Altered Brain Dynamics in Heavy Smokers Revealed by Dynamic Functional Network Connectivity Analysis

Cigarette smoking is known to be associated with altered static functional connectivity in the brain. However, investigating its dynamics may offer novel and insightful perspectives for elucidating the neural mechanisms underlying smoking addiction. The aim of this study was to explore the characteristics of dynamic functional network connectivity in heavy smokers. This study is a secondary analysis of a previously acquired dataset, leveraging novel dynamic functional network connectivity methodologies to investigate distinct research questions. Resting-state functional magnetic resonance imaging data were collected from 34 heavy smokers and 36 non-smokers. Forty-two meaningful independent components were selected after the group independent component analysis. Four distinct brain states were identified based on a sliding window approach and k-means clustering analysis. The temporal properties of these states were compared between the two groups, and correlations between these differences and smoking-related factors were examined in heavy smokers. Compared with non-smokers, heavy smokers exhibited a lower occurrence rate and mean dwell time in state 2 characterized by synchrony within the default mode network and anticorrelation with other domains, and a reduced mean dwell time in state 3 marked by high connectivity within the sensory domains. Network-based statistics revealed that cognitive control and cerebellar domains played important roles in the altered subnetworks. In heavy smokers, the occurrence rate showed negative relationships with the duration of smoking in state 2. These findings advance our understanding of the temporal and network-level dysfunctions associated with smoking addiction, offering a new framework for future studies aimed at developing targeted treatments and preventive strategies.