Lee David S, Sahib Ashish, Narr Katherine, Nunez Elvis, Joshi Shantanu
Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA, Los Angeles, USA.
Department of Bioengineering, UCLA, Los Angeles, USA.
Med Image Comput Comput Assist Interv. 2020 Oct;12267:518-527. doi: 10.1007/978-3-030-59728-3_51. Epub 2020 Sep 29.
We present a novel method for global diffeomorphic phase alignment of time-series data from resting-state functional magnetic resonance imaging (rsfMRI) signals. Additionally, we propose a multidimensional, continuous, invariant functional representation of brain time-series data and solve a general global cost function that brings both the temporal rotations and phase reparameterizations in alignment. We define a family of cost functions for spatiotemporal warping and compare time-series warps across them. This method achieves direct alignment of time-series, allows population analysis by aligning time-series activity across subjects and shows improved global correlation maps, as well as z-scores from independent component analysis (ICA), while showing new information exploited by phase alignment that was not previously recoverable.
我们提出了一种用于对静息态功能磁共振成像（rsfMRI）信号的时间序列数据进行全局微分同胚相位对齐的新方法。此外，我们提出了一种脑时间序列数据的多维、连续、不变的功能表示，并求解了一个通用的全局成本函数，该函数可实现时间旋转和相位重新参数化的对齐。我们定义了一系列用于时空扭曲的成本函数，并比较了它们之间的时间序列扭曲。该方法实现了时间序列的直接对齐，通过跨受试者对齐时间序列活动实现了群体分析，并显示出改进的全局相关图以及独立成分分析（ICA）的z分数，同时还展示了相位对齐所利用的以前无法恢复的新信息。
