High-Risk Drinkers Engage Distinct Stress-Predictive Brain Networks

Biol Psychiatry Cogn Neurosci Neuroimaging. 2022 Mar 7;S2451-9022(22)00049-0. doi: 10.1016/j.bpsc.2022.02.010. Online ahead of print.


Background: Excessive alcohol intake is a major public health problem and can be triggered by stress. Heavy drinking in patients with alcohol use disorder also alters neural, physiological, and emotional stress responses. However, it is unclear whether adaptations in stress-predictive brain networks can be an early marker of risky drinking behavior.

Methods: Risky social drinkers (regular bingers; n = 53) and light drinker control subjects (n = 51) aged 18 to 53 years completed a functional magnetic resonance imaging-based sustained stress protocol with repeated measures of subjective stress state, during which whole-brain functional connectivity was computed. This was followed by prospective daily ecological momentary assessment for 30 days. We used brain computational predictive modeling with cross-validation to identify unique brain connectivity predictors of stress in risky drinkers and determine the prospective utility of stress-brain networks for subsequent loss of control over drinking.

Results: Risky drinkers had anatomically and functionally distinct stress-predictive brain networks (showing stronger predictions from visual and motor networks) compared with light drinkers (default mode and frontoparietal networks). Stress-predictive brain networks defined for risky drinkers selectively predicted future real-world stress levels for risky drinkers and successfully predicted prospective future real-world loss of control over drinking across all participants.

Conclusions: These results indicate adaptations in computationally derived stress-related brain circuitry among high-risk drinkers, suggesting potential targets for early preventive intervention and revealing the malleability of the neural processes that govern stress responses.

Keywords: Binge drinking; Ecological momentary assessment; Loss of control; Predictive modeling; Stress; Ventromedial prefrontal cortex; Visuomotor networks; fMRI.