用神经动态信息构建个性化脑功能网络，提升复杂场景下的准确性。
Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction
- 基于神经动态先验知识，自适应学习个体化活动模式
- 在18个数据集上显著优于传统方法，尤其在异质场景下
- 适合研究脑连接差异、个性化医疗的学者使用
大脑活动本质上是受解剖空间约束的神经动态过程，导致在不同且异质的情境下，神经活动的空间分布和相关性模式存在显著差异。然而，主流脑功能网络构建方法依赖预定义脑图谱和线性假设，难以精确捕捉异质情境中的变化模式，限制了所构建网络的一致性和泛化能力。本文提出一种神经动态信息引导的预训练框架，用于个性化脑功能网络构建。该框架在异质情境中提取神经活动模式的个性化表征，并利用这些表征指导脑分区与神经活动相关性估计，从而获得个性化脑功能网络。在涵盖虚拟神经调控和异常神经回路识别等任务的18个数据集上进行了系统评估。实验结果表明，该框架在异质情境下表现出更优性能。总体而言，该框架挑战了主流脑功能网络构建范式。
原文摘要 · Abstract (English)
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.
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