Task activations produce spurious but systematic inflation of task functional connectivity estimates
Task activations produce spurious but systematic inflation of task functional connectivity estimates
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DOI：
10.1016/j.neuroimage.2018.12.054
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发表时间：
2019-04-01
期刊：
影响因子：
5.7
通讯作者：
Cocuzza, Carrisa
中科院分区：
文献类型：
--
作者：
Cole, Michael W.;Ito, Takuya;Cocuzza, Carrisa
大多数神经科学研究都集中在任务诱发的激活（特定大脑位置的活动幅度）上，对不同大脑位置之间的功能关系提供了有限的见解。任务状态功能连接（FC）-任务执行期间大脑活动时间序列之间的统计关联-通过量化任务期间的功能交互而超越任务诱发的激活。然而，许多任务状态FC的研究并没有消除任务诱发激活的一阶效应之前，估计任务状态FC。有人认为，这会导致模糊的推理“可能活跃或互动的任务期间”，而不是预期的推理“可能互动的任务期间”。利用神经质量计算模型，我们验证了任务诱发的激活大幅和不适当地膨胀任务状态FC估计，特别是在功能性MRI（fMRI）数据。已经开发了试图解决这个问题的各种方法，但这些方法的有效性尚未得到系统评估。我们发现，大多数标准的方法来拟合和删除平均任务诱发激活无法纠正这些膨胀的相关性。相比之下，灵活地拟合平均任务诱发反应形状的方法有效地纠正了膨胀的相关性，而不减少感兴趣的影响。实证fMRI数据的结果证实了该模型的预测，揭示激活引起的任务状态FC通货膨胀的皮尔逊相关性和心理生理相互作用（PPI）的方法。这些结果表明，使用灵活建模任务诱发反应形状的方法去除平均任务诱发激活是有效估计任务状态FC的重要预处理步骤。
Most neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (FC) - statistical association between brain activity time series during task performance - moves beyond task-evoked activations by quantifying functional interactions during tasks. However, many task-state FC studies do not remove the first-order effect of task-evoked activations prior to estimating task-state FC. It has been argued that this results in the ambiguous inference "likely active or interacting during the task", rather than the intended inference "likely interacting during the task". Utilizing a neural mass computational model, we verified that task-evoked activations substantially and inappropriately inflate task-state FC estimates, especially in functional MRI (fMRI) data. Various methods attempting to address this problem have been developed, yet the efficacies of these approaches have not been systematically assessed. We found that most standard approaches for fitting and removing mean task-evoked activations were unable to correct these inflated correlations. In contrast, methods that flexibly fit mean task-evoked response shapes effectively corrected the inflated correlations without reducing effects of interest. Results with empirical fMRI data confirmed the model's predictions, revealing activation-induced task-state FC inflation for both Pearson correlation and psychophysiological interaction (PPI) approaches. These results demonstrate that removal of mean task-evoked activations using an approach that flexibly models task-evoked response shape is an important preprocessing step for valid estimation of task-state FC.