MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom.
J Cogn Neurosci. 2020 Jul;32(7):1348-1368. doi: 10.1162/jocn_a_01554. Epub 2020 Feb 28.
The frontoparietal "multiple-demand" (MD) control network plays a key role in goal-directed behavior. Recent developments of multivoxel pattern analysis (MVPA) for fMRI data allow for more fine-grained investigations into the functionality and properties of brain systems. In particular, MVPA in the MD network was used to gain better understanding of control processes such as attentional effects, adaptive coding, and representation of multiple task-relevant features, but overall low decoding levels have limited its use for this network. A common practice of applying MVPA is by investigating pattern discriminability within a ROI using a template mask, thus ensuring that the same brain areas are studied in all participants. This approach offers high sensitivity but does not take into account differences between individuals in the spatial organization of brain regions. An alternative approach uses independent localizer data for each subject to select the most responsive voxels and define individual ROIs within the boundaries of a group template. Such an approach allows for a refined and targeted localization based on the unique pattern of activity of individual subjects while ensuring that functionally similar brain regions are studied for all subjects. In the current study, we tested whether using individual ROIs leads to changes in decodability of task-related neural representations as well as univariate activity across the MD network compared with when using a group template. We used three localizer tasks to separately define subject-specific ROIs: spatial working memory, verbal working memory, and a Stroop task. We then systematically assessed univariate and multivariate results in a separate rule-based criterion task. All the localizer tasks robustly recruited the MD network and evoked highly reliable activity patterns in individual subjects. Consistent with previous studies, we found a clear benefit of the subject-specific ROIs for univariate results from the criterion task, with increased activity in the individual ROIs based on the localizers' data, compared with the activity observed when using the group template. In contrast, there was no benefit of the subject-specific ROIs for the multivariate results in the form of increased discriminability, as well as no cost of reduced discriminability. Both univariate and multivariate results were similar in the subject-specific ROIs defined by each of the three localizers. Our results provide important empirical evidence for researchers in the field of cognitive control for the use of individual ROIs in the frontoparietal network for both univariate and multivariate analysis of fMRI data and serve as another step toward standardization and increased comparability across studies.
额顶“多需求”（MD）控制网络在目标导向行为中起着关键作用。多变量模式分析（MVPA）的最新发展可对大脑系统的功能和特性进行更精细的研究。特别是，在 MD 网络中进行 MVPA 有助于更好地理解控制过程，如注意力效应、自适应编码以及对多个与任务相关的特征的表示，但总体较低的解码水平限制了其在该网络中的应用。应用 MVPA 的一种常见做法是使用模板掩模在 ROI 内研究模式可辨别性，从而确保在所有参与者中研究相同的大脑区域。这种方法具有很高的灵敏度，但没有考虑到个体在大脑区域空间组织方面的差异。另一种方法是为每个受试者使用独立的定位器数据来选择最敏感的体素，并在群组模板的边界内定义个体 ROI。这种方法允许根据个体受试者的独特活动模式进行精细和有针对性的定位，同时确保对所有受试者研究功能相似的大脑区域。在当前研究中，我们测试了使用个体 ROI 是否会改变与任务相关的神经表示以及 MD 网络中的单变量活动的可解码性，与使用群组模板相比。我们使用三个定位器任务分别定义了受试者特定的 ROI：空间工作记忆、言语工作记忆和 Stroop 任务。然后，我们在一个单独的基于规则的标准任务中系统地评估了单变量和多变量的结果。所有定位器任务都可靠地招募了 MD 网络，并在个体受试者中引起了高度可靠的活动模式。与先前的研究一致，我们发现基于局部器数据的个体 ROI 在标准任务的单变量结果中具有明显的优势，与使用群组模板观察到的活动相比，个体 ROI 的活动增加了。相比之下，对于多变量结果，个体 ROI 没有增加辨别力的优势，也没有降低辨别力的成本。在基于三个定位器的个体 ROI 中，单变量和多变量的结果都相似。我们的结果为认知控制领域的研究人员提供了重要的经验证据，支持在额顶网络中使用个体 ROI 进行 fMRI 数据的单变量和多变量分析，这是朝着标准化和提高研究之间的可比性迈出的又一步。
