Abstract
In physiology, organ functions can be modelled as networks with individual regulatory mechanisms, forming a broader system through continuous interactions. The system not only interacts with itself, but can also respond to outside impulses. The paper proposes a functional graphical regression model to describe interconnected brain activities partly in response to other organs. The analysis focuses on the conditional independence structure of brain waves given the RR interval of the electrocardiographic waveform, the respiration amplitude and the blood volume pulse.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fici, R., Augugliaro, L., Wit, E.C.: Functional gaussian graphical regression models (2024). https://doi.org/10.48550/ar**v.2401.10196
Hsing, T., Eubank, R.: Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators. John Wiley & Sons, Ltd., Hoboken (2015)
Liu, H., Lafferty, J., Wasserman, L.: The nonparanormal: semiparametric estimation of high dimensional undirected graphs. J. Mach. Learn. Res. 10(80), 2295–2328 (2009)
Pernice, R., et al.: Multivariate correlation measures reveal structure and strength of brain-body physiological networks at rest and during mental stress. Front. Neurosci. 14 (2021)
Solea, E., Li, B.: Copula Gaussian graphical models for functional data. J. Am. Stat. Assoc. 117(538), 781–793 (2022)
Vujačić, I., Abbruzzo, A., Wit, E.C.: A computationally fast alternative to cross-validation in penalized gaussian graphical models. J. Stat. Comput. Simul. 85(18), 3628–3640 (2015)
Zapata, J., Oh, S.-Y., Petersen, A.: Partial separability and functional graphical models for multivariate gaussian processes. Biometrika 109(3), 665–681 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fici, R., Augugliaro, L., Wit, E.C. (2024). Functional Copula Graphical Regression Model for Analysing Brain-Body Rhythm. In: Einbeck, J., Maeng, H., Ogundimu, E., Perrakis, K. (eds) Developments in Statistical Modelling. IWSM 2024. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-65723-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-65723-8_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-65722-1
Online ISBN: 978-3-031-65723-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)