Combining multiple connectomes improves predictive modeling of phenotypic measures
Combining multiple connectomes improves predictive modeling of phenotypic measures
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DOI：
10.1016/j.neuroimage.2019.116038
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发表时间：
2019-11-01
期刊：
影响因子：
5.7
通讯作者：
Scheinost, Dustin
中科院分区：
文献类型：
--
作者：
Gao, Siyuan;Greene, Abigail S.;Scheinost, Dustin
静息状态和基于任务的功能连接矩阵或连接组是表型测量中个体差异的强有力预测因子。然而，大多数当前最先进的算法仅基于每个个体的单个连接体构建预测模型。这种方法忽略了来自不同来源的连接体中包含的互补信息，降低了预测性能。为了将联合收割机不同的任务连接体以原则性的方式组合到单个预测模型中，我们提出了一种新的预测框架，称为基于多维连接体的预测建模。两个具体的算法开发和实现在这个框架下。使用两个具有多个任务的大型开源数据集-人类连接组项目和费城神经发育队列，我们验证并比较了我们的框架对每个任务连接组独立执行基于连接组的预测建模（CPM），CPM是通过将个体的所有任务连接组平均在一起创建的一般功能连接矩阵，和CPM，其具有对多个连接体的朴素扩展，其中每个任务的每个边缘被独立地选择。我们的框架表现出上级性能的预测相比，其他竞争的方法。我们发现，不同的任务对最终预测模型的贡献不同，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：第一，两种方法结合多个连接体从不同的任务条件下在一个预测模型进行了演示;第二，我们表明，这些模型优于先前验证的单一的基于连接体的预测模型方法。
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 naive 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.