Biometry extraction and probabilistic anatomical atlas of the anterior Visual Pathway using dedicated high-resolution 3-D MRI.

Anterior Visual Pathway (aVP) damage may be linked to diverse inflammatory, degenerative and/or vascular conditions. Currently however, a standardized methodological framework for extracting MRI biomarkers of the aVP is not available. We used high-resolution, 3-D MRI data to generate a probabilistic anatomical atlas of the normal aVP and its intraorbital (iOrb), intracanalicular (iCan), intracranial (iCran), optic chiasm (OC), and tract (OT) subdivisions. We acquired 0.6 mm3 steady-state free-precession images from 24 healthy participants using a 3 T scanner. aVP masks were obtained by manual segmentation of each aVP subdivision. Mask straightening and normalization with cross-sectional area (CSA) preservation were obtained using scripts developed in-house. A probabilistic atlas ("aVP-24") was generated by averaging left and right sides of all subjects. Leave-one-out cross-validation with respect to interindividual variability was performed employing the Dice Similarity Index (DSI). Spatially normalized representations of the aVP subdivisions were generated. Overlapping CSA values before and after normalization demonstrate preservation of the aVP cross-section. Volume, length, CSA, and ellipticity index (ε) biometrics were extracted. The aVP-24 morphology followed previous descriptions from the gross anatomy. Atlas spatial validation DSI scores of 0.85 in 50% and 0.77 in 95% of participants indicated good generalizability across the subjects. The proposed MRI standardization framework allows for previously unavailable, geometrically unbiased biometric data of the entire aVP and provides the base for future spatial-resolved, group-level investigations.

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.

Cerebrospinal fluid NPTX2 changes and relationship with regional brain metabolism metrics across mild cognitive impairment due to Alzheimer's disease.

BACKGROUND: Neuronal pentraxin-2 (NPTX2), crucial for synaptic functioning, declines in cerebrospinal fluid (CSF) as cognition deteriorates. The variations of CSF NPTX2 across mild cognitive impairment (MCI) due to Alzheimer's disease (AD) and its association with brain metabolism remain elusive, albeit relevant for patient stratification and pathophysiological insights. METHODS: We retrospectively analyzed 49 MCI-AD patients grouped by time until dementia (EMCI, n = 34 progressing within 2 years; LMCI, n = 15 progressing later/stable at follow-up). We analyzed demographic variables, cognitive status (MMSE score), and CSF NPTX2 levels using a commercial ELISA assay in EMCI, LMCI, and a control group of age-/sex-matched individuals with other non-dementing disorders (OND). Using [18F]FDG PET scans for voxel-based analysis, we explored correlations between regional brain metabolism metrics and CSF NPTX2 levels in MCI-AD patients, accounting for age. RESULTS: Baseline and follow-up MMSE scores were lower in LMCI than EMCI (p value = 0.006 and p < 0.001). EMCI exhibited significantly higher CSF NPTX2 values than both LMCI (p = 0.028) and OND (p = 0.006). We found a significant positive correlation between NPTX2 values and metabolism of bilateral precuneus in MCI-AD patients (p < 0.005 at voxel level, p < 0.05 with family-wise error correction at the cluster level). CONCLUSIONS: Higher CSF NPTX2 in EMCI compared to controls and LMCI suggests compensatory synaptic responses to initial AD pathology. Disease progression sees these mechanisms overwhelmed, lowering CSF NPTX2 approaching dementia. Positive CSF NPTX2 correlation with precuneus glucose metabolism links to AD-related metabolic changes across MCI course. These findings posit CSF NPTX2 as a promising biomarker for both AD staging and progression risk stratification.

Familial Alzheimer's disease associated with heterozygous NPC1 mutation.

INTRODUCTION: NPC1 mutations are responsible for Niemann-Pick disease type C (NPC), a rare autosomal recessive neurodegenerative disease. Patients harbouring heterozygous NPC1 mutations may rarely show parkinsonism or dementia. Here, we describe for the first time a large family with an apparently autosomal dominant late-onset Alzheimer's disease (AD) harbouring a novel heterozygous NPC1 mutation. METHODS: All the five living siblings belonging to the family were evaluated. We performed clinical evaluation, neuropsychological tests, assessment of cerebrospinal fluid markers of amyloid deposition, tau pathology and neurodegeneration (ATN), structural neuroimaging and brain amyloid-positron emission tomography. Oxysterol serum levels were also tested. A wide next-generation sequencing panel of genes associated with neurodegenerative diseases and a whole exome sequencing analysis were performed. RESULTS: We detected the novel heterozygous c.3034G>T (p.Gly1012Cys) mutation in NPC1, shared by all the siblings. No other point mutations or deletions in NPC1 or NPC2 were found. In four siblings, a diagnosis of late-onset AD was defined according to clinical characterisation and ATN biomarkers (A+, T+, N+) and serum oxysterol analysis showed increased 7-ketocholesterol and cholestane-3ß,5α,6ß-triol. DISCUSSION: We describe a novel NPC1 heterozygous mutation harboured by different members of a family with autosomal dominant late-onset amnesic AD without NPC-associated features. A missense mutation in homozygous state in the same aminoacidic position has been previously reported in a patient with NPC with severe phenotype. The alteration of serum oxysterols in our family corroborates the pathogenic role of our NPC1 mutation. Our work, illustrating clinical and biochemical disease hallmarks associated with NPC1 heterozygosity in patients affected by AD, provides relevant insights into the pathogenetic mechanisms underlying this possible novel association.

Patterns of amyloid accumulation in amyloid-negative cases.

Amyloid staging models showed that regional abnormality occurs before global positivity. Several studies assumed that the trajectory of amyloid spread is homogeneous, but clinical evidence suggests that it is highly heterogeneous. We tested whether different amyloid-ß (Aß) patterns exist by applying clustering on negative scans and investigating their demographics, clinical, cognitive, and biomarkers correlates, and cognitive trajectories. 151 individuals from Geneva and Zurich cohorts with T1-MRI, negative Aß positron emission tomography (PET,centiloid<12) and clinical assessment were included. N=123 underwent tau PET, and N=65 follow-up neuropsychological assessment. We performed k-means clustering using 33 Aß regional Standardized Uptake Vales ratio. Demographics, clinical, cognitive, and biomarkers differences were investigated. Longitudinal cognitive changes by baseline cluster status were estimated using a linear mixed model. The cluster analysis identified two clusters: temporal predominant (TP) and cingulate predominant (CP). TP tau deposition was higher than CP. A trend for a higher cognitive decline in TP compared to CP was observed. This study suggests the existence of two Aß deposition patterns in the earliest phases of Aß accumulation, differently prone to tau pathology and cognitive decline.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.

BACKGROUND: The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. METHODS: LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. RESULTS: Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. CONCLUSIONS: Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. KEY POINTS: We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

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.

Discrepancy Between Patient and Caregiver Estimate of Apathy Predicts Dementia in Patients with Amnestic Mild Cognitive Impairment.

BACKGROUND: Apathy is a frequent behavioral symptom of Alzheimer's disease (AD). The Apathy Evaluation Scale (AES) is a tool exploring the perception of apathy by both caregivers (CG-AES) and patients (PT-AES), and the discrepancy in their ratings is a proxy of patients' disease unawareness. OBJECTIVE: To assess in a cohort study of patients with amnesic mild cognitive impairment (aMCI) whether apathy and awareness of apathy predict progression to dementia and timing. METHODS: From the global AES scores of 110 patients with aMCI and their caregivers, we obtained two principal indices for analysis: 1) 'Apathy', the mean of PT-AES and CG-AES, and 2) 'Discrepancy', obtained by subtracting CG-AES from PT-AES. Patients were followed with visits every six months for three years or until dementia. AES indices and the principal demographical/neuropsychological variables were filtered from multicollinearity. The most robust variables entered a logistic regression model and survival analyses (Cox regression, log-rank test of Kaplan-Meier curves) to estimate which predicted the risk and timing of progression, respectively. RESULTS: Sixty patients (54.5%) developed dementia (57 AD) after 6.0-36.0 months, 22 (20%) remained in an MCI stage, and 28 (25.5%) dropped out. 'Discrepancy' was a robust and accurate predictor of the risk of progression (AUC = 0.73) and, after binarization according to a computed cutoff, of timing to dementia. CONCLUSION: A structured evaluation of apathy, both self-assessed and estimated by caregivers, can provide useful information on the risk and timing of progression from aMCI to dementia. The discrepancy between the two estimates is a fairly reliable index for prediction purposes as a proxy of disease unawareness.

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones.

Exploring the brain metabolic correlates of process-specific CSF biomarkers in patients with MCI due to Alzheimer's disease: preliminary data.

We explored the brain metabolism correlates of emergent cerebrospinal fluid (CSF) biomarkers in a group of 26 patients with prodromal Alzheimer's disease (AD). Distinct volumes of interest (VOIs) expressed the sites of correlation between CSF biomarkers and brain metabolism as determined on [18F]FDG-PET images, as well as of significant hypometabolism in patients compared to healthy controls. Neurogranin- and α-synuclein-VOIs included left precuneus and/or posterior cingulate cortex (PC and/or PCC) and partially overlapped hypometabolism at those sites. ß-synuclein- and neurofilament light chain (NfL)-VOIs regarded either left or right lateral temporal areas, respectively, with partial overlap with hypometabolism only for the ß-synuclein-VOI, whereas the NfL-VOI did not include hypometabolic regions. We speculate that CSF neurogranin and α-synuclein express an already established hippocampal damage leading to PC and/or PCC deafferentation and hypometabolism. ß-synuclein may represent the progression of synaptopathy in the temporal lobe, while NfL the axonal injury in right temporal regions where neuronal loss is not yet evident.

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.

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

PURPOSE: The 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 METHODS: Retrospective 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. RESULTS: The 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. CONCLUSION: Using 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.

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.

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.

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.

Validation of FDG-PET datasets of normal controls for the extraction of SPM-based brain metabolism maps.

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.

The fate of patients with REM sleep behavior disorder and mild cognitive impairment.

OBJECTIVE: To investigate clinical and dopaminergic pre-synaptic brain imaging characteristics of subjects with idiopathic rapid eye movement (REM) behavior disorder (iRBD) and mild cognitive impairment (MCI), and to evaluate the combined predictive value of risk factors for short-term conversion to synucleinopathy. METHOD: In sum, 44 polysomnography (PSG)-confirmed iRBD patients (68.5 ± 7.2 years; 38 males) underwent 123I-FP-CIT-SPECT, comprehensive neuropsychological evaluation, clinical examination and clinical follow-up every six months (30.6 ± 21.5 months). Step-wise logistic regression was applied to identify those features discriminating iRBD patients with (iRBD-MCI; n = 14) and without MCI (normal cognition [NC], iRBD-NC; n = 30). The risk of neurodegeneration was estimated with Kaplan-Meier analysis. Predictors of phenoconversion were assessed with Cox proportional-hazards analysis, adjusting for age, gender and education. A generalized linear model (GLM) was applied to define the best combination of risk factors predicting conversion at follow-up. RESULTS: At baseline, patients with iRBD-MCI showed reduced striatal dopamine transporter (DAT) specific to non-displaceable binding ratio (SBR) and more constipation compared with iRBD-NC patients (p < 0.0001). During the follow-up, 10 patients (22.7%) develop an overt synucleinopathy. GLM analysis showed that patients with orthostatic hypotension, non-motor experiences of daily living, reduced putaminal DAT-SPECT SBR, and cognitive impairment in verbal memory/visuoconstruction abilities were at higher risk of phenoconversion (Hazard Ratio [HR] 26.05; Sensitivity 90%; Specificity 100%; Accuracy 97.73%; Positive Predictive Value 100%; Negative Predictive Value 97.14%). CONCLUSIONS: iRBD-MCI patients showed a more severe dopaminergic neuroimaging and clinical phenotype. Combining clinical and neuroimaging markers allowed to achieve excellent ability in identifying iRBD patients at high risk of developing a synucleinopathy within about three years from diagnosis.

Dopaminergic imaging and clinical predictors for phenoconversion of REM sleep behaviour disorder.

This is an international multicentre study aimed at evaluating the combined value of dopaminergic neuroimaging and clinical features in predicting future phenoconversion of idiopathic REM sleep behaviour (iRBD) subjects to overt synucleinopathy. Nine centres sent 123I-FP-CIT-SPECT data of 344 iRBD patients and 256 controls for centralized analysis. 123I-FP-CIT-SPECT images were semiquantified using DaTQUANTTM, obtaining putamen and caudate specific to non-displaceable binding ratios (SBRs). The following clinical variables were also analysed: (i) Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale, motor section score; (ii) Mini-Mental State Examination score; (iii) constipation; and (iv) hyposmia. Kaplan-Meier survival analysis was performed to estimate conversion risk. Hazard ratios for each variable were calculated with Cox regression. A generalized logistic regression model was applied to identify the best combination of risk factors. Bayesian classifier was used to identify the baseline features predicting phenoconversion to parkinsonism or dementia. After quality check of the data, 263 iRBD patients (67.6 ± 7.3 years, 229 males) and 243 control subjects (67.2 ± 10.1 years, 110 males) were analysed. Fifty-two (20%) patients developed a synucleinopathy after average follow-up of 2 years. The best combination of risk factors was putamen dopaminergic dysfunction of the most affected hemisphere on imaging, defined as the lower value between either putamina (P < 0.000001), constipation, (P < 0.000001) and age over 70 years (P = 0.0002). Combined features obtained from the generalized logistic regression achieved a hazard ratio of 5.71 (95% confidence interval 2.85-11.43). Bayesian classifier suggested that patients with higher Mini-Mental State Examination score and lower caudate SBR asymmetry were more likely to develop parkinsonism, while patients with the opposite pattern were more likely to develop dementia. This study shows that iRBD patients older than 70 with constipation and reduced nigro-putaminal dopaminergic function are at high risk of short-term phenoconversion to an overt synucleinopathy, providing an effective stratification approach for future neuroprotective trials. Moreover, we provide cut-off values for the significant predictors of phenoconversion to be used in single subjects.

Striatal dopamine transporter SPECT quantification: head-to-head comparison between two three-dimensional automatic tools.

PURPOSE: Our aim was to compare a widely distributed commercial tool with an older free software (i) one another, (ii) with a clinical motor score, (iii) versus reading by experts. PROCEDURES: We analyzed consecutive scans from one-hundred and fifty-one outpatients submitted to brain DAT SPECT for a suspected parkinsonism. Images were post-processed using a commercial (Datquant®) and a free (BasGanV2) software. Reading by expert was the gold standard. A subset of patients with pathological or borderline scan was evaluated with the clinical Unified Parkinson's Disease Rating Scale, motor part (MDS-UPDRS-III). RESULTS: SBR, putamen-to-caudate (P/C) ratio, and both P and C asymmetries were highly correlated between the two software with Pearson's 'r' correlation coefficients ranging from .706 to .887. Correlation coefficients with the MDS-UPDRS III score were higher with caudate than with putamen SBR values with both software, and in general higher with BasGanV2 than with Datquant®. Datquant® correspondence with expert reading was 84.1% (94.0% by additionally considering the P/C ratio as a further index). BasGanV2 correspondence with expert reading was 80.8% (86.1% by additionally considering the P/C ratio). CONCLUSIONS: Both Datquant® and BasGanV2 work reasonably well and similarly one another in semi-quantification of DAT SPECT. Both tools have their own strength and pitfalls that must be known in detail by users in order to obtain the best help in visual reading and reporting of DAT SPECT.