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绘制阿尔茨海默病、大脑发育和精神分裂症中的皮质变化图谱。

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia.

作者信息

Thompson Paul M, Hayashi Kiralee M, Sowell Elizabeth R, Gogtay Nitin, Giedd Jay N, Rapoport Judith L, de Zubicaray Greig I, Janke Andrew L, Rose Stephen E, Semple James, Doddrell David M, Wang Yalin, van Erp Theo G M, Cannon Tyrone D, Toga Arthur W

机构信息

Laboratory of Neuro Imaging, Brain Mapping Division, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA.

出版信息

Neuroimage. 2004;23 Suppl 1:S2-18. doi: 10.1016/j.neuroimage.2004.07.071.

Abstract

This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood- and adult-onset schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages.

摘要

本文描述了一些算法,这些算法能够基于在大量人群中收集的成像数据,识别与阿尔茨海默病、精神分裂症、正常衰老以及异常脑发育相关的脑结构和功能模式。利用这些技术能够发现非凡的信息:动态脑图谱揭示了大脑在儿童期如何生长、在疾病中如何变化以及对药物如何反应。基因脑图谱能够揭示基因对脑结构的影响,为先天与后天之争以及遗传性神经行为障碍的潜在机制提供线索。最近,我们为多种疾病制作了脑结构的延时影片。这些影片识别出了复杂多变的脑结构缺陷模式,揭示了疾病中脑衰退的路径在何处以及以何种速率偏离正常情况。然后,统计标准能够识别出这些变化异常加速的情况,或者药物或其他干预措施使其减缓的情况。在本文中,我们重点描述绘制皮质结构变化图谱的方法。这些方法已被用于揭示痴呆症、癫痫、抑郁症、儿童期和成年期精神分裂症、双相情感障碍、注意力缺陷多动障碍、胎儿酒精综合征、妥瑞氏综合征、威廉姆斯综合征以及甲基苯丙胺滥用者研究中的脑异常特征。具体而言,我们描述了一种称为皮质模式匹配的图像分析流程,它有助于随时间推移并跨受试者比较和整合皮质数据。接着定义统计量以识别组间的脑结构差异,包括皮质厚度的局部改变、灰质密度(GMD)以及皮质组织的不对称性。当以这种方式对基于人群的脑数据进行平均时,常常会出现个体脑扫描中未见到的细微特征。文中给出了示例来说明发育和各种疾病对人类皮质的深远影响。在痴呆症和精神分裂症中追踪了灰质损失的动态扩散波,并且这些序列与不同年龄健康受试者中正常发生的变化相关。

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