Rita Z. Goldstein, Ph.D. - US grants

DESCRIPTION (provided by applicant): 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.

? DESCRIPTION (provided by applicant): Among the many addictions, cocaine addiction is particularly treatment-resistant, with estimated relapse rates greater than 45%. While most abstaining individuals with cocaine use disorders (iCUD) relapse within the first few weeks of abstinence, relapse continues to occur long after acute withdrawal has abated. Re-exposure to cues previously associated with drug use that evoke craving is a major contributing factor in relapse to drug use. Pre-clinical studies using several animal models of drug addiction (including in cocaine, heroin, alcohol, nicotine, and methamphetamine addiction models) have shown that, contrary to common expectations for the reduction of craving and drug-seeking with abstinence, cue-induced drug craving increases progressively (i.e., incubates) in the first several months of abstinence. Primarily using a cross-sectional study design and subjective measures of craving assessed at different periods of abstinence (from 7 days post abstinence onset up to 24 months of abstinence), several human studies have shown similar effects in nicotine, methamphetamine and heroin use disorders. Together, these studies show highest cue-induced craving at 3 to 6 months of abstinence, suggesting that such incubation of craving renders abstaining drug addicted individuals vulnerable to relapse. However, a systematic investigation that uses objective markers and a within- subjects approach to quantify and longitudinally track the trajectory of cue-induced craving and its incubation for relapse monitoring and prediction is lacking. The late positive potential (LPP) component of the electroencephalography has been shown to objectively track motivated attention to salient stimuli including drug-cues, correlating with cue-induced craving and predicting relapse in cocaine and nicotine addicted individuals. Using LPP, our preliminary data suggests that cue-induced craving increases at 6-months and then decreases at 12-months follow-up compared to a treatment-seeking baseline in initially abstinent iCUD. Thus, we aim to use LPPs to assess cue-induced craving at carefully selected abstinence durations [at 7, 14, 30 days (assessed cross-sectionally) and at 3, 6, 9, and 12 months (assessed longitudinally)] to objectively measure the dynamics of craving incubation in human cocaine addiction. The hypothesized inverted-U shaped trajectory will be elucidated via changes in LPP amplitude, and will be supplemented by measures of simulated drug-seeking and self-reported craving. Our second aim is to use the longitudinal dynamics of craving incubation to monitor and prospectively predict relapse. Results could aid in developing time-sensitive personalized interventions to reduce risk of relapse in treatment-seeking individuals with substance use disorders.

ABSTRACT Over the past 15 years, the US has been affected by increasing prescription and illicit opiate/opioid abuse, addiction, and overdose. Research into the enhancement of treatment options for individuals with opiate/opioid use disorder (iOUD) is clearly a priority. The development of neuroscience-informed behavioral therapies that could be used as adjuncts to improve effectiveness of medication-assisted interventions in iOUD is a national priority, a response to the opiate crisis. The 8-week Mindfulness-Oriented Recovery Enhancement (MORE) decreases opioid misuse, craving, and cue-reactivity, and enhances natural reward responsiveness, effects attributed to restructuring of hedonic dysregulation, in opioid misusing patients. Improved function and increased gray matter in relevant brain regions [e.g., the prefrontal cortex (PFC)] have been shown with other 8-week mindfulness interventions in non- addicted individuals. However, the neural mechanisms underlying MORE-related changes in iOUD are unknown. The Impaired Response Inhibition and Salience Attribution (iRISA) model highlights the importance of mesocorticostriatal regions, including the PFC, to enhanced salience of drugs relative to natural rewards concomitant with decreased inhibitory control, core substrates underlying drug addiction. Given these commonalities, we propose to study the neural correlates of iRISA as contributing to and predictive of the impact of MORE on addiction outcome in iOUD. Using a pre-post randomized treatment design with a 3-months follow-up, we will examine the impact of MORE [vs. treatment-as-usual (TAU), as add-ons to methadone maintenance] on neural functional and structural plasticity, and clinical outcomes (including daily ecological momentary assessments), in treatment-seeking iOUD (with primary use of heroin). Treatment-seeking iOUD will be randomized to 8-weeks of MORE or psychosocial TAU and scanned with magnetic resonance imaging immediately before and after treatment. Healthy controls will be scanned at similar time intervals. We hypothesize that compared with the controls, TAU or pre-MORE, post-MORE subjects will show normalizations or changes in: a) frontostriatal function during reward processing (i.e., enhanced natural reward and reduced drug cue processing) and inhibitory control; b) meso- corticostriatal resting-state functional connectivity; and c) gray matter in regions associated with reward processing and inhibitory control. Clinical outcome will be assessed during, immediately and 3-months after MORE or TAU. We hypothesize that brain changes post-MORE>pre-MORE or TAU will predict clinical improvement (treatment retention, abstinence, amount/frequency of opiate use and craving) such that the more the neural change, the better the outcomes (healthy controls provide direction of results). In whole-brain analyses we consider the possibility that recovery entails effects in other networks that compensate for deficits. Results will help identify individual variability in the brain regions/circuits that support reward processing, including cue reactivity, and inhibitory control and that could change with, and predict, response to MORE, ultimately contributing to precision medicine in OUD.

Project Summary Substance use disorders (SUDs) are increasing in prevalence and are already a leading cause of disability, due in part to the fact that our understanding of the underlying pathophysiology is incomplete. Like most neuropsychiatric syndromes, SUDs are highly heterogeneous, and distinct mechanisms may be operative in some individuals but not in others, even within a single diagnostic category. Furthermore, SUDs frequently co- occur with depression, anxiety, and other psychiatric syndromes, complicating efforts to identify molecular and circuit-level mechanisms, and disentangle them from those involved in mood and anxiety disorders. Diagnostic heterogeneity is thus a fundamental obstacle to developing better treatments, identifying biomarkers for quantifying risk for different forms of addiction, and predicting treatment response and relapse. Recently, we developed and validated an approach to discovering and diagnosing subtypes of depression using fMRI measures of functional connectivity, which in turn predicted subtype-specific clinical symptom profiles and treatment outcomes. Here, in response to PAR-18-062, we propose a secondary data analysis that would extend this approach to SUDs, leveraging multiple deeply characterized and large-scale neuroimaging datasets. Our central hypothesis is that individual differences in mechanisms underlying impairments in response inhibition and salience attribution (iRISA) are mediated by distinct forms of dysfunctional connectivity in addiction-related circuits, which in turn interact and give rise to distinct neurophysiological addiction subtypes. In Aim 1, we will use statistical clustering and machine learning methods to delineate these subtypes and optimize classifiers (fMRI biomarkers) for diagnosing them in individual patients, focusing initially on cocaine addiction. In Aim 2, we will validate these subtype-specific biomarkers by first replicating them in a new dataset and then evaluating their longitudinal stability and predictive utility. In Aim 3, we will test whether subtype-specific circuit mechanisms generalize to mediate iRISA functions in other forms of addiction, and define their interactions with distinct mechanisms mediating anhedonia and anxious arousal in patients with comorbid depression and anxiety.

National studies show that drug use rates have increased in the last decade among women, comprising a major public health concern in the US. However, women are greatly underrepresented in neuroimaging studies, and the paucity of studies that explicitly target sex comparisons in addicted populations contributes to a gap in the study of the sex specific neurobiological mechanisms underlying drug addiction. Over the last decade, in a series of magnetic resonance imaging (MRI) studies (conducted with previous support including R01DA023579, R01DA020949), we have thoroughly mapped the clinical symptoms of cocaine addiction to the neural networks underlying impairments in Response Inhibition and Salience Attribution (iRISA). This model proposes that the drug assumes heightened salience at the expense of non-drug related reinforcement as associated with abnormalities in reward processing and concomitant decreases in inhibitory control, together increasing addiction severity (including craving, a proxy of relapse) in susceptible individuals. The iRISA model highlights the role of the dopaminergically innervated prefrontal cortex (PFC) and its connections to mesolimbic and striatal subcortical regions as assessed functionally and structurally. However, the majority of this neuroimaging research has been accomplished in male individuals with cocaine use disorders (iCUD). In the current project we aim to expand the reach of iRISA by comparing equal numbers of male to female iCUD; to test this model?s generalizability (vs. drug specificity effects), we will also include individuals with opioid use disorder (iOUD). We will conduct functional MRI during reward processing, inhibitory control and cue-reactivity tasks, and, to inspect generalizability of results beyond task-related activations, during resting-state. Beyond functional activations and connectivity, anatomical scans will assess the underlying gray matter integrity. Across all aims, healthy controls will be included to establish norms. We hypothesize female iCUD to differ from male iCUD, or female controls, in a pattern indicative of enhanced vulnerability to iRISA inclusive of compensatory PFC activations and abnormalities in structural measures; iCUD vs. iOUD comparisons will be exploratory. The novelty of this proposal is further enhanced by an exploratory aim to compare, in a within- subjects design, menstrual cycle (and hormonal) effects and by developing sophisticated machine-learning algorithms to incorporate data from all imaging modalities to yield an automated group classification and addiction severity (including craving) prediction tool. Considering that the majority of research in addiction occurs in males, clarification of the sex differences in the neural underpinnings of iRISA could reinforce the importance of studying both genders and suggest that different treatment strategies may be effective in women (potentially of most impact when timed vis-à-vis menstrual cycle), contributing to the development of tailored (gender-based) treatment options. Including equal numbers of women and men would advance basic studies of drug addiction and ultimately save resources by minimizing cost and adverse effects in future clinical trials.