Connectome‐based predictive modelling of smoking severity in smokers. (27th October 2022)

• Record Type: Journal Article
• Authors: Lin, Xiao; Zhu, Ximei; Zhou, Weiran; Zhang, Zhibo; Li, Peng; Dong, Guangheng; Meng, Shiqiu; Deng, Jiahui; Lu, Lin
• Is Part Of: Addiction biology. Volume 27:Number 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-27
• Subjects: connectome‐based predictive modelling -- cue‐reactivity task -- nicotine addiction; Substance abuse -- Periodicals; Substance abuse -- Physiological aspects -- Periodicals; Substance-Related Disorders -- periodicals; 616.86
• DOI: 10.1111/adb.13242
• Languages: English
• ISSNs: 1355-6215

Abstract

Abstract: The functional connectivity within and between networks could provide a framework to characterize the neurobiological mechanism of nicotine addiction. This study examined the brain regions that were functionally connected in response to smoking cues and established the brain–behaviour relationships in smokers. Sixty‐seven male smokers were enrolled and scanned while performing the cue‐reactivity and Stroop task. A whole‐brain analysis approach, connectome‐based predictive modelling (CPM), was conducted on the data from the cue‐reactivity task to identify the networks that could predict the smoking severity with the Shen atlas as templates. Then, the brain–behaviour relationships were verified in a different brain state (Stroop task). CPM identified the smoking severity‐related network, as indicated by a significant correlation between predicted and actual smoking severity scores ( r = 0.31, p = 0.02). Identified networks mainly involved the canonical networks implicated in the reward process (motor/sensory network and salience network) and executive control (frontoparietal network). Network strength in the Stroop task marginally significantly predicted smoking severity scores ( r = 0.23, p = 0.06), partially replicating the brain–behaviour relationship. The CPM results identified the whole‐brain neural network related to smoking severity, which was cross‐validated by the AAL and Shen atlas. These findings contribute to more profound insights into neural substrates underlying the smoking severity. Abstract : The present study identified a smoking severity‐related network, which was verified by significant correspondence between predicted and actual smoking severity scores through connectome‐based predictive modelling. The identified networks mainly involved canonical networks that are implicated in reward processing and executive control, which was cross‐validated by the AAL and Shen atlases.