Predicting Dementia in Cerebral Small Vessel Disease using an Automatic Diffusion Tensor Image Segmentation Technique

Background and Purpose: Cerebral small vessel disease (SVD) is the most common cause of vascular cognitive impairment, with a significant proportion of cases going on to develop dementia. We explore the extent to which DSEG (a diffusion tensor image, DTI segmentation technique that characterizes microstructural damage across the cerebrum) predicts both degree of cognitive decline and conversion to dementia, and hence may provide a useful prognostic procedure.

Methods: 99 SVD patients (aged 43-89) underwent annual MRI scanning (for three years) and cognitive assessment (for five years). DSEG-θ was used as a whole cerebrum measure of SVD severity. Dementia diagnosis was based DSM-V criteria. Cox regression identified which DSEG measures and vascular risk factors were related to increased risk of dementia. Linear discriminant analysis was used to classify groups of stable vs. subsequent dementia diagnosis individuals.

Results: DSEG-θ was significantly related to decline in executive function and global cognition (p <.001) Eighteen (18.2%) patients converted to dementia. Baseline DSEG-θ predicted dementia with a balanced classification rate (BCR) =75.95% and area under the receiver operator curve (AUC) =0.839. The best classification model included baseline DSEG-θ, change in DSEG-θ, age, sex and premorbid IQ (BCR of 79.65%, AUC=0.903).

Conclusions: DSEG is a fully automatic technique that provides an accurate method for assessing brain microstructural damage in SVD from a single imaging modality (DTI). DSEG-θ is an important tool in identifying SVD patients at increased risk of developing dementia and has potential as a clinical marker of SVD severity.