This application is written in response to the RFA on Basic Research on Self-Regulation (RFA-AG-11-010). Disorders where poor self-regulation is a prominent feature involve great harm and pose a serious concern to public health, yet little is known about their underlying neurobiological mechanisms. A series of brain-behavior studies at our laboratory brought forth an empirically based theoretical model of human drug addiction, characterized by Impaired Response Inhibition (RI) and Salience Attribution (SA) (hence, I-RISA). The model posits that addiction involves assigning a lower importance (salience) to non-drug emotional stimuli (while over-valuing drug-related stimuli) with a concomitant compromise in inhibiting disadvantageous responses (e.g., compulsive drug-taking). Neuroimaging mapped these I-RISA components onto dysfunctional striatal- prefrontal cortical circuitry demonstrating the diathesis for impaired self-regulation in this disorder. In the current proposal we will test the I-RISA model in another externalizing psychopathology characterized by impaired self-regulation. Specifically, we will target Intermittent Explosive Disorder (IED), that similarly to addiction, is a chronic and relapsing disorder, featuring a skewed SA and disrupted RI (individuals with IED perceive provocation where none may have been intended, reacting with disproportionate anger that intermittently culminates in assault behavior and damage to property). In both disorders, we will target sensitive brain-behavior measures of self-regulation, using the theory-informed multidimensional datasets to develop novel computer science algorithms to conduct group classification (distinguishing between cocaine addicted individuals, IED, and healthy controls). This project represents a major departure from the current functional neuroimaging and mental health research paradigms in its focus on: (1) abstract reinforcement with money (a universal secondary reinforcer that acquires its value and uniquely impacts human emotional learning and self- control through social communication);(2) positive but also negative reinforcement (going beyond the reward principle to study compromised sensitivity to punishment and adversity);using both to predict (3) self- regulation during neuroimaging (going beyond self-report as further bolstered by psychophysiological measures);and (4) the multimodal platform to automatically perform group classification (and other machine- learning techniques, e.g., multitask) such that the common neurobehavioral signatures (but also discriminative properties) of impaired self-regulation can be identified, a prototype to be generalized to other disorders of self- regulation. The size of the potentially impacted community is of significant proportions: according to current estimates, up to 20% of the adult population in the U.S. suffers from psychiatric symptoms that impair ability to exercise self-regulation. Bringing forth significant gains toward the goal of liberating patients from the cycle of relapsing behaviors (drug use or assault behaviors) that bear catastrophic consequences to the patients themselves and with devastating costs to the broader society, this tool is estimated to be of great value.

Public Health Relevance

In the current scientific endeavor we apply a well developed theoretical model to the study of the neurobiological underpinning of self-regulation in drug addicted individuals and those with Intermittent Explosive Disorder. In this functional neuroimaging research we will shift the research focus to abstract positive and negative reinforcers (emotional stimuli used to change behavior) and use sophisticated computer science algorithms to predict self-control. Thereby, integrating multimodal and cutting-edge behavioral and brain experimentation and analyses in humans, we will explore common signatures of disrupted self-regulation in two disorders of the most pressing public health concern.

National Institute of Health (NIH)
National Institute on Drug Abuse (NIDA)
Exploratory/Developmental Grants (R21)
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Grant, Steven J
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Brookhaven National Laboratory
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Moeller, Scott J; Zilverstand, Anna; Konova, Anna B et al. (2018) Neural Correlates of Drug-Biased Choice in Currently Using and Abstinent Individuals With Cocaine Use Disorder. Biol Psychiatry Cogn Neurosci Neuroimaging 3:485-494
McFarland, Dennis J; Parvaz, Muhammad A; Sarnacki, William A et al. (2017) Prediction of subjective ratings of emotional pictures by EEG features. J Neural Eng 14:016009
Bachi, Keren; Mani, Venkatesh; Jeyachandran, Devi et al. (2017) Vascular disease in cocaine addiction. Atherosclerosis 262:154-162
Gan, Gabriela; Preston-Campbell, Rebecca N; Moeller, Scott J et al. (2016) Reward vs. Retaliation-the Role of the Mesocorticolimbic Salience Network in Human Reactive Aggression. Front Behav Neurosci 10:179
Parvaz, Muhammad A; Moeller, Scott J; Goldstein, Rita Z (2016) Incubation of Cue-Induced Craving in Adults Addicted to Cocaine Measured by Electroencephalography. JAMA Psychiatry 73:1127-1134
Moeller, Scott J; Fleming, Stephen M; Gan, Gabriela et al. (2016) Metacognitive impairment in active cocaine use disorder is associated with individual differences in brain structure. Eur Neuropsychopharmacol 26:653-62
Zilverstand, Anna; Parvaz, Muhammad A; Moeller, Scott J et al. (2016) Cognitive interventions for addiction medicine: Understanding the underlying neurobiological mechanisms. Prog Brain Res 224:285-304
Parvaz, Muhammad A; Moeller, Scott J; Goldstein, Rita Z et al. (2015) Electrocortical evidence of increased post-reappraisal neural reactivity and its link to depressive symptoms. Soc Cogn Affect Neurosci 10:78-84
Parvaz, Muhammad A; Konova, Anna B; Proudfit, Greg H et al. (2015) Impaired neural response to negative prediction errors in cocaine addiction. J Neurosci 35:1872-9
Moeller, Scott J; Konova, Anna B; Goldstein, Rita Z (2015) Multiple ambiguities in the measurement of drug craving. Addiction 110:205-6

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