The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches

Frontiers in Systems Neuroscience

Yueyi Chen¹  Emma Wellmeyer²  Olivier George²  Brianna Aragon Montes²  Sierra Simpson²  Lauren C. Smith²  Adam Kimbrough³ 

[1] Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States;Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States;Purdue Institute for Inflammation, Immunology, and Infectious Disease, West Lafayette, IN, United States;Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States;

关键词: iDISCO;    fMRI;    immunohistochemistry;    modularity;    graph theory;    network neuroscience;   

DOI  :  10.3389/fnsys.2021.595507

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown