Document Type
Journal Article
Publication Title
Frontiers in Systems Neuroscience
Volume
14
Publisher
Frontiers Media S.A.
School
School of Medical and Health Sciences
RAS ID
35387
Funders
CNPq FAPESP CAPES
Abstract
© Copyright © 2020 Nascimento, Pinto-Orellana, Leite, Edwards, Louzada and Santos. Sparse time series models have shown promise in estimating contemporaneous and ongoing brain connectivity. This paper was motivated by a neuroscience experiment using EEG signals as the outcome of our established interventional protocol, a new method in neurorehabilitation toward developing a treatment for visual verticality disorder in post-stroke patients. To analyze the [complex outcome measure (EEG)] that reflects neural-network functioning and processing in more specific ways regarding traditional analyses, we make a comparison among sparse time series models (classic VAR, GLASSO, TSCGM, and TSCGM-modified with non-linear and iterative optimizations) combined with a graphical approach, such as a Dynamic Chain Graph Model (DCGM). These dynamic graphical models were useful in assessing the role of estimating the brain network structure and describing its causal relationship. In addition, the class of DCGM was able to visualize and compare experimental conditions and brain frequency domains [using finite impulse response (FIR) filter]. Moreover, using multilayer networks, the results corroborate with the susceptibility of sparse dynamic models, bypassing the false positives problem in estimation algorithms. We conclude that applying sparse dynamic models to EEG data may be useful for describing intervention-relocated changes in brain connectivity.
DOI
10.3389/fnsys.2020.527757
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Nascimento, D. C., Pinto-Orellana, M. A., Leite, J. P., Edwards, D. J., Louzada, F., & Santos, T. E. (2020). Brainwave nets: Are sparse dynamic models susceptible to brain manipulation experimentation?. Frontiers in Systems Neuroscience, 14, article 527757. https://doi.org/10.3389/fnsys.2020.527757