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

描述（由申请人提供）：本申请是为了响应RFA关于自我调节的基础研究（RFA-AG-11-010）而编写的。自我调节不良是一个突出特征的疾病涉及巨大的危害，并对公共健康构成严重关切，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，其特征是受损反应抑制（RI）和显著归因（SA）（因此，I-RISA）。该模型假设成瘾涉及对非药物情绪刺激分配较低的重要性（突出性）（同时高估药物相关刺激），同时在抑制不利反应（例如，强迫性吸毒）。神经影像学将这些I-RISA成分映射到功能障碍的纹状体-前额叶皮质回路上，证明了这种疾病中自我调节受损的素质。在当前的建议中，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。具体来说，我们将针对间歇性爆炸性障碍（IED），它与成瘾类似，是一种慢性和复发性障碍，其特征是SA倾斜和RI中断（患有IED的人会感知到无意的挑衅，做出不成比例的反应愤怒，间歇性地达到高潮，导致攻击行为和财产损失）。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（区分可卡因成瘾者，IED和健康对照）。这个项目代表了一个主要的偏离目前的功能神经影像学和心理健康研究范式，其重点是：（1）抽象的强化与金钱（一种普遍的次级认知，通过社会交往获得其价值并独特地影响人类的情感学习和自我控制）;（2）积极的，但也有消极的强化（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）神经成像期间的自我调节（超越自我报告，如通过心理生理学测量进一步支持的）;以及（4）自动执行组分类的多模式平台（以及其他机器学习技术，例如，多任务），从而可以识别受损的自我调节的常见神经行为特征（但也有区别性的特性），这是一种推广到其他自我调节障碍的原型。潜在受影响社区的规模很大：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。这一工具将患者从复发行为（吸毒或攻击行为）的循环中解放出来，给患者带来灾难性的后果，并给更广泛的社会带来毁灭性的代价。

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.