BackgroundPathogenic autoantibodies targeting the recently identified leucine rich glioma inactivated 1 protein and the subunit 1 of the N-methyl-D-aspartate receptor induce autoimmune encephalitis. A comparison of brain metabolic patterns in 18F-fluoro-2-deoxy-d-glucose positron emission tomography of anti-leucine rich glioma inactivated 1 protein and anti-N-methyl-D-aspartate receptor encephalitis patients has not been performed yet and shall be helpful in differentiating these two most common forms of autoimmune encephalitis.MethodsThe brain 18F-fluoro-2-deoxy-d-glucose uptake from whole-body positron emission tomography of six anti-N-methyl-D-aspartate receptor encephalitis patients and four patients with anti-leucine rich glioma inactivated 1 protein encephalitis admitted to Hannover Medical School between 2008 and 2012 was retrospectively analyzed and compared to matched controls.ResultsGroup analysis of anti-N-methyl-D-aspartate encephalitis patients demonstrated regionally limited hypermetabolism in frontotemporal areas contrasting an extensive hypometabolism in parietal lobes, whereas the anti-leucine rich glioma inactivated 1 protein syndrome was characterized by hypermetabolism in cerebellar, basal ganglia, occipital and precentral areas and minor frontomesial hypometabolism.ConclusionsThis retrospective 18F-fluoro-2-deoxy-d-glucose positron emission tomography study provides novel evidence for distinct brain metabolic patterns in patients with anti-leucine rich glioma inactivated 1 protein and anti-N-methyl-D-aspartate receptor encephalitis.

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In the recently revised diagnostic criteria for Alzheimer disease (AD), the National Institute on Aging and Alzheimer Association suggested that confidence in diagnosing dementia due to AD and mild cognitive impairment (MCI) due to AD could be improved by the use of certain biomarkers, such as 18F-FDG PET evidence of hypometabolism in AD-affected brain regions. Three groups have developed automated data analysis techniques to characterize the AD-related pattern of hypometabolism in a single measurement. In this study, we sought to directly compare the ability of these three 18F-FDG PET data analysis techniques—the PMOD Alzheimer discrimination analysis tool, the hypometabolic convergence index, and a set of meta-analytically derived regions of interest reflecting AD hypometabolism pattern (metaROI)—to distinguish moderate or mild AD dementia patients and MCI patients who subsequently converted to AD dementia from cognitively normal older adults. Methods One hundred sixty-six 18F-FDG PET patients from the AD Neuroimaging Initiative, 308 from the Network for Efficiency and Standardization of Dementia Diagnosis, and 176 from the European Alzheimer Disease Consortium PET study were categorized, with masking of group classification, as AD, MCI, or healthy control. For each AD-related 18F-FDG PET index, receiver-operating-characteristic curves were used to characterize and compare subject group classifications. Results The 3 techniques were roughly comparable in their ability to distinguish each of the clinical groups from cognitively normal older adults with high sensitivity and specificity. Accuracy of classification (in terms of area under the curve) in each clinical group varied more as a function of data-set than by technique. All techniques were differentially sensitive to disease severity, with the classification accuracy for MCI due to AD to moderate AD varying from 0.800 to 0.949 (PMOD Alzheimer tool), from 0.774 to 0.967 (metaROI), and from 0.801 to 0.983 (hypometabolic convergence index). Conclusion The 3 tested techniques have the potential to help detect AD in research and clinical settings. Additional efforts are needed to clarify their ability to address particular scientific and clinical questions. Their incremental diagnostic value over other imaging and biologic markers makes them easier to implement by other groups for these purposes.

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Aim: We investigated the performance of FDG PET using an automated procedure for discrimination between Alzheimer’s disease (AD) and controls, and studied the influence of demographic and technical factors. Methods: FDG PET data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) [102 controls (76.0 ± 4.9 years) and 89 AD patients (75.7 ± 7.6 years, MMSE 23.5 ± 2.1) and the Network for Standardisation of Dementia Diagnosis (NEST-DD) [36 controls (62.2 ± 5.0 years) and 237 AD patients (70.8 ± 8.3 years, MMSE 20.9 ± 4.4). The procedure created t-maps of abnormal voxels. The sum of t-values in predefined areas that are typically affected by AD (AD t-sum) provided a measure of scan abnormality associated with a preset threshold for discrimination between patients and controls. Results: AD patients had much higher AD t-sum scores compared to controls (p < 0.01), which were significantly related to dementia severity (ADNI: r = –0.62, p < 0.01; NEST-DD: r = –0.59, p < 0.01). Early-onset AD patients had significantly higher AD t-sum scores than late-onset AD patients (p < 0.01). Differences between databases were mainly due to different age distributions. The predefined AD t-sum threshold yielded a sensitivity and specificity of 83 and 78% in ADNI and 78 and 94% in NEST-DD, respectively. Conclusion: The automated FDG PET analysis procedure provided good discrimination power, and was most accurate for early-onset AD.