Ciric Rastko, Wolf Daniel H, Power Jonathan D, Roalf David R, Baum Graham L, Ruparel Kosha, Shinohara Russell T, Elliott Mark A, Eickhoff Simon B, Davatzikos Christos, Gur Ruben C, Gur Raquel E, Bassett Danielle S, Satterthwaite Theodore D
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Department of Psychiatry, Weill Cornell Medical College, NY, NY, USA.
Neuroimage. 2017 Jul 1;154:174-187. doi: 10.1016/j.neuroimage.2017.03.020. Epub 2017 Mar 14.
Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.
自从有关运动伪影对功能连接测量影响的初步报告以来，出现了大量参与者层面的混杂回归方法来限制其影响。然而，许多最常用的技术尚未使用广泛的结果测量进行系统评估。在此，我们对393名青少年中的14种参与者层面的混杂回归方法进行了系统评估。具体而言，我们根据四个基准对方法进行比较，包括运动与连接性之间的残余关系、运动对连接性的距离依赖性影响、网络可识别性以及混杂回归中损失的额外自由度。我们的结果描绘了方法之间两个明显的权衡。首先，包括全局信号回归的方法将连接性与运动之间的关系最小化，但会导致距离依赖性伪影。相比之下，截断方法既能减轻运动伪影又能减轻距离依赖性，但会使用额外的自由度。重要的是，效果较差的去噪方法也无法识别连接组中的模块化网络结构。综上所述，这些结果强调了现有方法的异质性功效，并表明在特定科学目标的背景下，不同的混杂回归策略可能是合适的。
