Statistical models for brain signals with properties that evolve across trials

Handle URI:
http://hdl.handle.net/10754/626380
Title:
Statistical models for brain signals with properties that evolve across trials
Authors:
Ombao, Hernando; Fiecas, Mark; Ting, Chee-Ming; Low, Yin Fen
Abstract:
Most neuroscience cognitive experiments involve repeated presentations of various stimuli across several minutes or a few hours. It has been observed that brain responses, even to the same stimulus, evolve over the course of the experiment. These changes in brain activation and connectivity are believed to be associated with learning and/or habituation. In this paper, we present two general approaches to modeling dynamic brain connectivity using electroencephalograms (EEGs) recorded across replicated trials in an experiment. The first approach is the Markovian regime-switching vector autoregressive model (MS-VAR) which treats EEGs as realizations of an underlying brain process that switches between different states both within a trial and across trials in the entire experiment. The second is the slowly evolutionary locally stationary process (SEv-LSP) which characterizes the observed EEGs as a mixture of oscillatory activities at various frequency bands. The SEv-LSP model captures the dynamic nature of the amplitudes of the band-oscillations and cross-correlations between them. The MS-VAR model is able to capture abrupt changes in the dynamics while the SEv-LSP directly gives interpretable results. Moreover, it is nonparametric and hence does not suffer from model misspecification. For both of these models, time-evolving connectivity metrics in the frequency domain are derived from the model parameters for both functional and effective connectivity. We illustrate these two models for estimating cross-trial connectivity in selective attention using EEG data from an oddball paradigm auditory experiment where the goal is to characterize the evolution of brain responses to target stimuli and to standard tones presented randomly throughout the entire experiment. The results suggest dynamic changes in connectivity patterns over trials with inter-subject variability.
KAUST Department:
Statistics Program, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Citation:
Ombao H, Fiecas M, Ting C-M, Low YF (2017) Statistical models for brain signals with properties that evolve across trials. NeuroImage. Available: http://dx.doi.org/10.1016/j.neuroimage.2017.11.061.
Publisher:
Elsevier BV
Journal:
NeuroImage
Issue Date:
7-Dec-2017
DOI:
10.1016/j.neuroimage.2017.11.061
Type:
Article
ISSN:
1053-8119
Additional Links:
http://www.sciencedirect.com/science/article/pii/S1053811917310078
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorOmbao, Hernandoen
dc.contributor.authorFiecas, Marken
dc.contributor.authorTing, Chee-Mingen
dc.contributor.authorLow, Yin Fenen
dc.date.accessioned2017-12-14T12:34:05Z-
dc.date.available2017-12-14T12:34:05Z-
dc.date.issued2017-12-07en
dc.identifier.citationOmbao H, Fiecas M, Ting C-M, Low YF (2017) Statistical models for brain signals with properties that evolve across trials. NeuroImage. Available: http://dx.doi.org/10.1016/j.neuroimage.2017.11.061.en
dc.identifier.issn1053-8119en
dc.identifier.doi10.1016/j.neuroimage.2017.11.061en
dc.identifier.urihttp://hdl.handle.net/10754/626380-
dc.description.abstractMost neuroscience cognitive experiments involve repeated presentations of various stimuli across several minutes or a few hours. It has been observed that brain responses, even to the same stimulus, evolve over the course of the experiment. These changes in brain activation and connectivity are believed to be associated with learning and/or habituation. In this paper, we present two general approaches to modeling dynamic brain connectivity using electroencephalograms (EEGs) recorded across replicated trials in an experiment. The first approach is the Markovian regime-switching vector autoregressive model (MS-VAR) which treats EEGs as realizations of an underlying brain process that switches between different states both within a trial and across trials in the entire experiment. The second is the slowly evolutionary locally stationary process (SEv-LSP) which characterizes the observed EEGs as a mixture of oscillatory activities at various frequency bands. The SEv-LSP model captures the dynamic nature of the amplitudes of the band-oscillations and cross-correlations between them. The MS-VAR model is able to capture abrupt changes in the dynamics while the SEv-LSP directly gives interpretable results. Moreover, it is nonparametric and hence does not suffer from model misspecification. For both of these models, time-evolving connectivity metrics in the frequency domain are derived from the model parameters for both functional and effective connectivity. We illustrate these two models for estimating cross-trial connectivity in selective attention using EEG data from an oddball paradigm auditory experiment where the goal is to characterize the evolution of brain responses to target stimuli and to standard tones presented randomly throughout the entire experiment. The results suggest dynamic changes in connectivity patterns over trials with inter-subject variability.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S1053811917310078en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in NeuroImage. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in NeuroImage, [, , (2017-12-07)] DOI: 10.1016/j.neuroimage.2017.11.061 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectAutoregressive modelen
dc.subjectCoherenceen
dc.subjectMarkov-switching modelen
dc.subjectPartial directed coherenceen
dc.subjectSpectral representationen
dc.subjectState-spaceen
dc.titleStatistical models for brain signals with properties that evolve across trialsen
dc.typeArticleen
dc.contributor.departmentStatistics Program, King Abdullah University of Science and Technology (KAUST), Saudi Arabiaen
dc.identifier.journalNeuroImageen
dc.eprint.versionPost-printen
dc.contributor.institutionDivision of Biostatistics, University of Minnesota, USAen
dc.contributor.institutionFaculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Malaysiaen
dc.contributor.institutionFaculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Malaysiaen
kaust.authorOmbao, Hernandoen
kaust.authorTing, Chee-Mingen
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