BCI-based feedback system to promote cognitive control of craving

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DESCRIPTION (provided by applicant): At the core of drug addiction is impairment in the brain's reward system where hypersensitivity to drug- related stimuli comes at the expense of insufficient salience attributed to all other non-drug-related reinforcers. This hypersensitivity t drug-related stimuli renders individuals with cocaine use disorder (CUD) particularly vulnerable to craving (and drug use), especially when presented with drug-related cues. Nevertheless, when instructed to volitionally inhibit cue-induced craving in a laboratory environment, some CUD reported lower craving and showed decreased activity in the brain regions that process the motivational value of rewards including drug-related cues (orbitofrontal cortex), thereby retaining some level of control over their drug- related cue reactivity. We propose to capitalize on this willed control of craving, using electroencephalogram (EEG) and subsequently ascertained event-related potentials (ERP) techniques, coupled with a Brain- Computer Interface (BCI) based real-time feedback system, to help bolster such cognitive control in drug addiction. In the current proposal, we aim to test the hypothesis that, when asked to volitionally reappraise drug stimuli, CUD will be able to modulate functionally significant drug-cue-induced electrocortical markers. We also hypothesize that providing a real-time feedback (generated by a BCI platform with advanced signal processing algorithms) of one's own EEG/ERP neuronal markers of drug-cue reactivity will be associated with reduced drug-seeking and craving and enhanced inhibitory control in treatment-seeking CUD. Given that EEG- and ERP-based BCI systems are non-invasive, ambulatory and affordable, this proposal has important clinical implications. In particular, once tested and validated, this system could be implemented in treatment centers for reducing drug-cue reactivity/drug-seeking/craving and enhancing self-control. Because a BCI-based real- time feedback system incorporates each patient's own brain signature of illness, it could be used to design an individually tailored treatment program to prevent relapse. Thus, given my background in biomedical engineering and graduate research experience in EEG data acquisition, signal processing, data analysis and the support, expertise and extensive supervision of the sponsor and the co-sponsors, I believe I am at a unique juncture in undertaking this multidisciplinary and technologically cutting-edge research proposal. Therefore, obtaining NRSA support to integrate my course work, research experience with the proposed research training plan will help me develop as an independent scientist, with a niche in implementing engineering principles to advance clinical and interventional neuroscience.