Duong-Tran Duy, Kaufmann Ralph, Chen Jiong, Wang Xuan, Garai Sumita, Xu Frederick, Bao Jingxuan, Amico Enrico, Kaplan Alan D, Petri Giovanni, Goni Joaquin, Zhao Yize, Shen Li
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA.
Department of Mathematics, United States Naval Academy, Annapolis, MD, USA.
Mathematics (Basel). 2024 Feb;12(3). doi: 10.3390/math12030455. Epub 2024 Jan 31.
Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicated that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the homological distance between rest and motor task was observed at both whole-brain and sub-circuit consolidated levels which suggested the self-similarity property of human brain functional connectivity unraveled by homological kernel. Furthermore, at the whole-brain level, the rest-task differentiation was found to be most prominent between rest and different tasks at different homological orders: i) Emotion task , ii) Motor task , and iii) Working memory task . At the functional sub-circuit level, the rest-task functional dichotomy of default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both task- and subject- domain which sheds light to subsequent investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study non-localized coordination patterns of localized structures stretching across complex network fibers.
人类全脑功能连接网络已被证明既具有局部/准局部特性(例如,由节点或边属性诱导的功能子电路集合),也具有非局部特性(例如,高阶功能协调模式)。然而,由局部/准局部功能子电路诱导的拓扑层次的非局部特性尚未得到探讨。为此,我们提出了一种同调形式主义,能够量化人类脑功能子电路的高阶特征。我们的结果表明,每个同调阶数都独特地揭示了人类脑功能子电路的多样且互补的特性。值得注意的是,在全脑和子电路整合水平上都观察到了静息态与运动任务之间的同调距离,这表明由同调核揭示的人类脑功能连接的自相似特性。此外,在全脑水平上,发现静息态与不同任务之间的差异在不同同调阶数下最为显著:i)情绪任务,ii)运动任务以及iii)工作记忆任务。在功能子电路水平上,默认模式网络的静息态 - 任务功能二分法在第一和第二同调支架上最为突出。同样在这样的尺度下,我们发现边缘网络在跨任务和跨主体领域的同调重构中发挥着重要作用,这为后续对该网络复杂神经生理作用的研究提供了线索。从更广泛的角度来看,我们这种形式主义除了可应用于脑连接组学之外,还可用于研究跨越复杂网络纤维的局部结构的非局部协调模式。