Reduced hippocampal metabolism in MCI and AD: automated FDG-PET image analysis.
ABSTRACT To facilitate image analysis, most recent 2-[18F]fluoro-2-deoxy-d-glucose PET (FDG-PET) studies of glucose metabolism (MRglc) have used automated voxel-based analysis (VBA) procedures but paradoxically none reports hippocampus MRglc reductions in mild cognitive impairment (MCI) or Alzheimer disease (AD). Only a few studies, those using regions of interest (ROIs), report hippocampal reductions. The authors created an automated and anatomically valid mask technique to sample the hippocampus on PET (HipMask).
Hippocampal ROIs drawn on the MRI of 48 subjects (20 healthy elderly [NL], 16 MCI, and 12 AD) were used to develop the HipMask. The HipMask technique was applied in an FDG-PET study of NL (n = 11), MCI (n = 13), and AD (n = 12), and compared to both MRI-guided ROIs and VBA methods.
HipMask and ROI hippocampal sampling produced significant and equivalent MRglc reductions for contrasts between MCI and AD relative to NL. The VBA showed typical cortical effects but failed to show hippocampal MRglc reductions in either clinical group. Hippocampal MRglc was the only discriminator of NL vs MCI (78% accuracy) and added to the cortical MRglc in classifying NL vs AD and MCI vs AD.
The new HipMask technique provides accurate and rapid assessment of the hippocampus on PET without the use of regions of interest. Hippocampal glucose metabolism reductions are found in both mild cognitive impairment and Alzheimer disease and contribute to their diagnostic classification. These results suggest re-examination of prior voxel-based analysis 2-[18F]fluoro-2-deoxy-d-glucose PET studies that failed to report hippocampal effects.
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ABSTRACT: Impaired brain glucose metabolism appears to be a potential pathogenic feature of mild cognitive impairment (MCI). This study examined the potential for increasing circulating ketone bodies through medium chain triglyceride (MCT) supplementation, as a means to beneficially modulate brain homeostasis in subjects with MCI.01/2015; 108. DOI:10.1016/j.bbacli.2015.01.001
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ABSTRACT: Controlling the false discovery rate is important when testing multiple hypotheses. To enhance the detection capability of a false discovery rate control test, we applied the likelihood ratio-based multiple testing method in neuroimage data and compared the performance with the existing methods. We analysed the performance of the likelihood ratio-based false discovery rate method using simulation data generated under independent assumption, and positron emission tomography data of Alzheimer's disease and questionable dementia. We investigated how well the method detects extensive hypometabolic regions and compared the results to those of the conventional Benjamini Hochberg-false discovery rate method. Our findings show that the likelihood ratio-based false discovery rate method can control the false discovery rate, giving the smallest false non-discovery rate (for a one-sided test) or the smallest expected number of false assignments (for a two-sided test). Even though we assumed independence among voxels, the likelihood ratio-based false discovery rate method detected more extensive hypometabolic regions in 22 patients with Alzheimer's disease, as compared to the 44 normal controls, than did the Benjamini Hochberg-false discovery rate method. The contingency and distribution patterns were consistent with those of previous studies. In 24 questionable dementia patients, the proposed likelihood ratio-based false discovery rate method was able to detect hypometabolism in the medial temporal region. This study showed that the proposed likelihood ratio-based false discovery rate method efficiently identifies extensive hypometabolic regions owing to its increased detection capability and ability to control the false discovery rate.BMC Medical Research Methodology 01/2015; 15(1):9. DOI:10.1186/1471-2288-15-9 · 2.17 Impact Factor