Resting Hypoconnectivity of Theoretically Defined Addiction Networks during Early Abstinence Predicts Subsequent...

Area of Science:

• Neuroscience
• Addiction Research
• Psychiatry

Background:

• Theoretical models link addiction domains (incentive salience, negative emotionality, executive control) to alcohol use disorder (AUD) relapse.
• Understanding neural network alterations is crucial for predicting and preventing AUD relapse.

Purpose of the Study:

• To investigate if the functional organization of neural networks associated with addiction domains predicts subsequent relapse in individuals with AUD.
• To determine the predictive value of resting-state functional connectivity (RSFC) within theoretically defined addiction networks for AUD relapse.

Main Methods:

• Resting functional magnetic resonance imaging (fMRI) data were collected from 45 individuals with AUD during early abstinence.
• Degree of RSFC within theoretically defined addiction networks was calculated.
• Regression analyses examined the relationship between RSFC strength and subsequent relapse metrics.

Main Results:

• Significantly lower RSFC within addiction networks was observed in individuals who relapsed compared to those who abstained.
• RSFC strength within these networks predicted the time to subsequent relapse.
• Specifically, lower incentive salience RSFC during early abstinence increased the odds of relapse.

Conclusions:

• Resting-state functional connectivity within domain-defined addiction networks, particularly incentive salience, is a significant predictor of relapse in AUD.
• Neither RSFC in a control network nor clinical self-report measures predicted relapse, underscoring the specificity of these findings.
• Findings highlight the need for timely interventions targeting RSFC in at-risk AUD individuals to prevent faster relapse.