Tompson Steve, Falk Emily B, Vettel Jean M, Bassett Danielle S
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 USA.
US Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA.
Personal Neurosci. 2018 Jul 5;1. doi: 10.1017/pen.2018.4. Epub 2018 Jul 2.
Over the past decade, advances in the interdisciplinary field of network science have provided a framework for understanding the intrinsic structure and function of human brain networks. A particularly fruitful area of this work has focused on patterns of functional connectivity derived from non-invasive neuroimaging techniques such as functional magnetic resonance imaging (fMRI). An important subset of these efforts has bridged the computational approaches of network science with the rich empirical data and biological hypotheses of neuroscience, and this research has begun to identify features of brain networks that explain individual differences in social, emotional, and cognitive functioning. The most common approach estimates connections assuming a single configuration of edges that is stable across the experimental session. In the literature, this is referred to as a static network approach, and researchers measure static brain networks while a subject is either at rest or performing a cognitively demanding task. Research on social and emotional functioning has primarily focused on linking static brain networks with individual differences, but recent advances have extended this work to examine temporal fluctuations in dynamic brain networks. Mounting evidence suggests that both the strength and flexibility of time-evolving brain networks influence individual differences in executive function, attention, working memory, and learning. In this review, we first examine the current evidence for brain networks involved in cognitive functioning. Then we review some preliminary evidence linking static network properties to individual differences in social and emotional functioning. We then discuss the applicability of emerging dynamic network methods for examining individual differences in social and emotional functioning. We close with an outline of important frontiers at the intersection between network science and neuroscience that will enhance our understanding of the neurobiological underpinnings of social behavior.
在过去十年中,网络科学这一跨学科领域的进展为理解人类大脑网络的内在结构和功能提供了一个框架。这项工作中一个特别富有成果的领域聚焦于从功能磁共振成像(fMRI)等非侵入性神经成像技术中得出的功能连接模式。这些努力中的一个重要子集将网络科学的计算方法与神经科学丰富的实证数据和生物学假设联系起来,并且这项研究已经开始识别大脑网络的特征,这些特征解释了社会、情感和认知功能方面的个体差异。最常见的方法是在假设边的单一配置在整个实验过程中稳定的情况下估计连接。在文献中,这被称为静态网络方法,研究人员在受试者休息或执行认知要求较高的任务时测量静态大脑网络。关于社会和情感功能的研究主要集中在将静态大脑网络与个体差异联系起来,但最近的进展已将这项工作扩展到研究动态大脑网络中的时间波动。越来越多的证据表明,随时间演变的大脑网络的强度和灵活性都会影响执行功能、注意力、工作记忆和学习方面的个体差异。在这篇综述中,我们首先考察目前关于参与认知功能的大脑网络的证据。然后我们回顾一些将静态网络属性与社会和情感功能方面的个体差异联系起来的初步证据。接着我们讨论新兴的动态网络方法在研究社会和情感功能方面个体差异的适用性。我们最后概述网络科学与神经科学交叉领域的重要前沿,这将增进我们对社会行为神经生物学基础的理解。