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PURPOSE: This study aimed to assess the glymphatic function and its correlation with clinical characteristics and the loss of dopaminergic neurons in Parkinson's disease (PD) using hybrid positron emission tomography (PET)-magnetic resonance imaging (MRI) combined with diffusion tensor image analysis along the perivascular space (DTI-ALPS), choroid plexus volume (CPV), and enlarged perivascular space (EPVS) volume. METHODS: Twenty-five PD patients and thirty matched healthy controls (HC) participated in the study. All participants underwent 18F-fluorodopa (18F-DOPA) PET-MRI scanning. The striatal standardized uptake value ratio (SUVR), DTI-ALPS index, CPV, and EPVS volume were calculated. Furthermore, we also analysed the relationship between the DTI-ALPS index, CPV, EPVS volume and striatal SUVR as well as clinical characteristics of PD patients. RESULTS: PD patients demonstrated significantly lower values in DTI-ALPS (t = 3.053, p = 0.004) and larger CPV (t = 2.743, p = 0.008) and EPVS volume (t = 2.807, p = 0.008) compared to HC. In PD group, the ALPS-index was negatively correlated with the Unified Parkinson's Disease Rating Scale III (UPDRS-III) scores (r = -0.730, p < 0.001), and positively correlated with the mean putaminal SUVR (r = 0.560, p = 0.007) and mean caudal SUVR (r = 0.459, p = 0.032). Moreover, the mean putaminal SUVR was negatively associated with the UPDRS-III scores (r = -0.544, p = 0.009). CONCLUSION: DTI-ALPS has the potential to uncover glymphatic dysfunction in patients with PD, with this dysfunction correlating strongly with the severity of disease, together with the mean putaminal and caudal SUVR. PET- MRI can serve as a potential multimodal imaging biomarker for early-stage PD.
ABSTRACT
RATIONALE AND OBJECTIVES: Parkinson's disease (PD) shows small structural changes in nigrostriatal pathways, which can be sensitively captured through diffusion kurtosis imaging (DKI). However, the value of DKI and its radiomic features in the classification performance of PD still need confirmation. This study aimed to compare the diagnostic efficiency of DKI-derived kurtosis metric and its radiomic features with different machine learning models for PD classification. MATERIALS AND METHODS: 75 people with PD and 80 healthy individuals had their brains scanned using DKI. These images were pre-processed and the standard atlas were non-linearly registered to them. With the labels in atlas, different brain regions in nigrostriatal pathways, including the caudate nucleus, putamen, pallidum, thalamus, and substantia nigra, were chosen as the region of interests (ROIs) to warped to the native space to measure the mean kurtosis (MK). Additionally, new radiomic features were developed for comparison. To handle the large amount of data, a statistical method called Z-score normalization and another method called LASSO regression were used to simplify the information. From this, a few most important features were chosen, and a combined score called Radscore was calculated using LASSO regression. For the comprehensive analyses, three different conventional machine learning models were then created: logistic regression (LR), support vector machine (SVM), and random forest (RF). To ensure the models were accurate, a process called 10-fold cross-validation was used, where the data were split into 10 parts for training and testing. RESULTS: Using MK alone, the models achieved good results in correctly identifying PD in the validation set, with LR at 0.90, RF at 0.93, and SVM at 0.90. When the radiomic features were added, the models performed even better, with LR at 0.92, RF at 0.95, and SVM at 0.91. Additionally, a nomogram combining all the information was created to predict the likelihood of someone having PD, which had an AUC of 0.91. CONCLUSION: These findings suggest that the combination of DKI measurements and radiomic features can effectively diagnose PD by providing more detailed information about the brain's condition and the processes involved in the disease.
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PURPOSE: Parkinson's disease (PD) involves pathological alterations that include cortical impairments at levels of region and network. However, its microstructural abnormalities remain to be further elucidated via an appropriate diffusion neuroimaging approach. This study aimed to comprehensively demonstrate the microstructural patterns of PD as mapped by diffusion kurtosis imaging (DKI). METHODS: The microstructure of grey matter in both the PD group and the matched healthy control group was quantified by a DKI metric (mean kurtosis). The intergroup difference and classification performance of global microstructural complexity were analyzed in a voxelwise manner and via a machine learning approach, respectively. The patterns of information flows were explored in terms of structural connectivity, network covariance and modular connectivity. RESULTS: Patients with PD exhibited global microstructural impairments that served as an efficient diagnostic indicator. Disrupted structural connections between the striatum and cortices as well as between the thalamus and cortices were widely distributed in the PD group. Aberrant covariance of the striatocortical circuitry and thalamocortical circuitry was observed in patients with PD, who also showed disrupted modular connectivity within the striatum and thalamus as well as across structures of the cortex, striatum and thalamus. CONCLUSION: These findings verified the potential clinical application of DKI for the exploration of microstructural patterns in PD, contributing complementary imaging features that offer a deeper insight into the neurodegenerative process.
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Prior efforts have manifested that functional connectivity (FC) network disruptions are concerned with cognitive disorder in presbycusis. The present research was designed to investigate the topological reorganization and classification performance of low-order functional connectivity (LOFC) and high-order functional connectivity (HOFC) networks in patients with presbycusis. Resting-state functional magnetic resonance imaging (Rs-fMRI) data were obtained in 60 patients with presbycusis and 50 matched healthy control subjects (HCs). LOFC and HOFC networks were then constructed, and the topological metrics obtained from the constructed networks were compared to evaluate topological differences in global, nodal network metrics, modularity and rich-club organization between patients with presbycusis and HCs. The use of HOFC profiles boosted presbycusis classification accuracy, sensitivity and specificity compared to that using LOFC profiles. The brain networks in both patients with presbycusis and HCs exhibited small-world properties within the given threshold range, and striking differences between groups in topological metrics were discovered in the constructed networks (LOFC and HOFC). NBS analysis identified a subnetwork involving 26 nodes and 23 signally altered internodal connections in patients with presbycusis in comparison to HCs in HOFC networks. This study highlighted the topological differences between LOFC and HOFC networks in patients with presbycusis, suggesting that HOFC profiles may help to further identify brain network abnormalities in presbycusis.
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PURPOSE: The purpose of this study was to analyze the intracerebral abnormalities of hemodynamics in patients with Parkinson's disease (PD) through arterial spin labelling (ASL) technique with multi-delay ASL (MDASL) and conventional single-delay ASL (SDASL) protocols and to verify the potential clinical application of these features for the diagnosis of PD. MATERIALS AND METHODS: Perfusion data of the brain obtained using MDASL and SDASL in patients with PD were compared to those obtained in healthy control (HC) subjects. Intergroup comparisons of z-scored cerebral blood flow (zCBF), arterial transit time (zATT) and cerebral blood volume (zCBV) were performed via voxel-based analysis. Performance of these perfusion metrics were estimated using area under the receiver operating characteristic curve (AUC) and compared using Delong test. RESULTS: A total of 47 patients with PD (29 men; 18 women; mean age, 69.0 ± 7.6 (standard deviation, [SD]) years; range: 50.0-84.0 years) and 50 HC subjects (28 men; 22 women; mean age, 70.1 ± 6.2 [SD] years; range: 50.0-93.0 years) were included. Relative to the uncorrected-zCBF map, the corrected-zCBF map further refined the distributed brain regions in the PD group versus the HC group, manifested as the extension of motor-related regions (PFWE < 0.001). Compared to the HC subjects, patients with PD had elevated zATT and zCBV in the right putamen, a shortened zATT in the superior frontal gyrus, and specific zCBV variations in the left precuneus and the right supplementary motor area (PFWE < 0.001). The corrected-zCBF (AUC, 0.90; 95% confidence interval [CI]: 0.84-0.96) showed better classification performance than uncorrected-zCBF (AUC, 0.84; 95% CI: 0.75-0.92) (P = 0.035). zCBV achieved an AUC of 0.89 (95% CI: 0.82-0.96) and zATT achieved an AUC of 0.66 (95% CI: 0.55-0.77). The integration model of hemodynamic features from MDASL provided improved performance (AUC, 0.97; 95% CI: 0.95-0.98) for the diagnosis of PD by comparison with each perfusion model (P < 0.001). CONCLUSION: ASL identifies impaired hemodynamics in patients with PD including regional abnormalities of CBF, CBV and ATT, which can better be mapped with MDASL compared to SDASL. These findings provide complementary depictions of perfusion abnormalities in patients with PD and highlight the clinical feasibility of MDASL.