A major line of our current research examines how stress can alter the motivational significance of drug-related cues. Using experimental psychosocial/pharmacological stress manipulations and EEG/ERP, peripheral psychophysiology, and behavioral measures, we investigate how stress influences responses to drug cues and how these responses relate to individual differences in addiction risk. Our current work focuses primarily on cannabis and CUD, including research on the role of stress-related systems (SAM, HPA, endogenous cannabinoids) in modulating drug cue reactivity. These studies are designed to move beyond identifying correlates of stress and addiction toward identifying specific biological mechanisms that may represent novel treatment targets.

​

Ultimately, this research aims to clarify when and for whom stress increases vulnerability to substance use and relapse and to identify modifiable stress-related mechanisms that can be targeted in interventions.

One major focus is drug cue reactivity, or the extent to which drug-associated cues acquire motivational significance and capture attention. We use EEG/ERP and other psychophysiological measures to characterize the neural processes underlying drug cue reactivity, including their reliability, temporal stability, and sensitivity to clinically relevant factors. Our current research focuses primarily on cannabis cues in individuals with CUD and examines how factors such as stress and individual differences influence neural responses to these cues.

​

A complementary focus is non-drug reward processing. We are interested in how individuals with substance use disorders respond to positive and negative outcomes and whether alterations in sensitivity to non-drug rewards contribute to continued substance use. Our research examines the neural mechanisms underlying reward processing and how these mechanisms interact with stress and drug-related motivation. Although our current work focuses on CUD, these mechanisms are broadly relevant to addiction across substance classes.

​

An emerging direction in this area uses transcranial alternating current stimulation (tACS), a form of non-invasive electrical brain stimulation, to causally manipulate facets of the reward circuit. Rather than simply identifying associations between reward dysfunction and substance use, this work experimentally tests whether reward-related neural processes can be modified. Our goal is to determine whether components of the non-drug reward system represent modifiable mechanisms and, ultimately, promising treatment targets for substance use disorders.

​

Together, this work seeks to understand how the balance between drug-related and alternative sources of reward contributes to addiction—and whether this balance can be shifted to support recovery.

Our earlier work developed and tested brief computerized interventions targeting distress intolerance, a mechanism associated with stress-related substance use. Our current intervention research focuses primarily on CUD and extends our earlier distress intolerance intervention work through smartphone-based support and just-in-time intervention strategies.

​

We are also increasingly using ecological momentary assessment (EMA) and wearable technology to measure affect, stress, substance use, and physiological functioning as they unfold in people's everyday environments. These methods allow us to move beyond retrospective self-report and laboratory snapshots to capture dynamic changes in clinically relevant processes in real time. This work also provides opportunities to develop more reliable and ecologically valid measures of substance use and addiction-related mechanisms.

​

A major goal of this research is to integrate real-time measurement with real-time intervention. Future directions include just-in-time adaptive interventions (JITAIs) that deliver support when individuals are experiencing heightened risk for substance use, as well as novel approaches to measuring addiction using digital phenotyping, EMA, and wearable sensors.

​

Although much of our current digital mental health research focuses on CUD, these approaches are designed to be broadly applicable across substance use disorders. By combining scalable technology with mechanistic models of addiction, we aim to develop interventions that are both accessible and responsive to the changing needs and circumstances of individual patients.