OBJECTIVE: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). METHODS: In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography-confirmed RBD who eventually phenoconverted to overt alpha-synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non-phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. RESULTS: Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD-converters from DLB-converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z-score cutoff of -1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of -1.0 at the most affected hemisphere putamen/caudate ratio characterized PD-converters. INTERPRETATION: Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease-modifying trials. ANN NEUROL 2024;95:1178-1192.

• MeSH
• Lewy Body Disease * diagnostic imaging   MeSH
• Dopamine * metabolism   MeSH
• Tomography, Emission-Computed, Single-Photon MeSH
• Middle Aged MeSH
• Humans MeSH
• Parkinson Disease * diagnostic imaging   complications   MeSH
• REM Sleep Behavior Disorder * diagnostic imaging   MeSH
• Presynaptic Terminals metabolism   MeSH
• Aged MeSH
• Machine Learning * MeSH
• Synucleinopathies * diagnostic imaging   MeSH
• Dopaminergic Imaging MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH

Transkraniální sonografie (TCS) je rychlé, levné a široce dostupné vyšetření, dobře využitelné v diagnostice Parkinsonovy nemoci (PN) a demence s Lewyho tělísky (DLB), zejména pro screening v časných a prodromálních stadiích těchto onemocnění. Pacienti s PN a DLB vykazují zvýšenou echogenitu substantia nigra (SN). Hyperechogenita SN odráží zejména nadměrné hromadění železa v tkáni a degeneraci. Cílem této přehledové práce je popsat roli a využitelnost TCS v diagnostice pacientů s PN a DLB, včetně zaměření se na jejich prodromální stadia a diferenciální diagnostiku. V bezpříznakovém období je nález hyperechogenity SN považován za jeden z rizikových faktorů pro rozvoj synukleinopatií, zejména PN. Výsledky TCS studií jsou dány do kontextu diagnostických kritérií pro PN a DLB.

Transcranial sonography (TCS) is a quick, inexpensive, widely available and well applicable examination in the diagnosis of Parkinson's disease (PD) and Lewy body dementia (LBD), especially for screening in the early and prodromal stages of these diseases. Patients with PD and DLB reveal increased echogenicity of substantia nigra (SN). The SN hyperechogenicity particularly reflects excessive iron accumulation and tissue degeneration. The aim of this review is to describe the role and utility of TCS in the diagnosis of patients with PD and LBD, including a focus on their prodromal stages and differential diagnosis. In the asymptomatic period, the finding of SN hyperechogenicity is considered to be one of the risk factors for the development of synucleinopathies, especially PD. The results of TCS studies are placed in the context of diagnostic criteria for PD and LBD.

• MeSH
• Lewy Body Disease * diagnostic imaging   diagnosis   MeSH
• Echoencephalography methods   MeSH
• Essential Tremor diagnostic imaging   diagnosis   MeSH
• Humans MeSH
• Parkinson Disease * diagnostic imaging   diagnosis   MeSH
• Parkinsonian Disorders diagnostic imaging   diagnosis   MeSH
• Substantia Nigra diagnostic imaging   pathology   MeSH
• Synucleinopathies diagnostic imaging   diagnosis   MeSH
• Ultrasonography, Doppler, Transcranial methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

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

• MeSH
• Tomography, Emission-Computed, Single-Photon MeSH
• Kaplan-Meier Estimate MeSH
• Middle Aged MeSH
• Humans MeSH
• Caudate Nucleus diagnostic imaging   metabolism   MeSH
• REM Sleep Behavior Disorder diagnostic imaging   metabolism   MeSH
• Dopamine Plasma Membrane Transport Proteins metabolism   MeSH
• Putamen diagnostic imaging   metabolism   MeSH
• Retrospective Studies MeSH
• ROC Curve MeSH
• Aged MeSH
• Synucleinopathies diagnostic imaging   metabolism   MeSH
• Tropanes MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Research Support, Non-U.S. Gov't MeSH

OBJECTIVES: Hyperechogenicity of the substantia nigra (SN) and abnormal dopamine transporter-single-photon emission computed tomography (DAT-SPECT) are biomarkers commonly used in the assessment of prodromal synucleinopathy. Our goals were as follows: (1) to compare echogenicity of SN in idiopathic rapid eye movement (REM) behavior disorder (iRBD), Parkinson's disease (PD) without RBD (PD-noRBD), PD with RBD (PD + RBD), and control subjects; and (2) to examine association between SN degeneration assessed by DAT-SPECT and SN echogenicity. PATIENTS/METHODS: A total of 61 subjects with confirmed iRBD were examined using Movement Disorders Society-unified PD rating scale (MDS-UPDRS), TCS (transcranial sonography) and DAT-SPECT. The results were compared with 44 patients with PD (25% PD + RBD) and with 120 age-matched healthy subjects. RESULTS AND CONCLUSION: The abnormal SN area was found in 75.5% PD, 23% iRBD and 7.3% controls. Median SN echogenicity area in PD (0.27 ± 0.22 cm2) was higher compared to iRBD (0.07 ± 0.07 cm2; p < 0.0001) and controls (0.05 ± 0.03 cm2; p < 0.0001). SN echogenicity in PD + RBD was not significantly different from PD-noRBD (0.30 vs. 0.22, p = 0.15). Abnormal DAT-SPECT was found in 16 iRBD (25.4%) and 44 PD subjects (100%). No correlation between the larger SN area and corresponding putaminal binding index was found in iRBD (r = -0.13, p = 0.29), nor in PD (r = -0.19, p = 0.22). The results of our study showed that: (1) SN echogenicity area in iRBD was higher compared to controls, but the hyperechogenicity was present only in a minority of iRBD patients; (2) SN echogenicity and DAT-SPECT binding index did not correlate in either group; and (3) SN echogenicity does not differ between PD with/without RBD.

• MeSH
• Tomography, Emission-Computed, Single-Photon MeSH
• Humans MeSH
• Nortropanes MeSH
• REM Sleep Behavior Disorder * diagnostic imaging   physiopathology   MeSH
• Iodine Radioisotopes MeSH
• Substantia Nigra * diagnostic imaging   physiopathology   MeSH
• Synucleinopathies * diagnostic imaging   physiopathology   MeSH
• Ultrasonography, Doppler, Transcranial MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Neuromelanin (NM) is a black pigment located in the brain in substantia nigra pars compacta (SN) and locus coeruleus. Its loss is directly connected to the loss of nerve cells in this part of the brain, which plays a role in Parkinson's Disease. Magnetic resonance imaging (MRI) is an ideal tool to monitor the amount of NM in the brain in vivo. The aim of the study was the development of tools and methodology for the quantification of NM in a special neuromelanin-sensitive MRI images. The first approach was done by creating regions of interest, corresponding to the anatomical position of SN based on an anatomical atlas and determining signal intensity threshold. By linking the anatomical and signal intensity information, we were able to segment the SN. As a second approach, the neural network U-Net was used for the segmentation of SN. Subsequently, the volume characterizing the amount of NM in the SN region was calculated. To verify the method and the assumptions, data available from various patient groups were correlated. The main benefit of this approach is the observer-independency of quantification and facilitation of the image processing process and subsequent quantification compared to the manual approach. It is ideal for automatic processing many image sets in one batch.

• MeSH
• Deep Learning * MeSH
• Middle Aged MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Melanins analysis   MeSH
• Image Processing, Computer-Assisted MeSH
• Prodromal Symptoms MeSH
• Aged MeSH
• Case-Control Studies MeSH
• Substantia Nigra diagnostic imaging   MeSH
• Synucleinopathies diagnostic imaging   MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Clinical Trial MeSH