1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
2 NeuroImaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
Brain Connect. 2019 Mar;9(2):113-127. doi: 10.1089/brain.2018.0587. Epub 2018 Nov 16.
Rapid advances in neuroimaging and network science have produced powerful tools and measures to appreciate human brain organization at multiple spatial and temporal scales. It is now possible to obtain increasingly meaningful representations of whole-brain structural and functional brain networks and to formally assess macroscale principles of network topology. In addition to its utility in characterizing healthy brain organization, individual variability, and life span-related changes, there is high promise of network neuroscience for the conceptualization and, ultimately, management of brain disorders. In the current review, we argue for a science of the human brain that, while strongly embracing macroscale connectomics, also recommends awareness of brain properties derived from meso- and microscale resolutions. Such features include MRI markers of tissue microstructure, local functional properties, as well as information from nonimaging domains, including cellular, genetic, or chemical data. Integrating these measures with connectome models promises to better define the individual elements that constitute large-scale networks, and clarify the notion of connection strength among them. By enriching the description of large-scale networks, this approach may improve our understanding of fundamental principles of healthy brain organization. Notably, it may also better define the substrate of prevalent brain disorders, including stroke, autism, as well as drug-resistant epilepsies that are each characterized by intriguing interactions between local anomalies and network-level perturbations.
神经影像学和网络科学的快速发展已经产生了强大的工具和方法，可以在多个时空尺度上欣赏人类大脑的组织。现在已经可以获得越来越有意义的全脑结构和功能脑网络表示，并正式评估网络拓扑的宏观原则。除了在描述健康大脑组织、个体变异性和与寿命相关的变化方面具有实用性外，网络神经科学在概念化和最终管理大脑疾病方面也具有很高的潜力。在当前的综述中，我们主张对人类大脑进行科学研究，既要强烈接受宏观尺度的连接组学，也要意识到从中尺度和微观尺度得出的大脑特性。这些特征包括 MRI 标记的组织微观结构、局部功能特性以及来自非成像领域的信息，包括细胞、遗传或化学数据。将这些测量值与连接组模型相结合，有望更好地定义构成大规模网络的个体元素，并阐明它们之间的连接强度概念。通过丰富对大规模网络的描述，这种方法可以提高我们对健康大脑组织基本原理的理解。值得注意的是，它还可能更好地定义常见大脑疾病的基础，包括中风、自闭症以及耐药性癫痫，这些疾病的特征是局部异常和网络级别的扰动之间存在有趣的相互作用。
