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.

中文翻译：

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隐藏的大脑：通过采用新颖的临床前无偏网络方法发现先前被忽视的大脑区域

基于先前的研究，现代神经科学的一大焦点围绕着预先选定的感兴趣的大脑区域。尽管有理由专注于涉及先前工作的大脑区域，但结果却是对脑功能的偏见评估。因此，许多大脑区域可能被证明在广泛的神经生物学问题中至关重要，包括神经退行性疾病和神经精神疾病。神经影像学和计算神经科学的进步使得对全脑功能进行公正评估并确定先前被忽视的大脑区域成为可能。这篇综述将讨论为进一步发展神经科学和基于网络的计算方法而开发的工具，这些方法可用于进一步分析大脑的互连性。此外，