Dynamic decomposition of spatiotemporal neural signals.
PLoS Comput Biol
; 13(5): e1005540, 2017 05.
Article
em En
| MEDLINE
| ID: mdl-28558039
ABSTRACT
Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Modelos Neurológicos
/
Rede Nervosa
Tipo de estudo:
Diagnostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
PLoS Comput Biol
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Holanda