Occipital hypometabolism is a risk factor for conversion to Parkinson's disease in isolated REM sleep behaviour disorder.

PURPOSE: Isolated REM sleep behaviour disorder (iRBD) patients are at high risk of developing clinical syndromes of the α-synuclein spectrum. Progression markers are needed to determine the neurodegenerative changes and to predict their conversion. Brain imaging with 18F-FDG PET in iRBD is promising, but longitudinal studies are scarce. We investigated the regional brain changes in iRBD over time, related to phenoconversion. METHODS: Twenty iRBD patients underwent two consecutive 18F-FDG PET brain scans and clinical assessments (3.7 ± 0.6 years apart). Seventeen patients also underwent 123I-MIBG and 123I-FP-CIT SPECT scans at baseline. Four subjects phenoconverted to Parkinson's disease (PD) during follow-up. 18F-FDG PET scans were compared to controls with a voxel-wise single-subject procedure. The relationship between regional brain changes in metabolism and PD-related pattern scores (PDRP) was investigated. RESULTS: Individual hypometabolism t-maps revealed three scenarios: (1) normal 18F-FDG PET scans at baseline and follow-up (N = 10); (2) normal scans at baseline but occipital or occipito-parietal hypometabolism at follow-up (N = 4); (3) occipital hypometabolism at baseline and follow-up (N = 6). All patients in the last group had pathological 123I-MIBG and 123I-FP-CIT SPECT. iRBD converters (N = 4) showed occipital hypometabolism at baseline (third scenario). At the group level, hypometabolism in the frontal and occipito-parietal regions and hypermetabolism in the cerebellum and limbic regions were progressive over time. PDRP z-scores increased over time (0.54 ± 0.36 per year). PDRP expression was driven by occipital hypometabolism and cerebellar hypermetabolism. CONCLUSIONS: Our results suggest that occipital hypometabolism at baseline in iRBD implies a short-term conversion to PD. This might help in stratification strategies for disease-modifying trials.

Derivation and Validation of a Phenoconversion-Related Pattern in Idiopathic Rapid Eye Movement Behavior Disorder.

BACKGROUND: Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents the prodromal stage of α-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 [18 F]FDG-PET (18 F-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. [18 F]FDG-PET pattern analysis has potential as a phenoconversion biomarker in iRBD patients. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Validation of the REM behaviour disorder phenoconversion-related pattern in an independent cohort.

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.

Rapid Eye Movement Sleep Behavior Disorder: Abnormal Cardiac Image and Progressive Abnormal Metabolic Brain Pattern.

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-2ß-carbomethoxy-3ß-(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.

Circuit imaging biomarkers in preclinical and prodromal Parkinson's disease.

Parkinson's disease (PD) commences several years before the onset of motor features. Pathophysiological understanding of the pre-clinical or early prodromal stages of PD are essential for the development of new therapeutic strategies. Two categories of patients are ideal to study the early disease stages. Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents a well-known prodromal stage of PD in which pathology is presumed to have reached the lower brainstem. The majority of patients with iRBD will develop manifest PD within years to decades. Another category encompasses non-manifest mutation carriers, i.e. subjects without symptoms, but with a known mutation or genetic variant which gives an increased risk of developing PD. The speed of progression from preclinical or prodromal to full clinical stages varies among patients and cannot be reliably predicted on the individual level. Clinical trials will require inclusion of patients with a predictable conversion within a limited time window. Biomarkers are necessary that can confirm pre-motor PD status and can provide information regarding lead time and speed of progression. Neuroimaging changes occur early in the disease process and may provide such a biomarker. Studies have focused on radiotracer imaging of the dopaminergic nigrostriatal system, which can be assessed with dopamine transporter (DAT) single photon emission computed tomography (SPECT). Loss of DAT binding represents an effect of irreversible structural damage to the nigrostriatal system. This marker can be used to monitor disease progression and identify individuals at specific risk for phenoconversion. However, it is known that changes in neuronal activity precede structural changes. Functional neuro-imaging techniques, such as 18F-2-fluoro-2-deoxy-D-glucose Positron Emission Tomography (18F-FDG PET) and functional magnetic resonance imaging (fMRI), can be used to model the effects of disease on brain networks when combined with advanced analytical methods. Because these changes occur early in the disease process, functional imaging studies are of particular interest in prodromal PD diagnosis. In addition, fMRI and 18F-FDG PET may be able to predict a specific future phenotype in prodromal cohorts, which is not possible with DAT SPECT. The goal of the current review is to discuss the network-level brain changes in pre-motor PD.

Four-Year Follow-up of [18 F]Fluorodeoxyglucose Positron Emission Tomography-Based Parkinson's Disease-Related Pattern Expression in 20 Patients with Isolated Rapid Eye Movement Sleep Behavior Disorder Shows Prodromal Progression.

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.

Functional impact of subthalamotomy by magnetic resonance-guided focused ultrasound in Parkinson's disease: a hybrid PET/MR study of resting-state brain metabolism.

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 [18F]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 < 0.001). Diffuse metabolic increase was found in the posterior parietal and occipital areas. Treatment also resulted in a significant decline in PDRP expression (p < 0.05), which correlated with clinical improvement in UPDRS motor scores (rho = 0.760; p = 0.002). CONCLUSIONS: MRgFUS-subthalamotomy induced metabolic alterations in distributed nodes of the motor, associative, and limbic circuits. Clinical improvement was associated with reduction in the PDRP expression. This treatment-induced modulation of the metabolic network is likely to mediate the clinical benefit achieved following MRgFUS-subthalamotomy.

Abnormal pattern of brain glucose metabolism in Parkinson's disease: replication in three European cohorts.

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.

Multicenter Validation of Metabolic Abnormalities Related to PSP According to the MDS-PSP Criteria.

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%) 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.

DNAJC6 Mutations Associated With Early-Onset Parkinson's Disease.

OBJECTIVE: DNAJC6 mutations were recently described in two families with autosomal recessive juvenile parkinsonism (onset age < 11), prominent atypical signs, poor or absent response to levodopa, and rapid progression (wheelchair-bound within ∼10 years from onset). Here, for the first time, we report DNAJC6 mutations in early-onset Parkinson's disease (PD). METHODS: The DNAJC6 open reading frame was analyzed in 274 patients with early-onset sporadic or familial PD. Selected variants were followed up by cosegregation, homozygosity mapping, linkage analysis, whole-exome sequencing, and protein studies. RESULTS: We identified two families with different novel homozygous DNAJC6 mutations segregating with PD. In each family, the DNAJC6 mutation was flanked by long runs of homozygosity within highest linkage peaks. Exome sequencing did not detect additional pathogenic variants within the linkage regions. In both families, patients showed severely decreased steady-state levels of the auxilin protein in fibroblasts. We also identified a sporadic patient carrying two rare noncoding DNAJC6 variants possibly effecting RNA splicing. All these cases fulfilled the criteria for a clinical diagnosis of early-onset PD, had symptoms onset in the third-to-fifth decade, and slow disease progression. Response to dopaminergic therapies was prominent, but, in some patients, limited by psychiatric side effects. The phenotype overlaps that of other monogenic forms of early-onset PD. INTERPRETATION: Our findings delineate a novel form of hereditary early-onset PD. Screening of DNAJC6 is warranted in all patients with early-onset PD compatible with autosomal recessive inheritance. Our data provide further evidence for the involvement of synaptic vesicles endocytosis and trafficking in PD pathogenesis.

FDG PET, dopamine transporter SPECT, and olfaction: Combining biomarkers in REM sleep behavior disorder.

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 18 F-fluorodeoxyglucose PET, dopamine transporter imaging, and olfactory testing. For reference, we included 18 F-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 < 0.0001). PD-related pattern expression was higher in idiopathic REM sleep behavior disorder subjects with hyposmia and in subjects with an abnormal dopamine transporter scan (P < 0.05, uncorrected). CONCLUSION: PD-related pattern expression, dopamine transporter binding, and olfaction may provide complementary information for predicting phenoconversion. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Clinical utility and research frontiers of neuroimaging in movement disorders.

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.

A disease-specific metabolic brain network associated with corticobasal degeneration.

Corticobasal degeneration is an uncommon parkinsonian variant condition that is diagnosed mainly on clinical examination. To facilitate the differential diagnosis of this disorder, we used metabolic brain imaging to characterize a specific network that can be used to discriminate corticobasal degeneration from other atypical parkinsonian syndromes. Ten non-demented patients (eight females/two males; age 73.9 ± 5.7 years) underwent metabolic brain imaging with (18)F-fluorodeoxyglucose positron emission tomography for atypical parkinsonism. These individuals were diagnosed clinically with probable corticobasal degeneration. This diagnosis was confirmed in the three subjects who additionally underwent post-mortem examination. Ten age-matched healthy subjects (five females/five males; age 71.7 ± 6.7 years) served as controls for the imaging studies. Spatial covariance analysis was applied to scan data from the combined group to identify a significant corticobasal degeneration-related metabolic pattern that discriminated (P < 0.001) the patients from the healthy control group. This pattern was characterized by bilateral, asymmetric metabolic reductions involving frontal and parietal cortex, thalamus, and caudate nucleus. These pattern-related changes were greater in magnitude in the cerebral hemisphere opposite the more clinically affected body side. The presence of this corticobasal degeneration-related metabolic topography was confirmed in two independent testing sets of patient and control scans, with elevated pattern expression (P < 0.001) in both disease groups relative to corresponding normal values. We next determined whether prospectively computed expression values for this pattern accurately discriminated corticobasal degeneration from multiple system atrophy and progressive supranuclear palsy (the two most common atypical parkinsonian syndromes) on a single case basis. Based upon this measure, corticobasal degeneration was successfully distinguished from multiple system atrophy (P < 0.001) but not progressive supranuclear palsy, presumably because of the overlap (∼ 24%) that existed between the corticobasal degeneration- and the progressive supranuclear palsy-related metabolic topographies. Nonetheless, excellent discrimination between these disease entities was achieved by computing hemispheric asymmetry scores for the corticobasal degeneration-related pattern on a prospective single scan basis. Indeed, a logistic algorithm based on the asymmetry scores combined with separately computed expression values for a previously validated progressive supranuclear palsy-related pattern provided excellent specificity (corticobasal degeneration: 92.7%; progressive supranuclear palsy: 94.1%) in classifying 58 testing subjects. In conclusion, corticobasal degeneration is associated with a reproducible disease-related metabolic covariance pattern that may help to distinguish this disorder from other atypical parkinsonian syndromes.

Subspace corrected relevance learning with application in neuroimaging.

In machine learning, data often comes from different sources, but combining them can introduce extraneous variation that affects both generalization and interpretability. For example, we investigate the classification of neurodegenerative diseases using FDG-PET data collected from multiple neuroimaging centers. However, data collected at different centers introduces unwanted variation due to differences in scanners, scanning protocols, and processing methods. To address this issue, we propose a two-step approach to limit the influence of center-dependent variation on the classification of healthy controls and early vs. late-stage Parkinson's disease patients. First, we train a Generalized Matrix Learning Vector Quantization (GMLVQ) model on healthy control data to identify a "relevance space" that distinguishes between centers. Second, we use this space to construct a correction matrix that restricts a second GMLVQ system's training on the diagnostic problem. We evaluate the effectiveness of this approach on the real-world multi-center datasets and simulated artificial dataset. Our results demonstrate that the approach produces machine learning systems with reduced bias - being more specific due to eliminating information related to center differences during the training process - and more informative relevance profiles that can be interpreted by medical experts. This method can be adapted to similar problems outside the neuroimaging domain, as long as an appropriate "relevance space" can be identified to construct the correction matrix.

Validation of parkinsonian disease-related metabolic brain patterns.

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.

Comparison of univariate and multivariate analyses for brain [18F]FDG PET data in α-synucleinopathies.

BACKGROUND: Brain imaging with [18F]FDG-PET can support the diagnostic work-up of patients with α-synucleinopathies. Validated data analysis approaches are necessary to evaluate disease-specific brain metabolism patterns in neurodegenerative disorders. This study compared the univariate Statistical Parametric Mapping (SPM) single-subject procedure and the multivariate Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) in a cohort of patients with α-synucleinopathies. METHODS: We included [18F]FDG-PET scans of 122 subjects within the α-synucleinopathy spectrum: Parkinson's Disease (PD) normal cognition on long-term follow-up (PD - low risk to dementia (LDR); n = 28), PD who developed dementia on clinical follow-up (PD - high risk of dementia (HDR); n = 16), Dementia with Lewy Bodies (DLB; n = 67), and Multiple System Atrophy (MSA; n = 11). We also included [18F]FDG-PET scans of isolated REM sleep behaviour disorder (iRBD; n = 51) subjects with a high risk of developing a manifest α-synucleinopathy. Each [18F]FDG-PET scan was compared with 112 healthy controls using SPM procedures. In the SSM/PCA approach, we computed the individual scores of previously identified patterns for PD, DLB, and MSA: PD-related patterns (PDRP), DLBRP, and MSARP. We used ROC curves to compare the diagnostic performances of SPM t-maps (visual rating) and SSM/PCA individual pattern scores in identifying each clinical condition across the spectrum. Specifically, we used the clinical diagnoses ("gold standard") as our reference in ROC curves to evaluate the accuracy of the two methods. Experts in movement disorders and dementia made all the diagnoses according to the current clinical criteria of each disease (PD, DLB and MSA). RESULTS: The visual rating of SPM t-maps showed higher performance (AUC: 0.995, specificity: 0.989, sensitivity 1.000) than PDRP z-scores (AUC: 0.818, specificity: 0.734, sensitivity 1.000) in differentiating PD-LDR from other α-synucleinopathies (PD-HDR, DLB and MSA). This result was mainly driven by the ability of SPM t-maps to reveal the limited or absent brain hypometabolism characteristics of PD-LDR. Both SPM t-maps visual rating and SSM/PCA z-scores showed high performance in identifying DLB (DLBRP = AUC: 0.909, specificity: 0.873, sensitivity 0.866; SPM t-maps = AUC: 0.892, specificity: 0.872, sensitivity 0.910) and MSA (MSARP: AUC: 0.921, specificity: 0.811, sensitivity 1.000; SPM t-maps: AUC: 1.000, specificity: 1.000, sensitivity 1.000) from other α-synucleinopathies. PD-HDR and DLB were comparable for the brain hypo and hypermetabolism patterns, thus not allowing differentiation by SPM t-maps or SSM/PCA. Of note, we found a gradual increase of PDRP and DLBRP expression in the continuum from iRBD to PD-HDR and DLB, where the DLB patients had the highest scores. SSM/PCA could differentiate iRBD from DLB, reflecting specifically the differences in disease staging and severity (AUC: 0.938, specificity: 0.821, sensitivity 0.941). CONCLUSIONS: SPM-single subject maps and SSM/PCA are both valid methods in supporting diagnosis within the α-synucleinopathy spectrum, with different strengths and pitfalls. The former reveals dysfunctional brain topographies at the individual level with high accuracy for all the specific subtype patterns, and particularly also the normal maps; the latter provides a reliable quantification, independent from the rater experience, particularly in tracking the disease severity and staging. Thus, our findings suggest that differences in data analysis approaches exist and should be considered in clinical settings. However, combining both methods might offer the best diagnostic performance.

Handedness correlates with the dominant Parkinson side: a systematic review and meta-analysis.

Parkinson's disease (PD) characteristically presents with asymmetrical symptoms, contralateral to the side of the most extensive cerebral affection. This intriguing asymmetry, even included in the definition for diagnosing PD, however, is still part of a mystery. The relation with handedness as a common indicator of cerebral asymmetry might provide a clue in the search for causal factors of asymmetrical symptom onset in PD. This possible relationship, however, is still under debate. The objective of this study was to establish whether a relation between handedness and dominant PD side exists. We searched for cross-sectional or cohort studies that registered handedness and onset side in PD patients in PubMed, EMBASE, and Web of Science from their first record until 14 February 2011. Data about handedness and dominant PD side was extracted. Authors who registered both but not described their relation were contacted for further information. Odds ratios (ORs) were analyzed with a fixed effect Mantel-Haenszel model. Heterogeneity and indications of publication bias were limited. Our electronic search identified 10 studies involving 4405 asymmetric PD patients. Of the right-handed patients, 2413 (59.5%) had right-dominant and 1644 (40.5%) had left-dominant PD symptoms. For the left-handed patients this relation was reversed, with 142 (40.8%) right-dominant and 206 (59.2%) left-dominant PD symptoms. Overall OR was 2.13 (95% confidence interval [CI], 1.71-2.66). Handedness and symptom dominance in PD are firmly related with each other in such a way that the PD symptoms emerge more often on the dominant hand-side. Possible causal factors are discussed.

FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder.

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.

Breakpoint mapping of 13 large parkin deletions/duplications reveals an exon 4 deletion and an exon 7 duplication as founder mutations.

Early-onset Parkinson's disease (EOPD) has been associated with recessive mutations in parkin (PARK2). About half of the mutations found in parkin are genomic rearrangements, i.e., large deletions or duplications. Although many different rearrangements have been found in parkin before, the exact breakpoints involving these rearrangements are rarely mapped. In the present study, the exact breakpoints of 13 different parkin deletions/duplications, detected in 13 patients out of a total screened sample of 116 EOPD patients using Multiple Ligation Probe Amplification (MLPA) analysis, were mapped using real time quantitative polymerase chain reaction (PCR), long-range PCR and sequence analysis. Deletion/duplication-specific PCR tests were developed as a rapid and low cost tool to confirm MLPA results and to test family members or patients with similar parkin deletions/duplications. Besides several different deletions, an exon 3 deletion, an exon 4 deletion and an exon 7 duplication were found in multiple families. Haplotype analysis in four families showed that a common haplotype of 1.2 Mb could be distinguished for the exon 7 duplication and a common haplotype of 6.3 Mb for the deletion of exon 4. These findings suggest common founder effects for distinct large rearrangements in parkin.

Biomarkers of conversion to α-synucleinopathy in isolated rapid-eye-movement sleep behaviour disorder.

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 α-synuclein pathology, such as Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. Abnormal α-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 α-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 α-synucleinopathy patients with isolated RBD might develop.