Greaves Matthew D, Novelli Leonardo, Mansour L Sina, Zalesky Andrew, Razi Adeel
School of Psychological Sciences, Monash University, Clayton, Victoria, Australia.
Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.
Nat Rev Neurosci. 2025 Jan;26(1):23-41. doi: 10.1038/s41583-024-00881-3. Epub 2024 Dec 11.
Understanding how one brain region exerts influence over another in vivo is profoundly constrained by models used to infer or predict directed connectivity. Although such neural interactions rely on the anatomy of the brain, it remains unclear whether, at the macroscale, structural (or anatomical) connectivity provides useful constraints on models of directed connectivity. Here, we review the current state of research on this question, highlighting a key distinction between inference-based effective connectivity and prediction-based directed functional connectivity. We explore the methods via which structural connectivity has been integrated into directed connectivity models: through prior distributions, fixed parameters in state-space models and inputs to structure learning algorithms. Although the evidence suggests that integrating structural connectivity substantially improves directed connectivity models, assessments of reliability and out-of-sample validity are lacking. We conclude this Review with a strategy for future research that addresses current challenges and identifies opportunities for advancing the integration of structural and directed connectivity to ultimately improve understanding of the brain in health and disease.
了解一个脑区在体内如何对另一个脑区施加影响，在很大程度上受到用于推断或预测定向连接性的模型的限制。尽管这种神经相互作用依赖于大脑的解剖结构，但在宏观尺度上，结构（或解剖）连接性是否为定向连接性模型提供有用的限制仍不清楚。在这里，我们回顾了关于这个问题的当前研究状况，强调了基于推断的有效连接性和基于预测的定向功能连接性之间的关键区别。我们探讨了将结构连接性整合到定向连接性模型中的方法：通过先验分布、状态空间模型中的固定参数以及结构学习算法的输入。尽管有证据表明，整合结构连接性可显著改善定向连接性模型，但仍缺乏可靠性和样本外有效性的评估。我们以未来研究策略结束本综述，该策略应对当前挑战，并确定推进结构连接性和定向连接性整合的机会，以最终增进对健康和疾病状态下大脑的理解。
