Cerebellar blood perfusion is a diagnostic, but not a prognostic, marker for parkinsonian-dominant type multiple system atrophy.

INTRODUCTION: Multiple system atrophy (MSA) is clinically characterized by various neurological symptoms. According to the diagnostic criteria, MSA is classified into parkinsonian-dominant type (MSA-P) or cerebellar ataxia-dominant type (MSA-C) based on the predominant signs displayed. Recently, N-isopropyl-p-[123I] iodoamphetamine (123I-IMP) single-photon emission computed tomography (SPECT), a radiological examination evaluating brain perfusion, has been successful in detecting cerebellar hypoperfusion in MSA-P patients, demonstrating its utility in the early detection of cerebellar dysfunction. In this study, we further explored whether this cerebellar hypoperfusion impacts the clinical features of MSA-P, whether it is observable in patients without cerebellar symptoms, and, most importantly, whether it influences the prognosis of MSA-P. METHODS: We conducted a retrospective analysis of 88 MSA patients who were admitted to our department for the last fifteen years. Clinical data were collected, and cerebellar perfusion was examined using 123I-IMP SPECT. This analysis includes the application of the three-dimensional stereotactic surface projection (3D-SSP) technique and Z-score. RESULTS: Cerebellar perfusion decreased in MSA-P patients without cerebellar ataxia, compared to healthy individuals (p = 0.0017). The Receiver Operating Characteristic (ROC) curve demonstrated a moderate ability to distinguish MSA-P patients without cerebellar ataxia (MSA-Pp) from healthy controls (AUC = 0.6832). Among MSA-Pp, those exhibiting cerebellar hypoperfusion showed relatively improved neurological prognosis, although the difference was not statistically significant when compared to those with normal cerebellar perfusion. CONCLUSION: Assessing cerebellar perfusion through IMP-SPECT proves valuable in detecting subclinical cerebellar dysfunction in MSA-Pp. Importantly, cerebellar hypoperfusion does not correlate with a poorer neurological prognosis.

Efficacy and Safety of Elobixibat in Parkinson's Disease with Chronic Constipation: CONST-PD Study.

BACKGROUND: Chronic constipation is a common digestive complication of Parkinson's disease (PD). OBJECTIVES: To verify the usefulness of elobixibat, an ileal bile acid transporter inhibitor, for chronic constipation in PD. METHODS: This double-blind, placebo-controlled study consisted of a 2-week observation/washout period and a 4-week treatment period. All patients received a Bowel Movement Diary at Week -2 and were allocated to elobixibat (10 mg) or placebo at Week 0. Patients visited at Weeks 2 and 4 to report daily spontaneous bowel movements (SBM), stool form, drug use, quality of life (QOL), and safety. Changes in these parameters were assessed. RESULTS: The study included 38 patients in the elobixibat group and 39 in the placebo group, and 37 each completed the study. SBM frequency/week (mean ± standard deviation) increased significantly from 4.2 ± 2.6 at baseline to 5.9 ± 3.2 at Week 4 in the elobixibat group (P = 0.0079), but not in the placebo group (4.5 ± 2.7 to 5.3 ± 3.5; P = 0.0889). On analysis of covariance, the between-group difference in frequency changes at Week 4 (primary endpoint) was not significant after adjustment by baseline and sex (point estimate = 0.8; 95% confidence interval = -0.57 to 2.09, P = 0.2601), although a significant difference (P = 0.0011) was evidenced at Week 1 by a similar analysis. Stool form and scores of satisfaction and stigma were improved by elobixibat. Adverse events were as previously reported. CONCLUSIONS: Elobixibat improved the SBM frequency, though the defined primary endpoint was not evidenced. QOL parameters (stool consistency and treatment satisfaction) were also improved. Elobixibat may have therapeutic benefits in PD patients suffering from chronic constipation. TRIAL REGISTRATION INFORMATION: Trial Registration Number: JPRN-jRCTs031200172 (submitted: October 26, 2020; first patient enrolment: December 23, 2020; https://jrct.niph.go.jp/en-latest-detail/jRCTs031200172).

Analytical and clinical validity of wearable, multi-sensor technology for assessment of motor function in patients with Parkinson's disease in Japan.

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.

Systemic Metabolic Alteration Dependent on the Thyroid-Liver Axis in Early PD.

OBJECTIVE: Parkinson's disease (PD) is a common neurodegenerative disease characterized by initial involvement of the olfactory bulb/amygdala or autonomic nerves followed by nigral degeneration. Although autonomic innervation strictly regulates multiorgan systems, including endocrine functions, circulation, and digestion, how dysautonomia in PD affects systemic metabolism has not been identified. In this study, we tried to estimate the pathogenic linkage of PD by nuclear medicine techniques, trans-omic analysis of blood samples, and cultured cell experiments. METHODS: Thyroid mediastinum ratio of 123 I-metaiodobenzylguanidine (MIBG) scintigraphy was measured in 1,158 patients with PD. Furthermore, serum exosome miRNA transcriptome analysis and plasma metabolome analysis followed by trans-omic analysis were performed in patients with de novo PD and age-matched healthy control persons. Additionally, thyroid hormone was administered to skeletal muscle and liver derived cells to evaluate the effect of hypothyroidism for these organs. RESULTS: Sympathetic denervation of thyroid correlating with its cardiac denervation was confirmed in 1,158 patients with PD by MIBG scintigraphy. Among patients with drug-naïve PD, comprehensive metabolome analysis revealed decreased levels of thyroxine and insufficient fatty acid ß-oxidation, which positively correlate with one another. Likewise, both plasma metabolome data and transcriptome data of circulating exosomal miRNAs, revealed specific enrichment of the peroxisome proliferator-activated receptor (PPARα) axis. Finally, association of thyroid hormone with PPARα-dependent ß-oxidation regulation was confirmed by in vitro experiments. INTERPRETATION: Our findings suggest that interorgan communications between the thyroid and liver are disorganized in the early stage of PD, which would be a sensitive diagnostic biomarker for PD. ANN NEUROL 2023;93:303-316.

Inverse renormalization group based on image super-resolution using deep convolutional networks.

The inverse renormalization group is studied based on the image super-resolution using the deep convolutional neural networks. We consider the improved correlation configuration instead of spin configuration for the spin models, such as the two-dimensional Ising and three-state Potts models. We propose a block-cluster transformation as an alternative to the block-spin transformation in dealing with the improved estimators. In the framework of the dual Monte Carlo algorithm, the block-cluster transformation is regarded as a transformation in the graph degrees of freedom, whereas the block-spin transformation is that in the spin degrees of freedom. We demonstrate that the renormalized improved correlation configuration successfully reproduces the original configuration at all the temperatures by the super-resolution scheme. Using the rule of enlargement, we repeatedly make inverse renormalization procedure to generate larger correlation configurations. To connect thermodynamics, an approximate temperature rescaling is discussed. The enlarged systems generated using the super-resolution satisfy the finite-size scaling.

Possible Neuroprotective Effects of l-Carnitine on White-Matter Microstructural Damage and Cognitive Decline in Hemodialysis Patients.

Although l-carnitine alleviated white-matter lesions in an experimental study, the treatment effects of l-carnitine on white-matter microstructural damage and cognitive decline in hemodialysis patients are unknown. Using novel diffusion magnetic resonance imaging (dMRI) techniques, white-matter microstructural changes together with cognitive decline in hemodialysis patients and the effects of l-carnitine on such disorders were investigated. Fourteen hemodialysis patients underwent dMRI and laboratory and neuropsychological tests, which were compared across seven patients each in two groups according to duration of l-carnitine treatment: (1) no or short-term l-carnitine treatment (NSTLC), and (2) long-term l-carnitine treatment (LTLC). Ten age- and sex-matched controls were enrolled. Compared to controls, microstructural disorders of white matter were widely detected on dMRI of patients. An autopsy study of one patient in the NSTLC group showed rarefaction of myelinated fibers in white matter. With LTLC, microstructural damage on dMRI was alleviated along with lower levels of high-sensitivity C-reactive protein and substantial increases in carnitine levels. The LTLC group showed better achievement on trail making test A, which was correlated with amelioration of disorders in some white-matter tracts. Novel dMRI tractography detected abnormalities of white-matter tracts after hemodialysis. Long-term treatment with l-carnitine might alleviate white-matter microstructural damage and cognitive impairment in hemodialysis patients.

Machine-learning study using improved correlation configuration and application to quantum Monte Carlo simulation.

We use the Fortuin-Kasteleyn representation-based improved estimator of the correlation configuration as an alternative to the ordinary correlation configuration in the machine-learning study of the phase classification of spin models. The phases of classical spin models are classified using the improved estimators, and the method is also applied to the quantum Monte Carlo simulation using the loop algorithm. We analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition of the spin-1/2 quantum XY model on the square lattice. We classify the BKT phase and the paramagnetic phase of the quantum XY model using the machine-learning approach. We show that the classification of the quantum XY model can be performed by using the training data of the classical XY model.

Machine-Learning Studies on Spin Models.

With the recent developments in machine learning, Carrasquilla and Melko have proposed a paradigm that is complementary to the conventional approach for the study of spin models. As an alternative to investigating the thermal average of macroscopic physical quantities, they have used the spin configurations for the classification of the disordered and ordered phases of a phase transition through machine learning. We extend and generalize this method. We focus on the configuration of the long-range correlation function instead of the spin configuration itself, which enables us to provide the same treatment to multi-component systems and the systems with a vector order parameter. We analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition with the same technique to classify three phases: the disordered, the BKT, and the ordered phases. We also present the classification of a model using the training data of a different model.