Smith Jason F, Chen Kewei, Johnson Sterling, Morrone-Strupinsky Jeannine, Reiman Eric M, Nelson Ann, Moeller James R, Alexander Gene E
Department of Psychology, Arizona State University, Tempe, AZ 85287, USA.
Neuroimage. 2006 Aug 1;32(1):325-32. doi: 10.1016/j.neuroimage.2005.12.010. Epub 2006 Jun 2.
The analysis of functional magnetic resonance imaging (fMRI) data has typically relied on univariate methods to identify areas of brain activity related to cognitive and behavioral task performance. We investigated the ability of multivariate network analysis using a modified form of principal component analysis, the Scaled Subprofile Model (SSM), applied to single-subject fMRI data to identify patterns of interactions among brain regions over time during an anatomically well-characterized simple motor task. We hypothesized that each subject would exhibit correlated patterns of brain activation in several regions known to participate in the regulation of movement including the contralateral motor cortex and the ipsilateral cerebellum. EPI BOLD images were acquired in six healthy participants as they performed a visually and auditorally paced finger opposition task. SSM analysis was applied to the fMR time series on a single-subject basis. Linear combinations of the major principal components that predicted the expected hemodynamic response to the order of experimental conditions were identified for each participant. These combinations of SSM patterns were highly associated with the expected hemodynamic response, an indicator of local neuronal activity, in each participant (0.84 </= R(2) </= 0.97, all P's < 0.0001). As predicted, the combined pattern in each subject was characterized most prominently by relatively increased activations in contralateral sensorimotor cortex and ipsilateral cerebellum. Additionally, all subjects showed areas of relatively decreased activation in the ipsilateral sensorimotor cortex and contralateral cerebellum. The application of network analysis methods, such as SSM, to single-subject fMRI data can identify patterns of task-specific, functionally interacting brain areas in individual subjects. This approach may help identify individual differences in the task-related functional connectivity, track changes in task-related patterns of activity within or between fMRI sessions, and provide a method to identify individual differences in response to treatment.
功能磁共振成像（fMRI）数据的分析通常依赖单变量方法来识别与认知和行为任务表现相关的脑活动区域。我们研究了使用主成分分析的一种改进形式——尺度子剖面模型（SSM）进行多变量网络分析的能力，该模型应用于单受试者fMRI数据，以识别在解剖结构明确的简单运动任务期间，脑区之间随时间的相互作用模式。我们假设每个受试者在几个已知参与运动调节的区域，包括对侧运动皮层和同侧小脑，会表现出相关的脑激活模式。在六名健康参与者执行视觉和听觉节奏的手指对指任务时，采集了EPI BOLD图像。SSM分析在单受试者基础上应用于fMR时间序列。为每个参与者确定了主要主成分的线性组合，这些组合预测了对实验条件顺序的预期血液动力学反应。这些SSM模式的组合在每个参与者中都与预期的血液动力学反应高度相关，预期血液动力学反应是局部神经元活动的一个指标（0.84≤R²≤0.97，所有P值＜0.0001）。如预期的那样，每个受试者的组合模式最显著的特征是对侧感觉运动皮层和同侧小脑的激活相对增加。此外，所有受试者在同侧感觉运动皮层和对侧小脑中均显示出激活相对减少的区域。将网络分析方法（如SSM）应用于单受试者fMRI数据，可以识别个体受试者中特定任务的、功能相互作用的脑区模式。这种方法可能有助于识别任务相关功能连接的个体差异，跟踪fMRI会话内或之间任务相关活动模式的变化，并提供一种识别治疗反应个体差异的方法。
