Null models for time-series

COEX 

Neural activity and functional interactions among neuronal populations are naturally variable from moment to moment, resulting in dynamic configurations of brain activity. Vast and interdisciplinary time-series analysis literature provides computational tools that are used to characterize temporal structure of neural activity, mapping neural dynamics to anatomical and functional brain architecture. Null models of neural time-series are algorithms that generate surrogate time-series, preserving certain temporal characteristics of neural signals while disturbing others. The generated surrogate data can then be used to statistically assess dynamical properties of neural activity and functional brain organization. In this talk, I will introduce commonly used null models of time-series in human neuroimaging, including phase randomization and auto-regressive null models that preserve temporal structure of the signal, and spatiotemporal null models that preserve the inherent spatial autocorrelation of the brain in addition to temporal features of neural activity. I will demonstrate examples of these algorithms and their applications for hypothesis testing in neural time-series analysis. I will conclude by overviewing advantages and disadvantages of using each framework and will provide resources and recommendations for best practices in time-series null model testing in human neuroimaging data analysis.