Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data

Intrinsic Connectivity Networks, patterns of correlated activity emerging from “resting-state” BOLD time series, are increasingly being associated with cognitive, clinical, and behavioral aspects, and compared with patterns of activity elicited by specific tasks. We study the reconfiguration of brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. To this end, we use a large cohort of publicly available data in both resting and task-based fMRI paradigms. By applying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy to predict ICNs is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.内在连接网络（Intrinsic Connectivity Networks，ICNs）是源自“静息态”血氧水平依赖（Blood Oxygen Level Dependent, BOLD）时间序列的相关活动模式，其正日益被证实与认知、临床及行为特征相关联，并常与特定任务诱发的活动模式进行对比。本研究采用机器学习方法，探究任务态与静息态下大脑网络的重构模式，旨在识别出在任务态与静息态的网络配置变化中受影响更为显著的内在连接网络（ICNs）。为此，我们使用了包含静息态与任务态功能磁共振成像（functional Magnetic Resonance Imaging, fMRI）范式的大规模公开共享数据集队列。通过应用一系列仅依赖任务态测量数据的监督分类器，我们证实，仅含单个隐藏层的简单神经网络可实现最高的内在连接网络（ICNs）预测准确率。此外，当将拟合后的模型应用于静息态测量数据进行测试时，该网络架构在与执行任务相关的脑区（主要涵盖视觉皮层与感觉运动皮层）上的预测性能接近90%，而在其余内在连接网络（ICNs）中则出现了性能的显著下降。一方面，本研究结果证实了两种范式（任务态与静息态）下内在连接网络（ICNs）的对应关系，从而为无法确保受试者配合完成指定任务的临床应用场景开辟了新的研究方向。另一方面，研究表明，未参与任务的脑区在两种范式下展现出了截然不同的连接模式。