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People with type 2 diabetes are at an increased risk of cognitive impairment and dementia (including Alzheimer's disease), as well as subtle forms of cognitive dysfunction. Current diabetes guidelines recommend screening for cognitive impairment in groups at high risk and providing guidance for diabetes management in patients with diabetes and cognitive impairment. Yet, no disease-modifying treatment is available and important questions remain about the mechanisms underlying diabetes-associated cognitive dysfunction. These mechanisms are likely to be multifactorial and different for subtle and more severe forms of diabetes-associated cognitive dysfunction. Over the past years, research on dementia, brain ageing, diabetes, and vascular disease has identified novel biomarkers of specific dementia aetiologies, brain parenchymal injury, and cerebral blood flow and metabolism. These markers shed light on the processes underlying diabetes-associated cognitive dysfunction, have clear applications in current research and increasingly in clinical diagnosis, and might ultimately guide targeted treatment.

Various biomarkers are available to support the diagnosis of neurodegenerative diseases in clinical and research settings. Among the molecular imaging biomarkers, amyloid-PET, which assesses brain amyloid deposition, and 18F-fluorodeoxyglucose (18F-FDG) PET, which assesses glucose metabolism, provide valuable and complementary information. However, uncertainty remains regarding the optimal timepoint, combination, and an order in which these PET biomarkers should be used in diagnostic evaluations because conclusive evidence is missing. Following an expert panel discussion, we reached an agreement on the specific use of the individual biomarkers, based on available evidence and clinical expertise. We propose a diagnostic algorithm with optimal timepoints for these PET biomarkers, also taking into account evidence from other biomarkers, for early and differential diagnosis of neurodegenerative diseases that can lead to dementia. We propose three main diagnostic pathways with distinct biomarker sequences, in which amyloid-PET and 18F-FDG-PET are placed at different positions in the order of diagnostic evaluations, depending on clinical presentation. We hope that this algorithm can support diagnostic decision making in specialist clinical settings with access to these biomarkers and might stimulate further research towards optimal diagnostic strategies.

PurposeAn 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.MethodsSelection 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.ResultsTwo-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.ConclusionsThe 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.

RationaleIn Parkinson’s disease (PD), spatial covariance analysis of 18F-FDG PET data has consistently revealed a characteristic PD-related brain pattern (PDRP). By quantifying PDRP expression on a scan-by-scan basis, this technique allows objective assessment of disease activity in individual subjects. We provide a further validation of the PDRP by applying spatial covariance analysis to PD cohorts from the Netherlands (NL), Italy (IT), and Spain (SP).MethodsThe PDRPNL was previously identified (17 controls, 19 PD) and its expression was determined in 19 healthy controls and 20 PD patients from the Netherlands. The PDRPIT was identified in 20 controls and 20 “de-novo” PD patients from an Italian cohort. A further 24 controls and 18 “de-novo” Italian patients were used for validation. The PDRPSP was identified in 19 controls and 19 PD patients from a Spanish cohort with late-stage PD. Thirty Spanish PD patients were used for validation. Patterns of the three centers were visually compared and then cross-validated. Furthermore, PDRP expression was determined in 8 patients with multiple system atrophy.ResultsA PDRP could be identified in each cohort. Each PDRP was characterized by relative hypermetabolism in the thalamus, putamen/pallidum, pons, cerebellum, and motor cortex. These changes co-varied with variable degrees of hypometabolism in posterior parietal, occipital, and frontal cortices. Frontal hypometabolism was less pronounced in “de-novo” PD subjects (Italian cohort). Occipital hypometabolism was more pronounced in late-stage PD subjects (Spanish cohort). PDRPIT, PDRPNL, and PDRPSP were significantly expressed in PD patients compared with controls in validation cohorts from the same center (P < 0.0001), and maintained significance on cross-validation (P < 0.005). PDRP expression was absent in MSA.ConclusionThe PDRP is a reproducible disease characteristic across PD populations and scanning platforms globally. Further study is needed to identify the topography of specific PD subtypes, and to identify and correct for center-specific effects.

PurposeTo investigate brain functional correlates of mild cognitive impairment (MCI) in idiopathic REM sleep behavior disorder (iRBD).MethodsThirty-nine consecutive iRBD patients, 17 with (RBD-MCI, 73.6±6.5 years), and 22 without (RBD-NC, 69.6±6.1 years) MCI underwent neuropsychological assessment, 18F-FDG-PET, and 123I-FP-CIT-SPECT as a marker of nigro-striatal dopaminergic function. Forty-two healthy subjects (69.6±8.5 years) were used as control for 18F-FDG-PET analysis. Brain metabolism was compared between the three groups by univariate analysis of variance. Post hoc comparison between RBD-MCI and RBD-NC was performed to investigate the presence of an MCI-related volume of interest (MCI-VOI). Brain functional connectivity was explored by interregional correlation analysis (IRCA), using the whole-brain normalized MCI-VOI uptake as the independent variable. Moreover, the MCI-VOI uptake was correlated with 123I-FP-CIT-SPECT specific-to-non displaceable binding ratios (SBR) and neuropsychological variables. Finally, the MCI-VOI white matter structural connectivity was analyzed by using a MRI-derived human atlas.ResultsThe MCI-VOI was characterized by a relative hypometabolism involving precuneus and cuneus (height threshold p<0.0001). IRCA (height threshold p<0.0001) revealed a brain functional network involving regions in frontal, temporal, parietal, and occipital lobes, thalamus, caudate, and red nuclei in iRBD patients. In controls, the network was smaller and involved temporal, occipital, cingulate cortex, and cerebellum. Moreover, MCI-VOI metabolism was correlated with verbal memory (p=0.01), executive functions (p=0.0001), and nigro-putaminal SBR (p=0.005). Finally, MCI-VOI was involved in a white matter network including cingulate fasciculus and corpus callosum.ConclusionOur data suggest that cuneus/precuneus is a hub of a large functional network subserving cognitive function in iRBD.

Abstract Study Objectives Increased phase synchronization in electroencephalography (EEG) bands might reflect the activation of compensatory mechanisms of cognitive decline in people with neurodegenerative diseases. Here, we investigated whether altered large-scale couplings of brain oscillations could be linked to the balancing of cognitive decline in a longitudinal cohort of people with idiopathic rapid eye-movement sleep behavior disorder (iRBD). Methods We analyzed 18 patients (17 males, 69.7 ± 7.5 years) with iRBD undergoing high-density EEG (HD-EEG), presynaptic dopaminergic imaging, and clinical and neuropsychological (NPS) assessments at two time points (time interval 24.2 ± 5.9 months). We thus quantified the HD-EEG power distribution, orthogonalized amplitude correlation, and weighted phase-lag index at both time points and correlated them with clinical, NPS, and imaging data. Results Four patients phenoconverted at follow-up (three cases of parkinsonism and one of dementia). At the group level, NPS scores decreased over time, without reaching statistical significance. However, alpha phase synchronization increased and delta amplitude correlations decreased significantly at follow-up compared to baseline. Both large-scale network connectivity metrics were significantly correlated with NPS scores but not with sleep quality indices or presynaptic dopaminergic imaging data. Conclusions These results suggest that increased alpha phase synchronization and reduced delta amplitude correlation may be considered electrophysiological signs of an active compensatory mechanism of cognitive impairment in people with iRBD. Large-scale functional modifications may be helpful biomarkers in the characterization of prodromal stages of alpha-synucleinopathies.

PurposeThe purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer’s disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model’s performance to that of multiple expert nuclear medicine physicians’ readers.Materials and methodsRetrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer’s disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model’s performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention.ResultsThe proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6–100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7–100) in AD, 71.4% (51.6–91.2) in MCI-AD, and 94.7% (90–99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders.ConclusionUsing only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.

PurposeWe aim to report the quality of accuracy studies investigating the utility of [18F]fluorodeoxyglucose (FDG)-PET in supporting the diagnosis of prodromal Alzheimer’s Disease (AD), frontotemporal lobar degeneration (FTLD) and prodromal dementia with Lewy bodies (DLB) in mild cognitive impairment (MCI) subjects, and the corresponding recommendations made by a panel of experts.MethodsSeven panellist, four from the European Association of Nuclear Medicine, and three from the European Academy of Neurology, produced recommendations taking into consideration the incremental value of FDG-PET, as added on clinical-neuropsychological examination, to ascertain the aetiology of MCI (AD, FTLD or DLB). A literature search using harmonized population, intervention, comparison, and outcome (PICO) strings was performed, and an evidence assessment consistent with the European Federation of Neurological Societies guidance was provided. The consensual recommendation was achieved based on Delphi rounds.ResultsFifty-four papers reported the comparison of interest. The selected papers allowed the identification of FDG patterns that characterized MCI due to AD, FTLD and DLB. While clinical outcome studies supporting the diagnosis of MCI due to AD showed varying accuracies (ranging from 58 to 100%) and varying areas under the receiver-operator characteristic curves (0.66 to 0.97), no respective data were identified for MCI due to FTLD or for MCI due to DLB. However, the high negative predictive value of FDG-PET and the existence of different disease-specific patterns of hypometabolism support the consensus recommendations for the clinical use of this imaging technique in MCI subjects.ConclusionsFDG-PET has clinical utility on a fair level of evidence in detecting MCI due to AD. Although promising also in detecting MCI due to FTLD and MCI due to DLB, more research is needed to ultimately judge the clinical utility of FDG-PET in these entities.

Abstract PurposeFDG-PET is an established supportive biomarker in dementia with Lewy bodies (DLB), but its diagnostic accuracy is unknown at the mild cognitive impairment (MCI-LB) stage when the typical metabolic pattern may be difficultly recognized at the individual level. Semiquantitative analysis of scans could enhance accuracy especially in less skilled readers, but its added role with respect to visual assessment in MCI-LB is still unknown.MethodsWe assessed the diagnostic accuracy of visual assessment of FDG-PET by six expert readers, blind to diagnosis, in discriminating two matched groups of patients (40 with prodromal AD (MCI-AD) and 39 with MCI-LB), both confirmed by in vivo biomarkers. Readers were provided in a stepwise fashion with (i) maps obtained by the univariate single-subject voxel-based analysis (VBA) with respect to a control group of 40 age- and sex-matched healthy subjects, and (ii) individual odds ratio (OR) plots obtained by the volumetric regions of interest (VROI) semiquantitative analysis of the two main hypometabolic clusters deriving from the comparison of MCI-AD and MCI-LB groups in the two directions, respectively.ResultsMean diagnostic accuracy of visual assessment was 76.8 ± 5.0% and did not significantly benefit from adding the univariate VBA map reading (77.4 ± 8.3%) whereas VROI-derived OR plot reading significantly increased both accuracy (89.7 ± 2.3%) and inter-rater reliability (ICC 0.97 [0.96–0.98]), regardless of the readers’ expertise.ConclusionConventional visual reading of FDG-PET is moderately accurate in distinguishing between MCI-LB and MCI-AD, and is not significantly improved by univariate single-subject VBA but by a VROI analysis built on macro-regions, allowing for high accuracy independent of reader skills.

Abstract PurposeHyposmia is a common feature of COVID-19 and Parkinson’s disease (PD). As parkinsonism has been reported after COVID-19, a link has been hypothesized between SARS-CoV2 infection and PD. We aimed to evaluate brain metabolic correlates of isolated persistent hyposmia after mild-to-moderate COVID-19 and to compare them with metabolic signature of hyposmia in drug-naïve PD patients.MethodsForty-four patients who experienced hyposmia after SARS-COV2 infection underwent brain [18F]-FDG PET in the first 6 months after recovery. Olfaction was assessed by means of the 16-item “Sniffin’ Sticks” test and patients were classified as with or without persistent hyposmia (COVID-hyposmia and COVID-no-hyposmia respectively). Brain [18F]-FDG PET of post-COVID subgroups were compared in SPM12. COVID-hyposmia patients were also compared with eighty-two drug-naïve PD patients with hyposmia. Multiple regression analysis was used to identify correlations between olfactory test scores and brain metabolism in patients’ subgroups.ResultsCOVID-hyposmia patients (n = 21) exhibited significant hypometabolism in the bilateral gyrus rectus and orbitofrontal cortex with respect to COVID-non-hyposmia (n = 23) (p < 0.002) and in middle and superior temporal gyri, medial/middle frontal gyri, and right insula with respect to PD-hyposmia (p < 0.012). With respect to COVID-hyposmia, PD-hyposmia patients showed hypometabolism in inferior/middle occipital gyri and cuneus bilaterally. Olfactory test scores were directly correlated with metabolism in bilateral rectus and medial frontal gyri and in the right middle temporal and anterior cingulate gyri in COVID-hyposmia patients (p < 0.006) and with bilateral cuneus/precuneus and left lateral occipital cortex in PD-hyposmia patients (p < 0.004).ConclusionMetabolic signature of persistent hyposmia after COVID-19 encompasses cortical regions involved in olfactory perception and does not overlap metabolic correlates of hyposmia in PD.