基于神经动态的预训练框架用于个性化脑功能网络构建
Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction
AI总结：
本文提出一种基于神经动态的预训练框架，用于构建个性化脑功能网络，通过提取个性化神经活动表示并指导脑分区和相关性估计，提升了异质场景下的性能。
摘要：
脑活动本质上是一种受解剖空间约束的神经动态过程。这导致了在变量和异质场景中，神经活动的空间分布模式和相关性模式存在显著差异。然而，主流的脑功能网络构建方法依赖于预定义的脑图谱和线性假设，无法精确捕捉异质场景中的变化神经活动模式。这限制了主流方法构建的脑功能网络的一致性和泛化能力。本文提出了一种基于神经动态的预训练框架，用于个性化脑功能网络构建。该框架提取了异质场景中神经活动模式的个性化表示。通过利用这些表示来指导脑分区和神经活动相关性估计，获得了个性化脑功能网络。系统评估在18个数据集上进行了跨任务评估，如虚拟神经调节和异常神经回路识别。实验结果表明，所提框架在异质场景中实现了优越的性能。总体而言，所提框架挑战了主流的脑功能网络构建方法。
英文摘要：
Brain activity is intrinsically a neural dynamic process constrained by anatomical space. This leads to significant variations in spatial distribution patterns and correlation patterns of neural activity across variable and heterogeneous scenarios. However, dominant brain functional network construction methods, which relies on pre-defined brain atlases and linear assumptions, fails to precisely capture varying neural activity patterns in heterogeneous scenarios. This limits the consistency and generalizability of the brain functional networks constructed by dominant methods. Here, a neural dynamics-informed pre-trained framework is proposed for personalized brain functional network construction. The proposed framework extracts personalized representations of neural activity patterns in heterogeneous scenarios. Personalized brain functional networks are obtained by utilizing these representations to guide brain parcellation and neural activity correlation estimation. Systematic evaluations were employed on 18 datasets across tasks, such as virtual neural modulation and abnormal neural circuit identification. Experimental results demonstrate that the proposed framework attains superior performance in heterogeneous scenarios. Overall, the proposed framework challenges the dominant brain functional network construction method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
