CRCNS US-Israel Research Proposal: Computational Phenotyping of Decision Making in Adolescent Psychopathology

青春期的特点是决策的变化，伴随着前额叶皮质的逐渐发展和支持目标导向决策的大脑网络的重新配置。青春期也是许多形式精神疾病的临床发病和流行高峰的典型年龄。认知过程计算模型的最新进展使控制学习和决策的参数得以量化，并表征了它们在精神疾病中的不同之处。大脑中的学习和决策过程有几个不同的特性：学习可以是无模型的(基于过去的试验和错误)，也可以是基于模型的(学习任务的结构并计算出给定该结构的最佳行动方案)，巴甫洛夫(对不同的动机相关结果具有天生的敏感性)和工具性(任意适应)，学习发生在积极和消极的结果中。此外，反应可能偏向于采取行动或不采取行动，也可能或多或少是探索性的(变量)。我们将使用三个强化学习任务，与计算模型一起，索引学习和决策的这些多个可区分的特征，以便共同定义学习和决策过程的个人“计算表型”。在目标1中，这种计算表型将在10-25岁的在线大样本中定义，以便绘制青少年发育过程中症状维度的变化图。 在目标2中，我们将使用神经成像来描述儿童、青少年和年轻人的决策表型和神经连通性之间的关系。在目标3中，我们将在一个诊断不同的患有广泛性焦虑、抑郁、ADHD或强迫症的青少年样本中表征决策表型和临床症状之间的关系。贯穿始终，任务行为和自我报告症状维度的计算建模将建立在对多模式和多任务数据的最先进的分层建模的基础上。这项建议中描述的研究活动有可能提高我们对支撑青少年精神病理学的认知和神经机制的理解，鉴于精神疾病的流行率和成本，以及早期发现和治疗的超级附加好处，这是一个具有广泛社会影响的问题。

Adolescence is characterized by changes in decision-making, accompanied by the progressive development of the prefrontal cortex and reconfiguration of brain networks that support goal-directed decision-making. Adolescence is also the typical age of clinical onset and peak prevalence for many forms of mental illness. Recent advances in computational modeling of cognitive processes have enabled the quantification of parameters that govern learning and decision and characterization of how they differ in mental illnesses. There are several differentiating properties of learning and decision making processes in the brain: learning can be model-free (based on past trial and error) vs. model-based (learning the structure of a task and computing a best course of action given that structure), Pavlovian (with innate sensitivities to different motivationally relevant outcomes) vs. instrumental (arbitrarily adaptive), and learning occurs from positive and negative consequences. Furthermore, responses can be biased toward action or inaction, and can be more or less exploratory (variable). We will use three reinforcement-learning tasks that, together with computational models, index these multiple differentiable features of learning and decision making, in order to jointly define an individual “computational phenotype” of learning and decision processes. In Aim 1 this computational phenotype will be defined in a large online sample age 10-25 in order to map changes in symptom dimensions across adolescent development. In Aim 2 we will use neuroimaging to characterize the relationship between decision-making phenotypes and neural connectivity in children, adolescents, and young adults. In Aim 3 we will characterize the relation between decision-making phenotypes and clinical symptomatology in a diagnostically heterogeneous sample of adolescents with generalized anxiety, depression, ADHD or OCD. Throughout, computational modeling of task behavior and self-reported symptom dimensions will build on state-of-the-art hierarchical modeling of multimodal and multi-task data. The research activities described in this proposal hold the potential to improve our understanding of the cognitive and neural mechanisms that underpin adolescent psychopathology, a question of broad societal impact given the prevalence and cost of mental illness, and the super-additive benefits of early detection and treatment.