Lang Jinwei, Yang Li-Zhuang, Li Hai
Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China.
Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China.
Brain Res. 2024 Dec 15;1845:149265. doi: 10.1016/j.brainres.2024.149265. Epub 2024 Oct 10.
The networks observed in the brain during resting-state activity are not entirely "task-free." Instead, they hint at a hierarchical structure prepared for adaptive cognitive functions. Recent studies have increasingly demonstrated the potential of resting-state fMRI to predict local activations or global connectomes during task performance. However, uncertainties remain regarding the unique and shared task-specific components within resting-state brain networks, elucidating local activations and global connectome patterns. A coherent framework is also required to integrate these task-specific components to predict local activations and global connectome patterns. In this work, we introduce the Rest2Task model based on the partial least squares-based multivariate regression algorithm, which effectively integrates mappings from resting-state connectivity to local activations and global connectome patterns. By analyzing the coefficients of the regression model, we extracted task-specific resting-state components corresponding to brain local activation or global connectome of various tasks and applied them to the brain lateralization prediction and psychiatric disorders diagnostic. Our model effectively substitutes traditional whole-brain functional connectivity (FC) in predicting functional lateralization and diagnosing brain disorders. Our research represents the inaugural effort to quantify the contribution of patterns (components) within resting-state FC to different tasks, endowing these components with specific task-related contextual information. The task-specific resting-state components offer new insights into brain lateralization processing and disease diagnosis, potentially providing fresh perspectives on the adaptive transformation of brain networks in response to tasks.
在静息状态活动期间大脑中观察到的网络并非完全“无任务”。相反，它们暗示了一种为适应性认知功能准备的层次结构。最近的研究越来越多地证明了静息态功能磁共振成像在预测任务执行期间局部激活或全局连接组方面的潜力。然而，关于静息态脑网络中独特且共享的特定任务成分、阐明局部激活和全局连接组模式仍存在不确定性。还需要一个连贯的框架来整合这些特定任务成分，以预测局部激活和全局连接组模式。在这项工作中，我们引入了基于偏最小二乘多元回归算法的Rest2Task模型，该模型有效地整合了从静息态连通性到局部激活和全局连接组模式的映射。通过分析回归模型的系数，我们提取了与各种任务的脑局部激活或全局连接组相对应的特定任务静息态成分，并将其应用于脑侧化预测和精神疾病诊断。我们的模型在预测功能侧化和诊断脑部疾病方面有效地替代了传统的全脑功能连通性（FC）。我们的研究代表了首次量化静息态FC内模式（成分）对不同任务的贡献的努力，赋予这些成分特定的任务相关上下文信息。特定任务的静息态成分对脑侧化处理和疾病诊断提供了新的见解，可能为脑网络响应任务的适应性转变提供新的视角。
