To determine if any association between previously identified alleles that confer risk for Parkinson disease and variables measuring disease progression.

Objective We conducted a study to obtain information that could be used to provide Parkinson's disease (PD) patients with appropriate advice on safe driving. Methods Consecutive PD patients who visited our office were studied. Among these patients, those who had experienced driving after being diagnosed with PD were interviewed by neurologists and a trained nurse to investigate their previous car accidents, motor function, cognitive function, sleepiness, levodopa equivalent dose (LED), and emotional dysregulation. The rates of major car accidents before and after the onset of PD were compared. Results Fifteen patients had experienced a major car accident resulting in human injury or serious property damage since the onset of PD. When the rates of major car accidents before and after the onset of PD were compared, the ratio was 4.3 [95% confidence interval (CI) 1.9-9.7]. The incidence of accidents after the onset of PD was correlated with age, disease duration, LED, the cognitive function Mini-Mental Scale Examination (MMSE), Japanese translation of the Montreal Cognitive Assessment (MoCA-J), but not the motor symptom score [Unified Pankinson's disease rating scale (UPDRS) part III at the time of the study]. The Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease (QUIP) score was also higher in patients with major car accidents. Conclusion The severity of symptoms (Hoehn-Yahr classification), cognitive function, and disease duration were expected to be risk factors for car accidents. However, the motor symptom score (UPDRS part III) was not associated with the incidence of major car accidents. In addition to a low cognitive function and the severity of symptoms, the QUIP score might be an independent factor that can be referenced when advising PD patients to refrain from driving.

A prominent view of language acquisition involves learning to ignore irrelevant auditory signals through functional reorganization, enabling more efficient processing of relevant information. Yet, few studies have characterized the neural spatiotemporal dynamics supporting rapid detection and subsequent disregard of irrelevant auditory information, in the developing brain. To address this unknown, the present study modeled the developmental acquisition of cost-efficient neural dynamics for auditory processing, using intracranial electrocorticographic responses measured in individuals receiving standard-of-care treatment for drug-resistant, focal epilepsy. We also provided evidence demonstrating the maturation of an anterior-to-posterior functional division within the superior-temporal gyrus (STG), which is known to exist in the adult STG.

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.

Treatments for neurodegenerative disorders remain rare, although recent FDA approvals, such as Lecanemab and Aducanumab for Alzheimer's Disease, highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use Summary-data-based Mendelian Randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer's disease, 3 amyotrophic lateral sclerosis, 5 Lewy body dementia, 46 Parkinson's disease, and 9 Progressive supranuclear palsy target genes passing multiple test corrections (pSMR_multi < 2.95×10-6 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics - classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these 69.8% are expressed in the disease relevant cell type from single nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as Riluzole in AD. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community [https://nih-card-ndd-smr-home-syboky.streamlit.app/].

Forty percent of Parkinson's disease patients develop levodopa-induced-dyskinesia (LiD) within 4 years of starting levodopa. The genetic basis of LiD remains poorly understood, and there have been few well powered studies.

Although many rare variants have been reportedly associated with Parkinson's disease (PD), many have not been replicated or have failed to replicate. Here, we conduct a large-scale replication of rare PD variants. We assessed a total of 27,590 PD cases, 6701 PD proxies, and 3,106,080 controls from three data sets: 23andMe, Inc., UK Biobank, and AMP-PD. Based on well-known PD genes, 834 variants of interest were selected from the ClinVar annotated 23andMe dataset. We performed a meta-analysis using summary statistics of all three studies. The meta-analysis resulted in five significant variants after Bonferroni correction, including variants in GBA1 and LRRK2. Another eight variants are strong candidate variants for their association with PD. Here, we provide the largest rare variant meta-analysis to date, providing information on confirmed and newly identified variants for their association with PD using several large databases. Additionally we also show the complexities of studying rare variants in large-scale cohorts.

The pathogenesis and clinical heterogeneity of Parkinson's disease (PD) have been evaluated from molecular, pathophysiological, and clinical perspectives. High-throughput proteomic analysis of cerebrospinal fluid (CSF) opened new opportunities for scrutinizing this heterogeneity. To date, this is the most comprehensive CSF-based proteomics profiling study in PD with 569 patients (350 idiopathic patients, 65 GBA + mutation carriers and 154 LRRK2 + mutation carriers), 534 controls, and 4135 proteins analyzed. Combining CSF aptamer-based proteomics with genetics we determined protein quantitative trait loci (pQTLs). Analyses of pQTLs together with summary statistics from the largest PD genome wide association study (GWAS) identified 68 potential causal proteins by Mendelian randomization. The top causal protein, GPNMB, was previously reported to be upregulated in the substantia nigra of PD patients. We also compared the CSF proteomes of patients and controls. Proteome differences between GBA + patients and unaffected GBA + controls suggest degeneration of dopaminergic neurons, altered dopamine metabolism and increased brain inflammation. In the LRRK2 + subcohort we found dysregulated lysosomal degradation, altered alpha-synuclein processing, and neurotransmission. Proteome differences between idiopathic patients and controls suggest increased neuroinflammation, mitochondrial dysfunction/oxidative stress, altered iron metabolism and potential neuroprotection mediated by vasoactive substances. Finally, we used proteomic data to stratify idiopathic patients into "endotypes". The identified endotypes show differences in cognitive and motor disease progression based on previously reported protein-based risk scores.Our findings not only contribute to the identification of new therapeutic targets but also to shape personalized medicine in CNS neurodegeneration.

Parkinson's disease has a large heritable component and genome-wide association studies have identified over 90 variants with disease-associated common variants, providing deeper insights into the disease biology. However, there have not been large-scale rare variant analyses for Parkinson's disease. To address this gap, we investigated the rare genetic component of Parkinson's disease at minor allele frequencies <1%, using whole genome and whole exome sequencing data from 7184 Parkinson's disease cases, 6701 proxy cases and 51 650 healthy controls from the Accelerating Medicines Partnership Parkinson's disease (AMP-PD) initiative, the National Institutes of Health, the UK Biobank and Genentech. We performed burden tests meta-analyses on small indels and single nucleotide protein-altering variants, prioritized based on their predicted functional impact. Our work identified several genes reaching exome-wide significance. Two of these genes, GBA1 and LRRK2, have variants that have been previously implicated as risk factors for Parkinson's disease, with some variants in LRRK2 resulting in monogenic forms of the disease. We identify potential novel risk associations for variants in B3GNT3, AUNIP, ADH5, TUBA1B, OR1G1, CAPN10 and TREML1 but were unable to replicate the observed associations across independent datasets. Of these, B3GNT3 and TREML1 could provide new evidence for the role of neuroinflammation in Parkinson's disease. To date, this is the largest analysis of rare genetic variants in Parkinson's disease.

Genome-wide genotyping platforms have the capacity to capture genetic variation across different populations, but there have been disparities in the representation of population-dependent genetic diversity. The motivation for pursuing this endeavor was to create a comprehensive genome-wide array capable of encompassing a wide range of neuro-specific content for the Global Parkinson's Genetics Program (GP2) and the Center for Alzheimer's and Related Dementias (CARD). CARD aims to increase diversity in genetic studies, using this array as a tool to foster inclusivity. GP2 is the first supported resource project of the Aligning Science Across Parkinson's (ASAP) initiative that aims to support a collaborative global effort aimed at significantly accelerating the discovery of genetic factors contributing to Parkinson's disease and atypical parkinsonism by generating genome-wide data for over 200,000 individuals in a multi-ancestry context. Here, we present the Illumina NeuroBooster array (NBA), a novel, high-throughput and cost-effective custom-designed content platform to screen for genetic variation in neurological disorders across diverse populations. The NBA contains a backbone of 1,914,934 variants (Infinium Global Diversity Array) complemented with custom content of 95,273 variants implicated in over 70 neurological conditions or traits with potential neurological complications. Furthermore, the platform includes over 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related GWAS loci across diverse populations. The NBA can identify low frequency variants and accurately impute over 15 million common variants from the latest release of the TOPMed Imputation Server as of August 2023 (reference of over 300 million variants and 90,000 participants). We envisage this valuable tool will standardize genetic studies in neurological disorders across different ancestral groups, allowing researchers to perform genetic research inclusively and at a global scale.

Parkinson's disease (PD) is a genetically complex neurodegenerative disease with ~20 genes known to contain mutations that cause PD or atypical parkinsonism. Large-scale next-generation sequencing projects have revolutionized genomics research. Applying these data to PD, many genes have been reported to contain putative disease-causing mutations. In most instances, however, the results remain quite limited and rather preliminary. Our aim was to assist researchers on their search for PD-risk genes and variant candidates with an easily accessible and open summary-level genomic data browser for the PD research community.

Whole-genome sequencing data are available from several large studies across a variety of diseases and traits. However, massive storage and computation resources are required to use these data, and to achieve sufficient power for discoveries, harmonization of multiple cohorts is critical.

The left prefrontal cortex is essential for verbal communication. It remains uncertain at what timing, to what extent, and what type of phrase initiates left-hemispheric dominant prefrontal activation during comprehension of spoken sentences. We clarified this issue by measuring event-related high-gamma activity during a task to respond to three-phrase questions configured in different orders. Questions beginning with a wh-interrogative deactivated the left posterior prefrontal cortex right after the 1st phrase offset and the anterior prefrontal cortex after the 2nd phrase offset. Left prefrontal high-gamma activity augmented subsequently and maximized around the 3rd phrase offset. Conversely, questions starting with a concrete phrase deactivated the right orbitofrontal region and then activated the left posterior prefrontal cortex after the 1st phrase offset. Regardless of sentence types, high-gamma activity emerged earlier, by one phrase, in the left posterior prefrontal than anterior prefrontal region. Sentences beginning with a wh-interrogative may initially deactivate the left prefrontal cortex to prioritize the bottom-up processing of upcoming auditory information. A concrete phrase may obliterate the inhibitory function of the right orbitofrontal region and facilitate top-down lexical prediction by the left prefrontal cortex. The left anterior prefrontal regions may be recruited for semantic integration of multiple concrete phrases.

Levodopa-induced dyskinesia (LID) is a common adverse effect of levodopa, one of the main therapeutics used to treat the motor symptoms of Parkinson's disease (PD). Previous evidence suggests a connection between LID and a disruption of the dopaminergic system as well as genes implicated in PD, including GBA1 and LRRK2.

Cognitive decline is common in Parkinson's disease (PD) and its genetic risk factors are not well known to date, besides variants in the GBA and APOE genes. However, variation in complex traits is caused by numerous variants and is usually studied with genome-wide association studies (GWAS), requiring a large sample size, which is difficult to achieve for outcome measures in PD. Taking an alternative approach, we computed 100 polygenic scores (PGS) related to cognitive, dementia, stroke, and brain anatomical phenotypes and investigated their association with cognitive decline in six longitudinal cohorts. The analysis was adjusted for age, sex, genetic ancestry, follow-up duration, GBA and APOE status. Then, we meta-analyzed five of these cohorts, comprising a total of 1702 PD participants with 6156 visits, using the Montreal Cognitive Assessment as a cognitive outcome measure. After correction for multiple comparisons, we found four PGS significantly associated with cognitive decline: intelligence (p = 5.26e-13), cognitive performance (p = 1.46e-12), educational attainment (p = 8.52e-10), and reasoning (p = 3.58e-5). Survival analyses highlighted an offset of several years between the first and last quartiles of PGS, with significant differences for the PGS of cognitive performance (5 years) and educational attainment (7 years). In conclusion, we found four PGS associated with cognitive decline in PD, all associated with general cognitive phenotypes. This study highlights the common genetic factors between cognitive decline in PD and the general population, and the importance of the participant's cognitive reserve for cognitive outcome in PD.

Parkinson's disease (PD) is a complex neurodegenerative disease with a variety of genetic and environmental factors contributing to disease. The SNCA gene encodes for the alpha-synuclein protein which plays a central role in PD, where aggregates of this protein are one of the pathological hallmarks of disease. Rare point mutations and copy number gains of the SNCA gene have been shown to cause autosomal dominant PD, and common DNA variants identified using Genome-Wide Association Studies (GWAS) are a moderate risk factor for PD. The UK Biobank is a large-scale population prospective study including ~500,000 individuals that has revolutionized human genetics. Here we assessed the frequency of SNCA variation in this cohort and identified 30 subjects carrying variants of interest including duplications (n = 6), deletions (n = 6) and large complex likely mosaic events (n = 18). No known pathogenic missense variants were identified. None of these subjects were reported to be a PD case, although it is possible that these individuals may develop PD at a later age, and whilst three had known prodromal features, these did not meet defined clinical criteria for being considered 'prodromal' cases. Seven of the 18 large complex carriers showed a history of blood based cancer. Overall, we identified copy number variants in the SNCA region in a large population based cohort without reported PD phenotype and symptoms. Putative mosaicism of the SNCA gene was identified, however, it is unclear whether it is associated with PD. These individuals are potential candidates for further investigation by performing SNCA RNA and protein expression studies, as well as promising clinical trial candidates to understand how duplication carriers potentially escape PD.

We examined 2-year longitudinal change in clinical features and biomarkers in LRRK2 non-manifesting carriers (NMCs) versus healthy controls (HCs) enrolled in the Parkinson's Progression Markers Initiative (PPMI). We analyzed 2-year longitudinal data from 176 LRRK2 G2019S NMCs and 185 HCs. All participants were assessed annually with comprehensive motor and non-motor scales, dopamine transporter (DAT) imaging, and biofluid biomarkers. The latter included cerebrospinal fluid (CSF) Abeta, total tau and phospho-tau; serum urate and neurofilament light chain (NfL); and urine bis(monoacylglycerol) phosphate (BMP). At baseline, LRRK2 G2019S NMCs had a mean (SD) age of 62 (7.7) years and were 56% female. 13% had DAT deficit (defined as <65% of age/sex-expected lowest putamen SBR) and 11% had hyposmia (defined as ≤15th percentile for age and sex). Only 5 of 176 LRRK2 NMCs developed PD during follow-up. Although NMCs scored significantly worse on numerous clinical scales at baseline than HCs, there was no longitudinal change in any clinical measures over 2 years or in DAT binding. There were no longitudinal differences in CSF and serum biomarkers between NMCs and HCs. Urinary BMP was significantly elevated in NMCs at all time points but did not change longitudinally. Neither baseline biofluid biomarkers nor the presence of DAT deficit correlated with 2-year change in clinical outcomes. We observed no significant 2-year longitudinal change in clinical or biomarker measures in LRRK2 G2019S NMCs in this large, well-characterized cohort even in the participants with baseline DAT deficit. These findings highlight the essential need for further enrichment biomarker discovery in addition to DAT deficit and longer follow-up to enable the selection of NMCs at the highest risk for conversion to enable future prevention clinical trials.

Understanding the genetic mechanisms underlying diseases in ancestrally diverse populations is a critical step towards the realization of the global application of precision medicine. The African and African admixed populations enable mapping of complex traits given their greater levels of genetic diversity, extensive population substructure, and distinct linkage disequilibrium patterns.

We built normative brain atlases that animate millisecond-scale intra- and inter-hemispheric white matter-level connectivity dynamics supporting object recognition and speech production. We quantified electrocorticographic modulations during three naming tasks using event-related high-gamma activity from 1,114 nonepileptogenic intracranial electrodes (i.e., non-lesional areas unaffected by epileptiform discharges). Using this electrocorticography data, we visualized functional connectivity modulations defined as significant naming-related high-gamma modulations occurring simultaneously at two sites connected by direct white matter streamlines on diffusion-weighted imaging tractography. Immediately after stimulus onset, intra- and inter-hemispheric functional connectivity enhancements were confined mainly across modality-specific perceptual regions. During response preparation, left intra-hemispheric connectivity enhancements propagated in a posterior-to-anterior direction, involving the left precentral and prefrontal areas. After overt response onset, inter- and intra-hemispheric connectivity enhancements mainly encompassed precentral, postcentral, and superior-temporal (STG) gyri. We found task-specific connectivity enhancements during response preparation as follows. Picture naming enhanced activity along the left arcuate fasciculus between the inferior-temporal and precentral/posterior inferior-frontal (pIFG) gyri. Nonspeech environmental sound naming augmented functional connectivity via the left inferior longitudinal and fronto-occipital fasciculi between the medial-occipital and STG/pIFG. Auditory descriptive naming task enhanced usage of the left frontal U-fibers, involving the middle-frontal gyrus. Taken together, the commonly observed network enhancements include inter-hemispheric connectivity optimizing perceptual processing exerted in each hemisphere, left intra-hemispheric connectivity supporting semantic and lexical processing, and inter-hemispheric connectivity for symmetric oral movements during overt speech. Our atlases improve the currently available models of object recognition and speech production by adding neural dynamics via direct intra- and inter-hemispheric white matter tracts.

Objective Although many rare variants have been reportedly associated with Parkinson's disease (PD), many have not been replicated or have failed to replicate. Here, we conduct a large-scale replication of rare PD variants. Methods We assessed a total of 27,590 PD cases, 6,701 PD proxies, and 3,106,080 controls from three data sets: 23andMe, Inc., UK Biobank, and AMP-PD. Based on well-known PD genes, 834 variants of interest were selected from the ClinVar annotated 23andMe dataset. We performed a meta-analysis using summary statistics of all three studies. Results The meta-analysis resulted in 11 significant variants after Bonferroni correction, including variants in GBA1 and LRRK2 . At least 9 previously reported pathogenic or risk variants for PD did not pass Bonferroni correction in this analysis. Conclusions Here, we provide the largest rare variant meta-analysis to date, providing thorough information of variants confirmed, newly identified, or rebutted for their association with PD.