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Background: Cognitive impairment is frequent in Parkinson’s disease (PD) and several neurotransmitter changes have been reported since the time of diagnosis, although seldom investigated altogether in the same patient cohort. Objective: Our aim was to evaluate the association between neurotransmitter impairment, brain metabolism, and cognition in a cohort of de novo, drug-naïve PD patients. Methods: We retrospectively selected 95 consecutive drug-naïve PD patients (mean age 71.89±7.53) undergoing at the time of diagnosis a brain [18F]FDG-PET as a marker of brain glucose metabolism and proxy measure of neurodegeneration, [123I]FP-CIT-SPECT as a marker and dopaminergic deafferentation in the striatum and frontal cortex, as well as a marker of serotonergic deafferentation in the thalamus, and quantitative electroencephalography (qEEG) as an indirect measure of cholinergic deafferentation. Patients also underwent a complete neuropsychological battery. Results: Positive correlations were observed between (i) executive functions and left cerebellar cortex metabolism, (ii) prefrontal dopaminergic tone and working memory (r = 0.304, p = 0.003), (iii) qEEG slowing in the posterior leads and both memory (r = 0.299, p = 0.004) and visuo-spatial functions (r = 0.357, p

Introduction: We assessed the influence of education as a proxy of cognitive reserve and age on the dementia with Lewy bodies (DLB) metabolic pattern. Methods: Brain 18F-fluorodeoxyglucose positron emission tomography and clinical/demographic information were available in 169 probable DLB patients included in the European DLB-consortium database. Principal component analysis identified brain regions relevant to local data variance. A linear regression model was applied to generate age- and education-sensitive maps corrected for Mini-Mental State Examination score, sex (and either education or age). Results: Age negatively covaried with metabolism in bilateral middle and superior frontal cortex, anterior and posterior cingulate, reducing the expression of the DLB-typical cingulate island sign (CIS). Education negatively covaried with metabolism in the left inferior parietal cortex and precuneus (making the CIS more prominent). Discussion: These findings point out the importance of tailoring interpretation of DLB biomarkers considering the concomitant effect of individual, non-disease-related variables such as age and cognitive reserve.

Theory of mind (ToM) deficit is a frequent finding in subjects with neurological and psychiatric conditions. While a number of brain regions play a role in ToM, to date the contribution of the diffuse projection systems is less understood. Here, we explored the topographical and neurochemical bases of ToM using multi-tracer molecular imaging and quantitative electroencephalography (qEEG) in a group of 30 drug-naive, de novo Parkinson's Disease (PD) patients (mean age 73.39 ± 8.93 years, 11 females). ToM was assessed using the “Reading the Mind in the Eyes Task” (RMET), while general cognition with the MMSE. We acquired FDG-PET images (as a marker of regional neurodegeneration), I-123 Ioflupane Single Photon Emission Computed Tomography (123 I-FP-CIT-SPECT, as a marker of dopaminergic impairment in the basal ganglia and in the cortex and as a proxy marker of serotoninergic deafferentation in the thalamus), and qEEG recordings (using the Theta/Alpha power ratio as marker of cholinergic deafferentation). PD presented with a significantly worse RMET score compared to 60 controls (20.7 ± 5.5 vs 27.5 ± 3.0 p = .001) while there was no difference between the two groups in age, education or MMSE. The voxel-wise analysis of total RMET score and regional metabolism showed a positive correlation in the superior temporal gyrus and in the insula. Among the proxy markers of dopaminergic degeneration, serotoninergic and cholinergic deafferentation, ToM presented only an inverse correlation with 123 I-FP-CIT thalamic specific binding ratio (SBR) values -a proxy serotoninergic marker-which remained significant after correction for FDG metabolism in the areas associated with ToM. On the other hand, MMSE only correlated with qEEG posterior Theta/Alpha power. These findings point to the presence of a specific cortical and neurochemical signature of ToM in PD, to the independence of ToM from general cognition, and suggest possible therapeutic targets to treat social cognition deficits.

Background: In clinical practice, the amy-PET is globally inspected to provide a binary outcome, but the role of a regional assessment has not been fully investigated yet. Objective: To deepen the role of regional amyloid burden and its implication on clinical-neuropsychological features. Materials: Amy-PET and a complete neuropsychological assessment (Trail Making Test, Rey Auditory Verbal Learning Test, semantic verbal fluency, Symbol Digit, Stroop, visuoconstruction) were available in 109 patients with clinical suspicion of Alzheimer’s disease. By averaging the standardized uptake value ratio and ELBA, a regional quantification was calculated for each scan. Patients were grouped according to their overall amyloid load: correlation maps, based on regional quantification, were calculated and compared. A regression analysis between neuropsychological assessment and the regional amyloid-β (Aβ) load was carried out. Results: Significant differences were observed between the correlation maps of patients at increasing levels of Aβ and the overall dataset. The Aβ uptake of the subcortical gray matter resulted not related to other brain regions independently of the global Aβ level. A significant association of semantic verbal fluency was observed with ratios of cortical and subcortical distribution of Aβ which represent a coarse measure of differences in regional distribution of Aβ. Conclusion: Our observations confirmed the different susceptibility to Aβ accumulation among brain regions. The association between cognition and Aβ distribution deserves further investigations: it is possibly due to a direct local effect or it represents a proxy marker of a more aggressive disease subtype. Regional Aβ assessment represents an available resource on amy-PET scan with possibly clinical and prognostic implications.

Background With the shift of research focus towards the pre-dementia stage of Alzheimer’s disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification. Methods We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects. Results In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures. Conclusions Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies.

Objective: To identify brain regions whose metabolic impairment contributes to dementia with Lewy bodies (DLB) clinical core features expression and to assess the influence of severity of global cognitive impairment on the DLB hypometabolic pattern. Methods: Brain fluorodeoxyglucose positron emission tomography and information on core features were available in 171 patients belonging to the imaging repository of the European DLB Consortium. Principal component analysis was applied to identify brain regions relevant to the local data variance. A linear regression model was applied to generate core-feature–specific patterns controlling for the main confounding variables (Mini-Mental State Examination [MMSE], age, education, gender, and center). Regression analysis to the locally normalized intensities was performed to generate an MMSE-sensitive map. Results: Parkinsonism negatively covaried with bilateral parietal, precuneus, and anterior cingulate metabolism; visual hallucinations (VH) with bilateral dorsolateral–frontal cortex, posterior cingulate, and parietal metabolism; and rapid eye movement sleep behavior disorder (RBD) with bilateral parieto-occipital cortex, precuneus, and ventrolateral–frontal metabolism. VH and RBD shared a positive covariance with metabolism in the medial temporal lobe, cerebellum, brainstem, basal ganglia, thalami, and orbitofrontal and sensorimotor cortex. Cognitive fluctuations negatively covaried with occipital metabolism and positively with parietal lobe metabolism. MMSE positively covaried with metabolism in the left superior frontal gyrus, bilateral–parietal cortex, and left precuneus, and negatively with metabolism in the insula, medial frontal gyrus, hippocampus in the left hemisphere, and right cerebellum. Interpretation: Regions of more preserved metabolism are relatively consistent across the variegate DLB spectrum. By contrast, core features were associated with more prominent hypometabolism in specific regions, thus suggesting a close clinical–imaging correlation, reflecting the interplay between topography of neurodegeneration and clinical presentation in DLB patients. Ann Neurol 2019;85:715–725.

Brain hypometabolism, as evaluated by PET with 18F-fluorodeoxyglucose, is considered a sensitive biomarker of synaptic dysfunction associated with neurodegeneration in Alzheimer disease. Statistical tools for image analysis showed promising capabilities in detecting and evaluating Alzheimer-related hypometabolism. The extended application of such tools requires their consolidation by proving generalizability and reproducibility. The aim of this study was to verify the reliability of an automatic tool for the detection of Alzheimer-related hypometabolic patterns based on a Support-Vector-Machine model. The model processed metabolic data from anatomical volumes of interest also considering interhemispheric asymmetrie. The model was developed and trained on a homogeneous dataset from a memory clinic center and then tested on an independent multicentric dataset drawn from the Alzheimer's Disease Neuroimaging Initiative. The accuracy of the discrimination between patients with Alzheimer disease, in either prodromal or dementia stage, and normal aging subjects was estimated at 95.8%, after cross-validation, in the training set. The accuracy of the same model in the testing set was 86.5%. The role of the two datasets was then reversed and the accuracy in the multicentric training set, after cross-validation, was 89.8% whereas the accuracy of the same model in the independent monocentric testing set was 88.0%. The classification rate was also evaluated in all subgroups in which the samples were partitioned, including non-converter mild cognitive impairment (MCI) patients, subjects with MCI reverted to normal conditions and subjects with significant memory concern (SMC) not confirmed with neuropsychological tests. The percent of positive detections was at the level of healthy controls (around or below 10%) for SMC and reverted MCI patients. An increasing rate of positive tests was found in patients from early prodromal Alzheimer disease (77%) to dementia (91%). A low rate of positivity was found in non-converted MCI patients. The two datasets exhibited similar trends of classification rate and related score through the different subgroups. The present findings show a good level of reproducibility and generalizability of a model for detecting hypometabolic pattern in Alzheimer disease and encourage further training to deepen the characterization of metabolic patterns in the early stage of the disease.