BACKGROUND In task-state functional magnetic resonance imaging (fMRI), hemodynamic response (HDR) shapes help identify cognitive process(es) supported by a brain network. However, when distinguishable networks have similar time courses, the low temporal resolution of the HDRs may result in spatial and temporal blurring of these networks. The present study demonstrated how task-merging and multivariate analysis allows data-driven separation of working memory (WM) processes. This was achieved by combining a WM task with the Thought Generation Task (TGT), a task which also requires attention to internal representations but no overt behavioral response. METHODS 69 adults completed one of two tasks: (1) a Sternberg WM task, whereby participants had to remember a string of letters over a 4-sec delay or no delay, and (2) the TGT task, whereby participants internally generated or listened to a function of an object. WM data were analyzed in isolation and then with the TGT data, using multi-experiment constrained principal component analysis for fMRI (fMRI-CPCA). The function of each network was interpreted by evaluating HDR shapes across conditions (within and between tasks). RESULTS The multi-experiment analysis produced three WM networks involving frontoparietal connectivity; two of these were combined when the WM task was analyzed alone. Notably, one network exhibited HDRs consistent with volitional attention to internal representations in both tasks (i.e., strongest in WM trials with a maintenance phase and in TGT trials involving silent thought). This network was separated from visual attention and motor response networks in the multi-experiment analysis only. CONCLUSIONS Task-merging and multivariate analysis allowed us to differentiate WM networks possibly underlying internal attention (maintenance), visual attention (encoding), and response processes. Further, it allowed postulation of the cognitive operations subserved by each network by providing HDR shapes. This approach facilitates characterization of network functions by allowing direct comparisons of activity across different cognitive domains.

中文翻译：

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任务合并可更好地分离工作记忆中的功能性大脑网络。

背景技术在任务状态功能磁共振成像（fMRI）中，血液动力学响应（HDR）形状有助于识别大脑网络支持的认知过程。但是，当可区分的网络具有相似的时程时，HDR的低时间分辨率可能会导致这些网络的空间和时间模糊。本研究证明了任务合并和多变量分析如何使数据驱动的工作记忆（WM）流程分离。这是通过将WM任务与“思想生成任务”（TGT）结合在一起来实现的，该任务还需要注意内部表示，但没有明显的行为反应。方法69位成年人完成了以下两项任务之一：（1）Sternberg WM任务，参与者必须在4秒的延迟或无延迟的情况下记住一串字母，以及（2）TGT任务，从而参与者在内部生成或收听对象的功能。使用多实验约束主成分分析（fMRI-CPCA）对WM数据进行孤立分析，然后与TGT数据进行分析。通过评估跨条件（任务内部和任务之间）的HDR形状来解释每个网络的功能。结果多元实验分析产生了三个涉及额顶连接的WM网络。当单独分析WM任务时，将其中两个结合在一起。值得注意的是，一个网络在两个任务中都表现出与内部关注一致的HDR（即，在具有维护阶段的WM试验中以及在涉及沉默思想的TGT试验中最强）。仅在多实验分析中，该网络才与视觉注意力和运动反应网络分开。结论任务合并和多变量分析使我们能够区分WM网络，这些网络可能是内部注意力（维护），视觉注意力（编码）和响应过程的基础。此外，它允许通过提供HDR形状来假设每个网络所服务的认知操作。通过允许直接比较不同认知域中的活动，此方法有助于表征网络功能。