Background and Purpose- Cerebral small vessel disease is characterized by a wide range of focal and global brain changes. We used a magnetic resonance imaging segmentation tool to quantify multiple types of small vessel disease-related brain changes and examined their individual and combined predictive value on cognitive and functional abilities. Methods- Magnetic resonance imaging scans of 560 older individuals from LADIS (Leukoaraiosis and Disability Study) were analyzed using automated atlas- and convolutional neural network-based segmentation methods yielding volumetric measures of white matter hyperintensities, lacunes, enlarged perivascular spaces, chronic cortical infarcts, and global and regional brain atrophy. The subjects were followed up with annual neuropsychological examinations for 3 years and evaluation of instrumental activities of daily living for 7 years. Results- The strongest predictors of cognitive performance and functional outcome over time were the total volumes of white matter hyperintensities, gray matter, and hippocampi (P<0.001 for global cognitive function, processing speed, executive functions, and memory and P<0.001 for poor functional outcome). Volumes of lacunes, enlarged perivascular spaces, and cortical infarcts were significantly associated with part of the outcome measures, but their contribution was weaker. In a multivariable linear mixed model, volumes of white matter hyperintensities, lacunes, gray matter, and hippocampi remained as independent predictors of cognitive impairment. A combined measure of these markers based on Z scores strongly predicted cognitive and functional outcomes (P<0.001) even above the contribution of the individual brain changes. Conclusions- Global burden of small vessel disease-related brain changes as quantified by an image segmentation tool is a powerful predictor of long-term cognitive decline and functional disability. A combined measure of white matter hyperintensities, lacunar, gray matter, and hippocampal volumes could be used as an imaging marker associated with vascular cognitive impairment.

中文翻译：

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MRI引起的与小血管疾病相关的脑部改变的全球负担可预测认知和功能下降。

背景和目的-脑小血管疾病的特征是广泛的局灶性和全局性脑部变化。我们使用了磁共振成像分割工具来量化多种类型的与小血管疾病相关的脑部改变，并检查了它们在认知和功能能力上的个体和组合预测价值。方法-使用自动地图集和基于卷积神经网络的分割方法分析了来自LADIS的560位年龄较大的个体的磁共振成像扫描（白细胞增多症和残疾研究），得出了白质高信号，腔隙，扩大的血管周空间，慢性皮质梗死，以及全球和区域性脑萎缩。对受试者进行了为期3年的年度神经心理学检查，并评估了7年的日常生活工具活动。结果-随时间推移认知能力和功能结局的最强预测指标是白质高信号，灰质和海马体的总量（整体认知功能，处理速度，执行功能和记忆力的P <0.001，较差的认知功能的P <0.001功能性结果）。腔隙的体积，血管周围间隙的扩大和皮质梗塞与部分结果指标显着相关，但它们的作用较弱。在多变量线性混合模型中，白质高信号，腔隙，灰质和海马体的体积仍然是认知障碍的独立预测因子。基于Z分数的这些标记物的组合度量甚至可以强烈预测认知和功能结局（P <0.001），甚至高于单个脑部变化的贡献。结论-通过图像分割工具量化的与小血管疾病相关的脑部改变的总体负担是长期认知功能下降和功能障碍的有力预测指标。白质高信号，腔隙，灰质和海马体积的综合量度可以用作与血管性认知障碍相关的影像学指标。结论-通过图像分割工具量化的与小血管疾病相关的脑部改变的总体负担是长期认知功能下降和功能障碍的有力预测指标。白质高信号，腔隙，灰质和海马体积的综合量度可以用作与血管性认知障碍相关的影像学指标。结论-通过图像分割工具量化的与小血管疾病相关的脑部改变的总体负担是长期认知功能下降和功能障碍的有力预测指标。白质高信号，腔隙，灰质和海马体积的综合量度可以用作与血管性认知障碍相关的影像学指标。