白质
认知功能衰退
磁共振成像
磁共振弥散成像
痴呆
神经科学
高强度
松弛法
髓鞘
流体衰减反转恢复
大脑大小
神经影像学
脑老化
部分各向异性
医学
衰老的大脑
病态的
病理
心理学
认知
中枢神经系统
放射科
疾病
自旋回波
作者
Akifumi Hagiwara,Satoru Kamio,Junko Kikuta,Moto Nakaya,Wataru Uchida,Shohei Fujita,Stikov Nikola,Toshiaki Akasahi,Akihiko Wada,Koji Kamagata,Shigeki Aoki
标识
DOI:10.1097/rli.0000000000001120
摘要
Abstract The aging process induces a variety of changes in the brain detectable by magnetic resonance imaging (MRI). These changes include alterations in brain volume, fluid-attenuated inversion recovery (FLAIR) white matter hyperintense lesions, and variations in tissue properties such as relaxivity, myelin, iron content, neurite density, and other microstructures. Each MRI technique offers unique insights into the structural and compositional changes occurring in the brain due to normal aging or neurodegenerative diseases. Age-related brain volume changes encompass a decrease in gray matter and an increase in ventricular volume, associated with cognitive decline. White matter hyperintensities, detected by FLAIR, are common and linked to cognitive impairments and increased risk of stroke and dementia. Tissue relaxometry reveals age-related changes in relaxivity, aiding the distinction between normal aging and pathological conditions. Myelin content, measurable by MRI, changes with age and is associated with cognitive and motor function alterations. Iron accumulation, detected by susceptibility-sensitive MRI, increases in certain brain regions with age, potentially contributing to neurodegenerative processes. Diffusion MRI provides detailed insights into microstructural changes such as neurite density and orientation. Neurofluid imaging, using techniques like gadolinium-based contrast agents and diffusion MRI, reveals age-related changes in cerebrospinal and interstitial fluid dynamics, crucial for brain health and waste clearance. This review offers a comprehensive overview of age-related brain changes revealed by various MRI techniques. Understanding these changes helps differentiate between normal aging and pathological conditions, aiding the development of interventions to mitigate age-related cognitive decline and other symptoms. Recent advances in machine learning and artificial intelligence have enabled novel methods for estimating brain age, offering also potential biomarkers for neurological and psychiatric disorders.
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