Hemmerling Kimberly J, Vigotsky Andrew D, Glanville Charlotte, Barry Robert L, Bright Molly G
Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
bioRxiv. 2025 Jan 23:2025.01.23.634596. doi: 10.1101/2025.01.23.634596.
Numerous approaches have been used to denoise spinal cord functional magnetic resonance imaging (fMRI) data. Principal component analysis (PCA)-based techniques, which derive regressors from a noise region of interest (ROI), have been used in both brain (e.g., CompCor) and spinal cord fMRI. However, spinal cord fMRI denoising methods have yet to be systematically evaluated. Here, we formalize and evaluate a PCA-based technique for deriving nuisance regressors for spinal cord fMRI analysis (SpinalCompCor). In this method, regressors are derived with PCA from a noise ROI, an area defined outside of the spinal cord and cerebrospinal fluid. A parallel analysis is used to systematically determine how many components to retain as regressors for modeling; this designated a median of 11 regressors across three fMRI datasets: motor task (n=26), breathing task (n=27), and resting state (n=10). First-level fMRI modeling demonstrated that principal component regressors did fit noise (e.g., physiological noise from blood vessels), particularly in the resting state fMRI dataset. However, group-level motor task activation maps themselves did not show a clear benefit from including SpinalCompCor regressors over our original denoising model. The potential for collinearity of principal component regressors with the task may be a concern, and this should be considered in future implementations for which task-correlated noise is anticipated.
已有多种方法用于对脊髓功能磁共振成像（fMRI）数据进行去噪。基于主成分分析（PCA）的技术，即从感兴趣的噪声区域（ROI）导出回归变量，已应用于脑功能磁共振成像（如CompCor）和脊髓功能磁共振成像。然而，脊髓功能磁共振成像的去噪方法尚未得到系统评估。在此，我们规范并评估一种基于PCA的技术，用于为脊髓功能磁共振成像分析导出干扰回归变量（SpinalCompCor）。在该方法中，回归变量通过PCA从一个噪声ROI导出，该区域定义在脊髓和脑脊液之外。采用平行分析来系统地确定保留多少成分作为回归变量用于建模；在三个功能磁共振成像数据集（运动任务，n = 26；呼吸任务，n = 27；静息状态，n = 10）中，这指定了11个回归变量的中位数。一级功能磁共振成像建模表明，主成分回归变量确实拟合了噪声（如来自血管的生理噪声），特别是在静息状态功能磁共振成像数据集中。然而，与我们原来的去噪模型相比，组水平的运动任务激活图在纳入SpinalCompCor回归变量后并未显示出明显优势。主成分回归变量与任务的共线性可能性可能是一个问题，在未来预期存在与任务相关噪声的实现中应予以考虑。
