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Published on: August 6, 2013
Inter-subject variability in brain connectivity predicts nicotine dependence severity and differentiates smokers via
Shaoyu Cui1, Xuefeng Xu2, Bo Yang3
1School of Psychology, Yunnan Normal University, Kunming, Yunnan Province, China.
Addictive Behaviors
|June 14, 2026
Summary
Inter-subject variability in resting-state brain functional connectivity (IVFC) may serve as a neurobiological marker for tobacco use disorder (TUD). Machine learning models accurately identified TUD using IVFC, with insular lobe connectivity predicting dependence severity.
Area of Science:
- Neuroscience
- Neuroimaging
- Machine Learning
Background:
- Tobacco use disorder (TUD) presents a significant public health challenge with considerable individual variability.
- Understanding the neurobiological underpinnings of TUD and identifying reliable biomarkers are crucial for effective treatment.
- This study explored inter-subject variability in resting-state functional connectivity (IVFC) as a potential neurobiological marker for TUD.
Purpose of the Study:
- To investigate whether IVFC patterns can differentiate individuals with TUD from healthy controls.
- To assess the potential of IVFC as a neurobiological marker for TUD.
- To explore the relationship between IVFC and the severity of nicotine dependence.
Main Methods:
- Resting-state functional magnetic resonance imaging (fMRI) data were acquired from 123 male TUD patients and 123 healthy controls.
- Inter-subject variability in functional connectivity (IVFC) was calculated across seven major brain lobes.
- Machine learning models, including extra trees classifier, were employed to classify TUD based on IVFC features, and regression analyses examined the association between IVFC and clinical scores.
Main Results:
- Significant alterations in IVFC were observed in six brain lobes of TUD patients compared to controls, most notably in the insular lobe.
- Machine learning models achieved up to 88% accuracy in classifying TUD, with temporal and limbic lobe features being highly influential.
- Higher IVFC in several lobes correlated with lower nicotine dependence severity, and insular lobe IVFC emerged as the sole independent predictor.
Conclusions:
- Widespread alterations in IVFC are characteristic of TUD and are linked to its clinical severity.
- IVFC analysis, particularly when combined with machine learning, shows promise as a neurobiological marker for distinguishing TUD.
- These findings contribute to a deeper understanding of the brain mechanisms underlying TUD.
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