Purpose: To quantify the effect sizes of regional metabolic and morphometric measures in patients with preclinical and mild Alzheimer disease (AD) to aid in the identification of noninvasive biomarkers for the early detection of AD.
Materials and methods: The study was conducted with institutional review board approval and in compliance with HIPAA regulations. Written informed consent was obtained from each participant or participant's legal guardian. Fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and magnetic resonance (MR) imaging data were analyzed from 80 healthy control (HC) subjects, 68 individuals with AD, and 156 with amnestic mild cognitive impairment (MCI), 69 of whom had single-domain amnestic MCI. Regions of interest (ROIs) were derived after coregistering FDG PET and MR images by using high-throughput, subject-specific procedures. The Cohen d effect sizes were calculated for 42 predefined ROIs across the brain. Statistical comparison of the largest overall effect sizes for MR imaging and PET was performed. Metabolic effect sizes were determined with and without accounting for regional atrophy. Discriminative accuracy of ROIs showing the largest effect sizes were compared by calculating receiver operating characteristic curves.
Results: For all disease groups, the hippocampus showed the largest morphometric effect size and the entorhinal cortex showed the largest metabolic effect size. In mild AD, the Cohen d effect size for hippocampal volume (1.92) was significantly larger (P < .05) than that for entorhinal metabolism (1.43). Regression of regional atrophy substantially reduced most metabolic effects. For all group comparisons, the areas under the receiver operating characteristic curves were significantly larger for hippocampal volume than for entorhinal metabolism.
Conclusion: The current results show no evidence that FDG PET is more sensitive than MR imaging to the degeneration occurring in preclinical and mild AD, suggesting that an MR imaging finding may be a more practical clinical biomarker for early detection of AD.
(c) RSNA, 2010.