ABSTRACT
A key problem in substance use disorders (SUD) is their etiological and functional heterogeneity, which is not
well captured by the current psychiatric nosology. An influential neuroscience-based heuristic framework,
Addictions Neuroclinical Assessment (ANA), proposes that to address this heterogeneity, the assessment of
addictions should be multi-dimensional and focus on three key domains: executive function (EF), incentive
salience (IS), and negative emotionality (NE), assessed with comprehensive batteries of self-report and
neurobehavioral tasks. While computational tools have increased the knowledge extracted from these tasks,
there are surprisingly few high-quality assays for monitoring and characterizing these domains. The burden of
administration of current assessment batteries may take up to 10 hours and most assessment instruments lack
precision in identifying underlying etiological mechanisms. Critically, most neurobehavioral and neuroimaging
tasks have low test-retest reliability, which limits their utility for biomarker discovery. To address these limitations,
we propose to apply Bayesian adaptive design optimization (ADO; Myung & Pitt, 2009) to established tasks that
index the three ANA domains, with the goal of developing rapid, robust, and reliable neurobehavioral probes of
these domains. ADO is a general-purpose computational machine-learning algorithm that optimizes data
collection and extracts the maximal information from participant responses in the fewest possible trials. Our
preliminary data show that ADO led to 0.95 or higher test-retest reliability of the delay discounting rate in under
1-2 minutes of testing, captured approximately 10% more variance in test-retest reliability, and was 3-5 times
more precise and 3-8 times more efficient than conventional assessment methods (Ahn et al., 2020). The current
study proposes to develop and evaluate a battery of ADO-based tasks, software, and mobile apps using state-
of-the-science computational approaches that will significantly reduce the time for neurocognitive task
administration, while increasing task reliability, precision, and efficiency. To capture the heterogeneity of
addiction, this battery will be tested with neurotypical individuals and several diverse populations with different
types of SUD (opioid, stimulant, alcohol, and tobacco) in three countries (USA, South Korea, Bulgaria) where
we have developed infrastructure for this type of research. This value-added perspective would be useful for out-
of-sample validation of our models and allow us to address not only the generalizability of the ANA domains to
different types of SUD, but also the cross-cultural generalizability of the domains, which has not been examined.
The specific aims of the study are to: (1) Develop a battery of reliable and efficient ADO-based neurobehavioral
tasks of the ANA domains and assess its test-retest reliability in neurotypical individuals; (2) Assess the predictive
utility of the newly developed ADO tasks for SUD outcomes by testing patients with different types of SUD; and
(3) Design web-based platforms and mobile apps for measuring cognition with the newly developed ADO tasks,
and open-source software platforms with the ADO and other computational methods we develop.
