Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials
KAUST DepartmentBiostatistics Group
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Embargo End Date2022-02-21
Permanent link to this recordhttp://hdl.handle.net/10754/667529
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AbstractWe propose an evolutionary state-space model (E-SSM) for analyzing high-dimensional brain signals, the statistical properties of which evolve over the course of a nonspatial memory experiment. Under the E-SSM, brain signals are modeled as mixtures of components (e.g., an AR(2) process) with oscillatory activity at predefined frequency bands. To account for the potential nonstationarity of these components (because brain responses can vary throughout an experiment), the parameters are allowed to vary over epochs. Compared with classical approaches, such as independent component analyses and filtering, the proposed method accounts for the entire temporal correlation of the components and accommodates nonstationarity. For inference purposes, we propose a novel computational algorithm based on a Kalman smoother, maximum likelihood, and blocked resampling. The E-SSM model is applied in simulation studies and applied to multi-epoch local field potential (LFP) signal data, collected from a nonspatial (olfactory) sequence memory task study. The results confirm that our method captures the evolution of the power of the components across different phases in the experiment, and identifies clusters of electrodes that behave similarly with respect to the decomposition of different sources. These findings suggest that the activity of electrodes does change over the course of an experiment in practice. Thus, treating these epoch recordings as realizations of an identical process could lead to misleading results. In summary, the proposed method underscores the importance of capturing the evolution in brain responses over the study period.
CitationGao, X., Shen, W., Shahbaba, B., Fortin, N. J., & Ombao, H. (2020). Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials. Statistica Sinica. doi:10.5705/ss.202017.0420
SponsorsShen was supported by the National Science Foundation (DMS-1509023) and the Simons Foundation (Award 512620). Shahbaba was supported by NSF grant DMS1622490 and NIH grants R01MH115697 and R01AI107034. Fortin was supported by the National Science Foundation (Awards IOS-1150292 and BCS-1439267) and Whitehall Foundation (Award 2010-05-84). The authors thank the editor, associate editor, and reviewers for their helpful comments and suggestions.