Objective

To identify brain regions whose metabolic impairment contributes to dementia with Lewy bodies (DLB) clinical core features expression and to assess the influence of severity of global cognitive impairment on the DLB hypometabolic pattern.

Methods

Brain fluorodeoxyglucose positron emission tomography and information on core features were available in 171 patients belonging to the imaging repository of the European DLB Consortium. Principal component analysis was applied to identify brain regions relevant to the local data variance. A linear regression model was applied to generate core‐feature–specific patterns controlling for the main confounding variables (Mini‐Mental State Examination [MMSE], age, education, gender, and center). Regression analysis to the locally normalized intensities was performed to generate an MMSE‐sensitive map.

Results

Parkinsonism negatively covaried with bilateral parietal, precuneus, and anterior cingulate metabolism; visual hallucinations (VH) with bilateral dorsolateral–frontal cortex, posterior cingulate, and parietal metabolism; and rapid eye movement sleep behavior disorder (RBD) with bilateral parieto‐occipital cortex, precuneus, and ventrolateral–frontal metabolism. VH and RBD shared a positive covariance with metabolism in the medial temporal lobe, cerebellum, brainstem, basal ganglia, thalami, and orbitofrontal and sensorimotor cortex. Cognitive fluctuations negatively covaried with occipital metabolism and positively with parietal lobe metabolism. MMSE positively covaried with metabolism in the left superior frontal gyrus, bilateral–parietal cortex, and left precuneus, and negatively with metabolism in the insula, medial frontal gyrus, hippocampus in the left hemisphere, and right cerebellum.

Interpretation

Regions of more preserved metabolism are relatively consistent across the variegate DLB spectrum. By contrast, core features were associated with more prominent hypometabolism in specific regions, thus suggesting a close clinical–imaging correlation, reflecting the interplay between topography of neurodegeneration and clinical presentation in DLB patients. Ann Neurol 2019;85:715–725