Regional brain volumes relate to Alzheimer’s disease cerebrospinal fluid biomarkers and neuropsychometry: A cross-sectional, observational study

Abstract

We hypothesized that automated assessment of brain volumes on MRI can predict presence of cerebrospinal fluid abnormal ß-amyloid42 and Tau protein levels and thus serve as a useful screening test for possible Alzheimer’s disease. 113 participants ranging from cognitively healthy to Alzheimer’s disease underwent MRI exams to obtain measurements of hippocampus, prefrontal cortex, precuneus, parietal cortex, and occipital lobe volumes. A non-exclusive subset (n = 107) consented to lumbar punctures to obtain cerebrospinal fluid for ß-amyloid42 and Tau protein assessment including cognitively health (n = 75), mild cognitively impaired (n = 22), and Alzheimer’s disease (n = 10). After adjustment for false discovery rate, ß-amyloid42 was significantly associated with volumes in the hippocampus (p = 0.043), prefrontal cortex (p = 0.010), precuneus (p = 0.024), and the posterior cingulate (p = 0.002). No association between Tau levels and regional brain volume survived multiple test correction. Secondary analysis was performed to determine associations between MRI brain volumes and CSF protein levels to neuropsychological impairment. A non-exclusive subset (n = 96) including cognitively healthy (n = 72), mild cognitively impaired (n = 21), and Alzheimer’s disease (n = 3) participants underwent Stroop Interference and Boston Naming neuropsychological testing. A higher score on the Boston Naming Test was optimally predicted in a selective regression model by greater hippocampus volume (p = 0.002), a higher ratio of ß-amyloid42 to Tau protein levels (p < 0.001), greater posterior cingulate volume (p = 0.0193), age (p = 0.0271), and a higher education level (p = 0.002). A better performance on the Stroop Interference Test was optimally predicted by greater hippocampus volume (p = 0.0003) and a higher education level (p < 0.001). Lastly, impaired cognitive status (mild cognitive impairment and Alzheimer’s Disease) was optimally predicted in a selective regression model by a worse performance on the Stroop Interference Test (p < 0.001), a worse performance on the Boston Naming Test (p < 0.001), along with lower prefrontal cortex volume (p = 0.002) and lower hippocampus volume (p = 0.007).