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，伴随着前额叶皮层的逐步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临床发病和流行高峰的典型年龄。认知过程的计算建模的最新进展使得能够量化控制学习和决策的参数，并表征它们在精神疾病中的差异。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）与基于模型（学习任务的结构并计算出给定结构的最佳行动方案），巴甫洛夫（对不同动机相关结果具有先天敏感性）与工具性（任意适应），学习发生在积极和消极的后果。此外，反应可能偏向于行动或不行动，并且可能或多或少是探索性的（变量）。我们将使用三个重复学习任务，连同计算模型，索引这些学习和决策的多个可区分的特征，以共同定义学习和决策过程的个体“计算表型”。在目标1中，将在10-25岁的大型在线样本中定义该计算表型，以绘制青少年发育过程中症状维度的变化。 在目标2中，我们将使用神经影像学来描述儿童，青少年和年轻人的决策表型和神经连接之间的关系。在目标3中，我们将描述决策表型和临床病理学之间的关系，在诊断异质性样本的青少年广泛性焦虑，抑郁症，多动症或强迫症。在整个过程中，任务行为和自我报告的症状维度的计算建模将建立在最先进的多模态和多任务数据的分层建模的基础上。本提案中描述的研究活动有可能提高我们对青少年精神病理学基础的认知和神经机制的理解，这是一个具有广泛社会影响的问题，因为精神疾病的患病率和成本，以及早期发现和治疗的超附加效益。

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.