Morishima Yosuke, van den Heuvel Martijn, Strik Werner, Dierks Thomas
Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Netw Neurosci. 2025 Apr 30;9(2):504-521. doi: 10.1162/netn_a_00443. eCollection 2025.
Recent advancements in neuroimaging data analysis facilitate the characterization of adaptive changes in brain network integration. This study introduces a distinctive approach that merges knowledge-informed and data-driven methodologies, offering a nuanced way to more effectively understand these changes. Utilizing graph network analysis, along with existing neurobiological knowledge of domain-specific brain network systems, we uncover a deeper understanding of brain network interaction and integration. As a proof of concept, we applied our approach to the language domain, a well-known large-scale network system as a representative model system, using functional imaging datasets with specific language tasks for validation of our proposed approach. Our results revealed a double dissociation between motor and sensory language modules during word generation and comprehension tasks. Furthermore, by introducing a hierarchical nature of brain networks and introducing local and global metrics, we demonstrated that hierarchical levels of networks exhibit distinct ways of integration of language brain networks. This innovative approach facilitates a differentiated and thorough interpretation of brain network function in local and global manners, marking a significant advancement in our ability to investigate adaptive changes in brain network integration in health and disease.
神经影像数据分析的最新进展有助于表征脑网络整合中的适应性变化。本研究引入了一种独特的方法，该方法融合了知识驱动和数据驱动的方法，为更有效地理解这些变化提供了一种细致入微的方式。利用图网络分析以及特定领域脑网络系统的现有神经生物学知识，我们对脑网络的相互作用和整合有了更深入的理解。作为概念验证，我们将我们的方法应用于语言领域，这是一个著名的大规模网络系统，作为一个代表性的模型系统，使用具有特定语言任务的功能成像数据集来验证我们提出的方法。我们的结果揭示了在单词生成和理解任务期间运动和感觉语言模块之间的双重分离。此外，通过引入脑网络的层次性质并引入局部和全局指标，我们证明了网络的层次水平表现出语言脑网络整合的不同方式。这种创新方法有助于以局部和全局方式对脑网络功能进行差异化和全面的解释，标志着我们在研究健康和疾病中脑网络整合的适应性变化方面的能力取得了重大进展。
