Dynamic functional connectivity between nucleus accumbens and the central executive network relates to chronic cannabis use

Abstract

The neural mechanisms of drug cue‐reactivity regarding the temporal fluctuations of functional connectivity, namely the dynamic connectivity, are sparsely studied. Quantifying the task‐modulated variability in dynamic functional connectivity at cue exposure can aid the understanding. We analyzed changes in dynamic connectivity in 54 adult cannabis users and 90 controls during a cannabis cue exposure task. The variability was measured as standard deviation in the (a) connectivity weights of the default mode, the central executive, and the salience networks and two reward loci (amygdalae and nuclei accumbens); and (b) topological indexes of the whole brain (global efficiency, modularity and network resilience). These were compared for the main effects of task conditions and the group (users vs. controls), and correlated with pre‐ and during‐scan subjective craving. The variability of connectivity weights between the central executive network and nuclei accumbens was increased in users throughout the cue exposure task, and, was positively correlated with during‐scan craving for cannabis. The variability of modularity was not different by groups, but positively correlated with prescan craving. The variability of dynamic connectivity during cannabis cue exposure task between the central executive network and the nuclei accumbens, and, the level of modularity, seem to relate to the neural underpinning of cannabis use and the subjective craving.

Patterns of brain function associated with cannabis cue-reactivity in regular cannabis users: a systematic review of fMRI studies

“…A total of 18 studies were included in this review (Bitter et al 2014 ; Charboneau et al 2013 ; Cousijn et al 2013 ; de Sousa Fernandes Perna et al 2017 ; Feldstein Ewing and Chung 2013 ; Filbey and Dunlop 2014 ; Filbey et al 2016 ; Filbey et al 2009 ; Goldman et al 2013 ; Karoly et al 2019 ; Kleinhans et al 2020 ; Kuhns et al 2020 ; Vingerhoets et al 2016 ; Wetherill et al 2014 ; Wetherill et al 2016 ; Wetherill et al 2015 ; Yoo et al 2020 ; Zhou et al 2019 ).…”

“…We extracted brain function during exposure to CAN vs NEU stimuli, in the following groups: (i) within cannabis users in 12 out of 18 studies (Charboneau et al 2013 ; Cousijn et al 2013 ; de Sousa Fernandes Perna et al 2017 ; Feldstein Ewing et al 2013 ; Filbey et al 2009 ; Filbey and Dunlop 2014 ; Filbey et al 2016 ; Goldman et al 2013 ; Karoly et al 2019 ; Kleinhans et al 2020 ; Wetherill et al 2014 ; Wetherill et al 2015 ) and (ii) between cannabis users and non-using controls in eight studies (Bitter et al 2014 ; Cousijn et al 2013 ; de Sousa Fernandes Perna et al 2017 ; Filbey et al 2016 ; Kleinhans et al 2020 ; Kuhns et al 2020 ; Yoo et al 2020 ; Zhou et al 2019 ). We additionally extracted brain function during exposure to CAN vs NEU stimuli between distinct cannabis subgroups, which was reported in 1–2 studies: dependent vs non-dependent users (2 studies) (Filbey and Dunlop 2014 ; Zhou et al 2019 ); high vs low problem cannabis use (2 studies) (Cousijn et al 2013 ; Vingerhoets et al 2016 ); early vs late cannabis use onset (1 study) (Cousijn et al 2013 ; Wetherill et al 2016 , frequent vs sporadic ( n = 1); cannabis use only vs cannabis and tobacco use (Kuhns et al 2020 ); and male vs female cannabis users (1 study) (Wetherill et al 2015 ).…”

Acute Effect of Betel Quid Chewing on Brain Network Dynamics: A Resting-State Functional Magnetic Resonance Imaging Study

“…Meanwhile, excessively increased temporal variability of FC during rest has been reported in multiple diseases including schizophrenia ( 28 ), major depressive disorder ( 29 ), bipolar disorder ( 30 ), and autism spectrum disorder ( 67 ), potentially reflecting a under-constrained brain network and disrupted information exchanges among brain systems ( 26 , 31 , 35 ). Interestingly, some other recent studies have reported that the uses of alcohol ( 31 ), nicotine ( 32 ), and cannabis ( 33 ) are all related to an increased temporal variability of functional brain network. Combining these previous findings with our results, it may be considered that increased temporal variability of FC is a common hallmark underlying the development of multiple substance use disorders including BQ dependence.…”

“…Thus, the “dynamic FC” has become a new hot spot in neuroimaging studies, which have been associated with learning ( 23 ), cognition ( 24 ), emotion ( 25 ), personality traits ( 26 , 27 ), as well as common psychiatric diseases including schizophrenia ( 28 ), major depressive disorder ( 29 ) and bipolar disorder ( 30 ). In particular, it was suggested that using addictive substances such as alcohol ( 31 ), nicotine ( 32 ), cannabis ( 33 ), and cocaine ( 34 ) could alter the dynamic FC patterns. For example, it was found that patients with alcohol use disorder exhibited significantly higher temporal variability of FC between the cerebellum and both the frontoparietal and ventral attention networks ( 31 ).…”

Revealing the relevant spatiotemporal scale underlying whole-brain dynamics

“…There has been convincing evidence that brain dynamics rest on the orchestrated activity of several networks of brain regions which transition in recurring patterns over time (Alexandrov 1999; Meer et al 2020). These transitions between brain networks have been associated with to cognition and (ab)normal behaviour (Engel et al 2001; Thompson et al 2013; Vidaurre et al 2017; Liégeois et al 2019; Lurie et al 2020; Yoo et al 2020). However, a fundamental question remains, namly at which particular spatiotemporal scale the whole-brain functional networks are able to optimally transition.…”