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Background: Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents the prodromal stage of cit-synucleinopathies. Reliable biomarkers are needed to predict phenoconversion.Objective: The aim was to derive and validate a brain glucose metabolism pattern related to phenoconversion in iRBD (iRBDconvRP) using spatial covariance analysis (Scaled Subprofile Model and Principal Component Analysis [SSM-PCA]).Methods: Seventy-six consecutive iRBD patients (70 +/- 6 years, 15 women) were enrolled in two centers and prospectively evaluated to assess phenoconversion (30 converters, 73 +/- 6 years, 14 Parkinson's disease and 16 dementia with Lewy bodies, follow-up time: 21 +/- 14 months; 46 nonconverters, 69 +/- 6 years, follow-up time: 33 +/- 19 months). All patients underwent [F-18]FDG-PET (F-18-fluorodeoxyglucose positron emitting tomography) to investigate brain glucose metabolism at baseline. SSM-PCA was applied to obtain the iRBDconvRP; nonconverter patients were considered as the reference group. Survival analysis and Cox regression were applied to explore prediction power.Results: First, we derived and validated two distinct center-specific iRBDconvRP that were comparable and significantly able to predict phenoconversion. Then, SSM-PCA was applied to the whole set, identifying the iRBDconvRP. The iRBDconvRP included positive voxel weights in cerebellum; brainstem; anterior cingulate cortex; lentiform nucleus; and middle, mesial temporal, and postcentral areas. Negative voxel weights were found in posterior cingulate, precuneus, middle frontal gyrus, and parietal areas. Receiver operating characteristic analysis showed an area under the curve of 0.85 (sensitivity: 87%, specificity: 72%), discriminating converters from nonconverters. The iRBDconvRP significantly predicted phenoconversion (hazard ratio: 7.42, 95% confidence interval: 2.6-21.4).Conclusions: We derived and validated an iRBDconvRP to efficiently discriminate converter from nonconverter iRBD patients. [F-18]FDG-PET pattern analysis has potential as a phenoconversion biomarker in iRBD patients. (C) 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Abstract Background A brain glucose metabolism pattern related to phenoconversion in patients with idiopathic/isolated REM sleep behaviour disorder (iRBDconvRP) was recently identified. However, the validation of the iRBDconvRP in an external, independent group of iRBD patients is needed to verify the reproducibility of such pattern, so to increase its importance in clinical and research settings. The aim of this work was to validate the iRBDconvRP in an independent group of iRBD patients. Methods Forty iRBD patients (70 ± 5.59 years, 19 females) underwent brain [18F]FDG-PET in Seoul National University. Thirteen patients phenoconverted at follow-up (7 Parkinson disease, 5 Dementia with Lewy bodies, 1 Multiple system atrophy; follow-up time 35 ± 20.56 months) and 27 patients were still free from parkinsonism/dementia after 62 ± 29.49 months from baseline. We applied the previously identified iRBDconvRP to validate its phenoconversion prediction power. Results The iRBDconvRP significantly discriminated converters from non-converters iRBD patients (p = 0.016; Area under the Curve 0.74, Sensitivity 0.69, Specificity 0.78), and it significantly predicted phenoconversion (Hazard ratio 4.26, C.I.95%: 1.18–15.39). Conclusions The iRBDconvRP confirmed its robustness in predicting phenoconversion in an independent group of iRBD patients, suggesting its potential role as a stratification biomarker for disease-modifying trials.

BACKGROUND: Isolated rapid eye movement sleep behavior disorder (iRBD) is prodromal for α-synucleinopathies.OBJECTIVE: The aim of this study was to determine whether pathological cardiac [123 I]meta-iodobenzylguanidine scintigraphy ([123 I]MIBG) is associated with progression of [18 F]fluorodeoxyglucose-positron emission tomography-based Parkinson's disease (PD)-related brain pattern (PDRP) expression in iRBD.METHODS: Seventeen subjects with iRBD underwent [18 F]fluorodeoxyglucose-positron emission tomography brain imaging twice ~3.6 years apart. In addition, [123 I]MIBG and [123 I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane single-photon emission computed tomography ([123 I]FP-CIT-SPECT) at baseline were performed. Olfactory, cognitive, and motor functions were tested annually.RESULTS: Twelve of 17 subjects had pathological [123 I]MIBG. At baseline, 6 of 12 of these expressed the PDRP (suprathreshold PDRP z score). At follow-up, 12 of 17 subjects had suprathreshold PDRP z scores, associated with pathological [123 I]MIBG in 92% and with pathological [123 I]FP-CIT-SPECT in 75%. Subjects with pathological [123 I]MIBG had higher PDRP z score change per year (P = 0.027). Three subjects phenoconverted to PD; all had pathological [123 I]MIBG and [123 I]FP-CIT-SPECT, suprathreshold baseline PDRP z scores, and hyposmia.CONCLUSIONS: Pathological [123 I]MIBG was associated with progressive and suprathreshold PDRP z scores at follow-up. Abnormal [123 I]MIBG likely identifies iRBD as prodromal PD earlier than pathological [123 I]FP-CIT-SPECT. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Background and Objectives 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson’s disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer’s disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory. Methods We applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories. Results The GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman’s rho =0.62, P=0.004). Conclusion In this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages.

It remains unclear whether the supportive imaging features described in the diagnostic criteria for progressive supranuclear palsy (PSP) are suitable for the full clinical spectrum. The aim of the current study was to define and cross-validate the pattern of glucose metabolism in the brain associated with a diagnosis of different PSP variants. A retrospective multicenter cohort study performed on 73 PSP patients who were referred for a fluorodeoxyglucose positron emission tomography PET scan: PSP-Richardson's syndrome, n = 47; PSP-parkinsonian variant, n = 18; and progressive gait freezing, n = 8. In addition, we included 55 healthy controls and 58 Parkinson's disease (PD) patients. Scans were normalized by global mean activity. We analyzed the regional differences in metabolism between the groups. Moreover, we applied a multivariate analysis to obtain a PSP-related pattern that was cross-validated in independent populations at the individual level. Group analysis showed relative hypometabolism in the midbrain, basal ganglia, thalamus, and frontoinsular cortices and hypermetabolism in the cerebellum and sensorimotor cortices in PSP patients compared with healthy controls and PD patients, the latter with more severe involvement in the basal ganglia and occipital cortices. The PSP-related pattern obtained confirmed the regions described above. At the individual level, the PSP-related pattern showed optimal diagnostic accuracy to distinguish between PSP and healthy controls (sensitivity, 80.4%; specificity, 96.9%)s and between PSP and PD (sensitivity, 80.4%; specificity, 90.7%). Moreover, PSP-Richardson's syndrome and PSP-parkinsonian variant patients showed significantly more PSP-related pattern expression than PD patients and healthy controls. The glucose metabolism assessed by fluorodeoxyglucose PET is a useful and reproducible supportive diagnostic tool for PSP-Richardson's syndrome and PSP-parkinsonian variant. © 2020 International Parkinson and Movement Disorder Society.

Background Isolated rapid eye movement sleep behavior disorder is known to be prodromal for alpha-synucleinopathies, such as Parkinson's disease (PD) and dementia with Lewy bodies. The [18 F]fluorodeoxyglucose-positron emission tomography (PET)-based PD-related brain pattern can be used to monitor disease progression. Objective We longitudinally investigated PD-related brain pattern expression changes in 20 subjects with isolated rapid eye movement sleep behavior disorder to investigate whether this may be a suitable technique to study prodromal PD progression in these patients and to identify potential phenoconverters. Methods Subjects underwent two [18 F]fluorodeoxyglucose-PET brain scans ~3.7 years apart, along with baseline and repeated motor, cognitive, and olfactory testing within roughly the same time frame. Results At baseline, 8 of 20 (40%) subjects significantly expressed the PD-related brain pattern (with z scores above the receiver operating characteristic-determined threshold). At follow-up, six additional subjects exhibited significant PD-related brain pattern expression (70% in total). PD-related brain pattern expression increased in all subjects (P = 0.00008). Four subjects (20%), all with significant baseline PD-related brain pattern expression, phenoconverted to clinical PD. Conclusions Suprathreshold PD-related brain pattern expression and greater score rate of change may signify greater shorter-term risk for phenoconversion. Our results support the use of serial PD-related brain pattern expression measurements as a prodromal PD progression biomarker in patients with isolated rapid eye movement sleep behavior disorder. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC. on behalf of International Parkinson and Movement Disorder Society.

Rationale In 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). Methods The 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. Results A 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. Conclusion The 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.

Purpose Subthalamotomy using magnetic resonance-guided focused ultrasound (MRgFUS) has become a potential treatment option for the cardinal features of Parkinson's disease (PD). The purpose of this study was to evaluate the effects of MRgFUS-subthalamotomy on brain metabolism using different scale levels. Methods We studied resting-state glucose metabolism in eight PD patients before and after unilateral MRgFUS-subthalamotomy using hybrid [F-18]FDG-PET/MR imaging. We used statistical nonparametric mapping (SnPM) to study regional metabolic changes following this treatment and also quantified whole-brain treatment-related changes in the expression of a spatial covariance-based Parkinson's disease-related metabolic brain pattern (PDRP). Modulation of regional and network activity was correlated with clinical improvement as measured by changes in Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor scores. Results After subthalamotomy, there was a significant reduction in FDG uptake in the subthalamic region, globus pallidus internus, motor and premotor cortical regions, and cingulate gyrus in the treated hemisphere, and the contralateral cerebellum (p

Patients with isolated rapid-eye-movement sleep behaviour disorder (RBD) are commonly regarded as being in the early stages of a progressive neurodegenerative disease involving alpha-synuclein pathology, such as Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. Abnormal alpha-synuclein deposition occurs early in the neurodegenerative process across the central and peripheral nervous systems and might precede the appearance of motor symptoms and cognitive decline by several decades. These findings provide the rationale to develop reliable biomarkers that can better predict conversion to clinically manifest alpha-synucleinopathies. In addition, biomarkers of disease progression will be essential to monitor treatment response once disease-modifying therapies become available, and biomarkers of disease subtype will be essential to enable prediction of which subtype of alpha-synucleinopathy patients with isolated RBD might develop.

BASICS: Nuclear Medicine Imaging Tracers for Neurology.- 18F-Fluorodeoxyglucose Positron Emission Tomography Procedures.- Tracer-Kinetic Modeling.- Quantification in Brain SPECT.- MRI/PET Brain Imaging.- An Investigation of Statistical Power of [15O]-H2O PET Perfusion Imaging.- Molecular Imaging Using Magnetic Resonance Spectroscopy in Neurology.- The Default Network of the Brain.- DEMENTIA: Dementia Due to Neurodegenerative Disease - Molecular Imaging Findings.- Abeta Imaging in Aging, Alzheimer's Disease and Other Neurodegenerative Conditions.- PET Imaging of the Alpha4Beta2*-Nicotinic Acetylcholine Receptors in Alzheimer's.- Neuroimaging Findings in Mild Cognitive Impairment.- Impact of the IWG/Dubois Criteria for Alzheimer's Disease in Imaging Studies.- Perfusion SPECT.- Nuclear Imaging in Frontotemporal.- Parkinson Dementia: PET Findings.- SPECT/PET Findings in Lewy Body Dementia.- Vascular.- Value of MIBG in the Differential Diagnosis of Neurodegenerative Disorders.- Linking Molecular Biology to Therapeutic Approaches for Alzheimer Disease with PET.- CEREBROVASCULAR DISORDERS: PET and SPECT Studies of Ageing and Cardiovascular Risk Factors for Alzheimer's Disease.- Carotid Plaque Imaging with SPECT-CT and PET-CT.- ET in Brain Arteriovenous Malformations and Cerebral Proliferative Angiopathy.- Transient Ischemic Attack.- PET Reveals Pathophysiology in Ischemic Stroke.- MOVEMENT DISORDERS: Parkinson Disease.- SPECT Imaging for Idiopathic M.Parkinson and Parkinsonian Syndromes.- PET and SPECT Imaging in Parkinsonian Syndromes.- Amyotrophic Lateral Sclerosis.- PET in Huntington's Disease.- PET and SPECT Imaging in Dystonia.- PET and SPECT Imaging in Hyperkinetic Movement Disorders.- Clinical Applications of [123I]FP-CIT SPECT Imaging.- INFLAMMATORY DISORDERS: PET Imaging of Microglia Activation in Neuropsychiatric Disorders with Potential Infectious Origin.- PET Imaging in Multiple Sclerosis.- PET and SPECT Imaging of Neurotoxicity.- PET and SPECT in Hepatic and Uremic Encephalopathy.- EPILEPSY: PET in Epilepsy.- Subtraction ictal SPECT coregistered to MRI (SISCOM).- Nuclear Medicine Neuroimaging and Electromagnetic Source Localization in Nonlesional Drug-Resistant Focal.- TUMORS OF THE NERVOUS SYSTEM: PET Imaging of Gliomas.- Single-Photon Emission Computed Tomography [Neuro-SPECT] Imaging of Brain Tumors.- The Value of 11C-Methionine PET in the Differential Diagnosis between Brain Tumor Recurrence and Radionecrosis.- Imaging Brain Metastases of Neuroendocrine Tumors.- OTHER SUBJECTS: Traumatic Brain Injury.- Whiplash: real or not real? A review and new concept.- Positron Emission Tomography Imaging in Altered States of Consciousness.- Anaesthesia and PET of the Brain.- Modulation of Brain Functioning by Deep Brain Stimulation.- Radionuclide Imaging Studies in Pediatric Neurology.

Imaging of brain glucose metabolism with 18F-2-fluoro-2-deoxy-d-glucose positron emission tomography (18F-FDG PET) can give important information regarding disease-related changes in underlying neuronal systems, when combined with appropriate analytical methods. One such method is the scaled subprofile model combined with principal component analysis (SSM PCA). This model takes into account the relationships (covariance) between voxels to identify disease-related patterns. By quantifying disease-related pattern expression on a scan-by-scan basis, this technique allows objective assessment of disease activity in individual subjects. This chapter provides an overview of steps involved in pattern identification in 18F-FDG PET data and is divided into three sections. Section 1 introduces basic concepts in nuclear imaging and explores the cellular underpinnings of signals measured with 18F-FDG PET. Section 2 describes relevant basic concepts in 18F-FDG PET image analysis including anatomical registration, normalization, and analysis of variance and covariance. Section 3 is dedicated to SSM PCA specifically. The goal of this chapter is to make the technique more accessible to readers without a mathematics or neuroimaging background. Although many excellent texts on this topic exist, the current chapter aims to provide a more conceptual overview, including some discussion points that are not always formally described in literature.

Functional impairment of spatially distributed brain regions in Parkinson's disease (PD) suggests changes in integrative and segregative network characteristics, for which novel analysis methods are available. To assess underlying structural network differences between PD patients and controls, we employed MRI T1 gray matter segmentation and diffusion MRI tractography to construct connectivity matrices to compare patients and controls with data originating from two different centers. In the Dutch dataset (Data-NL), 14 PD patients, and 15 healthy controls were analyzed, while 19 patients and 18 controls were included in the Canadian dataset (Data-CA). All subjects underwent T1 and diffusion-weighted MRI. Patients were assessed with Part 3 of the Unified Parkinson's Disease Rating Scale (UPDRS). T1 images were segmented using FreeSurfer, while tractography was performed using ExploreDTI. The regions of interest from the FreeSurfer segmentation were combined with the white matter streamline sets resulting from the tractography, to construct connectivity matrices. From these matrices, both global and local efficiencies were calculated, which were compared between the PD and control groups and related to the UPDRS motor scores. The connectivity matrices showed consistent patterns among the four groups, without significant differences between PD patients and control subjects, either in Data-NL or in Data-CA. In Data-NL, however, global and local efficiencies correlated negatively with UPDRS scores at both the whole-brain and the nodal levels [false discovery rate (FDR) 0.05]. At the nodal level, particularly, the posterior parietal cortex showed a negative correlation between UPDRS and local efficiency, while global efficiency correlated negatively with the UPDRS in the sensorimotor cortex. The spatial patterns of negative correlations between UPDRS and parameters for network efficiency seen in Data-NL suggest subtle structural differences in PD that were below sensitivity thresholds in Data-CA. These correlations are in line with previously described functional differences. The methodological approaches to detect such differences are discussed.

Background Isolated rapid eye movement sleep behavior disorder is known to be prodromal for alpha‐synucleinopathies, such as Parkinson's disease (PD) and dementia with Lewy bodies. The [18F]fluorodeoxyglucose‐positron emission tomography (PET)–based PD‐related brain pattern can be used to monitor disease progression. Objective We longitudinally investigated PD‐related brain pattern expression changes in 20 subjects with isolated rapid eye movement sleep behavior disorder to investigate whether this may be a suitable technique to study prodromal PD progression in these patients and to identify potential phenoconverters. Methods Subjects underwent two [18F]fluorodeoxyglucose‐PET brain scans ~3.7 years apart, along with baseline and repeated motor, cognitive, and olfactory testing within roughly the same time frame. Results At baseline, 8 of 20 (40%) subjects significantly expressed the PD‐related brain pattern (with z scores above the receiver operating characteristic–determined threshold). At follow‐up, six additional subjects exhibited significant PD‐related brain pattern expression (70% in total). PD‐related brain pattern expression increased in all subjects (P = 0.00008). Four subjects (20%), all with significant baseline PD‐related brain pattern expression, phenoconverted to clinical PD. Conclusions Suprathreshold PD‐related brain pattern expression and greater score rate of change may signify greater shorter‐term risk for phenoconversion. Our results support the use of serial PD‐related brain pattern expression measurements as a prodromal PD progression biomarker in patients with isolated rapid eye movement sleep behavior disorder. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Highlights • FDOPA PET allows quantification of presynaptic dopaminergic functioning in vivo. • Subregional analysis within the striatum provides additional diagnostic value for differentiation within Parkinsonian disorders. • Depletion of F-DOPA uptake in both patients with idiopathic Parkinson's disease and patients with atypical Parkinsonism preferentially affects the posterior part of the putamen. • A more linear decrease of FDOPA uptake from the head of the caudate nucleus to the posterior putamen is present in patients with IPD than in APD., Aim L -3,4-dihydroxy-6–18F-fluorophenylalanine (18F-DOPA PET may be used to distinguish subjects with Parkinsonism from those with symptoms not originating from impaired dopaminergic transmission. However, it is not routinely utilized to discriminate Idiopathic Parkinson's disease (IPD) from Atypical Parkinsonian Disorders (APD). We investigated the potential of FDOPA PET to discriminate between IPD and APD, with a focus on the anterior-to-posterior decline in het striatum, considered to be more specific for IPD. Materials and methods 18F-DOPA PET data from a total of 58 subjects were retrospectively analyzed. 28 subjects had idiopathic Parkinson's disease (14 male, 14 female; age at scan 61 +- 11,5), 13 atypical Parkinsonian disease (7 male, 6 females; age at scan: 69,6 +- 6,4) and 17 were controls (6 male, 11 female; age at scan 65,3 +-8,6). Regional striatal-to-occipital ratio's (RSOR's) were calculated, as well as multiple in-line VOI's from the caudate nucleus to the posterior part of the putamen. The linearity of anteroposterior decline was determined by a linear regression fit and associated R squared values. ROC curves were calculated to assess the diagnostic performance of these measurements. Data contralateral to the clinically most affected side were used for analysis. Results ROC curve analysis for differentiation between controls and Parkinsonism patients showed the highest AUC for the caudate nucleus-to-posterior putamen ratio (AUC = 0.930; p

BACKGROUND AND OBJECTIVE: Neurodegenerative diseases like Parkinson's disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ).METHODS: We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination's validity, we analyze FDG-PET data of Parkinson's disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists.RESULTS: One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients.CONCLUSION: We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully.

Summary Background Most patients with Parkinson's disease, Parkinson's disease dementia, and dementia with Lewy bodies do not carry mutations in known disease-causing genes. The aim of this study was to identify a novel gene implicated in the development of these disorders. Methods Our study was done in three stages. First, we did genome-wide linkage analysis of an Italian family with dominantly inherited Parkinson's disease to identify the disease locus. Second, we sequenced the candidate gene in an international multicentre series of unrelated probands who were diagnosed either clinically or pathologically with Parkinson's disease, Parkinson's disease dementia, or dementia with Lewy bodies. As a control, we used gene sequencing data from individuals with abdominal aortic aneurysms (who were not examined neurologically). Third, we enrolled an independent series of patients diagnosed clinically with Parkinson's disease and controls with no signs or family history of Parkinson's disease, Parkinson's disease dementia, or dementia with Lewy bodies from centres in Portugal, Sardinia, and Taiwan, and screened them for specific variants. We also did mRNA and brain pathology studies in three patients from the international multicentre series carrying disease-associated variants, and we did functional protein studies in in-vitro models, including neurons from induced pluripotent stem-like cells. Findings Molecular studies were done between Jan 1, 2008, and Dec 31, 2017. In the initial kindred of ten affected Italian individuals (mean age of disease onset 59·8 years [SD 8·7]), we detected significant linkage of Parkinson's disease to chromosome 14 and nominated LRP10 as the disease-causing gene. Among the international series of 660 probands, we identified eight individuals (four with Parkinson's disease, two with Parkinson's disease dementia, and two with dementia with Lewy bodies) who carried different, rare, potentially pathogenic LRP10 variants; one carrier was found among 645 controls with abdominal aortic aneurysms. In the independent series, two of these eight variants were detected in three additional Parkinson's disease probands (two from Sardinia and one from Taiwan) but in none of the controls. Of the 11 probands from the international and independent cohorts with LRP10 variants, ten had a positive family history of disease and DNA was available from ten affected relatives (in seven of these families). The LRP10 variants were present in nine of these ten relatives, providing independent—albeit limited—evidence of co-segregation with disease. Post-mortem studies in three patients carrying distinct LRP10 variants showed severe Lewy body pathology. Of nine variants identified in total (one in the initial family and eight in stage 2), three severely affected LRP10 expression and mRNA stability (1424+5delG, 1424+5G→A, and Ala212Serfs*17, shown by cDNA analysis), four affected protein stability (Tyr307Asn, Gly603Arg, Arg235Cys, and Pro699Ser, shown by cycloheximide-chase experiments), and two affected protein localisation (Asn517del and Arg533Leu; shown by immunocytochemistry), pointing to loss of LRP10 function as a common pathogenic mechanism. Interpretation Our findings implicate LRP10 gene defects in the development of inherited forms of α-synucleinopathies. Future elucidation of the function of the LRP10 protein and pathways could offer novel insights into mechanisms, biomarkers, and therapeutic targets. Funding Stichting ParkinsonFonds, Dorpmans-Wigmans Stichting, Erasmus Medical Center, ZonMw—Memorabel programme, EU Joint Programme Neurodegenerative Disease Research (JPND), Parkinson's UK, Avtal om Lakarutbildning och Forskning (ALF) and Parkinsonfonden (Sweden), Lijf and Leven foundation, and cross-border grant of Alzheimer Netherlands–Ligue Europeene Contre la Maladie d'Alzheimer (LECMA).

Introduction We aimed to uncover the pattern of network-level changes in neuronal function in Spinocerebellar ataxia type 3 (SCA3). Methods 17 genetically-confirmed SCA3 patients and 16 controls underwent structural MRI and static resting-state [18F]‑Fluoro‑deoxyglucose Positron Emission Tomography (FDG-PET) imaging. A SCA3-related pattern (SCA3-RP) was identified using a multivariate method (scaled subprofile model and principal component analysis (SSM PCA)). Participants were evaluated with the Scale for Assessment and Rating of Ataxia (SARA) and with neuropsychological examination including tests for language, executive dysfunction, memory, and information processing speed. The relationships between SCA3-RP expression and clinical scores were explored. Voxel based morphology (VBM) was applied on MRI-T1 images to assess possible correlations between FDG reduction and grey matter atrophy. Results The SCA3-RP disclosed relative hypometabolism of the cerebellum, caudate nucleus and posterior parietal cortex, and relatively increased metabolism in somatosensory areas and the limbic system. This topography, which was not explained by regional atrophy, correlated significantly with ataxia (SARA) scores (ρ = 0.72; P = 0.001). SCA3 patients showed significant deficits in executive function and information processing speed, but only letter fluency correlated with SCA3-RP expression (ρ = 0.51; P = 0.04, uncorrected for multiple comparisons). Conclusion The SCA3 metabolic profile reflects network-level alterations which are primarily associated with the motor features of the disease. Striatum decreases additional to cerebellar hypometabolism underscores an intrinsic extrapyramidal involvement in SCA3. Cerebellar-posterior parietal hypometabolism together with anterior parietal (sensory) cortex hypermetabolism may reflect a shift from impaired feedforward to compensatory feedback processing in higher-order motor control. The demonstrated SCA3-RP provides basic insight in cerebral network changes in this disease., Highlights • A metabolic cerebral pattern could be identified in FDG-PET data of SCA3 patients, which was not explained by regional atrophy. • Striatum decreases in the SCA3-pattern reflect extrapyramidal involvement. • The SCA3-pattern reflects changes in higher-order motor control.

We investigated the expression of the Alzheimer’s disease-related metabolic brain pattern (ADRP) in 18F-FDG-PET scans of 44 controls, 27 patients with mild cognitive impairment (MCI) who did not convert to Alzheimer’s disease (AD) after five or more years of clinical follow-up, 95 MCI patients who did develop AD dementia on clinical follow-up, and 55 patients with mild-to-moderate AD. The ADRP showed good sensitivity (84%) and specificity (86%) for MCI-converters when compared to controls, but limited specificity when compared to MCI non-converters (66%). Assessment of 18F-FDG-PET scans on a case-by-case basis using the ADRP may be useful for quantifying disease progression.

Background Idiopathic REM sleep behavior disorder is a prodromal stage of Parkinson's disease and dementia with Lewy bodies. Hyposmia, reduced dopamine transporter binding, and expression of the brain metabolic PD-related pattern were each associated with increased risk of conversion to PD. The objective of this study was to study the relationship between the PD-related pattern, dopamine transporter binding, and olfaction in idiopathic REM sleep behavior disorder. Methods In this cross-sectional study, 21 idiopathic REM sleep behavior disorder subjects underwent 18F-fluorodeoxyglucose PET, dopamine transporter imaging, and olfactory testing. For reference, we included 18F-fluorodeoxyglucose PET data of 19 controls, 20 PD patients, and 22 patients with dementia with Lewy bodies. PD-related pattern expression z-scores were computed from all PET scans. Results PD-related pattern expression was higher in idiopathic REM sleep behavior disorder subjects compared with controls (P = 0.048), but lower compared with PD (P = 0.001) and dementia with Lewy bodies (P

This review focuses on recent human 18F-FDG PET studies in Parkinson disease. First, an overview is given of the current analytic approaches to metabolic brain imaging data. Next, we discuss how 18F-FDG PET studies have advanced understanding of the relation between distinct brain regions and associated symptoms in Parkinson disease, including cognitive decline. In addition, the value of 18F-FDG PET studies in differential diagnosis, identifying prodromal patients, and the evaluation of treatment effects are reviewed. Finally, anticipated developments in the field are addressed.

The Patlak graphical analysis (PGAREF) for quantification of irreversible tracer binding with a reference tissue model was approximated by a dual time point imaging approach (DTPREF). The DTPREF was applied to 18 [18F]-FDOPA brain scans using the occipital cortex as reference region (DTPOCC) and compared to both PGAOCC and striatal-to-occipital ratios (SOR). Pearson correlation analysis and Bland–Altman plots showed an excellent correlation and good agreement between DTPOCC and PGAOCC, while correlations between SOR and PGAOCC were consistently lower. Linear discriminant analysis (LDA) demonstrated a similar performance for all methods in differentiating patients with Parkinson’s disease (PD) from healthy controls (HC). Specifically for [18F]-FDOPA brain imaging, these findings validate DTPOCC as an approximation for PGAOCC, providing the same quantitative information while reducing the acquisition time to two short static scans. For PD patients, this approach can greatly improve patient comfort while reducing motion artifacts and increasing image quality. In general, DTPREF can improve the clinical applicability of tracers with irreversible binding characteristics when a reference tissue is available.

Parkinson's disease is characterized not only by bradykinesia, rigidity, and tremor, but also by impairments of expressive and receptive linguistic prosody. The facilitating effect of music with a salient beat on patients' gait suggests that it might have a similar effect on vocal behavior, however it is currently unknown whether singing is affected by the disease. In the present study, fifteen Parkinson patients were compared with fifteen healthy controls during the singing of familiar melodies and improvised melodic continuations. While patients' speech could reliably be distinguished from that of healthy controls matched for age and gender, purely on the basis of aural perception, no significant differences in singing were observed, either in pitch, pitch range, pitch variability, and tempo, or in scale tone distribution, interval size or interval variability. The apparent dissociation of speech and singing in Parkinson's disease suggests that music could be used to facilitate expressive linguistic prosody. (C) 2016 The Authors. Published by Elsevier Inc.

Introduction: Distinguishing between different tremor disorders can be challenging. Some tremor disorders are thought to have typical tremor characteristics: the current study aims to provide sensitivity and specificity for five 'typical' tremor phenomena.Methods: Retrospectively, we examined 210 tremor patients referred for electrophysiological recordings between January 2008 and January 2014. The final clinical diagnosis was used as the gold standard. The first step was to determine whether patients met neurophysiological criteria for their type of tremor. Once established, we focused on 'typical' characteristics: tremor frequency decrease upon loading (enhanced physiological tremor (EPT)), amplitude increase upon loading, distractibility and entrainment (functional tremor (Fr)), and intention tremor (essential tremor (ET)). The prevalence of these phenomena in the 'typical' group was compared to the whole group.Results: Most patients (87%) concurred with all core clinical neurophysiological criteria for their tremor type. We found a frequency decrease upon loading to be a specific (95%), but not a sensitive (42%) test for EPT. Distractibility and entrainment both scored high on sensitivity (92%, 91%) and specificity (94%, 91%) in FL whereas a tremor amplitude increase was specific (92%), but not sensitive (22%). Intention tremor was a specific finding in ET (85%), but not a sensitive test (45%). Combination of characteristics improved sensitivity.Conclusion: In this study, we retrospectively determined sensitivity and specificity for five 'typical' tremor characteristics. Characteristics proved specific, but few were sensitive. These data on tremor phenomenology will help practicing neurologists to improve distinction between different tremor disorders. (C) 2016 Elsevier Ltd. All rights reserved.

Decision trees have been shown to be effective at classifying subjects with Parkinson's disease when provided with features (subject scores) derived from FDG-PET data. Such subject scores have strong discriminative power but are not intuitive to understand. We therefore augment each decision node with thumbnails of the principal component (PC) images from which the subject scores are computed, and also provide labeled scatter plots of the distribution of scores. These plots allow the progress of individual subjects to be traced through the tree and enable the user to focus on complex or unexpected classifications. In addition, we present a visual representation of a typical brain activity pattern arriving at each leaf node, and show how this can be compared to a known reference to validate the behaviour of the tree.

Objective DNAJC6 mutations were recently described in two families with autosomal recessive juvenile parkinsonism (onset age

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we employed 3D CNNs in FDG-PET brain images with the purpose of discriminating patients diagnosed with Parkinson’s disease (PD) from controls. We employed Scaled Subprofile Modeling using Principal Component Analysis as a preprocessing step to focus on specific brain regions and limit the number of voxels that are used as input for the CNNs, thereby increasing the signal-to-noise ratio in our data. We performed hyperparameter optimization on three CNN architectures to estimate the classification accuracy of the networks on new data. The best performance that we obtained was [Formula: see text] and area under the receiver operating characteristic curve [Formula: see text] on the test set. We believe that, with larger datasets, PD patients could be reliably distinguished from controls by FDG-PET scans alone and that this technique could be applied to more clinically challenging tasks, like the differential diagnosis of neurological disorders with similar symptoms, such as PD, Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA).

BACKGROUND AND PURPOSE: Therapeutic options are limited in functional neurological paresis disorder. Earlier intervention studies did not control for a placebo effect, hampering assessment of effectivity. A proof-of-principle investigation was conducted into the therapeutic potential of repetitive transcranial magnetic stimulation (rTMS), using a single-blind two-period placebo-controlled cross-over design.METHODS: Eleven patients received active 15 Hz rTMS over the contralateral motor cortex (hand area), in two periods of 5 days, for 30 min once a day at 80% of resting motor threshold, with a train length of 2 s and an intertrain interval of 4 s. Eight of these eleven patients were also included in the placebo treatment condition. Primary outcome measure was change in muscle strength as measured by dynamometry after treatment. Secondary outcome measure was the subjective change in muscle strength after treatment.RESULTS: In patients who received both treatments, active rTMS induced a significantly larger median increase in objectively measured muscle strength (24%) compared to placebo rTMS (6%; P < 0.04). Subjective ratings showed no difference due to treatment, i.e. patients did not perceive these objectively measured motor improvements (P = 0.40).CONCLUSIONS: Our findings suggest that rTMS by itself can potentially improve muscle weakness in functional neurological paresis disorder. Whereas patients' muscle strength increased as measured with dynamometry, patients did not report increased functioning of the affected hand, subjectively. The results may indicate that decreased muscle strength is not the core symptom and that rTMS should be added to behavioral approaches in functional neurological paresis.

Neuroimaging in Parkinson's disease (PD) and other primary Parkinsonian disorders has been increasingly used in the routine clinical work in the last years. The paradigm has changed from an "exclusionary" use, i.e., to rule out causes of secondary Parkinsonism, to an "inclusionary" one, i.e., finding image and network characteristics allowing to identify a specific disease. This is allowed by analyses spanning from the commonly used visual analysis to the most sophisticated postprocessing leading to the identification of covariance patterns both in morphological and functional neuroimaging. However, paralleling the advancement in covariance and connectivity analyses, the issues of standardization and harmonization of data acquisition, and image reconstruction and postprocessing among centers are emerging in the scientific community. Also, the building of scientific evidence still suffers from the lack of large, formal studies and relies on relatively small cohort studies from one or few centers. Joint actions to face these issues are now ongoing in Europe, supported by specific programs, such as the Joint Programming on Neurodegenerative Diseases (JPND). In the present review, some of the most recent and relevant achievements in the field of diffusion tensor magnetic resonance imaging (MRI), functional MRI, fludeoxyglucose-positron-emission tomography, dopamine transporter single-photon emission computed tomography and non-dopaminergic imaging in PD and primary Parkinsonisms are reported.

Background Idiopathic REM sleep behavior disorder is a prodromal stage of Parkinson's disease and dementia with Lewy bodies. Hyposmia, reduced dopamine transporter binding, and expression of the brain metabolic PD‐related pattern were each associated with increased risk of conversion to PD. The objective of this study was to study the relationship between the PD‐related pattern, dopamine transporter binding, and olfaction in idiopathic REM sleep behavior disorder. Methods In this cross‐sectional study, 21 idiopathic REM sleep behavior disorder subjects underwent 18F‐fluorodeoxyglucose PET, dopamine transporter imaging, and olfactory testing. For reference, we included 18F‐fluorodeoxyglucose PET data of 19 controls, 20 PD patients, and 22 patients with dementia with Lewy bodies. PD‐related pattern expression z‐scores were computed from all PET scans. Results PD‐related pattern expression was higher in idiopathic REM sleep behavior disorder subjects compared with controls (P = 0.048), but lower compared with PD (P = 0.001) and dementia with Lewy bodies (P

The clinical diagnosis of Huntington's disease (HD) is based on the motor symptoms, although these can be preceded by cognitive and behavioral changes. Biomarker studies have shown that structural imaging modalities are useful biomarkers of HD onset, while functional imaging measures have been studied less often for this purpose. Our aim was to investigate the combined value of 18-fluorodesoxyglucose (FDG)-PET and cognitive measures as biomarkers of HD onset. Twenty-two premanifest mutation carriers of HD (PMCs) and 11 healthy controls were assessed twice with FDG-PET scan, neurological and neuropsychological assessments over a 2-year interval. Seventeen PMCs had an additional third neurological evaluation, 10 years after baseline. Disease load was defined as the probability of motor onset within 5 years. Metabolism in putamen, caudate and pallidum of PMCs was significantly lower than that of controls, at both assessments. Almost half of the PMCs had converted to manifest HD 10 years later and all converters had low average or abnormal putaminal metabolism at 2 year follow-up. In contrast, all PMCs with normal putaminal metabolism at 2 year follow-up remained premanifest during the following 8 years. Furthermore, glucose metabolism of putamen explained a substantial part of the variance in disease load. A composite score of psychomotor tests contributed significantly to the prediction model as well, while cognitive performance was comparable for PMCs and controls. We conclude that in future clinical trials a combination of psychomotor tests and putaminal glucose metabolism may be used to identify PMCs close to motor onset of HD.

Introduction Under normal conditions, the spatial distribution of resting cerebral blood flow and cerebral metabolic rate of glucose are closely related. A relatively new magnetic resonance (MR) technique, pseudo-continuous arterial spin labeling (PCASL), can be used to measure regional brain perfusion. We identified a Parkinson's disease (PD)-related perfusion and metabolic covariance pattern in the same patients using PCASL and FDG-PET imaging and assessed (dis)similarities in the disease-related pattern between perfusion and metabolism in PD patients. Methods Nineteen PD patients and seventeen healthy controls underwent [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) imaging. Of 14 PD patients and all healthy controls PCASL-MRI could be obtained. Data were analyzed using scaled subprofile model/principal component analysis (SSM/PCA). Results Unique Parkinson's disease-related perfusion and metabolic covariance patterns were identified using PCASL and FDG-PET in the same patients. The PD-related metabolic covariance brain pattern is in high accordance with previously reports. Also our disease-related perfusion pattern is comparable to the earlier described perfusion pattern. The most marked difference between our perfusion and metabolic patterns is the larger perfusion decrease in cortical regions including the insula. Conclusion We identified PD-related perfusion and metabolic brain patterns using PCASL and FDG-PET in the same patients which were comparable with results of existing research. In this respect, PCASL appears to be a promising addition in the early diagnosis of individual parkinsonian patients., Highlights • Unique PD-related perfusion and metabolic covariance patterns were identified. • PD-related metabolic pattern is highly reproducible compared to earlier reports. • PCASL is a promising addition in the early diagnosis of parkinsonian patients.

The adenosine A(2A) receptor (A(2A)R) is highly concentrated in the striatum, and a therapeutic target for Parkinson's disorder (PD) and Huntington's disease. High affinity and selective radiolabeled A(2A)R antagonists can be important research and diagnostic tools for PD. Positron Emission Tomography (PET) can play an important role by measuring radiolabeled A(2A) antagonists non-invasively in the brain. However, till date no complete review on A(2A)R PET ligands is available. The present article has been therefore focused on available PET tracers for A(2A)R and their detailed biological evaluation in rodents, nonhuman primates and humans. Drug design and development by molecular modeling including new lead structures that are potential candidates for radiolabeling and mapping of cerebral A(2A)Rs is discussed in the present article. A brief overview of functions of adenosine in health and disease, including the relevance of A(2A)R for PD has also been presented.

Background: The objective of this study was to validate disease-related metabolic brain patterns for Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy. Methods: The study included 20 patients with Parkinson’s disease, 21 with multiple system atrophy, and 17 with progressive supranuclear palsy, all of whom had undergone a clinically motivated [18F]-fluoro-deoxyglucose positron emission tomography scan at an early stage of their disease. At a follow-up time after the scan of 2–4 years, a clinical diagnosis was made according to established clinical research criteria. Patient groups were compared with 18 healthy controls using a multivariate covariance image analysis technique called scaled subprofile model/principal component analysis. Results: Disease-related metabolic brain patterns for these parkinsonian disorders were identified. Validation showed that these patterns were highly discriminative of the 3 disorders. Conclusions: Early diagnosis of parkinsonian disorders is feasible when the expression of disease-related metabolic brain patterns is quantified at a single-subject level.

In Parkinson's disease (PD), basal ganglia dysfunction leads to disturbed sensorimotor integration and associated timing. Previous functional MRI and behavioural PD studies on timing indicated a specific striatal contribution to assessing spatial displacement in velocity estimation. In this computation, cerebral processing time implies demarcating discrete intervals of spatial change. To quantify these putative intervals, the threshold of perceived velocity change of a moving ball was assessed in healthy volunteers and PD patients. After rebound from the upper side of a monitor screen, the ball's velocity increased or decreased with variable magnitudes while participants indicated whether they noticed this velocity change. The threshold for detecting velocity change was around 0.014 rad/s in both groups. Moreover, velocity was perceived as equal when the ball decelerated; unchanged velocity was perceived as acceleration. This shift was 0.009 rad/s for healthy volunteers and 0.007 rad/s for PD patients, and was negatively correlated with the severity of bradykinesia. As the trajectory length before and after velocity change was the same, velocity change was also expressed as a change in stimulus duration (relative to 1 s initial duration). The temporal equivalent of a threshold for perceived velocity change was around 75 ms in both groups. The perceptual acceleration bias' is in line with the flash-lag' effect: the position of a moving stimulus is projected ahead compared with a stationary landmark. Such an extrapolation over adjacent past and predicted locations enables real-time' visuomotor control, notwithstanding delays because of intrinsic cerebral processing time. In PD, such impaired perceptual feed-forward processing may result in slow movements. (C) 2013 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins.

The delay associated with cerebral processing time implies a lack of real-time representation of changes in the observed environment. To bridge this gap for motor actions in a dynamical environment, the brain uses predictions of the most plausible future reality based on previously provided information. To optimise these predictions, adjustments to actual experiences are necessary. This requires a perceptual memory buffer. In our study we gained more insight how the brain treats (real-time) information by comparing cerebral activations related to judging past-, present- and future locations of a moving ball, respectively. Eighteen healthy subjects made these estimations while fMRI data was obtained. All three conditions evoked bilateral dorsal-parietal and premotor activations, while judgment of the location of the ball at the moment of judgment showed increased bilateral posterior hippocampus activation relative to making both future and past judgments at the one-second time-sale. Since the condition of such 'real-time' judgments implied undistracted observation of the ball's actual movements, the associated hippocampal activation is consistent with the concept that the hippocampus participates in a top-down exerted sensory gating mechanism. In this way, it may play a role in novelty (saliency) detection.

Objective: To compare the stepwise regression (SR) method and the decision tree (DT) method for classification of parkinsonian syndromes. Method: We applied the scaled subprofile model/principal component analysis (SSM/PCA) method to FDG-PET brain image data to obtain covariance patterns and the corresponding subject scores. The subject scores formed the input to the C4.5 decision tree algorithm to classify the subject brain images. For the SR method, scatter plots and receiver operating characteristic (ROC) curves indicate the subject classifications. We then compare the decision tree classifier results with those of the SR method. Results: We found out that the SR method performs slightly better than the DT method. We attribute this to the fact that the SR method uses a linear combination of the best features to form one robust feature, unlike the DT method. However, when the same robust feature is used as the input for the DT classifier, the performance is as high as that of the SR method. Conclusion: Even though the SR method performs better than the DT method, including the SR procedure in the DT classification yields a better performance. Additionally, the decision tree approach is more suitable for human interpretation and exploration than the SR method.