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结构神经影像学。

Structural neuroimaging.

机构信息

CIBORG Lab, Department of Radiology, Children's Hospital of Los Angeles and University of Southern California, Los Angeles, CA, United States.

CIBORG Lab, Department of Radiology, Children's Hospital of Los Angeles and University of Southern California, Los Angeles, CA, United States.

出版信息

Handb Clin Neurol. 2020;174:251-264. doi: 10.1016/B978-0-444-64148-9.00018-1.

DOI:10.1016/B978-0-444-64148-9.00018-1
PMID:32977882
Abstract

Characterizing the neuroanatomical correlates of brain development is essential in understanding brain-behavior relationships and neurodevelopmental disorders. Advances in brain MRI acquisition protocols and image processing techniques have made it possible to detect and track with great precision anatomical brain development and pediatric neurologic disorders. In this chapter, we provide a brief overview of the modern neuroimaging techniques for pediatric brain development and review key normal brain development studies. Characteristic disorders affecting neurodevelopment in childhood, such as prematurity, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), epilepsy, and brain cancer, and key neuroanatomical findings are described and then reviewed. Large datasets of typically developing children and children with various neurodevelopmental conditions are now being acquired to help provide the biomarkers of such impairments. While there are still several challenges in imaging brain structures specific to the pediatric populations, such as subject cooperation and tissues contrast variability, considerable imaging research is now being devoted to solving these problems and improving pediatric data analysis.

摘要

描述大脑发育的神经解剖学相关性对于理解大脑-行为关系和神经发育障碍至关重要。脑 MRI 采集协议和图像处理技术的进步使得精确检测和跟踪解剖学大脑发育和儿科神经疾病成为可能。在本章中,我们简要概述了用于儿科大脑发育的现代神经影像学技术,并回顾了关键的正常大脑发育研究。描述并回顾了影响儿童神经发育的特征性疾病,如早产、注意缺陷多动障碍(ADHD)、自闭症谱系障碍(ASD)、癫痫和脑癌,以及关键的神经解剖学发现。现在正在获取大量正常发育儿童和各种神经发育障碍儿童的数据集,以帮助提供这些障碍的生物标志物。虽然在对儿科人群的特定脑结构进行成像方面仍然存在一些挑战,例如受检者的配合和组织对比度的可变性,但现在已经有大量的影像学研究致力于解决这些问题并改善儿科数据分析。

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