Tasks for the Evaluation of the Reward Signature

The processing of reinforcers and punishers is crucial to adapt to an ever-changing environment and its dysregulation is prevalent in mental health and substance use disorders. While many human brain measures related to reward have been based on activity in individual brain regions, recent studies indicate that many affective and motivational processes are encoded in distributed systems that span multiple regions. Consequently, decoding these processes using individual regions yields small effect sizes and limited reliability, whereas predictive models based on distributed patterns yield larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, termed the Brain Reward Signature (BRS), we trained a model to predict the signed magnitude of monetary rewards on the Monetary Incentive Delay task (MID; N = 39) and achieved a highly significant decoding performance (92% for decoding rewards versus losses). We subsequently demonstrate the generalizability of our signature on another version of the MID in a different sample (92% decoding accuracy; N = 12) and on a gambling task from a large sample (73% decoding accuracy, N = 1084). We further provided preliminary data to characterize the specificity of the signature by illustrating that the signature map generates estimates that significantly differ between rewarding and negative feedback (92% decoding accuracy) but do not differ for conditions that differ in disgust rather than reward in a novel Disgust-Delay Task (N = 39). Finally, we show that passively viewing positive and negatively valenced facial expressions loads positively on our signature, in line with previous studies on morbid curiosity. We thus created a BRS that can accurately predict brain responses to rewards and losses in active decision making tasks, and that possibly relates to information seeking in passive observational tasks. <br> This is a repository for the Monetary-Incentive Delay Task and the Disgust Delay task. The uploaded data contains the preprocessed functional fMRI data and onset timing files that are needed to reproduce the analysis and results reported in the paper. <br> This is the link to the pre-print: https://www.biorxiv.org/content/10.1101/2022.06.16.496388v1.abstract <br> <br>

对强化物与惩罚物（reinforcers and punishers）的加工对于适应不断变化的环境至关重要，而其调节异常在心理健康与物质使用障碍中十分普遍。尽管诸多与奖赏相关的人类脑功能测量均基于单个脑区的活动，但近期研究表明，许多情感与动机过程编码于跨多个脑区的分布式系统（distributed systems）中。因此，仅使用单个脑区解码这些过程会得到较小的效应量与有限的信度，而基于分布式模式的预测模型则能获得更大的效应量与优异的信度。为构建针对奖赏与损失加工过程的预测模型——即大脑奖励特征（Brain Reward Signature, BRS），我们基于货币激励延迟任务（Monetary Incentive Delay Task, MID；N=39）训练模型以预测货币奖赏的符号化量级，并取得了极具统计学意义的解码性能（奖赏与损失的解码准确率达92%）。随后，我们在另一队列的MID任务版本（N=12，解码准确率92%）以及大样本赌博任务（N=1084，解码准确率73%）中验证了该特征的泛化性。我们还通过初步数据表征了该特征的特异性：结果显示，该特征图谱生成的估计值在奖赏与负性反馈间存在显著差异（解码准确率92%），但在一项全新的厌恶延迟任务（Disgust-Delay Task）（N=39）中，仅因厌恶而非奖赏存在差异的条件间并无显著区别。最后，我们发现被动观看正负效价面部表情时的脑活动会正向载荷于该特征，这与此前关于病态好奇心（morbid curiosity）的研究结果一致。综上，我们构建的大脑奖励特征能够精准预测主动决策任务中脑对奖赏与损失的响应，且可能与被动观察任务中的信息寻求行为相关。
本仓库用于存储货币激励延迟任务与厌恶延迟任务的相关数据。上传的数据包含预处理后的功能磁共振成像（functional MRI, fMRI）数据以及任务起始计时文件，可用于复现论文中报告的分析与结果。
本预印本（pre-print）的链接为：https://www.biorxiv.org/content/10.1101/2022.06.16.496388v1.abstract