Due to the spontaneous nature of resting-state fMRI (rs-fMRI) signals, cross-subject comparison and therefore, group studies of rs-fMRI are challenging. Most existing group comparison methods use features extracted from the fMRI time series, such as connectivity features, independent component analysis (ICA), and functional connectivity density (FCD) methods. However, in group studies, especially in the case of spectrum disorders, distances to a single atlas or a representative subject do not fully reflect the differences between subjects that may lie on a multi-dimensional spectrum. Moreover, there may not exist an individual subject or even an average atlas in such cases that is representative of all subjects. Here we describe an approach that measures pairwise distances between the synchronized rs-fMRI signals of pairs of subjects instead of to a single reference point. We also present a method for fMRI data comparison that leverages this generated pairwise feature to establish a radial basis function kernel matrix. This kernel matrix is used in turn to perform kernel regression of rs-fMRI to a clinical variable such as a cognitive or neurophysiological performance score of interest. This method opens a new pointwise analysis paradigm for fMRI data. We demonstrate the application of this method by performing a pointwise analysis on the cortical surface using rs-fMRI data to identify cortical regions associated with variability in ADHD index. While pointwise analysis methods are common in anatomical studies such as cortical thickness analysis and voxel- and tensor-based morphometry and its variants, such a method is lacking for rs-fMRI and could improve the utility of rs-fMRI for group studies. The method presented in this paper is aimed at filling this gap.
翻译:由于休息状态 FMRI (rs-fMRI) 信号的自发性质,交叉比较以及因此rs-fMRI 的分组研究具有挑战性。大多数现有集团比较方法使用FMRI时间序列中提取的功能性,例如连接特征、独立部件分析(ICA)和功能连接密度(FCD)方法。但在分组研究中,特别是在频谱障碍的情况下,与单一地图集或具有代表性的主体的距离并不充分反映多维度频谱上可能存在的主题之间的差异。此外,在代表所有主题的案例中,也许没有单个主题,甚至平均的 RDR 工具。我们在这里描述一种方法,即测量同步的、s-fRI 对象对双对齐的相距距离,而不是一个单一的参照点。我们还提出一种方法来进行FMRI数据比较,即利用这种对齐的特性来建立一个基于光基的电离心内核质函数矩阵矩阵。这种内脏矩阵被用来进行 RRS-fRI 的内脏回归到一个临床变量变量变量变量的临床变量分析,而我们用这个方法来进行这种对正认知或神经性分析。