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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 (β_standardized = 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 (β = 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 (β_standardized = 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 (β_standardized = 0.11, t(350) = 2.24, p = 0.03; β_standardized = -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 (β_standardized = 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.

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.

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

The association between glucocerebrosidase (GCase), encoded by GBA, and Parkinson’s disease highlights the role of the lysosome in Parkinson’s disease pathogenesis. Genome-wide association studies (GWAS) in Parkinson’s disease have revealed multiple associated loci, including the GALC locus on chromosome 14. GALC encodes the lysosomal enzyme galactosylceramidase (GalCase), 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 GalCase activity. We performed a GWAS in two independent cohorts from a)Columbia University and b)the Parkinson’s Progression Markers Initiative study, followed by a meta-analysis with a total of 976 Parkinson’s disease patients and 478 controls with available data on GalCase activity. We further analyzed the effects of common GALC variants on expression and GalCase activity using genomic colocalization methods. Mendelian randomization was used to study whether GalCase activity may be causal in Parkinson’s disease. To study the role of rare GALC variants we analyzed sequencing data from 5,028 Parkinson’s disease patients and 5,422 controls. Additionally, we studied the functional impact of GALC knock-out on alpha-synuclein accumulation and on GCase activity in neuronal cell models and performed in silico structural analysis of common GALC variants associated with altered GalCase activity.

The top hit in Parkinson's disease GWAS in the GALC locus, rs979812, is associated with increased GalCase activity (b = 1.2; se = 0.06; p = 5.10E-95). No other variants outside the GALC locus were associated with GalCase activity. Colocalization analysis demonstrated that rs979812 was also associated with increased GalCase expression. Mendelian randomization suggested that increased GalCase activity may be causally associated with Parkinson’s disease (b = 0.025, se = 0.007, p = 0.0008). We did not find an association between rare GALC variants and Parkinson’s disease. GALC knockout using CRISPR-Cas9 did not lead to alpha-synuclein accumulation, further supporting that increased rather than reduced GalCase levels may be associated with Parkinson’s disease. The structural analysis demonstrated that the common variant p.I562T may lead to improper maturation of GalCase affecting its activity.

Our results nominate GALC as the gene associated with Parkinson’s disease in this locus and suggest that the association of variants in the GALC locus may be driven by their effect of increasing GalCase expression and activity. Whether altering GalCase activity could be considered as a therapeutic target should be further studied.

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% men) 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.

Pre-operative Limbic System Functional Connectivity Distinguishes Responders From Non-responders to Surgical Treatment for Trigeminal Neuralgia

Background: Trigeminal neuralgia (TN) is a severe facial pain condition often requiring surgical treatment. Unfortunately, even technically successful surgery fails to achieve durable pain relief in many patients. The purpose of this study was to use resting-state functional magnetic resonance imaging (fMRI) to: (1) compare functional connectivity between limbic and accessory sensory networks in TN patients vs. healthy controls; and (2) determine if pre-operative variability in these networks can distinguish responders and non-responders to surgery for TN. Methods: We prospectively recruited 22 medically refractory classic or idiopathic TN patients undergoing surgical treatment over a 3-year period, and 19 age- and sex-matched healthy control subjects. fMRI was acquired within the month prior to surgery for all TN patients and at any time during the study period for controls. Functional connectivity analysis was restricted to six pain-relevant brain regions selected a priori: anterior cingulate cortex (ACC), posterior cingulate cortex, hippocampus, amygdala, thalamus, and insula. Two comparisons were performed: (1) TN vs. controls; and (2) responders vs. non-responders to surgical treatment for TN. Functional connectivity was assessed with a two-sample t-test, using a statistical significance threshold of p < 0.050 with false discovery rate (FDR) correction for multiple comparisons. Results: Pre-operative functional connectivity was increased in TN patients compared to controls between the right insular cortex and both the left thalamus [t ₍₃₉₎ = 3.67, p = 0.0007] and right thalamus [t ₍₃₉₎ = 3.22, p = 0.0026]. TN patients who were non-responders to surgery displayed increased functional connectivity between limbic structures, including between the left and right hippocampus [t ₍₁₈₎ = 2.85, p = 0.0106], and decreased functional connectivity between the ACC and both the left amygdala [t ₍₁₈₎ = 2.94, p = 0.0087] and right hippocampus [t ₍₁₈₎ = 3.20, p = 0.0049]. Across all TN patients, duration of illness was negatively correlated with connectivity between the ACC and left amygdala (r ² = 0.34, p = 0.00437) as well as the ACC and right hippocampus (r ² = 0.21, p = 0.0318). Conclusions: TN patients show significant functional connectivity abnormalities in sensory-salience regions. However, variations in the strength of functional connectivity in limbic networks may explain why some TN patients fail to respond adequately to surgery.

Comprehensive Analysis of Brain Volume in REM Sleep Behavior Disorder with Mild Cognitive Impairment

BACKGROUND

Rapid-eye-movement sleep behavior disorder (RBD) is a major risk factor for Parkinson's disease and dementia with Lewy bodies. More than a third of RBD patients have mild cognitive impairment (MCI), but their specific structural brain alterations remain poorly understood.

OBJECTIVE

This study aimed to investigate the local deformation and volume of gray and white matter tissue underlying MCI in RBD.

METHODS

Fifty-two idiopathic RBD patients, including 17 with MCI (33%), underwent polysomnography, neuropsychological, neurological, and magnetic resonance imaging assessments. MCI diagnosis was based on a subjective complaint, cognitive impairment on the neuropsychological battery, and preserved daily functioning. Forty-one controls were also included. Deformation-based morphometry (DBM), voxel-based morphometry (VBM), and regional volume analyses of the corpus callosum and basal forebrain cholinergic were performed. Multiple regressions models were also computed using anatomical, cognitive (composite z score), and motor parameters.

RESULTS

Globally, patients with MCI displayed a widespread pattern of local deformation and volume atrophy in the cortical (bilateral insula, cingulate cortex, precuneus, frontal and temporal regions, right angular gyrus, and mid-posterior segment of the corpus callosum) and subcortical (brainstem, corona radiata, basal ganglia, thalamus, amygdala, and right hippocampus) regions compared to patients without MCI (DBM) or controls (DBM and VBM). Moreover, brain deformation (DBM) in patients were associated with lower performance in attention and executive functions, visuospatial abilities, and higher motor symptoms severity.

CONCLUSION

The present study identified novel brain structural alterations in RBD patients with MCI which correlated with poorer cognitive performance. These results are consistent with those reported in patients with synucleinopathies-related cognitive impairment.

Clinical perception and management of Parkinson's disease during the COVID-19 pandemic: A Canadian experience

Background

The COVID-19 pandemic has necessitated the social isolation of the population and the rapid implementation of remote care for patients with neurodegenerative diseases. The objective of this study was to explore the perceived impact of confinement in patients with Parkinson's disease and document the effects of gender and living environment.

Methods

We recruited two cohorts from the Canadian provinces of Québec and Alberta, which differed in the dynamics of COVID-19 spreading at the time of the study, and administered a questionnaire on the perceived effects of confinement on daily living and disease management.

Results

The data reveals that approximately half of the patients experienced a change in one or more clinical symptoms, with differences observed between gender (e.g. day-to-day changes in slowness in men, aggravated headaches in women) and geographic location (e.g. increased depression in Alberta but reduced sleep quality in Québec). Furthermore, participants identifying as women or living in Alberta implemented more frequently home or online exercise. Lastly, high levels of satisfaction with phone or video consultations did not translate into a sustained interest to pursue this mode of healthcare.

Conclusions

This study suggests that COVID-19-related confinement affected Parkinson's disease manifestation and management. Patients also reported varying levels of interest to continue remote care. A number of differences reported in our study were seemingly related to gender and living environment.

Microstructural Abnormalities of the Dentatorubrothalamic Tract in Cervical Dystonia

BACKGROUND

The dentatorubrothalamic tract (DRTT) remains understudied in idiopathic cervical dystonia (CD), despite evidence that the pathway is relevant in the pathophysiology of the disorder.

OBJECTIVE

The aim of this study was to examine the DRTT in patients with CD using diffusion tensor imaging (DTI)-based tractography.

METHODS

Magnetic resonance imaging scans from 67 participants were collected to calculate diffusion tractography metrics using a binary tractography-based DRTT template. Fractional anisotropy and diffusivity measures of left and right DRTT were computed and compared between 32 subjects with CD and 35 age-matched healthy volunteers.

RESULTS

Fractional anisotropy of right DRTT and mean and axial diffusivity of left DRTT were significantly reduced in patients with CD. Similar abnormalities were observed in patients with focal CD and patients with CD without tremor. DTI metrics did not correlate with disease duration or severity.

CONCLUSIONS

Significant reductions in DTI measures suggest microstructural abnormalities within the DRTT in CD, characterized by a tractography pattern consistent with decreased axonal integrity. © 2021 International Parkinson and Movement Disorder Society.

The Quebec Parkinson Network: A Researcher-Patient Matching Platform and Multimodal Biorepository

BACKGROUND

Genetic, biologic and clinical data suggest that Parkinson's disease (PD) is an umbrella for multiple disorders with clinical and pathological overlap, yet with different underlying mechanisms. To better understand these and to move towards neuroprotective treatment, we have established the Quebec Parkinson Network (QPN), an open-access patient registry, and data and bio-samples repository.

OBJECTIVE

To present the QPN and to perform preliminary analysis of the QPN data.

METHODS

A total of 1,070 consecutively recruited PD patients were included in the analysis. Demographic and clinical data were analyzed, including comparisons between males and females, PD patients with and without RBD, and stratified analyses comparing early and late-onset PD and different age groups.

RESULTS

QPN patients exhibit a male:female ratio of 1.8:1, an average age-at-onset of 58.6 years, an age-at-diagnosis of 60.4 years, and average disease duration of 8.9 years. REM-sleep behavior disorder (RBD) was more common among men, and RBD was associated with other motor and non-motor symptoms including dyskinesia, fluctuations, postural hypotension and hallucinations. Older patients had significantly higher rates of constipation and cognitive impairment, and longer disease duration was associated with higher rates of dyskinesia, fluctuations, freezing of gait, falls, hallucinations and cognitive impairment. Since QPN's creation, over 60 studies and 30 publications have included patients and data from the QPN.

CONCLUSIONS

The QPN cohort displays typical PD demographics and clinical features. These data are open-access upon application (http://rpq-qpn.ca/en/), and will soon include genetic, imaging and bio-samples. We encourage clinicians and researchers to perform studies using these resources.

Probable REM sleep behavior disorder is associated with longitudinal cortical thinning in Parkinson's disease

REM sleep behavior disorder (RBD) has a poor prognostic implication in both motor and non-motor functions in Parkinson’s disease (PD) patients. However, to the best of our knowledge no study to date investigated the longitudinal cerebral changes underlying RBD symptoms in PD. We performed the longitudinal study to investigate the association between probable RBD and cortical and subcortical changes in early, de novo PD patients. We studied 78 participants from the Parkinson’s Progression Marker Initiative who underwent structural MRI at baseline and after 2 years. The presence of probable RBD (pRBD) was evaluated using the RBD screening questionnaire. We compared the cross-sectional and longitudinal cortical thickness and subcortical volume changes, between PD patients with and without pRBD. At baseline, we found bilateral inferior temporal cortex thinning in the PD-pRBD group compared with the PD-noRBD group. Longitudinally, the PD-pRBD group revealed a significant increase in the rate of thinning in the left insula compared with the PD-noRBD group, and the increased thinning correlated with decreased cognitive performance. In subcortical volume analyses, the presence of pRBD was linked with volume decrease over time in the left caudate nucleus, pallidum and amygdala. The volume changes in the left caudate nucleus revealed correlations with global cognition. These results support the idea that RBD is an important marker of rapid progression in PD motor and non-motor symptoms and suggest that the atrophy in the left insula and caudate nucleus might be the underlying neurobiological mechanisms of the poorer prognosis in PD patients with RBD.

Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson's disease using machine learning

Cognitive impairments are prevalent in Parkinson's disease (PD), but the underlying mechanisms of their development are unknown. In this study, we aimed to predict global cognition (GC) in PD with machine learning (ML) using structural neuroimaging, genetics and clinical and demographic characteristics. As a post-hoc analysis, we aimed to explore the connection between novel selected features and GC more precisely and to investigate whether this relationship is specific to GC or is driven by specific cognitive domains. 101 idiopathic PD patients had a cognitive assessment, structural MRI and blood draw. ML was performed on 102 input features including demographics, cortical thickness and subcortical measures, and several genetic variants (APOE, MAPT, SNCA, etc.). Using the combination of RRELIEFF and Support Vector Regression, 11 features were found to be predictive of GC including sex, rs894280, Edinburgh Handedness Inventory, UPDRS-III, education, five cortical thickness measures (R-parahippocampal, L-entorhinal, R-rostral anterior cingulate, L-middle temporal, and R-transverse temporal), and R-caudate volume. The rs894280 of SNCA gene was selected as the most novel finding of ML. Post-hoc analysis revealed a robust association between rs894280 and GC, attention, and visuospatial abilities. This variant indicates a potential role for the SNCA gene in cognitive impairments of idiopathic PD.

Patterns of brain activity during a set-shifting task linked to mild behavioral impairment in Parkinson's disease

•

PD with mild behavioral impairment revealed deficits in cognitive flexibility.

•

Brain activities during a set-shifting task linked with MBI in PD was evaluated.

•

PD-MBI revealed reduced activity in the prefrontal and posterior parietal cortices.

•

The prefrontal activity was associated with cognitive impairment in PD-MBI.

•

High MBI-C score was associated with reduced deactivation in the hippocampus.

Mild behavioral impairment (MBI) is a neurobehavioral syndrome characterized by later life emergence of sustained neuropsychiatric symptoms, as an at-risk state for incident cognitive decline and dementia. Prior studies have reported that neuropsychiatric symptoms are associated with cognitive abilities in Parkinson’s disease (PD) patients, and we have recently found a strong correlation between MBI and cognitive performance. However, the underlying neural activity patterns of cognitive performance linked to MBI in PD are unknown. Fifty-nine non-demented PD patients and 26 healthy controls were scanned using fMRI during performance of a modified version of the Wisconsin card sorting task. MBI was evaluated using the MBI-checklist, and PD patients were divided into two groups, PD-MBI and PD-noMBI. Compared to the PD-noMBI group and healthy controls, the PD-MBI group revealed less activation in the prefrontal and posterior parietal cortices, and reduced deactivation in the medial temporal region. These results suggest that in PD, MBI reflects deficits in the frontoparietal control network and the hippocampal memory system.

Association Between BDNF Val66Met Polymorphism and Mild Behavioral Impairment in Patients With Parkinson's Disease

Neuropsychiatric symptoms (NPS) are common in Parkinson's disease (PD) and have demonstrated an association with the p. Val66Met, a polymorphism in the BDNF gene. Mild behavioral impairment (MBI) is a validated syndrome describing emergent and persistent NPS in older adults as a marker of potential cognitive decline and dementia. This study investigated if PD patients with the Met allele were more likely to have MBI and whether they had impairments in specific domains of MBI using the Mild Behavioral Impairment Checklist (MBI-C) as the MBI ascertainment tool. One hundred forty-six PD patients were screened for neuropsychiatric and cognitive impairments with the MBI-C and the Montreal Cognitive Assessment (MoCA). All participants were genotyped for the BDNF p.Val66Met single-nucleotide polymorphism (SNP) using TaqMan Genotyping Assay. Statistical analysis was performed using multiple linear and logistic regression models. Met carriers had a 2 times higher likelihood of being MBI positive (MBI-C total score ≥8) than Val carriers. Met carriers had significantly higher MBI-C total scores and significantly greater impairments in the mood/anxiety and the psychotic domains of MBI-C compared to Val carriers. These findings indicate that the BDNF Met allele is associated with a higher neuropsychiatric burden in PD.

Theta-Burst Stimulation for Cognitive Enhancement in Parkinson's Disease With Mild Cognitive Impairment: A Randomized, Double-Blind, Sham-Controlled Trial

Background: Mild cognitive impairment is a common non-motor symptom of Parkinson's disease (PD-MCI) and has minimal treatment options.

Objective: In this double-blind, randomized, sham-controlled trial, we assessed the effect of repeated sessions of intermittent theta-burst stimulation over the left dorsolateral prefrontal cortex on cognition and brain connectivity in subjects with PD-MCI.

Methods: Forty-one subjects were randomized to receive real (n = 21) or sham stimulation (n = 20). All subjects underwent neuropsychological assessments before, 1 day, and 1 month after stimulation. Subjects also underwent resting-state functional magnetic resonance imaging before and 48 h after stimulation. The primary outcome was the change in the cognitive domain (executive function, attention, memory, language, and visuospatial abilities) z-scores across time.

Results: There was an insignificant effect on cognitive domain z-scores across time when comparing real with sham stimulation and correcting for multiple comparisons across cognitive domains (p > 0.05 Bonferroni correction). However, the real stimulation group demonstrated a trend toward improved executive functioning scores at the 1-month follow-up compared with sham (p < 0.05 uncorrected). After real stimulation, the connectivity of the stimulation site showed decreased connectivity to the left caudate head. There was no change in connectivity within or between the stimulation network (a network of cortical regions connected to the stimulation site) and the striatal network. However, higher baseline connectivity between the stimulation network and the striatal network was associated with improved executive function scores at 1 month.

Conclusions: These results suggest that intermittent theta-burst stimulation over the dorsolateral prefrontal cortex in subjects with PD-MCI has minimal effect on cognition compared with sham, although there were trends toward improved executive function. This intervention may be more effective in subjects with higher baseline connectivity between the stimulation network and the striatal network. This trial supports further investigation focusing on executive function and incorporating connectivity-based targeting.

Clinical Trial Registration:www.ClinicalTrials.gov, identifier NCT03243214.

A Prodromal Brain-Clinical Pattern of Cognition in Synucleinopathies

OBJECTIVE

Isolated (or idiopathic) rapid eye movement sleep behavior disorder (iRBD) is associated with dementia with Lewy bodies (DLB) and Parkinson's disease (PD). Biomarkers are lacking to predict conversion to a dementia or a motor-first phenotype. Here, we aimed at identifying a brain-clinical signature that predicts dementia in iRBD.

METHODS

A brain-clinical signature was identified in 48 patients with polysomnography-confirmed iRBD using partial least squares between brain deformation and 27 clinical variables. The resulting variable was applied to 78 patients with iRBD followed longitudinally to predict conversion to a synucleinopathy, specifically DLB. The deformation scores from patients with iRBD were compared with 207 patients with PD, DLB, or prodromal DLB to assess if scores were higher in DLB compared to PD.

RESULTS

One latent variable explained 31% of the brain-clinical covariance in iRBD, combining cortical and subcortical deformation and subarachnoid/ventricular expansion to cognitive and motor variables. The deformation score of this signature predicted conversion to a synucleinopathy in iRBD (p = 0.036, odds ratio [OR] = 2.249; 95% confidence interval [CI] = 1.053-4.803), specifically to DLB (OR = 4.754; 95% CI = 1.283-17.618, p = 0.020) and not PD (p = 0.286). Patients with iRBD who developed dementia had scores similar to clinical and prodromal patients with DLB but higher scores compared with patients with PD. The deformation score also predicted cognitive performance over 1, 2, and 4 years in patients with PD.

INTERPRETATION

We identified a brain-clinical signature that predicts conversion in iRBD to more severe/dementing forms of synucleinopathy. This pattern may serve as a new biomarker to optimize patient care, target risk reduction strategies, and administer neuroprotective trials. ANN NEUROL 2021;89:341-357.

Preoperative Transcranial Direct Current Stimulation in Glioma Patients: A Proof of Concept Pilot Study

Background: Transcranial direct current stimulation (tDCS) has been used extensively in patient populations to facilitate motor network plasticity. However, it has not been studied in patients with brain tumors. We aimed to determine the feasibility of a preoperative motor training and tDCS intervention in patients with glioma. In an exploratory manner, we assessed changes in motor network connectivity following this intervention and related these changes to predicted electrical field strength from the stimulated motor cortex.

Methods: Patients with left-sided glioma (n=8) were recruited in an open label proof of concept pilot trial and participated in four consecutive days of motor training combined with tDCS. The motor training consisted of a 60-min period where the subject learned to play the piano with their right hand. Concurrently, they received 40 min of 2 mA anodal tDCS of the left motor cortex. Patients underwent task and resting state fMRI before and after this intervention. Changes in both the connectivity of primary motor cortex (M1) and general connectivity across the brain were assessed. Patient specific finite element models were created and the predicted electrical field (EF) resulting from stimulation was computed. The magnitude of the EF was extracted from left M1 and correlated to the observed changes in functional connectivity.

Results: There were no adverse events and all subjects successfully completed the study protocol. Left M1 increased both local and global connectivity. Voxel-wide measures, not constrained by a specific region, revealed increased global connectivity of the frontal pole and decreased global connectivity of the supplementary motor area. The magnitude of EF applied to the left M1 correlated with changes in global connectivity of the right M1.

Conclusion: In this proof of concept pilot study, we demonstrate for the first time that tDCS appears to be feasible in glioma patients. In our exploratory analysis, we show preoperative motor training combined with tDCS may alter sensorimotor network connectivity. Patient specific modeling of EF in the presence of tumor may contribute to understanding the dose-response relationship of this intervention. Overall, this suggests the possibility of modulating neural networks in glioma patients.

Impact of Peritumoral Edema During Tumor Treatment Field Therapy: A Computational Modelling Study

BACKGROUND

Tumor treatment fields (TTFie-lds) are an approved adjuvant therapy for glioblastoma (GBM). The magnitude of applied electrical field has been shown to be related to the anti-tumoral response. However, peritumoral edema may result in shunting of electrical current around the tumor, thereby reducing the intra-tumoral electric field. In this study, we systematically address this issue with computational simulations.

METHODS

Finite element models are created of a human head with varying amounts of peritumoral edema surrounding a virtual tumor. The electric field distribution was simulated using the standard TTFields electrode montage. Electric field magnitude was extracted from the tumor and related to edema thickness. Two patient specific models were created to confirm these results.

RESULTS

The inclusion of peritumoral edema decreased the average magnitude of the electric field within the tumor. In the model considering a frontal tumor and an anterior-posterior electrode configuration, ≥6 mm of peritumoral edema decreased the electric field by 52%. In the patient specific models, peritumoral edema decreased the electric field magnitude within the tumor by an average of 26%. The effect of peritumoral edema on the electric field distribution was spatially heterogenous, being most significant at the tissue interface between edema and tumor.

CONCLUSIONS

The inclusion of peritumoral edema during TTFields modelling may have a dramatic effect on the predicted electric field magnitude within the tumor. Given the importance of electric field magnitude for the anti-tumoral effects of TTFields, the presence of edema should be considered both in future modelling studies and when planning TTField therapy.

Association study of DNAJC13, UCHL1, HTRA2, GIGYF2, and EIF4G1 with Parkinson's disease

Rare mutations in genes originally discovered in multigenerational families have been associated with increased risk of Parkinson's disease (PD). The involvement of rare variants in DNAJC13, UCHL1, HTRA2, GIGYF2, and EIF4G1 loci has been poorly studied or has produced conflicting results across cohorts. However, they are still being often referred to as "PD genes" and used in different models. To further elucidate the role of these 5 genes in PD, we fully sequenced them using molecular inversion probes in 2408 patients with PD and 3444 controls from 3 different cohorts. A total of 788 rare variants were identified across the 5 genes and 3 cohorts. Burden analyses and optimized sequence Kernel association tests revealed no significant association between any of the genes and PD after correction for multiple comparisons. Our results do not support an association of the 5 tested genes with PD. Combined with previous studies, it is unlikely that any of these genes plays an important role in PD. Their designation as "PARK" genes should be reconsidered.

Variants in the Niemann-Pick type C gene NPC1 are not associated with Parkinson's disease

Biallelic variants in NPC1, a gene coding for a lysosomal transmembrane protein involved in cholesterol trafficking, may cause Niemann-Pick disease type C (NPC). A few cases of NPC1 variant carriers with Parkinson's disease (PD) have been reported. In addition, pathologic studies have demonstrated phosphorylated alpha-synuclein and Lewy pathology in brains of NPC patients. Therefore, we aimed to examine whether NPC1 genetic variants may be associated with PD. Full sequencing of NPC1 was performed in 2657 PD patients and 3647 controls from 3 cohorts, using targeted sequencing with molecular inversion probes. A total of 9 common variants and 126 rare variants were identified across the 3 cohorts. To examine their association with PD, regression models adjusted for age, sex, and origin were performed for common variants, and optimal sequence Kernel association test (SKAT-O) was performed for rare variants. After correction for multiple comparisons, common and rare NPC1 variants were not associated with PD. Our results do not support a link between heterozygous variants in NPC1 and PD.

Common and unique connectivity at the interface of motor, neuropsychiatric, and cognitive symptoms in Parkinson's disease: A commonality analysis

Parkinson's disease (PD) is characterized by overlapping motor, neuropsychiatric, and cognitive symptoms. Worse performance in one domain is associated with worse performance in the other domains. Commonality analysis (CA) is a method of variance partitioning in multiple regression, used to separate the specific and common influence of collinear predictors. We apply, for the first time, CA to the functional connectome to investigate the unique and common neural connectivity underlying the interface of the symptom domains in 74 non-demented PD subjects. Edges were modeled as a function of global motor, cognitive, and neuropsychiatric scores. CA was performed, yielding measures of the unique and common contribution of the symptom domains. Bootstrap confidence intervals were used to determine the precision of the estimates and to directly compare each commonality coefficient. The overall model identified a network with the caudate nucleus as a hub. Neuropsychiatric impairment accounted for connectivity in the caudate-dorsal anterior cingulate and caudate-right dorsolateral prefrontal-right inferior parietal circuits, while caudate-medial prefrontal connectivity reflected a unique effect of both neuropsychiatric and cognitive impairment. Caudate-precuneus connectivity was explained by both unique and shared influence of neuropsychiatric and cognitive symptoms. Lastly, posterior cortical connectivity reflected an interplay of the unique and common effects of each symptom domain. We show that CA can determine the amount of variance in the connectome that is unique and shared amongst motor, neuropsychiatric, and cognitive symptoms in PD, thereby improving our ability to interpret the data while gaining novel insight into networks at the interface of these symptom domains.

Brain atrophy in Parkinson's disease with polysomnography-confirmed REM sleep behavior disorder

We aimed to investigate cortical and subcortical brain alterations in people with Parkinson's disease with polysomnography-confirmed rapid eye movement (REM) sleep behavior disorder (RBD). Thirty people with Parkinson's disease, including 15 people with RBD, were recruited and compared with 41 healthy controls. Surface-based cortical and subcortical analyses were performed on T1-weighted images to investigate thickness and shape abnormalities between groups, and voxel-based and deformation-based morphometry were performed to investigate local volume. Correlations were performed in patients to investigate the structural correlates of motor activity during REM sleep. People with RBD showed cortical thinning in the right perisylvian and inferior temporal cortices and shape contraction in the putamen compared with people without RBD. Compared with controls, people with RBD had extensive cortical thinning and volume loss, brainstem volume was reduced, and shape contraction was found in the basal ganglia and hippocampus. In comparison to controls, people without RBD showed more restricted thinning in the sensorimotor, parietal, and occipital cortices, reduced volume in the brainstem, temporal and more posterior areas, and shape contraction in the pallidum and hippocampus. In Parkinson's disease, higher tonic and phasic REM sleep motor activity was associated with contraction of the thalamic surface, extensive cortical thinning, and subtle volume reduction in the middle temporal gyrus. In Parkinson's disease, the presence of RBD is associated with extensive cortical and subcortical abnormalities, suggesting more severe neurodegeneration in people with RBD. This provides potential neuroanatomical correlates for the more severe clinical phenotype reported in people with Parkinson's disease with RBD.

Mild behavioral impairment in Parkinson's disease is associated with altered corticostriatal connectivity

•

Mild behavioral impairment in PD is linked to altered corticostriatal connectivity.

•

PD-MBI have less connectivity between the striatum and the DMN.

•

PD-MBI have increased atrophy of the SAN.

•

Caudate head and dorsal putamen connectivity is related to MBI-C scores in PD.

•

Caudate head-precuneus connectivity is linked to both MBI and MoCA scores.

Background

Mild behavioral impairment (MBI) is a syndrome characterized by later life onset, sustained neuropsychiatric symptoms as a marker of dementia risk. In Parkinson's disease (PD), MBI has been associated with worse cognitive abilities and increased cortical atrophy. However, the circuit level correlates of MBI have not been investigated in this population. Our objective was to investigate the relationship between MBI and corticostriatal connectivity in PD patients. This emphasis on corticostriatal connectivity was due to the significant role of these circuits in neuropsychiatric and cognitive symptoms across disease conditions.

Methods

Seventy-four non-demented patients with PD were administered the MBI-checklist, and classified as having high MBI (PD-MBI; n = 21) or low MBI scores (PD-noMBI; n = 53). Corticostriatal connectivity was assessed with both an atlas and seed-based analysis. The atlas analysis consisted of calculating the average connectivity between the striatal network and the default mode (DMN), central executive (CEN), and saliency networks (SAN). Structural measurements of cortical thickness and volume were also assessed. PD-MBI and PD-noMBI patients were compared, along with a group of age matched healthy control subjects (HC; n = 28). Subsequently, a seed analysis assessed the relationship of MBI scores with the connectivity of twelve seeds within the striatum while controlling for cognitive ability. A complementary analysis assessed the relationship between striatal connectivity and cognition, while controlling for MBI-C.

Results

PD-MBI demonstrated decreased connectivity between the striatum and both the DMN and SAN compared to PD-noMBI and HC. The decreased connectivity between the striatum and the SAN was explained partly by increased atrophy within the SAN in PD-MBI. The seed analysis revealed a relationship between higher MBI scores and lower connectivity of the left caudate head to the dorsal anterior cingulate cortex and left middle frontal gyrus. Higher MBI-C scores were also related to decreased connectivity of the right caudate head with the anterior cingulate cortex, precuneus, and left supramarginal gyrus, as well as increased connectivity to the left hippocampus and right cerebellar hemisphere. Caudate-precuneus connectivity was independently associated with both global behavioural and cognitive scores.

Conclusion

These results suggest PD-MBI is associated with altered corticostriatal connectivity, particularly between the head of the caudate and cortical regions associated with the DMN and SAN. In particular, caudate-precuneus connectivity is associated with both global behavioral and cognitive symptoms in PD.

The impact of traumatic brain injury on cognitive and neuropsychiatric symptoms of Parkinson's disease

The objective was to determine whether a history of traumatic brain injury (TBI) was associated with Parkinson's Disease (PD) and specific cognitive, motor, and neuropsychiatric symptoms. A cross-sectional cohort study of 120 participants aged 60-85 years old (48 females) were recruited (69 PD and 51 healthy controls). Assessments included demographic information, neuropsychological tests, a motor evaluation, neuropsychiatric questionnaires, and the Brain Injury Screening Questionnaire. A history of TBI or number of TBIs was not significantly related to an increased risk of developing PD or poorer motor scores on the United Parkinson Disease Rating Scale part 3. There was a significant negative correlation between number of TBI's and mean z-scores of global cognition (r_s(69) = -0.338, p = 0.004), executive function (r_s(69) = -0.251, p = 0.038), memory (r_s(69) = -0.262, p = 0.029), and language (r_s(69) = -0.245, p = 0.042), and a significant positive correlation on the Beck Depression Inventory II (r_s(69) = 0.285, p = 0.018) and the Patient Health Questionnaire-9 (PHQ-9) (r_s(69) = 0.326, p = 0.006) in the PD group only. In conclusion, a history of TBI was negatively associated with cognition and positively associated with depressive symptoms in patients with PD, but not with motor symptoms.

Characterization of cognition in mild cognitive impairment with and without Parkinson's disease

•

Screening tests can diagnose PD-MCI but do not give detailed cognitive profiles.

•

Criteria based on a complete neuropsychological battery identify more PD patients with MCI.

•

The overall cognitive profile is similar in PD-MCI and MCI.

•

Neuropsychological batteries and definition of impairment cut-offs should be refined.

Plasma Neurofilament Light: A Marker of Neurodegeneration in Mild Behavioral Impairment

BACKGROUND

Assessing neuropsychiatric symptoms (NPS) in older adults is important for determining dementia risk. Mild behavioral impairment (MBI) is an at-risk state for cognitive decline and dementia, characterized by emergent NPS in later life. MBI has significantly higher dementia incidence than late life psychiatric conditions. However, its utility as a proxy for neurodegeneration has not been demonstrated. Plasma neurofilament light (NfL) is a validated biomarker of axonal damage, and has been shown to associate with hallmarks of neurodegeneration.

OBJECTIVE

The purpose of this investigation was to identify associations between NfL rate of change and the presence of MBI symptomatology.

METHODS

We evaluated the association of MBI with changes in NfL in a cohort (n = 584; MBI + n = 190, MBI- n = 394) of non-demented participants from the Alzheimer's Disease Neuroimaging Initiative. MBI was determined by transforming Neuropsychiatric Inventory Questionnaire items using a published algorithm. Change in NfL was calculated over 2 years.

RESULTS

Time*MBI status was the only significant interaction to predict change in NfL concentrations (F(1,574) = 4.59, p = 0.032), even after controlling for age, mild cognitive impairment, and demographics. Analyses reclassifying 64 participants with new onset MBI over 2 years similarly demonstrated greater increases in NfL (F(1,574) = 5.82, p = 0.016).

CONCLUSION

These findings suggest MBI is a clinical proxy of early phase neurodegeneration with putative utility in identifying those at dementia risk. MBI can be used as a case ascertainment approach to capture those at high risk for cognitive decline and dementia, and is an important construct for clinicians dealing with cognitive and neuropsychiatric symptomatology in older adults.

Report from a multidisciplinary meeting on anxiety as a non-motor manifestation of Parkinson's disease

Anxiety is a severe problem for at least one-third of people living with Parkinson's disease (PD). Anxiety appears to have a greater adverse impact on quality of life than motor impairment. Despite its high prevalence and impact on daily life, anxiety is often undiagnosed and untreated. To better address anxiety in PD, future research must improve knowledge about the mechanism of anxiety in PD and address the lack of empirical evidence from clinical trials. In response to these challenges, the Parkinson's Foundation sponsored an expert meeting on anxiety on June 13th and 14th 2018. This paper summarizes the findings from that meeting informed by a review of the existing literature and discussions among patients, caregivers, and an international, clinician-scientist, expert panel working group. The goal is to provide recommendations to improve our understanding and treatment of anxiety in PD.

Neuroimaging biomarkers for clinical trials in atypical parkinsonian disorders: Proposal for a Neuroimaging Biomarker Utility System

INTRODUCTION

Therapeutic strategies targeting protein aggregations are ready for clinical trials in atypical parkinsonian disorders. Therefore, there is an urgent need for neuroimaging biomarkers to help with the early detection of neurodegenerative processes, the early differentiation of the underlying pathology, and the objective assessment of disease progression. However, there currently is not yet a consensus in the field on how to describe utility of biomarkers for clinical trials in atypical parkinsonian disorders.

METHODS

To promote standardized use of neuroimaging biomarkers for clinical trials, we aimed to develop a conceptual framework to characterize in more detail the kind of neuroimaging biomarkers needed in atypical parkinsonian disorders, identify the current challenges in ascribing utility of these biomarkers, and propose criteria for a system that may guide future studies.

RESULTS

As a consensus outcome, we describe the main challenges in ascribing utility of neuroimaging biomarkers in atypical parkinsonian disorders, and we propose a conceptual framework that includes a graded system for the description of utility of a specific neuroimaging measure. We included separate categories for the ability to accurately identify an intention-to-treat patient population early in the disease (Early), to accurately detect a specific underlying pathology (Specific), and the ability to monitor disease progression (Progression).

DISCUSSION

We suggest that the advancement of standardized neuroimaging in the field of atypical parkinsonian disorders will be furthered by a well-defined reference frame for the utility of biomarkers. The proposed utility system allows a detailed and graded description of the respective strengths of neuroimaging biomarkers in the currently most relevant areas of application in clinical trials.

Treatment of Persistent Post-Traumatic Headache and Post-Concussion Symptoms Using Repetitive Transcranial Magnetic Stimulation: A Pilot, Double-Blind, Randomized Controlled Trial

Persistent post-traumatic headache (PTH) after mild traumatic brain injury is one of the most prominent and highly reported persistent post-concussion symptoms (PPCS). Non-pharmacological treatments, including non-invasive neurostimulation technologies, have been proposed for use. Our objective was to evaluate headache characteristics at 1 month after repetitive transcranial magnetic stimulation (rTMS) treatment in participants with PTH and PPCS. A double-blind, randomized, sham-controlled, pilot clinical trial was performed on 20 participants (18-65 years) with persistent PTH (International Classification of Headache Disorders, 3rd edition) and PPCS (International Classification of Diseases, Tenth Revision). Ten sessions of rTMS therapy (10 Hz, 600 pulses, 70% resting motor threshold amplitude) were delivered to the left dorsolateral pre-frontal cortex. The primary outcome was a change in headache frequency or severity at 1 month post-rTMS. Two-week-long daily headache diaries and clinical questionnaires assessing function, PPCS, cognition, quality of life, and mood were completed at baseline, post-treatment, and at 1, 3, and 6 months post-rTMS. A two-way (treatment × time) mixed analyisis of variance indicated a significant overall time effect for average headache severity (F_(3,54) = 3.214; p = 0.03) and a reduction in headache frequency at 1 month post-treatment (#/2 weeks, REAL -5.2 [standard deviation {SD} = 5.8]; SHAM, -3.3 [SD = 7.7]). Secondary outcomes revealed an overall time interaction for headache impact, depression, post-concussion symptoms, and quality of life. There was a significant reduction in depression rating in the REAL group between baseline and 1 month post-treatment, with no change in the SHAM group (Personal Health Questionnaire-9; REAL, -4.3 [SD = 3.7[ p = 0.020]; SHAM, -0.7 [SD = 4.7; p = 1.0]; Bonferroni corrected). In the REAL group, 60% returned to work whereas only 10% returned in the SHAM group (p = 0.027). This pilot study demonstrates an overall time effect on headache severity, functional impact, depression, PPCS, and quality of life after rTMS treatment in participants with persistent PTH; however, findings were below clinical significance thresholds. There was a 100% response rate, no dropouts, and minimal adverse effects, warranting a larger phase II study. Clinicaltrials.gov: NCT03691272.

Mild behavioral impairment is linked to worse cognition and brain atrophy in Parkinson disease

OBJECTIVE

To evaluate the associations of mild behavioral impairment (MBI) with cognitive deficits and patterns of gray matter changes in Parkinson disease (PD).

METHODS

Sixty patients with PD without dementia and 29 healthy controls underwent a cognitive neuropsychological evaluation and structural MRI scan. MBI was evaluated with the MBI Checklist (MBI-C), a rating scale designed to elicit emergent neuropsychiatric symptoms in accordance with MBI criteria. We divided the patients with PD into 2 groups: 1 group with high MBI-C scores (PD-MBI) and the other with low MBI-C scores (PD-noMBI).

RESULTS

Among 60 patients with PD, 20 were categorized as having PD-MBI (33.33%). In healthy controls, no participants met the MBI cut-point threshold. The PD-MBI group had significantly lower Montreal Cognitive Assessment and z scores in all 5 domains and the global score compared to healthy controls and those with PD-noMBI. In addition, all cognitive domains except language and global cognition negatively correlated with the MBI-C total score in all patients with PD. For cortical structures, the PD-MBI group revealed middle temporal cortex thinning and decreased volume compared with the PD-noMBI group, and decreased volume in this area negatively correlated with the MBI-C total score.

CONCLUSIONS

The impaired cognitive function over all domains and atrophy in the temporal area in the PD-MBI group are in line with posterior cortical circuit deficits in PD, which have been associated with a faster rate of progression to dementia. These initial results suggest that MBI might be an early and important marker for incident cognitive decline and dementia in patients with PD.

Transcranial magnetic stimulation improves cognition over time in Parkinson's disease

INTRODUCTION

Cognitive impairment can occur in the early phase of Parkinson's disease and increases the risk of developing dementia. Cognitive deficits were shown to be associated with functional alterations in the dorsolateral prefrontal cortex (DLPFC) and caudate nucleus. Two previous transcranial magnetic stimulation studies over the left DLPFC showed short-term improvement in cognitive performance and focused on specific task.

METHODS

28 patients with idiopathic Parkinson's disease and mild cognitive impairment received intermittent "theta burst" stimulation (iTBS) (active, N = 14; or sham, N = 14) over the left DLPFC, twice a day for three days with 1-2 days in between. Detailed neuropsychological assessment of five cognitive domains was performed before iTBS and on days 1, 10, and 30 after the last iTBS session. Composite Z-scores were calculated for each domain and for overall cognition.

RESULTS

Our results showed an increase in overall cognition up to one month in both groups but this effect was only significant in the active group. Improvements were seen in the attention domain for both groups and in the visuospatial domain in the active group only. No significant differences were found between the groups.

CONCLUSION

These preliminary findings suggest that active iTBS might improve overall cognitive performance in patients with Parkinson's disease with mild cognitive impairment and that this effect can last up to one month. This cognitive improvement, is likely mediated by improvement on visuospatial abilities. Further studies are needed to explore the potential of iTBS as a therapeutical tool to slow cognitive decline in patients with Parkinson's disease.

Remnants of Cardinal Symptoms of Parkinson's Disease, Not Dyskinesia, Are Problematic for Dyskinetic Patients Performing Activities of Daily Living

Introduction: The impact of levodopa-induced dyskinesia (LID) on the daily lives of patients with Parkinson's disease (PD) remains to be determined. Furthermore, evidence suggests that cardinal motor symptoms of PD may coexist with LID, but their impact on activities of daily living (ADL) relative to LID is not known. This cross-sectional study aimed at determining the effect of LID and cardinal motor symptoms of PD on ADL in patients who were experiencing peak-dose choreic-type LID.

Method: One hundred and twenty-one patients diagnosed with PD known to experience choreic-type LID were recruited for the study. Patients were asked to perform a set of ADL. Levels of LID, tremor, bradykinesia, and freezing of gait (FoG) were measured using 17 inertial sensors design to capture full body movements, while rigidity, and postural instability were assessed using clinical evaluations. Cognition was also assessed using the mini-mental state examination. Success criteria were set for each ADL using the time needed to perform the task and errors measured in 69 age-gender-matched healthy controls. Binary logistic regressions were used to identify symptoms influencing success or failure for each activity. Receiver operating characteristic curves were computed on each significant symptom, and Youden indexes were calculated to determine the critical level of symptomatology at which the performance significantly changed.

Results: Results show that 97.7% of patients who presented with LID during the experiment also presented with at least one cardinal motor symptom. On average, patients took more time and did more errors during ADL. Multivariate analyses revealed that for the great majority of ADL, LID were not associated with worsening of performance; however, postural instability, tremor, rigidity, and cognitive decline significantly decreased the odds of success.

Conclusions: Residual symptoms of PD, such as tremor, rigidity, and postural instability still present at peak-dose were more problematic than LID in the performance of ADL for patients experiencing slight-to-moderate LID. We also found that cognitive decline was associated with decreased performance in certain tasks. Therefore, a strategy using lower doses of medication to manage LID may be counterproductive since it would not address most of these symptoms already present in patients.