Are rare heterozygous SYNJ1 variants associated with Parkinson's disease?

Previous studies have established that rare biallelic SYNJ1 mutations cause autosomal recessive parkinsonism and Parkinson's disease (PD). We analyzed 8165 PD cases, 818 early-onset-PD (EOPD, < 50 years) and 70,363 controls. Burden meta-analysis revealed an association between rare nonsynonymous variants and variants with high Combined Annotation-Dependent Depletion score (> 20) in the Sac1 SYNJ1 domain and PD (Pfdr = 0.

HarmonyTM: multi-center data harmonization applied to distributed learning for Parkinson's disease classification.

Purpose: Distributed learning is widely used to comply with data-sharing regulations and access diverse datasets for training machine learning (ML) models. The traveling model (TM) is a distributed learning approach that sequentially trains with data from one center at a time, which is especially advantageous when dealing with limited local datasets. However, a critical concern emerges when centers utilize different scanners for data acquisition, which could potentially lead models to exploit these differences as shortcuts.

Are rare heterozygous variants associated with Parkinson's disease?

Previous studies have suggested that rare biallelic mutations may cause autosomal recessive parkinsonism and Parkinson's disease (PD). Our study explored the impact of rare variants in non-familial settings, including 8,165 PD cases, 818 early-onset PD (EOPD, <50 years) and 70,363 controls. Burden meta-analysis using optimized sequence Kernel association test (SKAT-O) revealed an association between rare nonsynonymous variants in the Sac1 SYNJ1 domain and PD (P=0.

White Matter Microstructural Underpinnings of Mild Behavioral Impairment in Parkinson's Disease.

Background: Patients with Parkinson's disease (PD) experience changes in behavior, personality, and cognition that can manifest even in the initial stages of the disease. Previous studies have suggested that mild behavioral impairment (MBI) should be considered an early marker of cognitive decline. However, the precise neurostructural underpinnings of MBI in early- to mid-stage PD remain poorly understood.

The Parkinson's disease risk gene cathepsin B promotes fibrillar alpha-synuclein clearance, lysosomal function and glucocerebrosidase activity in dopaminergic neurons.

Background: Variants in the gene encoding the lysosomal hydrolase cathepsin B (catB) are associated with increased risk of Parkinson's disease (PD). However, neither the specific variants driving these associations nor the functional pathways that link catB to PD pathogenesis have been characterized. CatB activity contributes to lysosomal protein degradation and regulates signaling processes involved in autophagy and lysosome biogenesis.

Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data.

Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data.

A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm.

Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples.

Longitudinal brain structure changes in Parkinson's disease: A replication study.

Context: An existing major challenge in Parkinson's disease (PD) research is the identification of biomarkers of disease progression. While magnetic resonance imaging is a potential source of PD biomarkers, none of the magnetic resonance imaging measures of PD are robust enough to warrant their adoption in clinical research. This study is part of a project that aims to replicate 11 PD studies reviewed in a recent survey (JAMA neurology, 78(10) 2021) to investigate the robustness of PD neuroimaging findings to data and analytical variations.

Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier?

Sharing multicenter imaging datasets can be advantageous to increase data diversity and size but may lead to spurious correlations between site-related biological and non-biological image features and target labels, which machine learning (ML) models may exploit as shortcuts. To date, studies analyzing how and if deep learning models may use such effects as a shortcut are scarce. Thus, the aim of this work was to investigate if site-related effects are encoded in the feature space of an established deep learning model designed for Parkinson's disease (PD) classification based on T1-weighted MRI datasets.

The Parkinson's disease risk gene cathepsin B promotes fibrillar alpha-synuclein clearance, lysosomal function and glucocerebrosidase activity in dopaminergic neurons.

Variants in the gene encoding the lysosomal hydrolase cathepsin B (catB) are associated with increased risk of Parkinson's disease (PD). However, neither the specific variants driving these associations nor the functional pathways that link catB to PD pathogenesis have been characterized. CatB activity contributes to lysosomal protein degradation and regulates signaling processes involved in autophagy and lysosome biogenesis.

Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data.

Objective: This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences.

Material And Methods: A large database of 1880 T1-weighted MRI scans collected across 41 sites originally for Parkinson's disease (PD) classification was used to classify sites in this study. Forty-six percent of the datasets are from PD patients, while 54% are from healthy participants.

Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features.

Patients with Parkinson's Disease (PD) often suffer from cognitive decline. Accurate prediction of cognitive decline is essential for early treatment of at-risk patients. The aim of this study was to develop and evaluate a multimodal machine learning model for the prediction of continuous cognitive decline in patients with early PD.

Association of Rare Variants in ARSA with Parkinson's Disease.

Background: Several lysosomal genes are associated with Parkinson's disease (PD), yet the association between PD and ARSA remains unclear.

Objectives: To study rare ARSA variants in PD.

Methods: To study rare ARSA variants (minor allele frequency < 0.

Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets.

Introduction: Parkinson's disease (PD) is a severe neurodegenerative disease that affects millions of people. Early diagnosis is important to facilitate prompt interventions to slow down disease progression. However, accurate PD diagnosis can be challenging, especially in the early disease stages.

Association of rare variants in with Parkinson's disease.

Background: Several lysosomal genes are associated with Parkinson's disease (PD), yet the association between PD and , which encodes for the enzyme arylsulfatase A, remains controversial.

Objectives: To evaluate the association between rare variants and PD.

Methods: To study possible association of rare variants (minor allele frequency<0.

Motor symptoms in Parkinson's disease are related to the interplay between cortical curvature and thickness.

Introduction: Brain atrophy in Parkinson's disease occurs to varying degrees in different brain regions, even at the early stage of the disease. While cortical morphological features are often considered independently in structural brain imaging studies, research on the co-progression of different cortical morphological measurements could provide new insights regarding the progression of PD. This study's aim was to examine the interplay between cortical curvature and thickness as a function of PD diagnosis, motor symptoms, and cognitive performance.

Repetitive transcranial magnetic stimulation (rTMS) combined with multi-modality aphasia therapy for chronic post-stroke non-fluent aphasia: A pilot randomized sham-controlled trial.

Repetitive transcranial magnetic stimulation (rTMS) shows promise in improving speech production in post-stroke aphasia. Limited evidence suggests pairing rTMS with speech therapy may result in greater improvements. Twenty stroke survivors (>6 months post-stroke) were randomized to receive either sham rTMS plus multi-modality aphasia therapy (M-MAT) or rTMS plus M-MAT.

GALC variants affect galactosylceramidase enzymatic activity and risk of Parkinson's disease.

The association between glucocerebrosidase, encoded by GBA, and Parkinson's disease (PD) highlights the role of the lysosome in PD pathogenesis. Genome-wide association studies in PD have revealed multiple associated loci, including the GALC locus on chromosome 14. GALC encodes the lysosomal enzyme galactosylceramidase, which plays a pivotal role in the glycosphingolipid metabolism pathway.

Neuropsychiatric symptoms influence differently cognitive decline in older women and men.

Objective: The potential impact of sex on cognitive performance in normal aging and participants with Alzheimer's disease (AD) has been outlined previously. Nevertheless, differences in neuropsychiatric symptoms (NPS) have been also outlined. We aimed to study a potential association between NPS and cognitive performances according to sex, in older individuals with and without cognitive impairment.