Kyathanahally Sreenath P, Franco-Watkins Ana, Zhang Xiaoxia, Calhoun Vince D, Deshpande Gopikrishna
IEEE J Biomed Health Inform. 2017 May;21(3):814-825. doi: 10.1109/JBHI.2016.2590434. Epub 2016 Jul 12.
Human decision making is a multidimensional construct, driven by a complex interplay between external factors, internal biases, and computational capacity constraints. Here, we propose a layered approach to experimental design in which multiple tasks-from simple to complex-with additional layers of complexity introduced at each stage are incorporated for investigating decision making. This is demonstrated using tasks involving intertemporal choice between immediate and future prospects. Previous functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) studies have separately investigated the spatial and temporal neural substrates, respectively, of specific factors underlying decision making. In contrast, we performed simultaneous acquisition of EEG/fMRI data and fusion of both modalities using joint independent component analysis such that: 1) the native temporal/spatial resolutions of either modality is not compromised and 2) fast temporal dynamics of decision making as well as involved deeper striatal structures can be characterized. We show that spatiotemporal neural substrates underlying our proposed complex intertemporal task simultaneously incorporating rewards, costs, and uncertainty of future outcomes can be predicted (using a linear model) from neural substrates of each of these factors, which were separately obtained by simpler tasks. This was not the case for spatial and temporal features obtained separately from fMRI and EEG, respectively. However, certain prefrontal activations in the complex task could not be predicted from activations in simpler tasks, indicating that the assumption of pure insertion has limited validity. Overall, our approach provides a realistic and novel framework for investigating the neural substrates of decision making with high spatiotemporal resolution.
人类决策是一个多维度的概念，由外部因素、内部偏差和计算能力限制之间的复杂相互作用驱动。在此，我们提出一种分层的实验设计方法，其中纳入了多个任务——从简单到复杂——并在每个阶段引入额外的复杂层次，以研究决策。这通过涉及即时和未来前景之间跨期选择的任务得到了证明。先前的功能磁共振成像（fMRI）和脑电图（EEG）研究分别研究了决策背后特定因素的空间和时间神经基础。相比之下，我们同时采集了EEG/fMRI数据，并使用联合独立成分分析对两种模态进行融合，以便：1）不损害任何一种模态的原始时间/空间分辨率；2）能够表征决策的快速时间动态以及所涉及的更深层纹状体结构。我们表明，通过更简单的任务分别获得的这些因素的神经基础，可以（使用线性模型）从我们提出的同时纳入奖励、成本和未来结果不确定性的复杂跨期任务的时空神经基础中预测出来。分别从fMRI和EEG获得的空间和时间特征并非如此。然而，复杂任务中的某些前额叶激活无法从简单任务中的激活预测出来，这表明纯插入假设的有效性有限。总体而言，我们的方法为以高时空分辨率研究决策的神经基础提供了一个现实且新颖 的框架。
