Transactions on Signal Processing, 62, 5240–5250.
   Web of Science ®Google Scholar
• Priestley, M. (1965), “Evolutionary Spectra and Non-Stationary Processes,” Journal of the Royal Statistical Society, Series B, 28, 228–240.
   Google Scholar
• Qin, L., Guo, W., and Litt, B. (2009), “A Time–Frequency Functional Model for Locally Stationary Time Series Data,” Journal of Computational and Graphical Statistics, 18, 675–693.
   PubMed Web of Science ®Google Scholar
• Rosen, O., Wood, S., and Stoffer, D. (2012), “Adaptspec: Adaptive Spectral Estimation for Nonstationary Time Series,” Journal of the American Statistical Association, 107, 1575–1589.
   Web of Science ®Google Scholar
• Sanderson, J., Fryzlewicz, P., and Jones, M. (2010), “Estimating Linear Dependence Between Nonstationary Time Series Using the Locally Stationary Wavelet Model,” Biometrika, 97, 435–446.
   Web of Science ®Google Scholar
• Sato, J. R., Morettin, P. A., Arantes, P. R., and Amaro, Jr, E. (2007), “Wavelet Based Time-Varying Vector Autoregressive Modelling,” Computational Statistics & Data Analysis, 51, 5847–5866.
   Web of Science ®Google Scholar
• Seger, C., and Cincotta, C. (2006), “Dynamics of Frontal, Striatal, and Hippocampal Systems During Rule Learning,” Cerebral Cortex, 16, 1546–1555.
   PubMed Web of Science ®Google Scholar
• Shumway, R. (1983), “Replicated Time Series Regression: An Approach to Signal Estimation and Detection,” in Handbook of Statistics (Vol. 3), eds. D. Brillinger, and P. Krishnaiah, Amsterdam: North Holland, pp
   Google Scholar
• Siegel, S., and Castellan, N. (1988), Nonparmetric Statistics for the Behavioral Sciences, New York: McGraw-Hill.
   Google Scholar
• Smith, A., Frank, L., Wirth, S., Yanike, M., Hu, D., Kubota, Y., Graybiel, A., Suzuki, W., and Brown, E. (2004), “Dynamic Analysis of Learning in Behavioral Experiments,” The Journal of Neuroscience, 24, 447–461.
   PubMed Web of Science ®Google Scholar
• Truccolo, W., Ding, M., Knuth, K., Nakamura, R., and Bressler, S. (2002), “Trial-to-Trial Variability of Crortical Evoked Responses: Implications for the Analysis of Functional Connectivity,” Clinical Neurophysiology, 113, 206–226.
   PubMed Web of Science ®Google Scholar
• Turetsky, B., Raz, J., and Fein, G. (1989), “Estimation of Trial-to-Trial Variability in Evoked Potential Signals by Smoothing Across Trials,” Psychophysiology, 26, 700–712.
   PubMed Web of Science ®Google Scholar
• Wang, X.-F., and Ye, D. (2010), “On Nonparametric Comparison of Images and Regression Surfaces,” Journal of Statistical Planning and Inference, 140, 2875–2884.
   PubMed Web of Science ®Google Scholar
• Wirth, S., Yanike, M., Frank, L., Smith, A., brown, E., and Suzuki, W. (2003), “Single Neurons in the Monkey Hippocampus and Learning of New Associations,” Science, 300, 1578–1581.
   PubMed Web of Science ®Google Scholar