Loman Sophie, Caciagli Lorenzo, Patankar Shubhankar P, Kahn Ari E, Szymula Karol P, Nyema Nathaniel, Bassett Dani S
Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA.
Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.
bioRxiv. 2025 Jul 10:2024.07.04.602005. doi: 10.1101/2024.07.04.602005.
Humans naturally attend to patterns that emerge in our perceptual environments, building mental models that allow future experiences to be processed more effectively and efficiently. Perceptual events and statistical relations can be represented as nodes and edges in a graph. Recent work in graph learning has shown that human behavior is sensitive to graph topology, but little is known about how that topology might elicit distinct neural responses during learning. Here, we address this knowledge gap by applying time-resolved network analyses to fMRI data collected during a visuomotor graph learning task. We assess neural signatures of learning on two types of structures: modular and non-modular lattice graphs. We find that task performance is supported by a highly flexible visual system, relatively stable brain-wide community structure, cohesiveness within the dorsal attention, limbic, default mode, and subcortical systems, and an increasing degree of integration between the visual and ventral attention systems. Additionally, we find that the time-resolved connectivity of the limbic, default mode, temporoparietal, and subcortical systems is associated with enhanced performance on modular graphs but not on lattice-like graphs. These findings provide evidence for the differential processing of statistical patterns with distinct underlying graph topologies. Our work highlights the similarities between the neural correlates of graph learning and those of statistical learning.
人类天生会关注我们感知环境中出现的模式，构建心理模型，以便更有效且高效地处理未来的经历。感知事件和统计关系可以表示为图中的节点和边。近期图学习方面的研究表明，人类行为对图拓扑结构敏感，但对于这种拓扑结构在学习过程中如何引发不同的神经反应却知之甚少。在此，我们通过将时间分辨网络分析应用于在视觉运动图学习任务期间收集的功能磁共振成像（fMRI）数据来填补这一知识空白。我们评估在两种类型的结构上学习的神经特征：模块化和非模块化晶格图。我们发现任务表现得到高度灵活的视觉系统、相对稳定的全脑社区结构、背侧注意力、边缘系统、默认模式和皮质下系统内的凝聚力，以及视觉和腹侧注意力系统之间日益增强的整合程度的支持。此外，我们发现边缘系统、默认模式、颞顶叶和皮质下系统的时间分辨连通性与模块化图上的表现增强相关，但与晶格状图无关。这些发现为具有不同潜在图拓扑结构的统计模式的差异处理提供了证据。我们的工作突出了图学习和统计学习的神经关联之间的相似性。
