Neurocognitive networks mediating cognitive flexibility: Implications for neuropsychiatric disorders

介导认知灵活性的神经认知网络：对神经精神疾病的影响

• 批准号: 2426762
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2426762
• 关键词: Neurocognitive; networks; mediating; cognitive; flexibility

Individual variability has predominantly been investigated in the context of reward-based learning. Yet, another aspect of human flexibility crucial for adaptive behaviour includes structural/statistical learning i.e. learning temporal sequences without explicit reward (Kourtzi, & Welchman, 2019). When performing a structural learning task, healthy individuals have been shown to adapt to changes in the environment's temporal statistics -unbeknownst to them - and use probability matching and maximization decision strategies (Wang et al., 2017; Karlaftis et al., 2019). Interestingly, there is growing body of evidence to suggest that the brain architecture supporting learning predictive structures without explicit reward involves cortico-striatal circuits that have been previously implicated in reward-based learning (Robbins, 2007). Specifically, adopting a strategy closer to matching related to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes (maximization) related to plasticity in visual, motivational and executive corticostriatal circuits (Giorgio et al., 2018; Karlaftis et al., 2019). Further analysis is required to better understand the shared brain mechanisms mediating learning with and without reward contingencies. In addition, different decision strategies may be more efficacious in different contexts. A matching strategy has been associated with exploration and may be the optimal strategy when the environment changes (Iigaya et al.,2019) whereas maximization has been associated with exploitation and maybe the optimal strategy when under risk or uncertainty (Schulze, van Ravenzwaaij, & Newell, 2015). The proposed research seeks to determine whether a structural learning training paradigm could be utilised to optimize adaptive behaviour. Deficits in cognitive flexibility occur in OCD and many possibly related mental health disorders (e.g., addiction, Tourette Syndrome and eating disorders). OCD may be regarded as a prototypical disorder of compulsivity and an example of a "behavioural addiction" (Robbins & Clark, 2015). Therefore, the study of the neurocognitive networks underpinning cognitive flexibility in this disease model has broader relevance for the field of psychiatry. It is posited here that a structural learning intervention could be used for the symptomatic treatment of OCD. Aims:(1) The proposed research aims to examine the construct of cognitive flexibility, initially in a healthy population. In particular, a large-scale web-based assessment of cognitive flexibility and structural learning will be administered to general-population samples. (2) To identify sub-processes of cognitive flexibility and subsequently link these processes to specific functional and anatomical networks using multimodal imaging techniques. Further, the inter-relations between structural learning and reversal learning will be examined by use of computational methods.(3) To test the effects of a structural learning intervention on cognitive flexibility in individuals with and without OCD. Methods and techniques:(1) This research will compare a number of tests of cognitive flexibility and structural learning in healthy individuals. From these measures, latent variables of cognitive flexibility will be constructed and sub-processes that are conceptually and empirically separable will be identified by conducting a factor analysis. It is hypothesized here that there will be two differential aspects to cognitive flexibility relating to exploratory and exploitative strategies. (2) To map neurocognitive networks mediating the different aspects of cognitive flexibility the imaging protocol with consist of a combination of neuroimaging techniques such as functional magnetic resonance imaging, and magnetic resonance spectroscopy. Combined DTI-based segmentation analysis and rs-fMRI of the striatum will be used to compute the functional and structural connectivity between the striatal

个体差异性主要是在基于奖励的学习背景下研究的。然而，对适应行为至关重要的人类灵活性的另一个方面包括结构/统计学习，即在没有明确奖励的情况下学习时间序列（Kourtzi，& Welchman，2019）。当执行结构学习任务时，健康个体已经被证明能够适应环境的时间统计的变化-他们不知道-并使用概率匹配和最大化决策策略（Wang等人，2017; Karlaftis等人，2019年）。有趣的是，越来越多的证据表明，支持学习预测结构的大脑结构没有明确的奖励，涉及皮质-纹状体回路，这些回路以前涉及基于奖励的学习（罗宾斯，2007）。具体而言，采用更接近于与运动皮质纹状体回路中的可塑性相关的匹配的策略，同时选择与视觉、动机和执行皮质纹状体回路中的可塑性相关的最可能的结果（最大化）（Giorgio等人，2018; Karlaftis等人，2019年）。需要进一步分析才能更好地了解在有和没有奖励意外情况下介导学习的共享大脑机制。此外，不同的决策策略在不同的情境中可能更有效。匹配策略已经与探索相关联，并且当环境改变时可能是最佳策略（Iigaya等人，2019），而最大化与剥削有关，可能是风险或不确定性下的最佳策略（Schulze，货车Ravenzwaaij，&纽韦尔，2015）。拟议的研究旨在确定是否可以利用结构学习训练范式来优化适应行为。认知灵活性的缺陷发生在强迫症和许多可能相关的精神健康障碍中（例如，成瘾、抽动秽语综合症和饮食失调）。强迫症可以被认为是一种典型的强迫性障碍，也是“行为成瘾”的一个例子（Robbins & Clark，2015）。因此，在这种疾病模型中支持认知灵活性的神经认知网络的研究对精神病学领域具有更广泛的意义。它是假定在这里，结构性学习干预可用于强迫症的对症治疗。目的：（1）本研究旨在初步考察健康人群的认知灵活性结构。特别是，一个大规模的基于网络的认知灵活性和结构学习的评估将管理到一般人群的样本。(2)识别认知灵活性的子过程，并随后使用多模态成像技术将这些过程与特定的功能和解剖网络联系起来。此外，结构学习和反向学习之间的相互关系将通过使用计算方法进行检查。(3)测试结构性学习干预对患有和不患有强迫症的个体认知灵活性的影响。方法和技术：（1）本研究将比较健康个体的认知灵活性和结构学习的一些测试。从这些措施，认知灵活性的潜在变量将被构建和子过程，概念上和经验上分离将被确定进行因子分析。这里假设，将有两个不同方面的认知灵活性有关的探索和剥削的策略。(2)为了映射神经认知网络介导的认知灵活性的不同方面的成像协议，包括神经成像技术的组合，如功能性磁共振成像，磁共振波谱。结合基于DTI的分割分析和纹状体的rs-fMRI将用于计算纹状体之间的功能和结构连接，