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

Previous studies have suggested that rare biallelic SYNJ1 mutations may cause autosomal recessive parkinsonism and Parkinson's disease (PD). Our study explored the impact of rare SYNJ1 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 (Pfdr=0.040). Additionally, a meta-analysis focusing on patients with EOPD demonstrated an association between all rare SYNJ1 variants and PD (Pfdr=0.029). Rare SYNJ1 variants may be associated with sporadic PD, and more specifically with EOPD.

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 CTSB gene encoding the lysosomal hydrolase cathepsin B (catB) are associated with increased risk of Parkinson's disease (PD). However, neither the specific CTSB 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. Previous in vitro studies have found that catB can cleave monomeric and fibrillar alpha-synuclein, a key protein involved in the pathogenesis of PD that accumulates in the brains of PD patients. However, truncated synuclein isoforms generated by catB cleavage have an increased propensity to aggregate. Thus, catB activity could potentially contribute to lysosomal degradation and clearance of pathogenic alpha synuclein from the cell, but also has the potential of enhancing synuclein pathology by generating aggregation-prone truncations. Therefore, the mechanisms linking catB to PD pathophysiology remain to be clarified. Methods: Here, we conducted genetic analyses of the association between common and rare CTSB variants and risk of PD. We then used genetic and pharmacological approaches to manipulate catB expression and function in cell lines and induced pluripotent stem cell-derived dopaminergic neurons and assessed lysosomal activity and the handling of aggregated synuclein fibrils. Results: We first identified specific non-coding variants in CTSB that drive the association with PD and are linked to changes in brain CTSB expression levels. Using iPSC-derived dopaminergic neurons we then find that catB inhibition impairs autophagy, reduces glucocerebrosidase (encoded by GBA1) activity, and leads to an accumulation of lysosomal content. Moreover, in cell lines, reduction of CTSB gene expression impairs the degradation of pre-formed alpha-synuclein fibrils, whereas CTSB gene activation enhances fibril clearance. Similarly, in midbrain organoids and dopaminergic neurons treated with alpha-synuclein fibrils, catB inhibition or knockout potentiates the formation of inclusions which stain positively for phosphorylated alpha-synuclein. Conclusions: The results of our genetic and functional studies indicate that the reduction of catB function negatively impacts lysosomal pathways associated with PD pathogenesis, while conversely catB activation could promote the clearance of pathogenic alpha-synuclein.

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. OBJECTIVE: The aim was to explore the changes in white matter microstructure linked to MBI and mild cognitive impairment (MCI) in early- to mid-stage PD using diffusion magnetic resonance imaging (dMRI). METHODS: A total of 91 PD patients and 36 healthy participants were recruited and underwent anatomical MRI and dMRI, a comprehensive neuropsychological battery, and the completion of the Mild Behavioral Impairment-Checklist. Metrics of white matter integrity included tissue fractional anisotropy (FAt) and radial diffusivity (RDt), free water (FW), and fixel-based apparent fiber density (AFD). RESULTS: The connection between the left amygdala and the putamen was disrupted when comparing PD patients with MBI (PD-MBI) to PD-non-MBI, as evidenced by increased RDt (η2 = 0.09, P = 0.004) and both decreased AFD (η2 = 0.05, P = 0.048) and FAt (η2 = 0.12, P = 0.014). Compared to controls, PD patients with both MBI and MCI demonstrated increased FW for the connection between the left orbitofrontal gyrus (OrG) and the hippocampus (η2 = 0.22, P = 0.008), augmented RDt between the right OrG and the amygdala (η2 = 0.14, P = 0.008), and increased RDt (η2 = 0.25, P = 0.028) with decreased AFD (η2 = 0.10, P = 0.046) between the right OrG and the caudate nucleus. CONCLUSION: MBI is associated with abnormal microstructure of connections involving the orbitofrontal cortex, putamen, and amygdala. To our knowledge, this is the first assessment of the white matter microstructure in PD-MBI using dMRI. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The effect of transcranial ultrasound pulse repetition frequency on sustained inhibition in the human primary motor cortex: A double-blind, sham-controlled study.

BACKGROUND: Non-invasive brain stimulation techniques such as transcranial magnetic stimulation and transcranial direct current stimulation hold promise for inducing brain plasticity. However, their limited precision may hamper certain applications. In contrast, Transcranial Ultrasound Stimulation (TUS), known for its precision and deep brain targeting capabilities, requires further investigation to establish its efficacy in producing enduring effects for treating neurological and psychiatric disorders. OBJECTIVE: To investigate the enduring effects of different pulse repetition frequencies (PRF) of TUS on motor corticospinal excitability. METHODS: T1-, T2-weighted, and zero echo time magnetic resonance imaging scans were acquired from 21 neurologically healthy participants for neuronavigation, skull reconstruction, and the performance of transcranial ultrasound and thermal modelling. The effects of three different TUS PRFs (10, 100, and 1000 Hz) with a constant duty cycle of 10 % on corticospinal excitability in the primary motor cortex were assessed using TMS-induced motor evoked potentials (MEPs). Each PRF and sham condition was evaluated on separate days, with measurements taken 5-, 30-, and 60-min post-TUS. RESULTS: A significant decrease in MEP amplitude was observed with a PRF of 10 Hz (p = 0.007), which persisted for at least 30 min, and with a PRF of 100 Hz (p = 0.001), lasting over 60 min. However, no significant changes were found for the PRF of 1000 Hz and the sham conditions. CONCLUSION: This study highlights the significance of PRF selection in TUS and underscores its potential as a non-invasive approach to reduce corticospinal excitability, offering valuable insights for future clinical applications.

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. Our approach specifically makes use of the TM setup, which has proven effective in scenarios with limited data availability but has never been used for image-based disease classification. Our findings reveal that TM is effective for training CNN models, even in complex real-world scenarios with variable data distributions. After sufficient training cycles, the TM-trained CNN matches or slightly surpasses the performance of the centrally trained counterpart (AUROC of 83% vs. 80%). Our study highlights, for the first time, the effectiveness of TM in 3D medical image classification, especially in scenarios with limited training samples and heterogeneous distributed data. These insights are relevant for situations where ML models are supposed to be trained using data from small or remote medical centers, and rare diseases with sparse cases. The simplicity of this approach enables a broad application to many deep learning tasks, enhancing its clinical utility across various contexts and medical facilities.

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. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model's decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

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. Therefore, all layers of the PD classifier were frozen, except for the last layer of the network, which was replaced by a linear layer that was exclusively re-trained to predict three potential bias types (biological sex, scanner type, and originating site). Our findings based on a large database consisting of 1880 MRI scans collected across 41 centers show that the feature space of the established PD model (74% accuracy) can be used to classify sex (75% accuracy), scanner type (79% accuracy), and site location (71% accuracy) with high accuracies despite this information never being explicitly provided to the PD model during original training. Overall, the results of this study suggest that trained image-based classifiers may use unwanted shortcuts that are not meaningful for the actual clinical task at hand. This finding may explain why many image-based deep learning models do not perform well when applied to data from centers not contributing to the training set.

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. OBJECTIVE: This study attempts to replicate the results in Hanganu et al. (Brain, 137(4) 2014) using data from the Parkinson's Progression Markers Initiative (PPMI). METHODS: Using 25 PD subjects and 18 healthy controls, we analyzed the rate of change of cortical thickness and of the volume of subcortical structures, and we measured the relationship between structural changes and cognitive decline. We compared our findings to the results in the original study. RESULTS: (1) Similarly to the original study, PD patients with mild cognitive impairment (MCI) exhibited increased cortical thinning over time compared to patients without MCI in the right middle temporal gyrus, insula, and precuneus. (2) The rate of cortical thinning in the left inferior temporal and precentral gyri in PD patients correlated with the change in cognitive performance. (3) There were no group differences in the change of subcortical volumes. (4) We did not find a relationship between the change in subcortical volumes and the change in cognitive performance. CONCLUSION: Despite important differences in the dataset used in this replication study, and despite differences in sample size, we were able to partially replicate the original results. We produced a publicly available reproducible notebook allowing researchers to further investigate the reproducibility of the results in Hanganu et al. (2014) when more data is added to PPMI.

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

Variants in the CTSB gene encoding the lysosomal hydrolase cathepsin B (catB) are associated with increased risk of Parkinson's disease (PD). However, neither the specific CTSB 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. Previous in vitro studies have found that catB can cleave monomeric and fibrillar alpha-synuclein, a key protein involved in the pathogenesis of PD that accumulates in the brains of PD patients. However, truncated synuclein isoforms generated by catB cleavage have an increased propensity to aggregate. Thus, catB activity could potentially contribute to lysosomal degradation and clearance of pathogenic alpha synuclein from the cell, but also has the potential of enhancing synuclein pathology by generating aggregation-prone truncations. Therefore, the mechanisms linking catB to PD pathophysiology remain to be clarified. Here, we conducted genetic analyses of the association between common and rare CTSB variants and risk of PD. We then used genetic and pharmacological approaches to manipulate catB expression and function in cell lines and induced pluripotent stem cell-derived dopaminergic neurons and assessed lysosomal activity and the handling of aggregated synuclein fibrils. We find that catB inhibition impairs autophagy, reduces glucocerebrosidase (encoded by GBA1) activity, and leads to an accumulation of lysosomal content. In cell lines, reduction of CTSB gene expression impairs the degradation of pre-formed alpha-synuclein fibrils, whereas CTSB gene activation enhances fibril clearance. In midbrain organoids and dopaminergic neurons treated with alpha-synuclein fibrils, catB inhibition potentiates the formation of inclusions which stain positively for phosphorylated alpha-synuclein. These results indicate that the reduction of catB function negatively impacts lysosomal pathways associated with PD pathogenesis, while conversely catB activation could promote the clearance of pathogenic alpha-synuclein.

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. After preprocessing the T1-weighted scans, 2 additional data types were generated: intensity-harmonized T1-weighted scans and log-Jacobian deformation maps resulting from nonlinear atlas registration. Corresponding DL models were trained to classify sites for each data type. Additionally, logistic regression models were used to investigate the contribution of biological (age, sex, disease status) and non-biological (scanner type) variables to the models' decision. RESULTS: A comparison of the 3 different types of data revealed that DL models trained using T1-weighted and intensity-harmonized T1-weighted scans can classify sites with an accuracy of 85%, while the model using log-Jacobian deformation maps achieved a site classification accuracy of 54%. Disease status and scanner type were found to be significant confounders. DISCUSSION: Our results demonstrate that MRI scans encode relevant site-specific information that models could use as shortcuts that cannot be removed using simple intensity harmonization methods. CONCLUSION: The ability of DL models to exploit site-specific biases as shortcuts raises concerns about their reliability, generalization, and deployability in clinical settings.

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. We included 213 PD patients from the Parkinson's Progression Markers Initiative (PPMI) database. Machine learning was used to predict change in Montreal Cognitive Assessment (MoCA) score using the difference between baseline and 4-years follow-up data as outcome. Input features were categorized into four sets: clinical test scores, cerebrospinal fluid (CSF) biomarkers, brain volumes, and genetic variants. All combinations of input feature sets were added to a basic model, which consisted of demographics and baseline cognition. An iterative scheme using RReliefF-based feature ranking and support vector regression in combination with tenfold cross validation was used to determine the optimal number of predictive features and to evaluate model performance for each combination of input feature sets. Our best performing model consisted of a combination of the basic model, clinical test scores and CSF-based biomarkers. This model had 12 features, which included baseline cognition, CSF phosphorylated tau, CSF total tau, CSF amyloid-beta1-42, geriatric depression scale (GDS) scores, and anxiety scores. Interestingly, many of the predictive features in our model have previously been associated with Alzheimer's disease, showing the importance of assessing Alzheimer's disease pathology in patients with Parkinson's disease.

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 < 0.01) in PD, we performed burden analyses in six independent cohorts with 5801 PD patients and 20,475 controls, followed by a meta-analysis. RESULTS: We found evidence for associations between functional ARSA variants and PD in four cohorts (P ≤ 0.05 in each) and in the meta-analysis (P = 0.042). We also found an association between loss-of-function variants and PD in the United Kingdom Biobank cohort (P = 0.005) and in the meta-analysis (P = 0.049). These results should be interpreted with caution as no association survived multiple comparisons correction. Additionally, we describe two families with potential co-segregation of ARSA p.E382K and PD. CONCLUSIONS: Rare functional and loss-of-function ARSA variants may be associated with PD. Further replications in large case-control/familial cohorts are required. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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. The aim of this work was to develop and evaluate a robust explainable deep learning model for PD classification trained from one of the largest collections of T1-weighted magnetic resonance imaging datasets. MATERIALS AND METHODS: A total of 2,041 T1-weighted MRI datasets from 13 different studies were collected, including 1,024 datasets from PD patients and 1,017 datasets from age- and sex-matched healthy controls (HC). The datasets were skull stripped, resampled to isotropic resolution, bias field corrected, and non-linearly registered to the MNI PD25 atlas. The Jacobian maps derived from the deformation fields together with basic clinical parameters were used to train a state-of-the-art convolutional neural network (CNN) to classify PD and HC subjects. Saliency maps were generated to display the brain regions contributing the most to the classification task as a means of explainable artificial intelligence. RESULTS: The CNN model was trained using an 85%/5%/10% train/validation/test split stratified by diagnosis, sex, and study. The model achieved an accuracy of 79.3%, precision of 80.2%, specificity of 81.3%, sensitivity of 77.7%, and AUC-ROC of 0.87 on the test set while performing similarly on an independent test set. Saliency maps computed for the test set data highlighted frontotemporal regions, the orbital-frontal cortex, and multiple deep gray matter structures as most important. CONCLUSION: The developed CNN model, trained on a large heterogenous database, was able to differentiate PD patients from HC subjects with high accuracy with clinically feasible classification explanations. Future research should aim to investigate the combination of multiple imaging modalities with deep learning and on validating these results in a prospective trial as a clinical decision support system.

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 , which encodes for the enzyme arylsulfatase A, remains controversial. Objectives: To evaluate the association between rare ARSA variants and PD. Methods: To study possible association of rare variants (minor allele frequency<0.01) in ARSA with PD, we performed burden analyses in six independent cohorts with a total of 5,801 PD patients and 20,475 controls, using optimized sequence Kernel association test (SKAT-O), followed by a meta-analysis. Results: We found evidence for an association between functional ARSA variants and PD in four independent cohorts (P≤0.05 in each) and in the meta-analysis (P=0.042). We also found an association between loss-of-function variants and PD in the UKBB cohort (P=0.005) and in the meta-analysis (P=0.049). However, despite replicating in four independent cohorts, these results should be interpreted with caution as no association survived correction for multiple comparisons. Additionally, we describe two families with potential co-segregation of the ARSA variant p.E384K and PD. Conclusions: Rare functional and loss-of-function ARSA variants may be associated with PD. Further replication in large case-control cohorts and in familial studies is required to confirm these associations.

Mitochondrial function-associated genes underlie cortical atrophy in prodromal synucleinopathies.

Isolated rapid eye movement sleep behaviour disorder (iRBD) is a sleep disorder characterized by the loss of rapid eye movement sleep muscle atonia and the appearance of abnormal movements and vocalizations during rapid eye movement sleep. It is a strong marker of incipient synucleinopathy such as dementia with Lewy bodies and Parkinson's disease. Patients with iRBD already show brain changes that are reminiscent of manifest synucleinopathies including brain atrophy. However, the mechanisms underlying the development of this atrophy remain poorly understood. In this study, we performed cutting-edge imaging transcriptomics and comprehensive spatial mapping analyses in a multicentric cohort of 171 polysomnography-confirmed iRBD patients [67.7 ± 6.6 (49-87) years; 83% men] and 238 healthy controls [66.6 ± 7.9 (41-88) years; 77% men] with T1-weighted MRI to investigate the gene expression and connectivity patterns associated with changes in cortical thickness and surface area in iRBD. Partial least squares regression was performed to identify the gene expression patterns underlying cortical changes in iRBD. Gene set enrichment analysis and virtual histology were then done to assess the biological processes, cellular components, human disease gene terms, and cell types enriched in these gene expression patterns. We then used structural and functional neighbourhood analyses to assess whether the atrophy patterns in iRBD were constrained by the brain's structural and functional connectome. Moreover, we used comprehensive spatial mapping analyses to assess the specific neurotransmitter systems, functional networks, cytoarchitectonic classes, and cognitive brain systems associated with cortical changes in iRBD. All comparisons were tested against null models that preserved spatial autocorrelation between brain regions and compared to Alzheimer's disease to assess the specificity of findings to synucleinopathies. We found that genes involved in mitochondrial function and macroautophagy were the strongest contributors to the cortical thinning occurring in iRBD. Moreover, we demonstrated that cortical thinning was constrained by the brain's structural and functional connectome and that it mapped onto specific networks involved in motor and planning functions. In contrast with cortical thickness, changes in cortical surface area were related to distinct genes, namely genes involved in the inflammatory response, and to different spatial mapping patterns. The gene expression and connectivity patterns associated with iRBD were all distinct from those observed in Alzheimer's disease. In summary, this study demonstrates that the development of brain atrophy in synucleinopathies is constrained by specific genes and networks.

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. It is still unclear whether GALC is the gene driving the association in the chromosome 14 locus and, if so, by which mechanism. We first aimed to examine whether variants in the GALC locus and across the genome are associated with galactosylceramidase activity. We performed a genome-wide association study in two independent cohorts from (i) Columbia University; and (ii) the Parkinson's Progression Markers Initiative study, followed by a meta-analysis with a total of 976 PD patients and 478 controls with available data on galactosylceramidase activity. We further analysed the effects of common GALC variants on expression and galactosylceramidase activity using genomic colocalization methods. Mendelian randomization was used to study whether galactosylceramidase activity may be causal in PD. To study the role of rare GALC variants, we analysed sequencing data from 5028 PD patients and 5422 controls. Additionally, we studied the functional impact of GALC knockout on alpha-synuclein accumulation and on glucocerebrosidase activity in neuronal cell models and performed in silico structural analysis of common GALC variants associated with altered galactosylceramidase activity. The top hit in PD genome-wide association study in the GALC locus, rs979812, is associated with increased galactosylceramidase activity (b = 1.2; SE = 0.06; P = 5.10 × 10-95). No other variants outside the GALC locus were associated with galactosylceramidase activity. Colocalization analysis demonstrated that rs979812 was also associated with increased galactosylceramidase expression. Mendelian randomization suggested that increased galactosylceramidase activity may be causally associated with PD (b = 0.025, SE = 0.007, P = 0.0008). We did not find an association between rare GALC variants and PD. GALC knockout using CRISPR-Cas9 did not lead to alpha-synuclein accumulation, further supporting that increased rather than reduced galactosylceramidase levels may be associated with PD. The structural analysis demonstrated that the common variant p.I562T may lead to improper maturation of galactosylceramidase affecting its activity. Our results nominate GALC as the gene associated with PD in this locus and suggest that the association of variants in the GALC locus may be driven by their effect of increasing galactosylceramidase expression and activity. Whether altering galactosylceramidase activity could be considered as a therapeutic target should be further studied.

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. For the first time, we demonstrate that rTMS combined with M-MAT is feasible, with zero adverse events and minimal attrition. Both groups improved significantly over time on all speech and language outcomes. However, improvements did not differ between rTMS or sham. We found that rTMS and sham groups differed in lesion location, which may explain speech and language outcomes as well as unique patterns of BOLD signal change within each group. We offer practical considerations for future studies and conclude that while combination therapy of rTMS plus M-MAT in chronic post-stroke aphasia is safe and feasible, personalized intervention may be necessary.

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. METHODS: A total of 359 de novo PD patients and 159 healthy controls (HC) from the Parkinson's Progression Markers Initiative (PPMI) database were included in this study. Additionally, an independent cohort from four databases (182 PD, 132 HC) with longer disease durations was included to assess the effects of PD diagnosis in more advanced cases. Pearson correlation was used to determine subject-specific associations between cortical curvature and thickness estimated from T1-weighted MRI images. General linear modeling (GLM) was then used to assess the effect of PD diagnosis, motor symptoms, and cognitive performance on the curvature-thickness association. Next, longitudinal changes in the curvature-thickness correlation as well as the predictive effect of the cortical curvature-thickness association on changes in motor symptoms and cognitive performance across four years were investigated. Finally, Akaike information criterion (AIC) was used to build a GLM to model PD motor symptom severity cross-sectionally. RESULTS: A significant interaction effect between PD motor symptoms and age on the curvature-thickness correlation was found (ßstandardized = 0.11; t(350) = 2.12; p = 0.03). This interaction effect showed that motor symptoms in older patients were related to an attenuated curvature-thickness association. No significant effect of PD diagnosis was observed for the PPMI database (ß = 0.03; t(510) = 0.35; p = 0.72). However, in patients with a longer disease duration, a significant effect of diagnosis on the curvature-thickness association was found (ßstandardized = 0.31; t(306.7) = 3.49; p = 0.0006). Moreover, rigidity, but not tremor, in PD was significantly related to the curvature-thickness correlation (ßstandardized = 0.11, t(350) = 2.24, p = 0.03; ßstandardized = -0.03, t(350) = -0.58, p = 0.56, respectively). The curvature-thickness association was attenuated over time in both PD and HC, but the two groups did not show a significantly different effect (ßstandardized = 0.03, t(184.7) = 0.78, p = 0.44). No predictive effects of the CC-CT correlation on longitudinal changes in cognitive performance or motor symptoms were observed (all p-values > 0.05). The best cross-sectional model for PD motor symptoms included the curvature-thickness correlation, cognitive performance, and putamen dopamine transporter (DAT) binding, which together explained 14 % of variance. CONCLUSION: The association between cortical curvature and thickness is related to PD motor symptoms and age. This research shows the potential of modeling the curvature-thickness interplay in PD.

Brain atrophy in prodromal synucleinopathy is shaped by structural connectivity and gene expression.

Isolated REM sleep behaviour disorder (iRBD) is a synucleinopathy characterized by abnormal behaviours and vocalizations during REM sleep. Most iRBD patients develop dementia with Lewy bodies, Parkinson's disease or multiple system atrophy over time. Patients with iRBD exhibit brain atrophy patterns that are reminiscent of those observed in overt synucleinopathies. However, the mechanisms linking brain atrophy to the underlying alpha-synuclein pathophysiology are poorly understood. Our objective was to investigate how the prion-like and regional vulnerability hypotheses of alpha-synuclein might explain brain atrophy in iRBD. Using a multicentric cohort of 182 polysomnography-confirmed iRBD patients who underwent T1-weighted MRI, we performed vertex-based cortical surface and deformation-based morphometry analyses to quantify brain atrophy in patients (67.8 years, 84% male) and 261 healthy controls (66.2 years, 75%) and investigated the morphological correlates of motor and cognitive functioning in iRBD. Next, we applied the agent-based Susceptible-Infected-Removed model (i.e. a computational model that simulates in silico the spread of pathologic alpha-synuclein based on structural connectivity and gene expression) and tested if it recreated atrophy in iRBD by statistically comparing simulated regional brain atrophy to the atrophy observed in patients. The impact of SNCA and GBA gene expression and brain connectivity was then evaluated by comparing the model fit to the one obtained in null models where either gene expression or connectivity was randomized. The results showed that iRBD patients present with cortical thinning and tissue deformation, which correlated with motor and cognitive functioning. Next, we found that the computational model recreated cortical thinning (r = 0.51, P = 0.0007) and tissue deformation (r = 0.52, P = 0.0005) in patients, and that the connectome's architecture along with SNCA and GBA gene expression contributed to shaping atrophy in iRBD. We further demonstrated that the full agent-based model performed better than network measures or gene expression alone in recreating the atrophy pattern in iRBD. In summary, atrophy in iRBD is extensive, correlates with motor and cognitive function and can be recreated using the dynamics of agent-based modelling, structural connectivity and gene expression. These findings support the concepts that both prion-like spread and regional susceptibility account for the atrophy observed in prodromal synucleinopathies. Therefore, the agent-based Susceptible-Infected-Removed model may be a useful tool for testing hypotheses underlying neurodegenerative diseases and new therapies aimed at slowing or stopping the spread of alpha-synuclein pathology.