Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.
Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut, USA.
Nat Protoc. 2017 Mar;12(3):506-518. doi: 10.1038/nprot.2016.178. Epub 2017 Feb 9.
Neuroimaging is a fast-developing research area in which anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale data sets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: (i) feature selection, (ii) feature summarization, (iii) model building, and (iv) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a considerable amount of the variance in these measures. It has been demonstrated that the CPM protocol performs as well as or better than many of the existing approaches in brain-behavior prediction. As CPM focuses on linear modeling and a purely data-driven approach, neuroscientists with limited or no experience in machine learning or optimization will find it easy to implement these protocols. Depending on the volume of data to be processed, the protocol can take 10-100 min for model building, 1-48 h for permutation testing, and 10-20 min for visualization of results.
神经影像学是一个快速发展的研究领域，它使用功能磁共振成像 (fMRI)、弥散张量成像 (DTI) 和脑电图 (EEG) 等技术来收集人类大脑的解剖和功能图像。技术进步和大规模数据集使得能够开发出使用来自神经影像学数据的脑连接测量值来预测个体差异的特质和行为的模型。在这里，我们提出了基于连接体的预测建模 (CPM)，这是一种使用交叉验证从连接体数据中开发脑-行为关系预测模型的基于数据的协议。该协议包括以下步骤：(i)特征选择，(ii)特征总结，(iii)模型构建，以及 (iv)预测显著性评估。我们还包括了可视化最具预测性特征（即脑连接）的建议。最终的结果应该是一个可推广的模型，它将脑连接数据作为输入，并对新的受试者的行为测量值进行预测，解释这些测量值的相当大的方差。已经证明，CPM 协议在脑-行为预测方面的表现与许多现有的方法一样好或更好。由于 CPM 专注于线性建模和纯粹的数据驱动方法，因此具有有限或没有机器学习或优化经验的神经科学家将发现实施这些协议很容易。根据要处理的数据量，模型构建可能需要 10-100 分钟，置换测试可能需要 1-48 小时，结果可视化可能需要 10-20 分钟。
