Does multivariate pattern analysis of functional magnetic resonance data benefit from high resolution and sensitivity at high magnetic field strength (7T)?

功能磁共振数据的多变量模式分析（MVPA）近年来在神经科学中得到了广泛的应用。MVPA有望以高空间分辨率提供有关大脑功能的信息。然而，最近发表了相互矛盾的结果，在不同的空间尺度上的功能磁共振数据及其分类分析的贡献所携带的信息。在本项目中，我们打算研究在高磁场强度下产生的高空间分辨率和灵敏度在多大程度上有助于改善功能激活模式的分类。为此，我们改变场强（3T和7T），空间分辨率和灵敏度，并研究其影响下的刺激条件下，创造神经元的兴奋模式在亚毫米与多毫米尺度。该项目的总体目标是表征影响功能磁共振数据的多变量模式分析的因素，并随后使用MVPA优化未来的实验设计。

Multivariate pattern analysis (MVPA) of functional magnetic resonance data has recently been widely used in the neurosciences. MVPA promises to yield information about brain function with high spatial resolution. Recently, however, conflicting results were published concerning the information carried by functional magnetic resonance data at different spatial scales and their contribution to classification analyses. In the present project, we intend to investigate to what degree the high spatial resolution and sensitivity yielded at high magnetic field strength contributes to an improved classification of functional activation patterns. To this end we vary field strength (3T and 7T), spatial resolution and sensitivity and investigate their influence under stimulation conditions that create neuronal excitation patterns in the sub-millimeter versus multiple-millimeter scale. The overall goal of the project is the characterization of factors influencing multivariate pattern analysis of functional magnetic resonance data and, subsequently, the optimization of future experimental designs using MVPA.