There is growing interest in non-invasive brain stimulation (NIBS) as a novel treatment option for substance-use disorders (SUDs). Recent momentum stems from a foundation of preclinical neuroscience demonstrating links between neural circuits and drug consuming behavior, as well as recent FDA-approval of NIBS treatments for mental health disorders that share overlapping pathology with SUDs. As with any emerging field, enthusiasm must be tempered by reason; lessons learned from the past should be prudently applied to future therapies. Here, an international ensemble of experts provides an overview of the state of transcranial-electrical (tES) and transcranial-magnetic (TMS) stimulation applied in SUDs. This consensus paper provides a systematic literature review on published data-emphasizing the heterogeneity of methods and outcome measures while suggesting strategies to help bridge knowledge gaps. The goal of this effort is to provide the community with guidelines for best practices in tES/TMS SUD research. We hope this will accelerate the speed at which the community translates basic neuroscience into advanced neuromodulation tools for clinical practice in addiction medicine.

Given conceptual frameworks of addiction as a disease of intercommunicating brain networks, examinations of network interactions may provide a holistic characterization of addiction-related dysfunction. One such methodological approach is the examination of resting-state functional connectivity, which quantifies correlations in low frequency fluctuations of the blood oxygen level–dependent magnetic resonance imaging signal between disparate brain regions in the absence of task performance. Here, evidence of differentiated effects of chronic nicotine exposure, which reduces the efficiency of network communication across the brain, and acute nicotine exposure, which increases connectivity within specific limbic circuits, is discussed. Several large-scale resting networks, including the salience, default, and executive control networks, have also been implicated in nicotine addiction. The dynamics of connectivity changes among and between these large-scale networks during nicotine withdrawal and satiety provide a heuristic framework with which to characterize the neurobiological mechanism of addiction. The ability to simultaneously quantify effects of both chronic (trait) and acute (state) nicotine exposure provides a platform to develop a neuroimaging-based addiction biomarker. While such development remains in its early stages, evidence of coherent modulations in resting-state functional connectivity at various stages of nicotine addiction suggests potential network interactions on which to focus future addiction biomarker development.

Perceptual load is known to influence the locus of attentional selection in the brain but through an unknown underlying mechanism. We used event-related potentials (ERPs) to investigate how perceptual load interacts with cue-driven involuntary attention. Perceptual load was manipulated in a line orientation discrimination task in which target location was cued involuntarily by means of peripheral cues. Attentional modulation was observed for P1m (the posterior midline P1 component with peak latency between 108 and 140 ms) with invalid trials eliciting larger P1m than valid trials. This attentional effect on P1m increased as a function of perceptual load, suggesting an early temporal locus for the interaction of perceptual load and involuntary attention. Attentional modulation for the C1 component (peak latency at approximately 80 ms) was also observed, but only for high load stimuli that were presented intermixed with low load stimuli. Results suggest that (a) perceptual load affects attentional selection at early processing stages; (b) perceptual load interacts with involuntary attention earlier and with different brain mechanisms relative to voluntary attention; and (c) attentional modulation in the C1 time range is possible under optimal experimental conditions.