Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Nat Protoc. 2018 Dec;13(12):2801-2826. doi: 10.1038/s41596-018-0065-y.
Participant motion during functional magnetic resonance image (fMRI) acquisition produces spurious signal fluctuations that can confound measures of functional connectivity. Without mitigation, motion artifact can bias statistical inferences about relationships between connectivity and individual differences. To counteract motion artifact, this protocol describes the implementation of a validated, high-performance denoising strategy that combines a set of model features, including physiological signals, motion estimates, and mathematical expansions, to target both widespread and focal effects of subject movement. This protocol can be used to reduce motion-related variance to near zero in studies of functional connectivity, providing up to a 100-fold improvement over minimal-processing approaches in large datasets. Image denoising requires 40 min to 4 h of computing per image, depending on model specifications and data dimensionality. The protocol additionally includes instructions for assessing the performance of a denoising strategy. Associated software implements all denoising and diagnostic procedures, using a combination of established image-processing libraries and the eXtensible Connectivity Pipeline (XCP) software.
参与者在功能磁共振成像（fMRI）采集过程中的运动产生虚假的信号波动，可能会混淆功能连接的测量。如果不加以缓解，运动伪影可能会影响关于连接性和个体差异之间关系的统计推断。为了抵消运动伪影，本方案描述了一种经过验证的高性能去噪策略的实施，该策略结合了一组模型特征，包括生理信号、运动估计和数学扩展，以针对受试者运动的广泛和焦点效应。在功能连接研究中，该方案可以将与运动相关的方差降低到接近零，在大型数据集上比最小处理方法提高多达 100 倍。图像去噪每幅图像需要 40 分钟到 4 小时的计算时间，具体取决于模型规格和数据维度。该方案还包括评估去噪策略性能的说明。相关软件使用成熟的图像处理库和可扩展连接管道（XCP）软件的组合来实现所有的去噪和诊断过程。
