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

描述(由申请人提供)：本申请是针对RFA《关于自我调节的基础研究》(RFA-AG-11-010)而写的。自我调节能力差的疾病具有巨大的危害性，并对公共卫生构成严重关切，但人们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大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，I-RISA)。该模型假设，成瘾涉及对非毒品情绪刺激赋予较低的重要性(显著)(同时高估与毒品相关的刺激)，并伴随着在抑制不利反应(例如，强制吸毒)方面的妥协。神经成像将这些I-RISA成分映射到功能障碍的纹状体-前额叶皮质回路，显示了这种疾病自我调节受损的素质。在目前的提案中，我们将在另一种以自我调节受损为特征的外化精神病理学中测试I-RISA模型。具体地说，我们将针对间歇性爆炸性障碍(IED)，它类似于成瘾，是一种慢性和复发性障碍，特征是SA扭曲和RI中断(IED患者感知到可能不是故意的挑衅，以不成比例的愤怒反应，间歇性地最终导致攻击行为和财产损失)。在这两种障碍中，我们将针对自我调节的敏感大脑行为指标，使用理论知情的多维数据集来开发新的计算机科学算法来进行群体分类(区分可卡因成瘾者、IED和健康对照组)。这个项目在以下方面与当前的功能神经成像和心理健康研究范式有很大的不同：(1)抽象的金钱强化(一种普遍的次级强化工具，获得其价值，并通过社会交流独特地影响人类的情感学习和自我控制)；(2)积极但也负面的强化(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，并得到心理生理措施的进一步支持)；以及(4)自动执行组分类(和其他机器学习技术，例如，多任务)的多模式平台，从而可以识别受损的自我调节受损的常见神经行为特征(但也可以辨别特性)，这是要推广到其他自我调节障碍的原型。潜在受影响社区的规模是相当大的：根据目前的估计，美国高达20%的成年人患有削弱自我调节能力的精神症状。在将患者从复发行为(药物使用或攻击行为)的循环中解放出来的目标上取得了重大进展，这些行为给患者本身带来了灾难性的后果，并给更广泛的社会带来了毁灭性的代价，据估计，这一工具具有很大的价值。

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.