Four-YearFollow-upof [F-18]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 [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

Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases

Alzheimer's disease (AD) and Parkinson's disease (PD) are two common, progressive neurodegenerative brain disorders. Their diagnosis is very challenging at an early disease stage, if based on clinical symptoms only. Brain imaging techniques such as [18F]-fluoro-deoxyglucose positron emission tomography (FDG-PET) can provide important additional information with respect to changes in the cerebral glucose metabolism. In this study, we use machine learning techniques to perform an automated classification of FDG-PET data. The approach is based on the extraction of features by applying the scaled subprofile model with principal component analysis (SSM/PCA) in order to extract characteristics patterns of glucose metabolism. These features are then used for discriminating healthy controls, PD and AD patients by means of two machine learning frameworks: Generalized Matrix Learning Vector Quantization (GMLVQ) with local and global relevance matrices, and Support Vector Machines (SVMs) with a linear kernel. Datasets from different neuroimaging centers are considered. Results obtained for the individual centers, show that reliable classification is possible. We demonstrate, however, that cross-center classification can be problematic due to potential center-specific characteristics of the available FDG-PET data

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

Brain Imaging in RBD

Neuroimaging studies can provide in vivo insights to the early structural and functional brain changes in patients with idiopathic RBD (iRBD) and may help give a prognosis of disease course. This chapter summarizes the major findings of neuroimaging studies in iRBD, a specific prodromal stage of Parkinson’s disease (PD) and other α-synucleinopathies. Molecular imaging techniques, magnetic resonance imaging (MRI), and transcranial sonography (TCS) are all discussed

Feasibility of a brain PET harmonization program for state of the art PET/CT systems

Purpose/Introduction: Use of brain PET studies in multicentre trials or as a quantitative imaging biomarker for (automated) differential diagnosis of neurogenerative diseases require harmonized quantitative image characteristics. In this study we explored the feasibility of developing a harmonizing performance standard for brain PET studies on state of the art PET/CT systems. Subjects & Methods: In this exploratory study 6 state of the art PET/CT systems were included: Philips Gemini TF, Ingenuity TF and digital Vereos systems, 2 Siemens Biograph mCTs and a GE 710. Only systems with EARL compliant (calibration and image quality) performances were included. A 30 min dynamic PET scan of the 3D Hoffmann brain phantom was acquired. The phantom was filled with an exact known FDG stock solution (aimed at 40 kBq/mL). Each scan was reconstructedusing various clinically relevant reconstruction settings. Depending on PET/CT system reconstruction settings were varied as follows: time of flight (TOF) on/off; resolution modelling (RM) on/off, voxel size, number of iterations/subsets and Gaussian smoothing FWHM (mm). The reconstructed images were analysed using a coregistered eroded binary map of both grey (GM) and white matter (WM). GM and WM recovery coefficients were calculated as the ratio of observed and expected activity concentrations. Results: For all systems distinct differences in both GM and WM recoveries and GM/WM ratios were observed between reconstructions that did or did not apply RM. Across the various systems/reconstructions a harmonized GM recovery between 0.77 and 0.85 (RM OFF) or between 0.81 and 0.94 (RM ON) seems feasible. WM recoveries (0.25 expected) were less affected by reconstruction settings, but showed a larger difference between Philips (0.28 to 0.33) versus Siemens (0.20 to 0.20) and GE (0.22 to 0.23) systems. GM/WM ratios were 4.2 to 4.4 for the Siemens and 3.7 to 4.0 for the GE systems, while the Philips systems showed somewhat lower values of 3.1 to 3.5 mainly because of difference in WM recovery. Discussion/Conclusion: Harmonization of PET/CT system performance for brain studies appears to be feasible, in particular for GM uptake assessment. Use of RM increases GM recovery at the cost of a wider (worse) harmonized performance range. There seems to be a vendor specific difference in WM recovery. The cause of this finding (possibly scatter correction) as well as its implication for PET/CT performance harmonization needs to be further explored. Currently, more data are being collected prospectively as part of JPND granted European networks

Factors affecting the harmonization of disease-related metabolic brain pattern expression quantification in [18F]FDG-PET (PETMETPAT): Working Group Summaries for European Joint Programming For Neurodegenerative Research (JPND)

Introduction:The implementation of spatial-covariance [18F]fluorodeoxyglucose positron emissiontomography–based disease-related metabolic brain patterns as biomarkers has been hampered by in-tercenter imaging differences. Within the scope of the JPND-PETMETPAT working group, we illus-trate the impact of these differences on Parkinson’s disease–related pattern (PDRP) expressionscores.Methods:Five healthy controls, 5 patients with idiopathic rapid eye movement sleep behavior dis-order, and 5 patients with Parkinson’s disease were scanned on one positron emission tomography/computed tomography system with multiple image reconstructions. In addition, one Hoffman 3DBrain Phantom was scanned on several positron emission tomography/computed tomography sys-tems using various reconstructions. Effects of image contrast on PDRP scores were also examined.Results:Human and phantom raw PDRP scores were systematically influenced by scanner andreconstruction effects. PDRP scores correlated inversely to image contrast. A Gaussian spatial filterreduced contrast while decreasing intercenter score differences.Discussion:Image contrast should be considered in harmonization efforts. A Gaussian filter mayreduce noise and intercenter effects without sacrificing sensitivity. Phantom measurements will beimportant for correcting PDRP score offsets