空间导航的脑网络基础和调控机制

空间导航在生活中时刻发生,空间能力衰退是阿尔兹海默症的重要早期表现。早期关于空间导航神经机制的研究主要关注单个脑区的特异性功能,但这些脑区如何交互以整合不同模态的信息支持复杂导航行为尚不清楚。脑成像技术、脑网络建模方法和神经调控手段的发展,为在脑网络水平理解人类空间导航的认知神经机制提供了重要研究手段。本研究试图融合空间导航认知神经机制研究的最新进展,借助脑网络建模、大数据分析、微电流刺激等前沿研究手段,研究空间导航脑网络的关键拓扑属性特征(如模块化、核心节点等),探寻该功能特异性神经网络的重要影响因素和调控机制,并构建空间导航的脑网络理论模型。研究成果将有利于理解人类复杂导航行为的脑网络基础,为阿尔兹海默症等相关认知障碍脑疾病的筛查和诊断提供重要参考。

The functional brain network that supports human spatial navigation

The system for representing space/places is one of the core knowledge systems in the human brain. Spatial navigation is also emerging as a potential cost-effective cognitive biomarker to detect Alzheimer’s disease (AD) in the preclinical stages. Existing studies have revealed multiple regions across the brain that are specific for different cognitive components of spatial navigation, such as the scene selective areas located in the parahippocampal gyrus and the retrosplenial cortex. Recently, it has been suggested that a non-aggregate network process involving multiple interacting brain regions could better characterize the neural basis of spatial navigation. But little is known about how these regions work together as a network (referred to as navigation network) to support flexible navigation behaviors. This work presents a conceptual framework for research to explore the brain network basis of spatial navigation. Various cutting-edge techniques including multimodal brain imaging, brain network modeling, big data analysis and brain stimulation are integrated for this purpose. Accordingly, three different studies are described as following.Study 1 focuses on localization, modeling and analyses of the network for spatial navigation. Specifically, a large-scale neuroimaging meta-analysis which integrates thousands of functional activations in relevant tasks will be used to localize brain regions important for spatial navigation. Then, multimodal brain networks will be modeled based on data of different imaging modalities, including structural magnetic resonance imaging (MRI), diffusion MRI, and resting-state and task functional MRI. Next, graph-theorical techniques will be used to investigate the topological properties such as modularity and hub distribution of the networks. We are also interested in the interactions of these different networks. Study 2 aims to identify influencing factors of spatial navigation. Using a public database of large samples (e.g., UK Biobank), association analyses will be conducted between the network properties and a large number of genetic and environmental variables including early life experience factors (e.g., birthplace, family economic status, and home street layouts) and genetic variants. Integrative approaches including multivariate analysis (e.g., partial least squares, PLS), genome-wide association analysis (GWAS) and genetic functional analysis will be applied. Genetic-environmental interactions will also be investigated. Based on results of this project, in Study 3 we focus on the stimulation of the navigation network. We are particularly interested in the hub regions within this network. Specifically, direct electrical stimulation and stereoelectroencephalography (SEEG) will be combined. This work plan to recruit a group of patients with medically refractory epilepsy who has undergone SEEG electrode implantation due to clinical needs. Stimulation targets will be determined according to the network modeling results of the present project and previous studies (e.g., hippocampus and retrosplenial cortex). With SEEG and direct electrical stimulation, we will explore the stimulation effects of different targeting regions on navigation network and behavioral performance. Effects of various stimulation parameters will also be explored. In sum, this project integrates interdisciplinary research techniques and computational methods, and aims to establish a brain network model for spatial navigation, reveal important factors that influence the development of this network, and explore the underlying mechanisms of such influences. Results of the present work would provide new insights into understanding the neural network basis of spatial navigation in humans. The established network model and the potential stimulation mechanisms would provide new data and perspectives for studying brain disorders of cognitive impairments such as AD, and help develop new methods for early diagnosis and precise treatment of relevant disorders. In addition, results of this project would spur new theoretical thinking for study of spatial navigation as well as other cognitive functions, which in turn would facilitate new research questions and hypotheses on human cognition and relevant disorders.