Machine learning discovery of patterns of self regulation in drug addiction and I

描述（由申请人提供）：本申请是根据RFA关于自我调节基础研究（RFA- ag -11-010）撰写的。以自我调节能力差为突出特征的疾病危害巨大，对公众健康构成严重关切，但对其潜在的神经生物学机制知之甚少。我们实验室的一系列脑行为研究提出了一个基于经验的人类药物成瘾理论模型，其特征是反应抑制受损（RI）和显著归因（SA）（因此称为I-RISA）。该模型假定，成瘾包括将非药物情绪刺激的重要性（显著性）降低（同时高估与药物相关的刺激），同时在抑制不利反应（例如，强迫性吸毒）方面做出妥协。神经成像将这些I-RISA成分映射到功能失调的纹状体-前额叶皮层回路上，证明了这种疾病中自我调节受损的特质。在当前的提案中，我们将在另一种以自我调节受损为特征的外化精神病理中测试I-RISA模型。具体来说，我们将针对间歇性爆炸障碍（IED），它与成瘾类似，是一种慢性和复发性疾病，以扭曲的SA和中断的RI为特征（IED患者感知到可能没有意图的挑衅，反应出不成比例的愤怒，间歇性地以攻击行为和财产损害为高潮）。在这两种疾病中，我们将针对自我调节的敏感大脑行为测量，使用理论信息的多维数据集开发新的计算机科学算法来进行群体分类（区分可卡因成瘾个体，IED和健康对照）。本项目与目前的功能神经影像学和心理健康研究范式有很大的不同，其重点是：(1)金钱的抽象强化（一种普遍的次级强化物，通过社会交流获得其价值并独特地影响人类的情绪学习和自我控制）；(2)正强化和负强化（超越奖励原则，研究对惩罚和逆境的妥协敏感性）；使用两者来预测(3)神经成像过程中的自我调节（超越自我报告，进一步得到心理生理测量的支持）；(4)自动执行群体分类（以及其他机器学习技术，例如多任务）的多模态平台，这样就可以识别受损自我调节的常见神经行为特征（但也有区别属性），这是一个可以推广到其他自我调节障碍的原型。潜在受影响群体的规模相当大：根据目前的估计，美国多达20%的成年人患有损害自我调节能力的精神症状。将患者从对患者自身造成灾难性后果并对更广泛的社会造成毁灭性代价的复发行为（吸毒或攻击行为）的循环中解放出来的目标取得了重大进展，这一工具估计具有很大的价值。

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. 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.