The relationship between brain structure and function plays a crucial role in cognitive and clinical neuroscience. We present a supervised machine learning based approach that captures this relationship by predicting the spatial extent of activations that are observed with task based functional Magnetic Resonance Imaging (fMRI) from the local white matter connectivity, as reflected in diffusion MRI (dMRI) tractography. In particular, we explore three different feature representations of local connectivity patterns that do not require a pre-defined parcellation of cortical and subcortical structures. Instead, they employ cluster-based Bag of Features, Gaussian Mixture Models, and Fisher vectors. We demonstrate that our framework can be used to test the statistical significance of structure-function relationships, compare it to parcellation-based and group-average benchmarks, and propose an algorithm for visualizing our chosen feature representations that permits a neuroanatomical interpretation of our results.

大脑结构和功能之间的关系在认知和临床神经科学中起着至关重要的作用。我们提出了一种基于监督机器学习的方法，该方法通过预测使用基于任务的功能磁共振成像 (fMRI) 从局部白质连接中观察到的激活的空间范围来捕捉这种关系，这反映在扩散 MRI (dMRI) 纤维束成像中。特别是，我们探索了三种不同的局部连接模式的特征表示，它们不需要预先定义的皮质和皮质下结构的分割。相反，他们采用基于集群的特征包、高斯混合模型和 Fisher 向量。我们证明了我们的框架可用于测试结构-功能关系的统计显着性，