Department of Biomedical Engineering, Yale University, United States.
Interdepartmental Neuroscience Program, Yale School of Medicine, United States.
Neuroimage. 2019 Nov 1;201:116038. doi: 10.1016/j.neuroimage.2019.116038. Epub 2019 Jul 20.
Resting-state and task-based functional connectivity matrices, or connectomes, are powerful predictors of individual differences in phenotypic measures. However, most of the current state-of-the-art algorithms only build predictive models based on a single connectome for each individual. This approach neglects the complementary information contained in connectomes from different sources and reduces prediction performance. In order to combine different task connectomes into a single predictive model in a principled way, we propose a novel prediction framework, termed multidimensional connectome-based predictive modeling. Two specific algorithms are developed and implemented under this framework. Using two large open-source datasets with multiple tasks-the Human Connectome Project and the Philadelphia Neurodevelopmental Cohort, we validate and compare our framework against performing connectome-based predictive modeling (CPM) on each task connectome independently, CPM on a general functional connectivity matrix created by averaging together all task connectomes for an individual, and CPM with a naïve extension to multiple connectomes where each edge for each task is selected independently. Our framework exhibits superior performance in prediction compared with the other competing methods. We found that different tasks contribute differentially to the final predictive model, suggesting that the battery of tasks used in prediction is an important consideration. This work makes two major contributions: First, two methods for combining multiple connectomes from different task conditions in one predictive model are demonstrated; Second, we show that these models outperform a previously validated single connectome-based predictive model approach.
静息态和任务态功能连接矩阵（即连接组）是预测个体表型差异的有力指标。然而，目前大多数最先进的算法仅为每个个体构建基于单个连接组的预测模型。这种方法忽略了来自不同来源的连接组中包含的互补信息，从而降低了预测性能。为了以一种有原则的方式将不同的任务连接组组合到单个预测模型中，我们提出了一种新的预测框架，称为多维连接组预测建模。在这个框架下，开发并实现了两种特定的算法。我们使用两个具有多个任务的大型开源数据集——人类连接组计划和费城神经发育队列，验证并比较了我们的框架与以下方法的性能：在每个任务连接组上分别进行基于连接组的预测建模（CPM）；在通过平均每个个体的所有任务连接组创建的一般功能连接矩阵上进行 CPM；以及在一种简单的扩展方法上进行 CPM，其中每个任务的每条边都是独立选择的。与其他竞争方法相比，我们的框架在预测方面表现出了优越的性能。我们发现，不同的任务对最终预测模型的贡献程度不同，这表明预测中使用的任务组合是一个重要的考虑因素。这项工作有两个主要贡献：首先，展示了两种在一个预测模型中结合来自不同任务条件的多个连接组的方法；其次，我们表明这些模型优于以前验证的基于单个连接组的预测模型方法。
