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.
Keywords: brain; cerebral small vessel diseases; humans; image processing, computer assisted; neuropsychology.