Dissociating Statistically-Determined Alzheimer's Disease/Vascular Dementia Neuropsychological Syndromes Using White and Gray Neuroradiological Parameters

Background: There is remarkable heterogeneity in clinical Alzheimer's disease (AD) or vascular dementia (VaD). Objectives: 1) To statistically examine neuropsychological data to determine dementia subgroups for individuals clinically diagnosed with AD or VaD and then 2) examine group differences in specific gray/white matter regions of interest. Methods: A k-means cluster analysis requested a 3-group solution from neuropsychological data acquired from individuals diagnosed clinically with AD/VaD. MRI measures of hippocampal, caudate, ventricular, subcortical lacunar infarction, whole brain volume, and leukoaraiosis (LA) were analyzed. Three regions of LA volumes were quantified and these included the periventricular (5mm around the ventricles), infracortical (5mm beneath the gray matter), and deep (between periventricular and infracortical) regions. Results: Cluster analysis sorted AD/VaD patients into single domain amnestic (n=41), single-domain dysexecutive (n=26), and multi-domain (n=26) phenotypes. Multi-domain patients exhibited worst performance on language tests; however, multi-domain patients were equally impaired on memory tests when compared to amnestic patients. Statistically-determined groups dissociated using neuroradiological parameters: amnestic and multi-domain groups presented with smaller hippocampal volume while the dysexecutive group presented with greater deep, periventricular, and whole brain LA. Neither caudate nor lacunae volume differed by group. Caudate nucleus volume negatively correlated with total LA in the dysexecutive and multi-domain groups. Conclusions: There are at least three distinct subtypes embedded within patients diagnosed clinically with AD/VaD spectrum dementia. We encourage future research to assess a) the neuroradiological substrates underlying statistically-determined AD/VaD spectrum dementia and b) how statistical modeling can be integrated into existing diagnostic criteria.