Global Burden of Small Vessel Disease–Related Brain Changes on MRI Predicts Cognitive and Functional Decline

Abstract

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.

Cysteine-Altering NOTCH3 Variants Are a Risk Factor for Stroke in the Elderly Population

“…Although SVD is associated with mild cognitive impairment and vascular dementia, 1 , 33 we did not find an increased frequency of mild cognitive impairment and dementia in the individuals with a NOTCH3 cys variant. However, the frequency of cognitive impairment may be an underestimation as this was evaluated using only ICD-10 codes, and the majority of patients did not have a formal neuropsychological evaluation.…”

Evaluating severity of white matter lesions from computed tomography images with convolutional neural network

“…We used the automated FLAIR WML segmentations from the LADIS cohort as the ground truth segmentations when training the CNN model and validating the CT segmentation results. Our previous cross-validated study [ 24 ] has shown that the CNN-based WML segmentation on MRIs produces very similar results when compared with the semi-manual segmentation (correlation 0.99, average Dice similarity index 0.72). This suggests that the MRI-based CNN WML segmentation can be used as ground truth in training CT CNN models and can also be used in validation.…”

Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression

“…Repeatedly, biomarkers of accelerated brain aging in both gray matter and the white matter are associated with increased disability and impairment in activities of daily livings. These findings include associations between disability and hippocampal volume loss, cortical gray matter changes, WMH, infarcts, and other measures of white matter microstructure 50 – 52 . Our observation utilizing a BAG measure derived from structural differences is concordant with this past work.…”