Behavioural and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Sep;3(9):754-766. doi: 10.1016/j.bpsc.2018.03.003. Epub 2018 Mar 14.
Human brain networks based on neuroimaging data have already proven useful in characterizing both normal and abnormal brain structure and function. However, many brain disorders are neurodevelopmental in origin, highlighting the need to go beyond characterizing brain organization in terms of static networks. Here, we review the fast-growing literature shedding light on developmental changes in network phenotypes. We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of follow-up. We also discuss the subtle ways in which healthy brain network development can inform our understanding of disorders, including work bridging the gap between macroscopic neuroimaging results and the microscopic level. Finally, we turn to studies of three specific neurodevelopmental disorders that first manifest primarily in childhood and adolescence/early adulthood, namely psychotic disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorder. In each case we discuss recent progress in understanding the atypical features of brain network development associated with the disorder, and we conclude the review with some suggestions for future directions.
基于神经影像学数据的人类大脑网络已经被证明在描述正常和异常大脑结构和功能方面非常有用。然而，许多大脑疾病都起源于神经发育，这突出表明需要超越基于静态网络来描述大脑组织。在这里，我们回顾了快速发展的文献，这些文献揭示了网络表型的发育变化。我们首先概述了最近大规模努力绘制健康大脑发育图谱的情况，并描述了包括在长时间随访期间进行多次重复测量的纵向数据所发挥的关键作用。我们还讨论了健康大脑网络发育如何以微妙的方式为我们理解疾病提供信息，包括将宏观神经影像学结果与微观水平联系起来的工作。最后，我们转向研究三种特定的神经发育障碍，这些障碍首先在儿童和青少年/成年早期表现出来，即精神病、注意缺陷/多动障碍和自闭症谱系障碍。在每种情况下，我们都讨论了理解与该障碍相关的大脑网络发育异常特征的最新进展，并在评论结束时提出了一些未来方向的建议。
