Sochat Vanessa, Supekar Kaustubh, Bustillo Juan, Calhoun Vince, Turner Jessica A, Rubin Daniel L
Stanford Graduate Fellow, Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, California, United States of America.
Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California, United States of America.
PLoS One. 2014 Apr 18;9(4):e95493. doi: 10.1371/journal.pone.0095493. eCollection 2014.
Analyzing Functional Magnetic Resonance Imaging (fMRI) of resting brains to determine the spatial location and activity of intrinsic brain networks--a novel and burgeoning research field--is limited by the lack of ground truth and the tendency of analyses to overfit the data. Independent Component Analysis (ICA) is commonly used to separate the data into signal and Gaussian noise components, and then map these components on to spatial networks. Identifying noise from this data, however, is a tedious process that has proven hard to automate, particularly when data from different institutions, subjects, and scanners is used. Here we present an automated method to delineate noisy independent components in ICA using a data-driven infrastructure that queries a database of 246 spatial and temporal features to discover a computational signature of different types of noise. We evaluated the performance of our method to detect noisy components from healthy control fMRI (sensitivity = 0.91, specificity = 0.82, cross validation accuracy (CVA) = 0.87, area under the curve (AUC) = 0.93), and demonstrate its generalizability by showing equivalent performance on (1) an age- and scanner-matched cohort of schizophrenia patients from the same institution (sensitivity = 0.89, specificity = 0.83, CVA = 0.86), (2) an age-matched cohort on an equivalent scanner from a different institution (sensitivity = 0.88, specificity = 0.88, CVA = 0.88), and (3) an age-matched cohort on a different scanner from a different institution (sensitivity = 0.72, specificity = 0.92, CVA = 0.79). We additionally compare our approach with a recently published method. Our results suggest that our method is robust to noise variations due to population as well as scanner differences, thereby making it well suited to the goal of automatically distinguishing noise from functional networks to enable investigation of human brain function.
分析静息大脑的功能磁共振成像（fMRI）以确定内在脑网络的空间位置和活动——这是一个新兴的研究领域——受到缺乏地面真值以及分析过度拟合数据倾向的限制。独立成分分析（ICA）通常用于将数据分离为信号和高斯噪声成分，然后将这些成分映射到空间网络上。然而，从这些数据中识别噪声是一个繁琐的过程，事实证明很难实现自动化，尤其是在使用来自不同机构、受试者和扫描仪的数据时。在此，我们提出一种自动化方法，利用数据驱动的基础设施在ICA中描绘有噪声的独立成分，该基础设施查询一个包含246个空间和时间特征的数据库，以发现不同类型噪声的计算特征。我们评估了我们的方法从健康对照fMRI中检测有噪声成分的性能（灵敏度 = 0.91，特异性 = 0.82，交叉验证准确率（CVA） = 0.87，曲线下面积（AUC） = 0.93），并通过在以下方面展示等效性能来证明其通用性：（1）来自同一机构的年龄和扫描仪匹配的精神分裂症患者队列（灵敏度 = 0.89，特异性 = 0.83，CVA = 0.86），（2）来自不同机构的等效扫描仪上的年龄匹配队列（灵敏度 = 0.88，特异性 = 0.88，CVA = 0.88），以及（3）来自不同机构的不同扫描仪上的年龄匹配队列（灵敏度 = 0.72，特异性 = 0.92，CVA = 0.79）。我们还将我们的方法与最近发表的一种方法进行了比较。我们的结果表明，我们的方法对由于人群以及扫描仪差异导致的噪声变化具有鲁棒性，从而使其非常适合自动区分噪声与功能网络以实现对人类脑功能进行研究的目标。
