Zhang Yunhao, Wang Shaonan, Lin Nan, Fan Lingzhong, Zong Chengqing
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Neuroimage. 2025 Apr 1;309:121096. doi: 10.1016/j.neuroimage.2025.121096. Epub 2025 Feb 18.
Constructing task-state large-scale brain networks can enhance our understanding of the organization of brain functions during cognitive tasks. The primary goal of brain network partitioning is to cluster functionally homogeneous brain regions. However, a brain region often serves multiple cognitive functions, complicating the partitioning process. This study proposes a novel clustering method for partitioning large-scale brain networks based on specific cognitive functions, selecting semantic representation as the target cognitive function to evaluate the validity of the proposed method. Specifically, we analyzed functional magnetic resonance imaging (fMRI) data from 11 subjects, each exposed to 672 concepts, and correlated this with semantic rating data related to these concepts. We identified distinct semantic networks based on the concept comprehension task and validated the robustness of our network partitioning through multiple methods. We found that the semantic networks derived from multidimensional semantic activation clustering exhibit high reliability and cross-semantic model consistency (semantic ratings and word embeddings extracted from GPT-2), particularly in networks associated with high semantic functions. Moreover, these semantic networks exhibits significant differences from the resting-state and task-based brain networks obtained using traditional methods. Further analysis revealed functional differences between semantic networks, including disparities in their multidimensional semantic representation capabilities, differences in the information modalities they rely on to acquire semantic information, and varying associations with general cognitive domains. This study introduces a novel approach for analyzing brain networks tailored to specific cognitive functions, establishing a standard semantic parcellation with seven networks for future research, potentially enriching our understanding of complex cognitive processes and their neural bases.
构建任务状态下的大规模脑网络可以增强我们对认知任务期间脑功能组织的理解。脑网络划分的主要目标是将功能上同质的脑区聚类。然而，一个脑区通常服务于多种认知功能，这使得划分过程变得复杂。本研究提出了一种基于特定认知功能对大规模脑网络进行划分的新型聚类方法，选择语义表征作为目标认知功能来评估所提出方法的有效性。具体而言，我们分析了11名受试者的功能磁共振成像（fMRI）数据，每名受试者接触672个概念，并将其与这些概念相关的语义评分数据进行关联。我们基于概念理解任务识别出不同的语义网络，并通过多种方法验证了我们网络划分的稳健性。我们发现，从多维语义激活聚类中得出的语义网络具有高可靠性和跨语义模型一致性（语义评分和从GPT-2中提取的词嵌入），特别是在与高语义功能相关的网络中。此外，这些语义网络与使用传统方法获得的静息态和任务型脑网络存在显著差异。进一步分析揭示了语义网络之间的功能差异，包括它们在多维语义表征能力上的差异、获取语义信息所依赖的信息模态的差异以及与一般认知领域的不同关联。本研究引入了一种针对特定认知功能分析脑网络的新方法，建立了一个具有七个网络的标准语义分割，以供未来研究使用，这可能会丰富我们对复杂认知过程及其神经基础的理解。
