Explore the vast range of titles available.

Parkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic dermatitis, bullous pemphigoid, and rosacea), skin pathological changes (decreased nerve endings and alpha-synuclein deposition), and metabolic changes of sebum. Recently, a transcriptome method using RNA in skin surface lipids (SSL-RNAs) which can be obtained non-invasively with an oil-blotting film was reported as a novel analytic method of sebum. Here we report transcriptome analyses using SSL-RNAs and the potential of these expression profiles with machine learning as diagnostic biomarkers for PD in double cohorts (PD [n = 15, 50], controls [n = 15, 50]). Differential expression analysis between the patients with PD and healthy controls identified more than 100 differentially expressed genes in the two cohorts. In each cohort, several genes related to oxidative phosphorylation were upregulated, and gene ontology analysis using differentially expressed genes revealed functional processes associated with PD. Furthermore, machine learning using the expression information obtained from the SSL-RNAs was able to efficiently discriminate patients with PD from healthy controls, with an area under the receiver operating characteristic curve of 0.806. This non-invasive gene expression profile of SSL-RNAs may contribute to early PD diagnosis based on the neurodegeneration background.

Objective The purpose of this study was to examine how the radiopharmaceutical accumulation in the brain changes with the elapsed time between the administration of 18 F-flutemetamol and the start of imaging, and to determine its effect on quantitative indices. Methods The study population consisted of 25 subjects who agreed to participate in the study. After visual evaluation by the radiologist, 14 subjects tested negative for Aβ accumulation, and 11 subjects tested positive as well. The study population was treated with 18 F-flutemeamol, and list mode acquisition was performed for 50 min starting at 60 min after the time of administration. From the acquired list data, five PET images were extracted at 10-min intervals from the start to the end of acquisition, a PET image corresponding to 20 min of acquisition from 60 min after administration, and a PET image corresponding to 20 min of acquisition from 90 min after administration, respectively. Pixel values were measured for the PET images created and quantitative indices (pixel value, SUVr, Centiloid scale) were calculated and compared. Results In most of the PET images, pixel values showed a decreasing trend with elapsed time after radiopharmaceutical administration. Accordingly, calculated SUVr and Centiloid Scale also changed. Conclusions Elapsed time after radiopharmaceutical administration resulted in a washout of the radiopharmaceutical accumulation in the brain. From this, it was suggested that the quantitative indices change.

Multiple system atrophy (MSA) is classified into two main types: parkinsonian and cerebellar ataxia with oligodendrogliopathy. We examined microstructural alterations in the white matter and the substantia nigra pars compacta (SNc) of patients with MSA of parkinsonian type (MSA-P) using multishell diffusion magnetic resonance imaging (dMRI) and myelin sensitive imaging techniques. Age- and sex-matched patients with MSA-P (n = 21, n = 10 first and second cohorts, respectively), Parkinson’s disease patients (n = 19, 17), and healthy controls (n = 20, 24) were enrolled. Magnetization transfer saturation imaging (MT-sat) and dMRI were obtained using 3-T MRI. Measurements obtained from diffusion tensor imaging (DTI), free-water elimination DTI, neurite orientation dispersion and density imaging (NODDI), and MT-sat were compared between groups. Tract-based spatial statistics analysis revealed differences in diffuse white matter alterations in the free-water fractional volume, myelin volume fraction, and intracellular volume fraction between the patients with MSA-P and healthy controls, whereas free-water and MT-sat differences were limited to the middle cerebellar peduncle in comparison with those with Parkinson’s disease. Region-of-interest analysis of white matter and SNc revealed significant differences in the middle and inferior cerebellar peduncle, pontine crossing tract, corticospinal tract, and SNc between the MSA-P and healthy controls and/or Parkinson’s disease patients. Our results shed light on alterations to brain microstructure in MSA.

Background Impulse control behaviors (ICBs) in Parkinson's disease (PD) are thought to be caused by an overdose of dopaminergic therapy in the relatively spared ventral striatum, or by hypersensitivity of this region to dopamine. Alterations in brain networks are now also thought to contribute to the development of ICBs. Objective To comprehensively assess white matter microstructures in PD patients with ICBs using advanced diffusion MRI and magnetization transfer saturation (MT‐sat) imaging. Methods This study included 19 PD patients with ICBs (PD‐ICBs), 18 PD patients without ICBs (PD‐nICBs), and 20 healthy controls (HCs). Indices of diffusion tensor imaging (DTI), diffusion kurtosis imaging, neurite orientation dispersion and density imaging, and MT‐sat imaging were evaluated using tract‐based spatial statistics (TBSS), regions of interest (ROIs), and tract‐specific analysis (TSA). Results Compared with HCs, PD‐nICBs had significant alterations in many major white matter tracts in most parameters. In contrast, PD‐ICBs had only partial changes in several parameters. Compared with PD‐ICBs, TBSS, ROI, and TSA analyses revealed that PD‐nICBs had lower axial kurtosis, myelin volume fraction, and orientation dispersion index in the uncinate fasciculus and external capsule, as well as in the retrolenticular part of the internal capsule. These are components of the reward system and the visual and emotional perception areas, respectively. Interpretation Myelin and axonal changes in fibers related to the reward system and visual emotional recognition might be more prominent in PD‐nICBs than in PD‐ICBs.

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

Objective To investigate the oxidized albumin ratio, which is the redox ratio of human nonmercaptalbumin (HNA) to serum albumin (%HNA), as a biomarker in idiopathic Parkinson’s disease (iPD) and related neurodegenerative disorders. Methods This prospective study enrolled 216 iPD patients, 15 patients with autosomal recessive familial PD due to parkin mutations (PARK2), 30 multiple system atrophy (MSA) patients, 32 progressive nuclear palsy (PSP) patients, and 143 healthy controls. HNA was analyzed using modified high‐performance liquid chromatography and was evaluated alongside other parameters. Results iPD and PARK2 patients had a higher %HNA than controls (iPD vs. controls: odds ratio (OR) 1.325, P < 0.001; PARK2 vs. controls: OR 1.712, P < 0.001). Even iPD patients at an early Hoehn & Yahr stage (I and II) showed a higher %HNA than controls. iPD patients had a higher %HNA than MSA and PSP patients (iPD vs. MSA: OR 1.249, P < 0.001, iPD vs. PSP: OR 1.288, P < 0.05). When discriminating iPD patients from controls, %HNA corrected by age achieved an AUC of 0.750; when discriminating iPD patients from MSA and PSP patients, an AUC of 0.747 was achieved. Furthermore, uric acid, an antioxidant compound, was decreased in iPD patients, similar to the change in %HNA. Interpretation %HNA was significantly increased in iPD and PARK2 patients compared with controls, regardless of disease course and severity. Oxidative stress might be increased from the early stages of iPD and PARK2 and play an important role in their pathomechanisms.

Purpose To investigate whether Parkinson’s disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by applying a deep learning technique to parameter-weighted and number of streamlines (NOS)–based structural connectome matrices calculated from diffusion-weighted MRI. Methods In this prospective study, 115 PD patients and 115 healthy controls were enrolled. NOS-based and parameter-weighted connectome matrices were calculated from MRI images obtained with a 3-T MRI unit. With 5-fold cross-validation, diagnostic performance of convolutional neural network (CNN) models using those connectome matrices in differentiating patients with PD from healthy controls was evaluated. To identify the important brain connections for diagnosing PD, gradient-weighted class activation mapping (Grad-CAM) was applied to the trained CNN models. Results CNN models based on some parameter-weighted structural matrices (diffusion kurtosis imaging (DKI)–weighted, neurite orientation dispersion and density imaging (NODDI)–weighted, and g -ratio-weighted connectome matrices) showed moderate performance (areas under the receiver operating characteristic curve (AUCs) = 0.895, 0.801, and 0.836, respectively) in discriminating PD patients from healthy controls. The DKI-weighted connectome matrix performed significantly better than the conventional NOS-based matrix (AUC = 0.761) (DeLong’s test, p  < 0.0001). Alterations of neural connections between the basal ganglia and cerebellum were indicated by applying Grad-CAM to the NODDI- and g -ratio-weighted matrices. Conclusion Patients with PD can be differentiated from healthy controls by applying the deep learning technique to the parameter-weighted connectome matrices, and neural circuit disorders including those between the basal ganglia on one side and the cerebellum on the contralateral side were visualized.

Continuous, objective monitoring of motor signs and symptoms may help improve tracking of disease progression and treatment response in Parkinson’s disease (PD). This study assessed the analytical and clinical validity of multi-sensor smartwatch measurements in hospitalized and home-based settings (96 patients with PD; mean wear time 19 h/day) using a twice-daily virtual motor examination (VME) at times representing medication OFF/ON states. Digital measurement performance was better during inpatient clinical assessments for composite V-scores than single-sensor–derived features for bradykinesia (Spearman |r|= 0.63, reliability = 0.72), tremor (|r|= 0.41, reliability = 0.65), and overall motor features (|r|= 0.70, reliability = 0.67). Composite levodopa effect sizes during hospitalization were 0.51–1.44 for clinical assessments and 0.56–1.37 for VMEs. Reliability of digital measurements during home-based VMEs was 0.62–0.80 for scores derived from weekly averages and 0.24–0.66 for daily measurements. These results show that unsupervised digital measurements of motor features with wrist-worn sensors are sensitive to medication state and are reliable in naturalistic settings. Trial Registration: Japan Pharmaceutical Information Center Clinical Trials Information (JAPIC-CTI): JapicCTI-194825; Registered June 25, 2019.

In super-aged societies, dementia has become a critical issue, underscoring the urgent need for tools to assess cognitive status effectively in various sectors, including financial and business settings. Facial and speech features have been tried as cost-effective biomarkers of dementia including Alzheimer’s disease (AD). We aimed to establish an easy, automatic, and extensive screening tool for AD using a chatbot and artificial intelligence. Smile images and visual and auditory data of natural conversations with a chatbot from 99 healthy controls (HCs) and 93 individuals with AD or mild cognitive impairment due to AD (PwA) were analyzed using machine learning. A subset of 8 facial and 21 sound features successfully distinguished PwA from HCs, with a high area under the receiver operating characteristic curve of 0.94 ± 0.05. Another subset of 8 facial and 20 sound features predicted the cognitive test scores, with a mean absolute error as low as 5.78 ± 0.08. These results were superior to those obtained from face or auditory data alone or from conventional image depiction tasks. Thus, by combining spontaneous sound and facial data obtained through conversations with a chatbot, the proposed model can be put to practical use in real-life scenarios.

Purpose This study aimed to evaluate the accuracy and diagnostic test performance of the U-net-based segmentation method in neuromelanin magnetic resonance imaging (NM-MRI) compared to the established manual segmentation method for Parkinson’s disease (PD) diagnosis. Methods NM-MRI datasets from two different 3T-scanners were used: a “principal dataset” with 122 participants and an “external validation dataset” with 24 participants, including 62 and 12 PD patients, respectively. Two radiologists performed SNpc manual segmentation. Inter-reader precision was determined using Dice coefficients. The U-net was trained with manual segmentation as ground truth and Dice coefficients used to measure accuracy. Training and validation steps were performed on the principal dataset using a 4-fold cross-validation method. We tested the U-net on the external validation dataset. SNpc hyperintense areas were estimated from U-net and manual segmentation masks, replicating a previously validated thresholding method, and their diagnostic test performances for PD determined. Results For SNpc segmentation, U-net accuracy was comparable to inter-reader precision in the principal dataset (Dice coefficient: U-net, 0.83 ± 0.04; inter-reader, 0.83 ± 0.04), but lower in external validation dataset (Dice coefficient: U-net, 079 ± 0.04; inter-reader, 0.85 ± 0.03). Diagnostic test performances for PD were comparable between U-net and manual segmentation methods in both principal (area under the receiver operating characteristic curve: U-net, 0.950; manual, 0.948) and external (U-net, 0.944; manual, 0.931) datasets. Conclusion U-net segmentation provided relatively high accuracy in the evaluation of the SNpc in NM-MRI and yielded diagnostic performance comparable to that of the established manual method.