Sanford Nicole, Whitman Jennifer C, Woodward Todd S
Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; BC Mental Health & Substance Use Services Research Institute, Vancouver, BC, Canada.
Department of Psychology, Northwestern University, IL, USA.
Cortex. 2020 Apr;125:246-271. doi: 10.1016/j.cortex.2019.12.014. Epub 2020 Jan 14.
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.
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).
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.
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.
在任务态功能磁共振成像（fMRI）中，血流动力学反应（HDR）的形状有助于识别脑网络所支持的认知过程。然而，当可区分的网络具有相似的时间进程时，HDR的低时间分辨率可能会导致这些网络在空间和时间上的模糊。本研究展示了任务合并和多变量分析如何实现对工作记忆（WM）过程的数据驱动分离。这是通过将一个WM任务与思维生成任务（TGT）相结合来实现的，TGT任务也需要关注内部表征，但没有明显的行为反应。
69名成年人完成了两项任务中的一项：（1）斯特恩伯格WM任务，参与者必须在4秒延迟或无延迟的情况下记住一串字母；（2）TGT任务，参与者在内部生成或听取一个物体的功能。WM数据先单独分析，然后与TGT数据一起分析，使用功能磁共振成像的多实验约束主成分分析（fMRI-CPCA）。通过评估不同条件（任务内和任务间）下的HDR形状来解释每个网络的功能。
多实验分析产生了三个涉及额顶叶连接的WM网络；单独分析WM任务时，其中两个网络合并了。值得注意的是，在两项任务中，一个网络的HDR与对内部表征的意志性注意一致（即在有维持阶段的WM试验和涉及静思的TGT试验中最强）。仅在多实验分析中，这个网络与视觉注意和运动反应网络分离。
任务合并和多变量分析使我们能够区分可能潜在支持内部注意（维持）、视觉注意（编码）和反应过程的WM网络。此外，通过提供HDR形状，它还能推测每个网络所支持的认知操作。这种方法通过允许直接比较不同认知领域的活动，有助于表征网络功能。
