Department of Fundamental Electricity and Instrumentation, Vrije Universiteit Brussel, Brussels, Belgium.
IEEE Trans Biomed Eng. 2012 Aug;59(8):2264-72. doi: 10.1109/TBME.2012.2202117. Epub 2012 Jun 1.
The postprocessing of functional magnetic resonance imaging (fMRI) data to study the brain functions deals mainly with two objectives: signal detection and extraction of the haemodynamic response. Signal detection consists of exploring and detecting those areas of the brain that are triggered due to an external stimulus. Extraction of the haemodynamic response deals with describing and measuring the physiological process of activated regions in the brain due to stimulus. The haemodynamic response represents the change in oxygen levels since the brain functions require more glucose and oxygen upon stimulus that implies a change in blood flow. In the literature, different approaches to estimate and model the haemodynamic response have been proposed. These approaches can be discriminated in model structures that either provide a proper representation of the obtained measurements but provide no or a limited amount of physiological information, or provide physiological insight but lacks a proper fit to the data. In this paper, a novel model structure is studied for describing the haemodynamics in fMRI measurements: fractional models. We show that these models are flexible enough to describe the gathered data with the additional merit of providing physiological information.
功能磁共振成像（fMRI）数据的后处理主要涉及两个目标：信号检测和血流动力学响应的提取。信号检测包括探索和检测由于外部刺激而触发的大脑区域。血流动力学响应的提取涉及描述和测量由于刺激而激活的大脑区域的生理过程。血流动力学响应代表了由于大脑功能在刺激时需要更多的葡萄糖和氧气而导致的氧气水平的变化，这意味着血液流动的变化。在文献中，已经提出了不同的方法来估计和模拟血流动力学响应。这些方法可以根据模型结构进行区分，这些结构要么提供对获得的测量值的适当表示，但提供很少或没有生理信息，要么提供生理洞察力，但与数据的适当拟合不足。在本文中，研究了一种用于描述 fMRI 测量中血液动力学的新模型结构：分数模型。我们表明，这些模型具有足够的灵活性，可以用额外的生理信息来描述所收集的数据。
