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dc.contributor.authorGao, Xu
dc.contributor.authorShen, Weining
dc.contributor.authorShahbaba, Babak
dc.contributor.authorFortin, Norbert J.
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2021-02-21T12:31:29Z
dc.date.available2021-02-21T12:31:29Z
dc.date.issued2020
dc.identifier.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
dc.identifier.issn1017-0405
dc.identifier.doi10.5705/ss.202017.0420
dc.identifier.urihttp://hdl.handle.net/10754/667529
dc.description.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.
dc.description.sponsorshipShen 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.
dc.publisherStatistica Sinica (Institute of Statistical Science)
dc.relation.urlhttp://www3.stat.sinica.edu.tw/statistica/J30N3/J30N319/J30N319.html
dc.rightsArchived with thanks to Statistica Sinica
dc.titleEvolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials
dc.typeArticle
dc.contributor.departmentBiostatistics Group
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalStatistica Sinica
dc.rights.embargodate2022-02-21
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Statistics, University of California, Irvine, 2226 Donald Bren Hall, Irvine, CA 92697-1250, USA.
dc.contributor.institutionDepartment of Statistics, University of California, Irvine, 2224 Donald Bren Hall, Irvine, CA 92697-1250, USA.
dc.contributor.institutionUniversity of California, Irvine, 106 Bonney Research Laboratory Building Department of Neurobiology and Behavior, Irvine, CA 92697, USA.
dc.identifier.volume30
dc.identifier.issue3
dc.identifier.pages1561-1582
dc.identifier.arxivid1610.07271
kaust.personOmbao, Hernando
dc.identifier.eid2-s2.0-85089740103
refterms.dateFOA2021-02-21T13:46:28Z


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