Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission.

PURPOSE: Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, multiple system atrophy) at individual patient level via principal component analysis (PCA). METHODS: FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level. RESULTS: Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912). CONCLUSION: Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset.

Towards an Automated Approach to the Semi-Quantification of [18F]F-DOPA PET in Pediatric-Type Diffuse Gliomas.

BACKGROUND: This study aims to evaluate the use of a computer-aided, semi-quantification approach to [18F]F-DOPA positron emission tomography (PET) in pediatric-type diffuse gliomas (PDGs) to calculate the tumor-to-background ratio. METHODS: A total of 18 pediatric patients with PDGs underwent magnetic resonance imaging and [18F]F-DOPA PET, which were analyzed using both manual and automated procedures. The former provided a tumor-to-normal-tissue ratio (TN) and tumor-to-striatal-tissue ratio (TS), while the latter provided analogous scores (tn, ts). We tested the correlation, consistency, and ability to stratify grading and survival between these methods. RESULTS: High Pearson correlation coefficients resulted between the ratios calculated with the two approaches: ρ = 0.93 (p < 10-4) and ρ = 0.814 (p < 10-4). The analysis of the residuals suggested that tn and ts were more consistent than TN and TS. Similarly to TN and TS, the automatically computed scores showed significant differences between low- and high-grade gliomas (p ≤ 10-4, t-test) and the overall survival was significantly shorter in patients with higher values when compared to those with lower ones (p < 10-3, log-rank test). CONCLUSIONS: This study suggested that the proposed computer-aided approach could yield similar results to the manual procedure in terms of diagnostic and prognostic information.

The Role of Monoaminergic Tones and Brain Metabolism in Cognition in De Novo Parkinson's Disease.

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 < 0.001). CONCLUSIONS: In subjects with PD, the impact of regional metabolism and diffuse projection systems degeneration differs across cognitive domains. These findings suggest possible tailored approaches to the treatment of cognitive deficits in PD.

A comparison of advanced semi-quantitative amyloid PET analysis methods.

PURPOSE: To date, there is no consensus on how to semi-quantitatively assess brain amyloid PET. Some approaches use late acquisition alone (e.g., ELBA, based on radiomic features), others integrate the early scan (e.g., TDr, which targets the area of maximum perfusion) and structural imaging (e.g., WMR, that compares kinetic behaviour of white and grey matter, or SI based on the kinetic characteristics of the grey matter alone). In this study SUVr, ELBA, TDr, WMR, and SI were compared. The latter - the most complete one - provided the reference measure for amyloid burden allowing to assess the efficacy and feasibility in clinical setting of the other approaches. METHODS: We used data from 85 patients (aged 44-87) who underwent dual time-point PET/MRI acquisitions. The correlations with SI were computed and the methods compared with the visual assessment. Assuming SUVr, ELBA, TDr, and WMR to be independent measures, we linearly combined them to obtain more robust indices. Finally, we investigated possible associations between each quantifier and age in amyloid-negative patients. RESULTS: Each quantifier exhibited excellent agreement with visual assessment and strong correlation with SI (average AUC = 0.99, ρ = 0.91). Exceptions to this were observed for subcortical regions with ELBA and WMR (ρELBA = 0.44, ρWMR = 0.70). The linear combinations showed better performances than the individual methods. Significant associations were observed between TDr, WMR, SI, and age in amyloid-negative patients (p < 0.05). CONCLUSION: Among the other methods, TDr came closest to the reference with less implementation complexity. Moreover, this study suggests that combining independent approaches gives better results than the individual procedure, so efforts should focus on multi-classifier systems for amyloid PET. Finally, the ability of techniques integrating blood perfusion to depict age-related variations in amyloid load in amyloid-negative subjects demonstrates the goodness of the estimate.

The Free and Cued Selective Reminding Test: Discriminative Values in a Naturalistic Cohort.

BACKGROUND: Neuropsychological assessment is still the basis for the first evaluation of patients with cognitive complaints. The Free and Cued Selective Reminding Test (FCSRT) generates several indices that could have different accuracy in the differential diagnosis between Alzheimer's disease (AD) and other disorders. OBJECTIVE: In a consecutive series of naturalistic patients, the accuracy of the FCSRT indices in differentiating patients with either mild cognitive impairment (MCI) due to AD or AD dementia from other competing conditions was evaluated. METHODS: We evaluated the accuracy of the seven FCSRT indices in differentiating patients with AD from other competing conditions in 434 consecutive outpatients, either at the MCI or at the early dementia stage. We analyzed these data through the receiver operating characteristics curve, and we then generated the odds-ratio map of the two indices with the best discriminative value between pairs of disorders. RESULTS: The immediate and the delayed free total recall, the immediate total recall, and the index of sensitivity of cueing were the most useful indices and allowed to distinguish AD from dementia with Lewy bodies and psychiatric conditions with very high accuracy. Accuracy was instead moderate in distinguishing AD from behavioral variant frontotemporal dementia, vascular cognitive impairment, and other conditions. CONCLUSION: By using odd-ratio maps and comparison-customized cut-off scores, we confirmed that the FCSRT represents a useful tool to characterize the memory performance of patients with MCI and thus to assist the clinician in the diagnosis process, though with different accuracy values depending on the clinical hypothesis.

The Role of Hub and Spoke Regions in Theory of Mind in Early Alzheimer's Disease and Frontotemporal Dementia.

Theory of mind (ToM, the ability to attribute mental states to others) deficit is a frequent finding in neurodegenerative conditions, mediated by a diffuse brain network confirmed by 18F-FDG-PET and MR imaging, involving frontal, temporal and parietal areas. However, the role of hubs and spokes network regions in ToM performance, and their respective damage, is still unclear. To study this mechanism, we combined ToM testing with brain 18F-FDG-PET imaging in 25 subjects with mild cognitive impairment due to Alzheimer's disease (MCI−AD), 24 subjects with the behavioral variant of frontotemporal dementia (bvFTD) and 40 controls. Regions included in the ToM network were divided into hubs and spokes based on their structural connectivity and distribution of hypometabolism. The hubs of the ToM network were identified in frontal regions in both bvFTD and MCI−AD patients. A mediation analysis revealed that the impact of spokes damage on ToM performance was mediated by the integrity of hubs (p < 0.001), while the impact of hubs damage on ToM performance was independent from the integrity of spokes (p < 0.001). Our findings support the theory that a key role is played by the hubs in ToM deficits, suggesting that hubs could represent a final common pathway leading from the damage of spoke regions to clinical deficits.

Polysomnographic correlates of sleep disturbances in de novo, drug naïve Parkinson's Disease.

BACKGROUND: Sleep disturbances are common non-motor symptoms of Parkinson's Disease (PD). METHODS: The aim of this study was to investigate the polysomnographic correlates of sleep changes, as investigated by the Parkinson's Disease Sleep Scale-2 (PDSS-2), in a cohort of sixty-two consecutive de novo, drug naïve PD patients (71.40 ± 7.84 y/o). RESULTS: PDSS-2 total score showed a direct correlation with stage shifts (p = 0.008). Fragmented sleep showed an inverse correlation with sleep efficiency (p = 0.012). Insomnia symptoms showed an inverse correlation with wake after sleep onset (p = 0.005) and direct correlation with periodic leg movements (p = 0.006) and stage shift indices (p = 0.003). Motor Symptoms showed a direct correlation with Apnoea-Hypopnoea (AHI; p = 0.02) and awakenings indices (p = 0.003). Dream distressing showed a direct correlation with REM without atonia (RWA, p = 0.042) and an inverse correlation with AHI (p = 0.012). Sleep quality showed an inverse correlation with RWA (p = 0.008). CONCLUSION: PDSS-2 features are significantly correlated with polysomnography objective findings, thus further supporting its reliability to investigate sleep disturbances in PD patients.

Added value of semiquantitative analysis of brain FDG-PET for the differentiation between MCI-Lewy bodies and MCI due to Alzheimer's disease.

PURPOSE: FDG-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. METHODS: We 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. RESULTS: Mean 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. CONCLUSION: Conventional 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.

Associations among education, age, and the dementia with Lewy bodies (DLB) metabolic pattern: A European-DLB consortium project.

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.

Probing the Role of a Regional Quantitative Assessment of Amyloid PET.

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-ß (Aß) load was carried out. RESULTS: Significant differences were observed between the correlation maps of patients at increasing levels of Aß and the overall dataset. The Aß uptake of the subcortical gray matter resulted not related to other brain regions independently of the global Aß level. A significant association of semantic verbal fluency was observed with ratios of cortical and subcortical distribution of Aß which represent a coarse measure of differences in regional distribution of Aß. CONCLUSION: Our observations confirmed the different susceptibility to Aß accumulation among brain regions. The association between cognition and Aß 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 Aß assessment represents an available resource on amy-PET scan with possibly clinical and prognostic implications.

Anatomical and neurochemical bases of theory of mind in de novo Parkinson's Disease.

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-naïve, 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.

Metabolic Correlates of Dopaminergic Loss in Dementia with Lewy Bodies.

BACKGROUND: Striatal dopamine deficiency and metabolic changes are well-known phenomena in dementia with Lewy bodies and can be quantified in vivo by 123 I-Ioflupane brain single-photon emission computed tomography of dopamine transporter and 18 F-fluorodesoxyglucose PET. However, the linkage between both biomarkers is ill-understood. OBJECTIVE: We used the hitherto largest study cohort of combined imaging from the European consortium to elucidate the role of both biomarkers in the pathophysiological course of dementia with Lewy bodies. METHODS: We compared striatal dopamine deficiency and glucose metabolism of 84 dementia with Lewy body patients and comparable healthy controls. After normalization of data, we tested their correlation by region-of-interest-based and voxel-based methods, controlled for study center, age, sex, education, and current cognitive impairment. Metabolic connectivity was analyzed by inter-region coefficients stratified by dopamine deficiency and compared to healthy controls. RESULTS: There was an inverse relationship between striatal dopamine availability and relative glucose hypermetabolism, pronounced in the basal ganglia and in limbic regions. With increasing dopamine deficiency, metabolic connectivity showed strong deteriorations in distinct brain regions implicated in disease symptoms, with greatest disruptions in the basal ganglia and limbic system, coincident with the pattern of relative hypermetabolism. CONCLUSIONS: Relative glucose hypermetabolism and disturbed metabolic connectivity of limbic and basal ganglia circuits are metabolic correlates of dopamine deficiency in dementia with Lewy bodies. Identification of specific metabolic network alterations in patients with early dopamine deficiency may serve as an additional supporting biomarker for timely diagnosis of dementia with Lewy bodies. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Metabolic patterns across core features in dementia with lewy bodies.

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.

Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease.

PURPOSE: The aim of this study was to verify the reliability and generalizability of an automatic tool for the detection of Alzheimer-related hypometabolic pattern based on a Support-Vector-Machine (SVM) model analyzing 18F-fluorodeoxyglucose (FDG) PET data. METHODS: The SVM model processed metabolic data from anatomical volumes of interest also considering interhemispheric asymmetries. It was trained on a homogeneous dataset from a memory clinic center and tested on an independent multicentric dataset drawn from the Alzheimer's Disease Neuroimaging Initiative. Subjects were included in the study and classified based on a diagnosis confirmed after an adequate follow-up time. RESULTS: The accuracy of the discrimination between patients with Alzheimer Disease (AD), in either prodromal or dementia stage, and normal aging subjects was 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 was 89.8% in the multicentric training set and 88.0% in the monocentric testing set. The classification rate was also evaluated in different subgroups, including non-converter mild cognitive impairment (MCI) patients, subjects with MCI reverted to normal conditions and subjects with non-confirmed memory concern. The percent of pattern detections increased from 77% in early prodromal AD to 91% in AD dementia, while it was about 10% for healthy controls and non-AD patients. CONCLUSIONS: The present findings show a good level of reproducibility and generalizability of a model for detecting the hypometabolic pattern in AD and confirm the accuracy of FDG-PET in Alzheimer disease.

MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study.

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.