State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis.
BMC Neurosci
; 25(1): 14, 2024 Mar 04.
Article
em En
| MEDLINE
| ID: mdl-38438838
ABSTRACT
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Cognição
/
Eletroencefalografia
Limite:
Humans
Idioma:
En
Revista:
BMC Neurosci
Assunto da revista:
NEUROLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos