Staff View: Statistical models for brain signals with properties that evolve across trials

Statistical models for brain signals with properties that evolve across trials

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 beli...

Full description

Saved in:

Bibliographic Details
Main Authors:	Ombao, Hernando, Fiecas, Mark, Ting, Chee Ming, Low, Yin Fen
Format:	Article
Published:	Elsevier Inc. 2018
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/84311/ https://doi.org/10.1016/j.neuroimage.2017.11.061
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.utm.84311
record_format	eprints
spelling	my.utm.843112019-12-28T01:46:38Z http://eprints.utm.my/id/eprint/84311/ Statistical models for brain signals with properties that evolve across trials Ombao, Hernando Fiecas, Mark Ting, Chee Ming Low, Yin Fen TP Chemical technology 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. Elsevier Inc. 2018 Article PeerReviewed Ombao, Hernando and Fiecas, Mark and Ting, Chee Ming and Low, Yin Fen (2018) Statistical models for brain signals with properties that evolve across trials. Neuroimage, 180 . pp. 609-618. ISSN 1053-8119 https://doi.org/10.1016/j.neuroimage.2017.11.061
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TP Chemical technology
spellingShingle	TP Chemical technology Ombao, Hernando Fiecas, Mark Ting, Chee Ming Low, Yin Fen Statistical models for brain signals with properties that evolve across trials
description	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.
format	Article
author	Ombao, Hernando Fiecas, Mark Ting, Chee Ming Low, Yin Fen
author_facet	Ombao, Hernando Fiecas, Mark Ting, Chee Ming Low, Yin Fen
author_sort	Ombao, Hernando
title	Statistical models for brain signals with properties that evolve across trials
title_short	Statistical models for brain signals with properties that evolve across trials
title_full	Statistical models for brain signals with properties that evolve across trials
title_fullStr	Statistical models for brain signals with properties that evolve across trials
title_full_unstemmed	Statistical models for brain signals with properties that evolve across trials
title_sort	statistical models for brain signals with properties that evolve across trials
publisher	Elsevier Inc.
publishDate	2018
url	http://eprints.utm.my/id/eprint/84311/ https://doi.org/10.1016/j.neuroimage.2017.11.061
_version_	1654960069684494336
score	13.211869

Statistical models for brain signals with properties that evolve across trials

Similar Items