轻度认知障碍(mild cognitive impairment,MCI)是一种介于正常老化与阿尔茨海默病(Alzheimer's disease,AD)之间不稳定的认知损害状态.MCI症状较轻,有逆转正常、保持稳定、进展或死亡的4种不同类型的转归,但仍有2/3的MCI患者有可能进展为痴呆.故早期识别稳定型MCI(stable MCI,sMCI)和进展型MCI(progressive MCI,pMCI),有利于及时干预,延缓MCI的进展,改善患者生存质量.结构磁共振(structural magnetic resonance,sMRI)能够预测痴呆相关神经退行性变和认知衰退.大量研究发现,除了临床量表的异常外,sMCI向pMCI进展过程中存在着明显的sMRI的改变,主要集中在皮质厚度和脑萎缩、海马的体积、结构脑网络连接的差异,特别是基于大数据的神经卷积网络等机器学习方法,有助于早期预测sMCI和pMCI,这些研究有助于发掘sMCI向pMCI转化早期的影像学标志物.
Progressing researches on brain structural magnetic resonance in the conversion from stable to pro-gressive mild cognitive impairment
Mild cognitive impairment(MCI)is an unstable cognitive impairment state between nor-mal aging and Alzheimer's disease(AD).The symptoms of MCI are mild and it has four different types of outcomes:reversing normal,maintaining stability,progression and death.However,2/3 of MCI patients may still progress to dementia.Therefore,early identification of stable MCI(sMCI)and progressive MCI(pMCI)is beneficial for timely intervention,and delaying the progression of MCI,then improving patients'quality of life.Structural magnetic resonance imaging(sMRI)can predict dementia related neurodegeneration and cog-nitive decline.A large number of studies have found that,in addition to abnormalities in clinical scales,there are significant changes in sMRI during the progression of sMCI to pMCI,mainly including differences in cor-tical thickness and brain atrophy,hippocampal volume,and structural brain network connectivity.Especially,machine learning methods such as big data based neural convolutional networks are helpful in early prediction of sMCI and pMCI.These studies contribute to the discovery of early imaging markers for the conversion of sMCI to pMCI.
Mild cognitive impairment,stableMild cognitive impairment,progressiveStruc-tural magnetic resonance
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