Kapur Kush, Roy Anindya, Bhaumik Dulal K, Gibbons Robert D, Lazar Nicole A, Sweeney John A, Aryal Subhash, Patterson Dave
Center for Health Statistics, University of Illinois at Chicago, Chicago, Illinois, USA.
Commun Stat Theory Methods. 2009;38(16-17):3099-3113. doi: 10.1080/03610920902947576.
In this article, we model functional magnetic resonance imaging (fMRI) data for event-related experiment data using a fourth degree spline to fit voxel specific blood oxygenation level-dependent (BOLD) responses. The data are preprocessed for removing long term temporal components such as drifts using wavelet approximations. The spatial dependence is incorporated in the data by the application of 3D Gaussian spatial filter. The methodology assigns an activation score to each trial based on the voxel specific characteristics of the response curve. The proposed procedure has a capability of being fully automated and it produces activation images based on overall scores assigned to each voxel. The methodology is illustrated on real data from an event-related design experiment of visually guided saccades (VGS).
在本文中，我们使用四次样条函数对事件相关实验数据的功能磁共振成像（fMRI）数据进行建模，以拟合体素特异性血氧水平依赖（BOLD）响应。数据经过预处理，使用小波近似法去除长期时间成分，如漂移。通过应用三维高斯空间滤波器将空间依赖性纳入数据中。该方法基于响应曲线的体素特异性特征为每个试验分配一个激活分数。所提出的程序具有完全自动化的能力，并根据分配给每个体素的总体分数生成激活图像。该方法在视觉引导扫视（VGS）事件相关设计实验的真实数据上进行了说明。
