Schwarz Adam J, Whitcher Brandon, Gozzi Alessandro, Reese Torsten, Bifone Angelo
Department of Neuroimaging, Psychiatry Centre of Excellence in Drug Discovery, GlaxoSmithKline Medicines Research Centre, Via Fleming 4, 37135 Verona, Italy.
J Neurosci Methods. 2007 Jan 30;159(2):346-60. doi: 10.1016/j.jneumeth.2006.07.017. Epub 2006 Aug 28.
In pharmacological MRI (phMRI) studies tracking signal changes following the acute administration of a compound, the spatiotemporal pattern of response is often unknown a priori. Moreover, when analysed within a general linear model (GLM) framework, the experimental paradigm of a single injection point under-informs the construction of an appropriate signal model, and information from pharmacokinetics or ancillary in vivo studies may be unavailable or insufficient to accurately describe the dynamic signal changes observed following injection of the drug. Here, we extend the application of a data-driven clustering algorithm, wavelet cluster analysis (WCA), to phMRI data from one or more groups of subjects in a study. A WCA decomposition of spatially concatenated time series' provides a compact overview of spatiotemporal response patterns across cohorts, highlighting typical temporal signatures, brain regions implicated in the response and inter-subject variability. Further, we demonstrate the use of regressors based on selected temporal components as suitable signal models in GLM-based analyses, resulting in a close fit to dynamic phMRI signal changes. This approach is illustrated with simulated data and two representative in vivo phMRI studies in the rat (nicotine and apomorphine challenges).
在药物磁共振成像（phMRI）研究中，追踪化合物急性给药后的信号变化时，反应的时空模式通常事先并不清楚。此外，在一般线性模型（GLM）框架内进行分析时，单一注射点的实验范式对构建合适的信号模型提供的信息不足，并且来自药代动力学或辅助体内研究的信息可能无法获取或不足以准确描述注射药物后观察到的动态信号变化。在此，我们将数据驱动的聚类算法——小波聚类分析（WCA）的应用扩展到研究中一组或多组受试者的phMRI数据。对空间串联时间序列进行WCA分解，能提供整个队列时空反应模式的简要概述，突出典型的时间特征、参与反应的脑区以及受试者间的变异性。此外，我们展示了在基于GLM的分析中，使用基于选定时间成分的回归因子作为合适的信号模型，从而紧密拟合动态phMRI信号变化。通过模拟数据以及大鼠体内两项代表性的phMRI研究（尼古丁和阿扑吗啡激发试验）对该方法进行了说明。
