UNLABELLED - Evaluating the symptomatic progression of mild cognitive impairment (MCI) caused by Alzheimer disease (AD) is practically accomplished by tracking performance on cognitive tasks, such as the Alzheimer Disease Assessment Scale's cognitive subscale (ADAS_cog), the Mini-Mental Status Examination (MMSE), and the Functional Activities Questionnaire (FAQ). The longitudinal relationships between cognitive decline and metabolic function as assessed using (18)F-FDG PET are needed to address both the cognitive and the biologic progression of disease state in individual subjects. We conducted an exploratory investigation to evaluate longitudinal changes in brain glucose metabolism of individual subjects and their relationship to the subject's changes of cognitive status.
METHODS - We describe a method to determine correlations in (18)F-FDG spatial distribution over time. This parameter is termed the regional (18)F-FDG time correlation coefficient (rFTC). By using linear mixed-effects models, we determined the difference in the rFTC decline rate between controls and subjects at high risk of developing AD, such as individuals with MCI or the presence of apolipoprotein E (APOE)-ε4 allele. The association between each subject's rFTC and performance on cognitive tests (ADAS_cog, MMSE, and FAQ) was determined with 2 different correlation methods. All subject data were downloaded from the Alzheimer Disease Neuroimaging Initiative.
RESULTS - The rFTC values of controls remained fairly constant over time (-0.003 annual change; 95% confidence interval, -0.010-0.004). In MCI patients, the rFTC declined faster than in controls by an additional annual change of -0.02 (95% confidence interval, -0.030 to -0.010). In MCI patients, the decline in rFTC was associated with cognitive decline (ADAS_cog, P = 0.011; FAQ, P = 0.0016; MMSE, P = 0.004). After a linear effect of time was accounted for, visit-to-visit changes in rFTC correlated with visit-to-visit changes in all 3 cognitive tests.
CONCLUSION - Longitudinal changes in rFTC detect subtle metabolic changes in individuals associated with variations in their cognition. This analytic tool may be useful for a patient-based monitoring of cognitive decline.