Modeling High-Dimensional Multichannel Brain Signals

Handle URI:
http://hdl.handle.net/10754/626662
Title:
Modeling High-Dimensional Multichannel Brain Signals
Authors:
Hu, Lechuan; Fortin, Norbert J.; Ombao, Hernando ( 0000-0001-7020-8091 )
Abstract:
Our goal is to model and measure functional and effective (directional) connectivity in multichannel brain physiological signals (e.g., electroencephalograms, local field potentials). The difficulties from analyzing these data mainly come from two aspects: first, there are major statistical and computational challenges for modeling and analyzing high-dimensional multichannel brain signals; second, there is no set of universally agreed measures for characterizing connectivity. To model multichannel brain signals, our approach is to fit a vector autoregressive (VAR) model with potentially high lag order so that complex lead-lag temporal dynamics between the channels can be captured. Estimates of the VAR model will be obtained by our proposed hybrid LASSLE (LASSO + LSE) method which combines regularization (to control for sparsity) and least squares estimation (to improve bias and mean-squared error). Then we employ some measures of connectivity but put an emphasis on partial directed coherence (PDC) which can capture the directional connectivity between channels. PDC is a frequency-specific measure that explains the extent to which the present oscillatory activity in a sender channel influences the future oscillatory activity in a specific receiver channel relative to all possible receivers in the network. The proposed modeling approach provided key insights into potential functional relationships among simultaneously recorded sites during performance of a complex memory task. Specifically, this novel method was successful in quantifying patterns of effective connectivity across electrode locations, and in capturing how these patterns varied across trial epochs and trial types.
KAUST Department:
Statistics Program
Citation:
Hu L, Fortin NJ, Ombao H (2017) Modeling High-Dimensional Multichannel Brain Signals. Statistics in Biosciences. Available: http://dx.doi.org/10.1007/s12561-017-9210-3.
Publisher:
Springer Nature
Journal:
Statistics in Biosciences
Issue Date:
12-Dec-2017
DOI:
10.1007/s12561-017-9210-3
Type:
Article
ISSN:
1867-1764; 1867-1772
Sponsors:
N.J. Fortin’s research was supported in part by the National Science Foundation (Awards IOS-1150292 and BCS-1439267), the Whitehall Foundation (Award 2010-05-84), and the University of California, Irvine. H. Ombao’s work was supported in part by grants from the US NSF Division of Mathematical Sciences (DMS 15-09023) and the Division of Social and Economic Sciences (SES 14-61534).
Additional Links:
https://link.springer.com/article/10.1007%2Fs12561-017-9210-3
Appears in Collections:
Articles; Statistics Program

Full metadata record

DC FieldValue Language
dc.contributor.authorHu, Lechuanen
dc.contributor.authorFortin, Norbert J.en
dc.contributor.authorOmbao, Hernandoen
dc.date.accessioned2018-01-01T12:19:06Z-
dc.date.available2018-01-01T12:19:06Z-
dc.date.issued2017-12-12en
dc.identifier.citationHu L, Fortin NJ, Ombao H (2017) Modeling High-Dimensional Multichannel Brain Signals. Statistics in Biosciences. Available: http://dx.doi.org/10.1007/s12561-017-9210-3.en
dc.identifier.issn1867-1764en
dc.identifier.issn1867-1772en
dc.identifier.doi10.1007/s12561-017-9210-3en
dc.identifier.urihttp://hdl.handle.net/10754/626662-
dc.description.abstractOur goal is to model and measure functional and effective (directional) connectivity in multichannel brain physiological signals (e.g., electroencephalograms, local field potentials). The difficulties from analyzing these data mainly come from two aspects: first, there are major statistical and computational challenges for modeling and analyzing high-dimensional multichannel brain signals; second, there is no set of universally agreed measures for characterizing connectivity. To model multichannel brain signals, our approach is to fit a vector autoregressive (VAR) model with potentially high lag order so that complex lead-lag temporal dynamics between the channels can be captured. Estimates of the VAR model will be obtained by our proposed hybrid LASSLE (LASSO + LSE) method which combines regularization (to control for sparsity) and least squares estimation (to improve bias and mean-squared error). Then we employ some measures of connectivity but put an emphasis on partial directed coherence (PDC) which can capture the directional connectivity between channels. PDC is a frequency-specific measure that explains the extent to which the present oscillatory activity in a sender channel influences the future oscillatory activity in a specific receiver channel relative to all possible receivers in the network. The proposed modeling approach provided key insights into potential functional relationships among simultaneously recorded sites during performance of a complex memory task. Specifically, this novel method was successful in quantifying patterns of effective connectivity across electrode locations, and in capturing how these patterns varied across trial epochs and trial types.en
dc.description.sponsorshipN.J. Fortin’s research was supported in part by the National Science Foundation (Awards IOS-1150292 and BCS-1439267), the Whitehall Foundation (Award 2010-05-84), and the University of California, Irvine. H. Ombao’s work was supported in part by grants from the US NSF Division of Mathematical Sciences (DMS 15-09023) and the Division of Social and Economic Sciences (SES 14-61534).en
dc.publisherSpringer Natureen
dc.relation.urlhttps://link.springer.com/article/10.1007%2Fs12561-017-9210-3en
dc.subjectElectroencephalogramsen
dc.subjectLocal field potentialsen
dc.subjectBrain effective connectivityen
dc.subjectMultivariate time seriesen
dc.subjectVector autoregressive modelen
dc.subjectPartial directed coherenceen
dc.titleModeling High-Dimensional Multichannel Brain Signalsen
dc.typeArticleen
dc.contributor.departmentStatistics Programen
dc.identifier.journalStatistics in Biosciencesen
dc.contributor.institutionDepartment of Statistics, University of California, Irvine, USAen
dc.contributor.institutionDepartment of Neurobiology and Behavior, University of California, Irvine, USAen
kaust.authorOmbao, Hernandoen
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