Gössl C, Fahrmeir L, Auer D P
Max-Planck-Institute of Psychiatry, Munich, Germany.
Neuroimage. 2001 Jul;14(1 Pt 1):140-8. doi: 10.1006/nimg.2001.0795.
In functional magnetic resonance imaging (fMRI), modeling the complex link between neuronal activity and its hemodynamic response via the neurovascular coupling requires an elaborate and sensitive response model. Methods based on physiologic assumptions as well as direct, descriptive models have been proposed. The focus of this study is placed on such a direct approach that allows for a robust pixelwise determination of hemodynamic characteristics, such as time to peak or the poststimulus undershoot. A Bayesian procedure is presented that can easily be adapted to different hemodynamic properties in question and can be estimated without numerical problems known from nonlinear optimization algorithms. The usefulness of the model is demonstrated by thorough analyzes of the poststimulus undershoot in visual and acoustic stimulation paradigms. Further, we show the capability of this approach to improve analysis of fMRI data in altered hemodynamic conditions.
在功能磁共振成像（fMRI）中，通过神经血管耦合对神经元活动与其血液动力学反应之间的复杂联系进行建模，需要一个精细且灵敏的反应模型。已经提出了基于生理假设的方法以及直接的描述性模型。本研究的重点是这样一种直接方法，它能够对血液动力学特征进行稳健的逐像素测定，比如峰值时间或刺激后下冲。本文提出了一种贝叶斯方法，该方法可以轻松适应所讨论的不同血液动力学特性，并且无需非线性优化算法中存在的数值问题即可进行估计。通过对视觉和听觉刺激范式下刺激后下冲的深入分析，证明了该模型的实用性。此外，我们展示了这种方法在改变的血液动力学条件下改善fMRI数据分析的能力。
