Wang Yingying, Qiao Chen, Qu Gang, Calhoun Vince D, Stephen Julia M, Wilson Tony W, Wang Yu-Ping
IEEE Trans Biomed Eng. 2024 Dec;71(12):3390-3401. doi: 10.1109/TBME.2024.3423803. Epub 2024 Nov 21.
Brain dynamic effective connectivity (dEC), characterizes the information transmission patterns between brain regions that change over time, which provides insight into the biological mechanism underlying brain development. However, most existing methods predominantly capture fixed or temporally invariant EC, leaving dEC largely unexplored.
Herein we propose a deep dynamic causal learning model specifically designed to capture dEC. It includes a dynamic causal learner to detect time-varying causal relationships from spatio-temporal data, and a dynamic causal discriminator to validate these findings by comparing original and reconstructed data.
Our model outperforms established baselines in the accuracy of identifying dynamic causalities when tested on the simulated data. When applied to the Philadelphia Neurodevelopmental Cohort, the model uncovers distinct patterns in dEC networks across different age groups. Specifically, the evolution process of brain dEC networks in young adults is more stable than in children, and significant differences in information transfer patterns exist between them.
This study highlights the brain's developmental trajectory, where networks transition from undifferentiated to specialized structures with age, in accordance with the improvement of an individual's cognitive and information processing capability.
The proposed model consists of the identification and verification of dynamic causality, utilizing the spatio-temporal fusing information from fMRI. As a result, it can accurately detect dEC and characterize its evolution over age.
脑动态有效连接性（dEC）表征了脑区之间随时间变化的信息传递模式，这为洞察脑发育的生物学机制提供了线索。然而，大多数现有方法主要捕捉固定的或时间不变的有效连接性，使得dEC在很大程度上未被探索。
在此，我们提出了一种专门设计用于捕捉dEC的深度动态因果学习模型。它包括一个动态因果学习者，用于从时空数据中检测随时间变化的因果关系，以及一个动态因果判别器，通过比较原始数据和重建数据来验证这些发现。
在模拟数据上进行测试时，我们的模型在识别动态因果关系的准确性方面优于已建立的基线。当应用于费城神经发育队列时，该模型揭示了不同年龄组dEC网络中的独特模式。具体而言，年轻人脑dEC网络的演化过程比儿童更稳定，并且他们之间在信息传递模式上存在显著差异。
本研究突出了脑的发育轨迹，即随着年龄增长，网络从未分化结构转变为专门化结构，这与个体认知和信息处理能力的提高相一致。
所提出的模型包括动态因果关系的识别和验证，利用功能磁共振成像的时空融合信息。因此，它可以准确检测dEC并表征其随年龄的演变。
