Temporal Dynamics of Large-Scale Networks Predict Neural Cue Reactivity and Cue-Induced Craving

Area of Science:

• Neuroscience
• Addiction Research
• Cognitive Neuroscience

Background:

• Cue reactivity is central to substance use disorders.
• Key brain networks like the default mode network (DMN) and salience network (SN) are involved in cue reactivity.
• The relationship between resting-state dynamics of these networks and subsequent cue reactivity/craving is not well understood.

Purpose of the Study:

• To investigate the link between resting-state temporal dynamics of the DMN and SN and neural/subjective responses to smoking cues in nicotine-dependent individuals.
• To explore whether neural cue reactivity mediates the association between resting-state network dynamics and craving.

Main Methods:

• Assessed resting-state fMRI data from 46 nicotine-dependent participants.
• Quantified temporal dynamic properties (total time in specific states) of the DMN and SN.
• Used regression and mediation analyses to examine relationships between resting-state dynamics, neural cue reactivity, and subjective craving.

Main Results:

• More time spent in the DMN state predicted increased neural activity in response to cues within the anterior insula (a salience network node).
• Less time spent in the SN state predicted increased neural activity in the dorsal anterior cingulate cortex (a salience network node).
• Neural activity in the anterior insula and dorsal anterior cingulate cortex significantly mediated the link between resting-state DMN/SN dynamics and subjective craving.

Conclusions:

• Resting-state temporal dynamics of the DMN and SN are significantly associated with subsequent cue reactivity in key salience network nodes.
• These resting-state dynamics predict cue-induced craving in nicotine dependence.
• Findings suggest a neurobiological pathway where resting-state network dynamics influence how the brain responds to external stimuli, ultimately impacting craving.