Abstract

Reward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we used emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants. We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution that represented dissociable patterns of convergent brain activity across reward processing tasks. Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value (MAG-1 & MAG-2) and processing a variety of emotional (MAG-3), external (MAG-4 & MAG-5), and internal (MAG-6 & MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.

中文翻译：

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元分析聚类在奖励处理范例中分离大脑活动和行为特征。

摘要

奖励学习是一种普遍存在的认知机制，可指导适应性选择和行为，并且在受损时会导致相当大的心理健康后果。奖励相关的功能性神经影像学研究已经开始涉及大脑区域的网络，这些网络对于处理参与多面体奖励处理结构的各种外围影响（例如，风险，主观偏好，延迟，社会背景）至关重要。为了提供关于奖励过程的更完整的神经认知观点，该观点综合了整个文献中的发现，同时还了解了这些周围的影响，我们使用了新兴的元分析技术来阐明大脑区域，进而通过网络来始终参与奖励过程的各个方面。使用数据驱动的元分析k-均值聚类方法，我们从176个涉及13358名健康参与者的奖励处理研究中的749个实验对比中分离了七个神经成像结果（即大脑活动图）的元分析分组（MAG）。然后，我们执行了探索性功能解码方法，以深入了解与每个MAG相关的推定功能。我们确定了一种七MAG聚类解决方案，该解决方案代表了奖励处理任务中大脑活动的可分解模式。此外，我们的功能解码分析表明，这些MAG中的每一个都映射到离散的行为配置文件上，这些行为建议在预测价值（MAG-1＆MAG-2）和处理各种情感（MAG-3），外部（MAG-4）方面发挥特殊作用＆MAG-5）和内部（MAG-6＆MAG-7）对奖励处理范例的影响。这些发现支持并扩展了公认的奖励学习理论的各个方面，并突出了与奖励处理各个方面相关的大规模脑网络活动。