title: Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots.
abstract: For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain.
authors.affiliation: Team Neurosys, InriaVillers-lès-Nancy, France; Loria, Centre National de la Recherche Scientifique, UMR no 7503Villers-lès-Nancy, France; Université de Lorraine, Loria, UMR no 7503Villers-lès-Nancy, France.
authors.forename: Tamara
authors.initials: T
authors.lastname: Tošić
authors.affiliation: Department of Psychiatry, University of North Carolina at Chapel HillChapel Hill, NC, USA; Neurobiology Curriculum, University of North Carolina at Chapel HillChapel Hill, NC, USA.
authors.forename: Kristin K
authors.initials: KK
authors.lastname: Sellers
authors.affiliation: Department of Psychiatry, University of North Carolina at Chapel HillChapel Hill, NC, USA; Neurobiology Curriculum, University of North Carolina at Chapel HillChapel Hill, NC, USA; Department of Cell Biology and Physiology, University of North Carolina at Chapel HillChapel Hill, NC, USA; Department of Biomedical Engineering, University of North Carolina at Chapel HillChapel Hill, NC, USA; Neuroscience Center, University of North Carolina at Chapel HillChapel Hill, NC, USA.
authors.forename: Flavio
authors.initials: F
authors.lastname: Fröhlich
authors.affiliation: Team Neurosys, InriaVillers-lès-Nancy, France; Loria, Centre National de la Recherche Scientifique, UMR no 7503Villers-lès-Nancy, France; Université de Lorraine, Loria, UMR no 7503Villers-lès-Nancy, France.
authors.forename: Mariia
authors.initials: M
authors.lastname: Fedotenkova
authors.affiliation: Department of German Studies and LinguisticsBerlin, Germany; Bernstein Center for Computational NeuroscienceBerlin, Germany.
authors.forename: Peter
authors.initials: P
authors.lastname: Beim Graben
authors.affiliation: Team Neurosys, InriaVillers-lès-Nancy, France; Loria, Centre National de la Recherche Scientifique, UMR no 7503Villers-lès-Nancy, France; Université de Lorraine, Loria, UMR no 7503Villers-lès-Nancy, France.
authors.forename: Axel
authors.initials: A
authors.lastname: Hutt
keywords: anesthesia; ferret; local field potentials; recurrence plot analysis; statistical inference; surrogate data; time-frequency analysis; trial-to-trial variability
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