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Towards treatment stratification for successful smoking cessation: Harnessing predictive neurocognitive models

Smoking is the single biggest preventable cause of death in Ireland. Nicotine is a highly addictive substance; only 4% of unaided quit attempts are ultimately successful. Even state-of-the-art therapies, such as Contingency Management (CM) and Acceptance and Commitment Therapy (ACT) often have success rates of less than 20%. The former treatment involves providing alternative reinforcement (e.g., money) for smoking whereas the latter involves training to cope with negative sensations during withdrawal. A key barrier to improving the effectiveness of such treatments is that individual differences in treatment response are traditionally not researched much nor therefore understood: the proposed study will seek to address this deficit. Individuals will be assigned to either CM, ACT or Treatment-as-Usual (TAU) followed prospectively as they attempt to abstain from smoking. We plan to use a sophisticated machine learning approach recently developed by the applicant (Whelan et al., Nature, 2014) to quantify pre-existing differences and changes in the key brain systems of reward processing, cognitive control, and stress responsivity as putative neurocognitive endophenotypes using behavioural measures and a non-invasive measure of brain functioning (EEG). In addition, other types of information, such as SES, personality, gender and life events will be incorporated to provide a wide-ranging assay of relevant factors. By examining these background influences, along with both common neural change responses and those specific to the CM, ACT and TAU groups, it will be possible to identify individual differences in successful response to treatment. Crucially, this type of research may ultimately lead to the development of stratified treatment options. Indeed, our research plan includes a cost-benefit analysis to identify the most cost-effective subset of features for accurate stratification. Overall, we thus expect this project to contribute to the translational scientific literature on smoking cessation, and to further understanding of diverse responses to treatment for nicotine addiction.