Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Magn Reson Med. 2021 Jul;86(1):487-498. doi: 10.1002/mrm.28690. Epub 2021 Feb 2.
Spatial normalization is an essential step in resting-state functional MRI connectomic analysis with atlas-based parcellation, but brain lesions can confound it. Cost-function masking (CFM) is a popular compensation approach, but may not benefit modern normalization methods. This study compared three normalization methods with and without CFM and determined their impact on connectomic measures in patients with glioma.
Fifty patients with glioma were included. T -weighted images were normalized using three different methods in SPM12, with and without CFM, which were then overlaid on the ICBM152 template and scored by two neuroradiologists. The Dice coefficient of gray-matter correspondence was also calculated. Normalized resting-state functional MRI data were parcellated using the AAL90 atlas to construct an individual connectivity matrix and calculate connectomic measures. The R among the different normalization methods was calculated for the connectivity matrices and connectomic measures.
The older method (Original) performed significantly worse than the modern methods (Default and DARTEL; P < .005 in observer ranking). The use of CFM did not significantly improve the normalization results. The Original method had lower correlation with the Default and DARTEL methods (R = 0.71-0.74) than Default with DARTEL (R = 0.96) in the connectivity matrix. The clustering coefficient appears to be the most, and modularity the least, sensitive connectomic measures to normalization performance.
The spatial normalization method can have an impact on resting-state functional MRI connectome and connectomic measures derived using atlas-based brain parcellation. In patients with glioma, this study demonstrated that Default and DARTEL performed better than the Original method, and that CFM made no significant difference.
基于图谱的分区的静息态功能磁共振连接组学分析中，空间标准化是一个重要步骤，但脑损伤可能会对此产生干扰。代价函数掩蔽（CFM）是一种常用的补偿方法，但可能对现代标准化方法没有益处。本研究比较了三种带有和不带有 CFM 的标准化方法，并确定了它们对脑肿瘤患者连接组学测量的影响。
纳入 50 例脑肿瘤患者。使用 SPM12 中的三种不同方法对 T1 加权图像进行标准化，分别为带和不带 CFM，然后将其叠加到 ICBM152 模板上，并由两名神经放射科医生进行评分。还计算了灰质对应性的 Dice 系数。使用 AAL90 图谱对归一化的静息态功能磁共振数据进行分区，构建个体连接矩阵并计算连接组学测量值。不同归一化方法之间的 R 用于连接矩阵和连接组学测量值。
较旧的方法（原始方法）的表现明显差于现代方法（默认方法和 DARTEL；观察者排名中 P<0.005）。使用 CFM 并不能显著改善归一化结果。在连接矩阵中，原始方法与默认方法和 DARTEL 方法的相关性较低（R=0.71-0.74），而默认方法与 DARTEL 方法的相关性较高（R=0.96）。聚类系数似乎是对归一化性能最敏感的连接组学测量值，而模块度则是最不敏感的。
空间标准化方法会对基于图谱的脑分区的静息态功能磁共振连接组和连接组学测量值产生影响。在脑肿瘤患者中，本研究表明，默认方法和 DARTEL 优于原始方法，而 CFM 没有显著差异。
