Simpson Sierra, Chen Yueyi, Wellmeyer Emma, Smith Lauren C, Aragon Montes Brianna, George Olivier, Kimbrough Adam
Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States.
Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States.
Front Syst Neurosci. 2021 Apr 21;15:595507. doi: 10.3389/fnsys.2021.595507. eCollection 2021.
A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.
现代神经科学的一个主要关注点围绕着基于先前研究预先选定的感兴趣脑区。虽然有理由关注先前研究中涉及的脑区,但结果却是对脑功能的评估存在偏差。因此,许多在包括神经退行性疾病和神经精神疾病在内的广泛神经生物学问题中可能至关重要的脑区被忽视了。神经成像和计算神经科学的进展使得对全脑功能进行无偏评估并识别先前被忽视的脑区成为可能。本综述将讨论为推动神经科学发展而开发的工具以及用于进一步分析脑内相互连接性的基于网络的计算方法。此外,它将审视在临床(即人类)和临床前(即动物模型)研究中评估连接性的神经网络方法实例,并讨论神经退行性疾病和神经精神疾病的临床前研究如何能够从全脑成像和网络神经科学的无偏特性中大大受益。