Alexander Gene E, Lin Lan, Yoshimaru Eriko S, Bharadwaj Pradyumna K, Bergfield Kaitlin L, Hoang Lan T, Chawla Monica K, Chen Kewei, Moeller James R, Barnes Carol A, Trouard Theodore P
Department of Psychology, University of Arizona, Tucson, AZ, United States.
Department of Psychiatry, University of Arizona, Tucson, AZ, United States.
Front Aging Neurosci. 2020 Aug 26;12:267. doi: 10.3389/fnagi.2020.00267. eCollection 2020.
Healthy human aging has been associated with brain atrophy in prefrontal and selective temporal regions, but reductions in other brain areas have been observed. We previously found regional covariance patterns of gray matter with magnetic resonance imaging (MRI) in healthy humans and rhesus macaques, using multivariate network Scaled Subprofile Model (SSM) analysis and voxel-based morphometry (VBM), supporting aging effects including in prefrontal and temporal cortices. This approach has yet to be applied to neuroimaging in rodent models of aging. We investigated 7.0T MRI gray matter covariance in 10 young and 10 aged adult male Fischer 344 rats to identify, using SSM VBM, the age-related regional network gray matter covariance pattern in the rodent. SSM VBM identified a regional pattern that distinguished young from aged rats, characterized by reductions in prefrontal, temporal association/perirhinal, and cerebellar areas with relative increases in somatosensory, thalamic, midbrain, and hippocampal regions. Greater expression of the age-related MRI gray matter pattern was associated with poorer spatial learning in the age groups combined. Aging in the rat is characterized by a regional network pattern of gray matter reductions corresponding to aging effects previously observed in humans and non-human primates. SSM MRI network analyses can advance translational aging neuroscience research, extending from human to small animal models, with potential for evaluating mechanisms and interventions for cognitive aging.
健康的人类衰老与前额叶和特定颞叶区域的脑萎缩有关，但在其他脑区也观察到了萎缩现象。我们之前利用多变量网络缩放子轮廓模型（SSM）分析和基于体素的形态测量法（VBM），在健康人类和恒河猴中发现了灰质与磁共振成像（MRI）的区域协方差模式，证实了衰老效应，包括前额叶和颞叶皮质的衰老效应。这种方法尚未应用于衰老啮齿动物模型的神经成像研究。我们对10只年轻成年雄性和10只老年成年雄性费希尔344大鼠进行了7.0T MRI灰质协方差研究，以使用SSM VBM识别啮齿动物中与年龄相关的区域网络灰质协方差模式。SSM VBM识别出一种区分年轻大鼠和老年大鼠的区域模式，其特征是前额叶、颞叶联合/嗅周区域和小脑区域减少，而体感、丘脑、中脑和海马区域相对增加。在合并的年龄组中，与年龄相关的MRI灰质模式表达越高，空间学习能力越差。大鼠衰老的特征是灰质减少的区域网络模式，这与之前在人类和非人灵长类动物中观察到的衰老效应相对应。SSM MRI网络分析可以推动转化衰老神经科学研究，从人类扩展到小动物模型，具有评估认知衰老机制和干预措施的潜力。
