Voxel-based analysis of confounding effects of age and dementia severity on cerebral metabolism in Alzheimer's disease

Alzheimer's disease is characterized by early hippocampal lesions, but neuropathological and functional imaging studies have also demonstrated involvement of associative cortices in patients suffering from this illness. New image-processing technologies have led to demonstration of predominant posteromedial cortical metabolic impairment in the disease. Confounding effects of both age and dementia severity on brain metabolism were assessed using categorical and correlational analyses performed with Statistical Parametric Mapping. Posterior cingulate and precuneus metabolism, assessed by positron emission tomography, was significantly correlated with age in a population of 46 patients with probable Alzheimer's disease. Metabolism in posterior cingulate and precuneus was higher in elderly than in younger patients with a diagnosis of Alzheimer's disease, even when dementia severity was taken as a confounding covariate. The data suggest that the sensitivity of positron emission tomography for the diagnosis of Alzheimer's disease is reduced in elderly cases, where less severe pathology is sufficient to induce clinical symptoms of dementia. Conversely, higher posteromedial metabolic impairment in early onset cases may reflect greater density of regional cerebral lesions or major decrease of functional afferences in a richly connected multimodal associative area. Posterior cingulate metabolism was also correlated to dementia severity, even when age was taken as a confounding covariate, whereas metabolism in the hippocampal formation was not shown to correlate with global cognitive deficit. Functional correlation was maintained between posterior cingulate and middle frontal cortex in demented patients as in elderly controls. The key role of posteromedial cortex in cognitive dysfunction assessed in Alzheimer's disease is probably related to its highly integrated position within attentional, visuospatial and memory neuronal networks.