Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods
Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods
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DOI：
10.1016/j.neurobiolaging.2018.06.013
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发表时间：
2018-11-01
影响因子：
4.2
通讯作者：
Davatzikos, Christos
中科院分区：
文献类型：
--
作者：
Eavani, Harini;Habes, Mohamad;Davatzikos, Christos
在认知正常的老年人中分离大脑老化的异质性是具有挑战性的，因为多个共同发生的病理过程导致不同的功能和结构变化。利用机器学习方法对巴尔的摩老龄化纵向研究中400名年龄在50岁至96岁之间的参与者的磁共振成像数据进行分析，我们构建了标准的大脑结构和功能变化的横断面衰老轨迹。与典型轨迹的偏离确定了具有弹性脑老化和多种亚型晚期脑老化的个体。我们确定了5种不同的晚期脑老化表型。一组包括相对广泛的结构和功能丧失和高白质高强度负荷的个体。另一个亚组显示海马局灶性萎缩和较低的后扣带功能连贯性，低白质高强度负荷，以及较高的中颞部连通性，潜在地反映了与阿尔茨海默病早期阶段一致的高脑组织储备抵消了脑损失。其他亚群表现出明显的格局。这些结果表明，不应该通过寻找大脑老化的单一特征来测量大脑的变化，而应该通过捕捉大脑老化的异质性和亚型的方法来测量。我们的发现为未来的研究提供了参考，这些研究旨在更好地理解大脑老化成像模式的神经生物学基础。(C)2018 Elsevier Inc.保留所有权利。
Disentangling the heterogeneity of brain aging in cognitively normal older adults is challenging, as multiple co-occurring pathologic processes result in diverse functional and structural changes. Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative cross-sectional brain aging trajectories of structural and functional changes. Deviations from typical trajectories identified individuals with resilient brain aging and multiple subtypes of advanced brain aging. We identified 5 distinct phenotypes of advanced brain aging. One group included individuals with relatively extensive structural and functional loss and high white matter hyperintensity burden. Another subgroup showed focal hippocampal atrophy and lower posterior-cingulate functional coherence, low white matter hyperintensity burden, and higher medial-temporal connectivity, potentially reflecting high brain tissue reserve counterbalancing brain loss that is consistent with early stages of Alzheimer's disease. Other subgroups displayed distinct patterns. These results indicate that brain changes should not be measured seeking a single signature of brain aging but rather via methods capturing heterogeneity and subtypes of brain aging. Our findings inform future studies aiming to better understand the neurobiological underpinnings of brain aging imaging patterns. (C) 2018 Elsevier Inc. All rights reserved.