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Introduction: The detection in mild cognitive impairment (MCI) of metabolic alterations suggestive of depression and/or of evolution to dementia. Methods: Sixty-nine MCI patients underwent clinical and imaging evaluation including position emission tomography/computed tomography with fluorodeoxy-glucose (FDG-PET/CT). Results: The metabolism mean values in parietal, temporal and pre-cuneus areas were lower in subjects who evolved to dementia, and in frontal and in anterior cingulate areas in depressed subjects. Abnormal metabolism values were higher in the frontal and parietal lobes, and in the precuneus in subjects who evolved to dementia independently from depression. Conclusions: In MCI FDG-PET/CT abnormality patterns suggest the presence of depression or the evolution to dementia.
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PURPOSE: An appropriate healthy control dataset is mandatory to achieve good performance in voxel-wise analyses. We aimed at evaluating [18F]FDG PET brain datasets of healthy controls (HC), based on publicly available data, for the extraction of voxel-based brain metabolism maps at the single-subject level. METHODS: Selection of HC images was based on visual rating, after Cook's distance and jack-knife analyses, to exclude artefacts and/or outliers. The performance of these HC datasets (ADNI-HC and AIMN-HC) to extract hypometabolism patterns in single patients was tested in comparison with the standard reference HC dataset (HSR-HC) by means of Dice score analysis. We evaluated the performance and comparability of the different HC datasets in the assessment of single-subject SPM-based hypometabolism in three independent cohorts of patients, namely, ADD, bvFTD and DLB. RESULTS: Two-step Cook's distance analysis and the subsequent jack-knife analysis resulted in the selection of n = 125 subjects from the AIMN-HC dataset and n = 75 subjects from the ADNI-HC dataset. The average concordance between SPM hypometabolism t-maps in the three patient cohorts, as obtained with the new datasets and compared to the HSR-HC standard reference dataset, was 0.87 for the AIMN-HC dataset and 0.83 for the ADNI-HC dataset. Pattern expression analysis revealed high overall accuracy (> 80%) of the SPM t-map classification according to different statistical thresholds and sample sizes. CONCLUSIONS: The applied procedures ensure validity of these HC datasets for the single-subject estimation of brain metabolism using voxel-wise comparisons. These well-selected HC datasets are ready-to-use in research and clinical settings.