Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials

Files
1-s2.0-S2452306219300450-main.pdf (2.29 MB)

Embargo End Date
2021-09-09

Type
Article

Authors
Fontaine, Charles
Frostig, Ron D.
Ombao, Hernando

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Statistics Program

Preprint Posting Date
2018-09-24

Online Publication Date
2019-09-09

Print Publication Date
2019-09

Date
2019-09-09

Abstract
Tools for characterizing non-linear spectral dependence between spontaneous brain signals are developed, based on the use of parametric copula models (both bivariate and vine models) applied on the magnitude of Fourier coefficients rather than using coherence. The motivation is an experiment on rats that studied the impact of stroke on the connectivity structure (dependence) between local field potentials recorded by various microelectrodes. The following major questions are addressed. The first is to determine changepoints in the regime within a microelectrode for a given frequency band based on a difference between the cumulative distribution functions modeled for each epoch (small window of time). The proposed approach is an iterative algorithm which compares each successive bivariate copulas on all the epochs range, using a bivariate Kolmogorov-Smirnov statistic. The second is to determine if such changes are present only in some microelectrodes versus generalized across the entire network. These issues are addressed by comparing Vine-copulas models fitted for each epoch. The necessary framework is provided and the effectiveness of the methods is shown through the results for the local field potential data analysis of a rat.

Citation
Fontaine, C., Frostig, R. D., & Ombao, H. (2020). Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials. Econometrics and Statistics, 15, 85–103. doi:10.1016/j.ecosta.2019.06.003

Acknowledgements
Hernando Ombao was supported by KAUST Baseline Funds and Ron D. Frostig was supported by the Leducq Foundation (15CVD02).

Publisher
Elsevier BV

Journal
Econometrics and Statistics

DOI
10.1016/j.ecosta.2019.06.003

arXiv
1809.08785

Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S2452306219300450

Permanent link to this record
http://hdl.handle.net/10754/656783
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