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基于解剖结构指导的时空图卷积网络(AG-STGCNs)用于建模多个任务领域中脑回和脑沟之间的功能连接。

Anatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains.

出版信息

IEEE Trans Neural Netw Learn Syst. 2024 Jun;35(6):7435-7445. doi: 10.1109/TNNLS.2022.3194733. Epub 2024 Jun 3.

Abstract

The cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is further supported by morphological, genetic, and structural evidences. Therefore, systematically investigating the gyro-sulcal (G-S) functional difference can help deeply understand the functional mechanism of brain. By integrating functional magnetic resonance imaging (fMRI) with advanced deep learning models, recent studies have unveiled the temporal difference in functional activity between gyri and sulci. However, the potential difference of functional connectivity, which represents functional dependency between gyri and sulci, is much unknown. Moreover, the regularity and variability of the G-S functional connectivity difference across multiple task domains remains to be explored. To address the two concerns, this study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on 830 subjects with seven different task-based and one resting state fMRI (rs-fMRI) datasets from the public Human Connectome Project (HCP), we consistently found that there are significant differences of functional connectivity between gyral and sulcal regions within task domains compared with resting state (RS). Furthermore, there is considerable variability of such functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors. Our study helps better understand the functional segregation of gyri and sulci within task domains as well as the anatomo-functional-behavioral relationship of the human brain.

摘要

大脑皮层折叠形成脑回和脑沟,为揭示大脑的解剖-功能关系提供了基础。先前的研究广泛表明,脑回和脑沟具有内在的功能差异,形态学、遗传学和结构证据进一步支持了这一观点。因此,系统地研究脑回-脑沟(G-S)的功能差异有助于深入了解大脑的功能机制。通过将功能磁共振成像(fMRI)与先进的深度学习模型相结合,最近的研究揭示了脑回和脑沟之间功能活动的时间差异。然而,功能连接的潜在差异,即脑回和脑沟之间的功能依赖性,还知之甚少。此外,多个任务领域之间 G-S 功能连接差异的规律性和可变性仍有待探索。为了解决这两个问题,本研究开发了新的解剖指导时空图卷积网络(AG-STGCNs),以研究多个任务领域内脑回和脑沟之间功能连接差异的规律性和可变性。基于来自公共人类连接组计划(HCP)的 830 名受试者的七个不同任务和一个静息状态 fMRI(rs-fMRI)数据集,我们一致发现,与静息状态(RS)相比,任务状态下脑回和脑沟之间的功能连接存在显著差异。此外,这种功能连接和信息在不同任务领域之间在脑回和脑沟之间的流动存在相当大的可变性,这与个体认知行为相关。我们的研究有助于更好地理解任务状态下脑回和脑沟的功能分离以及人类大脑的解剖-功能-行为关系。

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