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Converging patterns of ageing-associated brain volume loss and tissue microstructure differences
Neurobiology of Aging ( IF 3.7 ) Pub Date : 2020-04-01 , DOI: 10.1016/j.neurobiolaging.2020.01.006
Marco Taubert 1 , Elisabeth Roggenhofer 2 , Lester Melie-Garcia 2 , Sandrine Muller 2 , Nico Lehmann 3 , Martin Preisig 4 , Peter Vollenweider 5 , Pedro Marques-Vidal 5 , Antoine Lutti 2 , Ferath Kherif 2 , Bogdan Draganski 6
Affiliation  

Given the worldwide increasing socioeconomic burden of aging-associated brain diseases, there is pressing need to gain in-depth knowledge about the neurobiology of brain anatomy changes across the life span. Advances in quantitative magnetic resonance imaging sensitive to brain's myelin, iron, and free water content allow for a detailed in vivo investigation of aging-related changes while reducing spurious morphometry differences. Main aim of our study is to link previous morphometry findings in aging to microstructural tissue properties in a large-scale cohort (n = 966, age range 46-86 y). Addressing previous controversies in the field, we present results obtained with different approaches to adjust local findings for global effects. Beyond the confirmation of age-related atrophy, myelin, and free water decreases, we report proportionally steeper volume, iron, and myelin decline in sensorimotor and subcortical areas paralleled by free water increase. We demonstrate aging-related white matter volume, myelin, and iron loss in frontostriatal projections. Our findings provide robust evidence for spatial overlap between volume and tissue property differences in aging that affect predominantly motor and executive networks.

中文翻译:

衰老相关脑容量损失和组​​织微观结构差异的收敛模式

鉴于与衰老相关的脑部疾病在全球范围内不断增加的社会经济负担,迫切需要深入了解大脑解剖结构在整个生命周期中的神经生物学变化。对大脑髓鞘、铁和游离水含量敏感的定量磁共振成像的进展允许对与衰老相关的变化进行详细的体内研究,同时减少虚假的形态测量差异。我们研究的主要目的是将先前在衰老中的形态测量结果与大规模队列(n = 966,年龄范围 46-86 岁)中的微观结构组织特性联系起来。针对该领域以前的争议,我们展示了使用不同方法获得的结果,以调整局部发现以适应全球影响。除了确认与年龄相关的萎缩,髓鞘和游离水减少,我们报告了与游离水增加平行的感觉运动和皮层下区域的体积、铁和髓鞘按比例下降。我们在额纹状体投影中展示了与衰老相关的白质体积、髓鞘和铁损失。我们的发现为衰老过程中体积和组织特性差异之间的空间重叠提供了有力的证据,这些差异主要影响运动和执行网络。
更新日期:2020-04-01
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