Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI

Abstract

BOLD-fMRI technology provides a good foundation for the research of human brain dynamic functional connectivity and brain state analysis. However, due to the complexity of brain function connectivity and the high dimensionality expression of brain dynamic attributions, more research studies are focusing on tracking the time-varying characteristics through the transition between different brain states. The transition process is considered to occur instantaneously at some special time point in the above research studies, whereas our work found the brain state transition may be completed in a time section gradually rather than instantaneously. In this paper, a brain state conversion rate model is constructed to observe the procedure of brain state transition trend at each time point, and the state change can be observed by the values of conversion rate. According to the results, the transition of status always lasts for a few time points, and a brain state network model with both steady state and transition state is presented. Network topological overlap coefficient is built to analyze the features of time-varying networks. With this method, some common regular patterns of time-varying characteristics can be observed strongly in healthy children but not in the autism children. This distinct can help us to distinguish children with autism from healthy children.

The dynamic modular fingerprints of the human brain at rest

“…Also, the process of association and dissociation within DMN components is also revealed by Allen et al ) where brain states were described using K-means clustering. More importantly, several studies also showed that the dynamic states transition leads to the inclusion of some FPN regions in the DMN in some brain states Liu et al, 2019), which was also obtained in our study (results of dataset 1). Similarly, the temporal network alternates its reconfiguration between left, right and complete modules.…”

“…For instance, DAN expands dynamically its network to include the visual components. The dynamic inclusion of these networks echoes the presence of high correlation between them, which was supported in previous studies Liu et al, 2019).…”

“…These findings highlight the key important role of DMN in integrating information in spontaneous functional brain activity, in accordance with the presence of a large proportion of hubs associated to this network (de Pasquale et al, 2015;de Reus and van den Heuvel, 2013;Kabbara et al, 2017b;van den Heuvel and Sporns, 2013 (Delamillieure et al, 2010;Doucet et al, 2012). Thus, looking at the brain as a dynamic system where stable activity is intertwined by transient functional variabilities was supported by many studies (Honey et al, 2007;Liu et al, 2019;Van De Ville et al, 2010).…”

“…Features derived from these fast-dynamic analyses were also shown as potential markers for some brain diseases (Filippi et al, 2019;Liu et al, 2019) and behavioral characteristics (Kenett et al, 2020;Tompson et al, 2018).…”

Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs

“…The investigation of functional brain network connectivity (FNC) as a time-varying property in resting state functional magnetic resonance imaging (rs-fMRI) studies began relatively recently and, to date, has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan ( Allen et al, 2014 ; Damaraju et al, 2014 ; Ou et al, 2015 ; Yaesoubi et al, 2015 , 2017 ; Abrol et al, 2017a , b ; Marusak et al, 2017 ; Barber et al, 2018 ; Diez-Cirarda et al, 2018 ; Faghiri et al, 2018 ; Patanaik et al, 2018 ; Rashid et al, 2018 ; Smith et al, 2018 ; Vergara et al, 2018 ; Xie et al, 2018 , 2019 ; Denkova et al, 2019 ; Espinoza et al, 2019a ; Fiorenzato et al, 2019 ; Fu et al, 2019 ; Gonzalez-Castillo et al, 2019 ; Hou et al, 2019 ; Klugah-Brown et al, 2019 ; Li et al, 2019 , 2021 ; Liu et al, 2019 ; Mash et al, 2019 ; Rabany et al, 2019 ; Yao et al, 2019 ; Zhou et al, 2019 ; Agcaoglu et al, 2020 ; d’Ambrosio et al, 2020 ; Mennigen et al, 2020 ; Shappell et al, 2021 ). Temporal variation in fMRI has been employed primarily to establish evidence of stable hemodynamic covariation between pairs of functionally or anatomically defined brain regions or functionally coherent distributed spatial networks.…”