Université de Lyon, F-69622, Lyon, France; Université Lyon 1, Villeurbanne, France; INSERM, U 1028, Lyon Neuroscience Research Center, Lyon, F-69000, France; CNRS, UMR 5292, Lyon Neuroscience Research Center, Lyon, F-69000, France.
Department of Cognition, Development and Educational Psychology, Institute of Neurosciences, University of Barcelona, Spain; Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute, Spain.
Neuroimage. 2021 Nov 15;242:118478. doi: 10.1016/j.neuroimage.2021.118478. Epub 2021 Aug 14.
Understanding how the brain processes reward is an important and complex endeavor, which has involved the use of a range of complementary neuroimaging tools, including electroencephalography (EEG). EEG has been praised for its high temporal resolution but, because the signal recorded at the scalp is a mixture of brain activities, it is often considered to have poor spatial resolution. Besides, EEG data analysis has most often relied on event-related potentials (ERPs) which cancel out non-phase locked oscillatory activity, thus limiting the functional discriminative power of EEG attainable through spectral analyses. Because these three dimensions -temporal, spatial and spectral- have been unequally leveraged in reward studies, we argue that the full potential of EEG has not been exploited. To back up our claim, we first performed a systematic survey of EEG studies assessing reward processing. Specifically, we report on the nature of the cognitive processes investigated (i.e., reward anticipation or reward outcome processing) and the methods used to collect and process the EEG data (i.e., event-related potential, time-frequency or source analyses). A total of 359 studies involving healthy subjects and the delivery of monetary rewards were surveyed. We show that reward anticipation has been overlooked (88% of studies investigated reward outcome processing, while only 24% investigated reward anticipation), and that time-frequency and source analyses (respectively reported by 19% and 12% of the studies) have not been widely adopted by the field yet, with ERPs still being the dominant methodology (92% of the studies). We argue that this focus on feedback-related ERPs provides a biased perspective on reward processing, by ignoring reward anticipation processes as well as a large part of the information contained in the EEG signal. Finally, we illustrate with selected examples how addressing these issues could benefit the field, relying on approaches combining time-frequency analyses, blind source separation and source localization.
理解大脑如何处理奖励是一项重要而复杂的任务，涉及使用一系列互补的神经影像学工具，包括脑电图 (EEG)。EEG 因其具有高时间分辨率而受到赞誉，但由于头皮上记录的信号是大脑活动的混合，因此通常被认为空间分辨率较差。此外，EEG 数据分析最常依赖于事件相关电位 (ERP)，ERP 会消除非锁相振荡活动，从而限制通过频谱分析获得的 EEG 的功能辨别力。由于这三个维度——时间、空间和频谱——在奖励研究中利用程度不同，我们认为 EEG 的全部潜力尚未得到充分利用。为了支持我们的观点，我们首先对评估奖励处理的 EEG 研究进行了系统调查。具体来说，我们报告了所研究的认知过程的性质（即奖励预期或奖励结果处理）以及用于收集和处理 EEG 数据的方法（即事件相关电位、时频或源分析）。共调查了 359 项涉及健康受试者和金钱奖励的研究。我们表明，奖励预期被忽视了（88%的研究调查了奖励结果处理，而只有 24%的研究调查了奖励预期），时频和源分析（分别有 19%和 12%的研究报告）尚未被该领域广泛采用，ERP 仍然是占主导地位的方法（92%的研究）。我们认为，这种对反馈相关 ERP 的关注提供了一种对奖励处理的有偏见的观点，忽略了奖励预期过程以及 EEG 信号中包含的大部分信息。最后，我们通过选择的例子说明了如何通过结合时频分析、盲源分离和源定位的方法来解决这些问题，从而使该领域受益。
