[P2–411]: PREDICTING DEMENTIA IN CEREBRAL SMALL‐VESSEL DISEASE USING AN AUTOMATIC DIFFUSION TENSOR IMAGE SEGMENTATION TECHNIQUE

Cerebral small vessel disease (SVD) is the primary cause of vascular cognitive impairment and vascular dementia. Clinically, patients with SVD present with lacunar strokes and are characterized by a decline in executive function (EF) and information processing speed (IPS), whereas memory functions appear to be relatively stable. Developing accurate biomarkers to track disease severity and identify individuals most at risk of converting to dementia is important to administer effective treatments and interventions. Markers derived from magnetic resonance imaging (MRI) have been associated with cognitive decline in SVD. These include presence of white matter hyperintensities (WMH), gray matter (GM) atrophy, lacunar infarcts, cerebral microbleeds, and white matter (WM) microstructural damage detected using diffusion tensor imaging (DTI). WMH volume–derived and DTI-derived measures have also been shown to predict risk of receiving a dementia diagnosis in SVD. Markers of structural damage measured by MRI often co-occur in patients with SVD, and there is potential to combine multiple MRI markers into a unitary burden score. Such combined burden scores may provide a more accurate method for monitoring disease progress and establishing 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 diffusion tensor image segmentation technique (DSEG; which 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—Ninety-nine SVD patients (aged 43–89 years) underwent annual magnetic resonance imaging scanning (for 3 years) and cognitive assessment (for 5 years). DSEG-θ was used as a whole-cerebrum measure of SVD severity. Dementia diagnosis was based Diagnostic and Statistical Manual of Mental Disorders 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 versus subsequent dementia diagnosis individuals. Results—DSEG-θ was significantly related to decline in executive function and global cognition (P<0.001). Eighteen (18.2%) patients converted to dementia. Baseline DSEG-θ predicted dementia with a balanced classification rate=75.95% and area under the receiver operating characteristic curve=0.839. The best classification model included baseline DSEGθ, change in DSEG-θ, age, sex, and premorbid intelligence quotient (balanced classification rate of 79.65%; area under the receiver operating characteristic curve=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 (diffusion tensor imaging). 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. (Stroke. 2019;50:2775-2782. DOI: 10.1161/STROKEAHA.119.025843.)