近日，南方科技大学教授刘泉影及其小组揭示了通过干扰替代大脑来绘制有效连接图。该研究于2025年4月22日发表于国际一流学术期刊《自然—方法学》杂志上。

为了解决这一差距，该课题组人员引入了神经微扰推理（NPI），这是一个数据驱动的框架，用于绘制全脑EC。NPI使用人工神经网络训练来模拟大规模的神经动力学，作为大脑的计算代理。通过系统地干扰代理脑的所有区域并分析其他区域的反应，NPI绘制了全脑EC的方向性、强度和兴奋/抑制特性。NPI在具有已知地真值EC的生成模型上的验证证明了其优于现有方法，如格兰杰成因和动态成因建模。当应用于不同数据集的静息状态功能磁共振成像数据时，NPI显示出一致的、结构支持的EC模式。

此外，与皮质-皮质诱发电位数据的比较表明，NPI推断的EC与真实刺激传播模式之间存在很强的相似性。通过从对大脑功能的相关理解过渡到对大脑功能的纵向理解，NPI标志着在解码大脑功能结构和促进神经科学研究和临床应用方面迈出了一大步。

据介绍，有效连通性（Effective connectivity， EC）是理解大脑信息处理的基础，它反映了大脑区域之间的相互作用。然而，传统的获取电导的方法依赖于对刺激的神经反应，通常具有侵入性或空间覆盖范围有限，因此不适合人类全脑电导的绘制。

附：英文原文

Title: Mapping effective connectivity by virtually perturbing a surrogate brain

Author: Luo, Zixiang, Peng, Kaining, Liang, Zhichao, Cai, Shengyuan, Xu, Chenyu, Li, Dan, Hu, Yu, Zhou, Changsong, Liu, Quanying

Issue&Volume: 2025-04-22

Abstract: Effective connectivity (EC), which reflects the causal interactions between brain regions, is fundamental to understanding information processing in the brain; however, traditional methods for obtaining EC, which rely on neural responses to stimulation, are often invasive or limited in spatial coverage, making them unsuitable for whole-brain EC mapping in humans. Here, to address this gap, we introduce Neural Perturbational Inference (NPI), a data-driven framework for mapping whole-brain EC. NPI employs an artificial neural network trained to model large-scale neural dynamics, serving as a computational surrogate of the brain. By systematically perturbing all regions in the surrogate brain and analyzing the resulting responses in other regions, NPI maps the directionality, strength and excitatory/inhibitory properties of brain-wide EC. Validation of NPI on generative models with known ground-truth EC demonstrates its superiority over existing methods such as Granger causality and dynamic causal modeling. When applied to resting-state functional magnetic resonance imaging data across diverse datasets, NPI reveals consistent, structurally supported EC patterns. Furthermore, comparisons with cortico-cortical evoked potential data show a strong resemblance between NPI-inferred EC and real stimulation propagation patterns. By transitioning from correlational to causal understandings of brain functionality, NPI marks a stride in decoding the brain’s functional architecture and facilitating both neuroscience studies and clinical applications.

DOI: 10.1038/s41592-025-02654-x

Source: https://www.nature.com/articles/s41592-025-02654-x