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3,476 results for “Parkinsons Disease”
Raw dataset and additional data for article "Nonmotor symptoms associated with progressive loss of dopaminergic neurons in a mouse model of Parkinson's disease"
<p>Dataset from the project investigating the presence of nonmotor symptoms of Parkinson's disease in a mouse model of progressive loss of dopaminergic neurons (namely,TIF-IADATCreERT2 strain). Mice were tested for executive and cognitive functions (males: Operant Sensation Seeking test, OSS; females: Probabilistic Reversal Learning Task in Intellicages), olfactory acuity (males: buried food test), saccharin preference (males and females), and motor performance (males and females: test using CatWalk apparatus).</p><p>The dataset includes files used to perform statistical analyses but their names may vary from the ones used in the scripts. For the purpose of recreating our analyses, please refer to the GitHub page, where both scripts and input data file names (in 'Raw data files' section) are compliant: https://github.com/annaradli/tif-pd-behavior.</p><p><strong>Description of files:</strong></p><p><i>Raw data files:</i></p><ul><li>animals_info.csv - animals data: genotype, sex, age, Intellicage tag identifier</li><li>catwalk_run_statistics_all_females.csv - data recorded in CatWalk apparatus for females</li><li>catwalk_run_statistics_all_males.csv - data recorded in CatWalk apparatus for males</li><li>females_weight_raw_data_revised.csv - females' body weight (revised for containing Polish words)</li><li>intellicage_raw_data.csv - data recorded in IntelliCage exported to .csv format</li><li>intellicage_raw_data_R.RData - data recorded in IntelliCage in .RData format</li><li>males_weight_raw_data.csv - males' body weight</li><li>olfactory_time_digging_raw_data.csv - time to start digging at the right place in the buried food test</li><li>olfactory_time_retrieve_raw_data.csv- time to retrieve cracker in the buried food test</li><li>oss_raw_data.csv - data recorded in the OSS test</li><li>saccharin_preference_males_raw_data.csv - saccharin preference test results for males</li><li>snvta_cells_count.csv - number of TH+ cells in SN and VTA in male mice (3+3) 14 weeks after tamoxifen treatment</li></ul><p><i>Additional data files:</i></p><ul><li>all_anova.xlsx - summary of two-way ANOVAs of all behavioral tests and weight measurements for males and females</li><li>catwalk_complete.xlsx - CatWalk complete dataset with datapoints</li><li>catwalk_correlation_between_paws.xlsx - correlation coefficients of CatWalk parameters between the left and right paws</li><li>catwalk_reduced.xlsx - CatWalk parameters used in linear regression model reduction of data</li><li>intelli.xlsx - IntelliCage data summarized in bins</li><li>oss.xlsx - operant sensation-seeking data</li></ul><p>v2 contains the corrected 'animals_info.csv' file without an unnecessary column.</p><p>v3 has a revised version of file containing females' weight measurements and also added a file with midbrain cell counts</p><p>v4 has a whole section of 'Additional data files' added</p>
Parkinson's Disease Hoehn&Yahr Dataset
<p><span>The ALAMEDA_PD_HoehnYahr_dataset.csv contains 76 preprocessed features extracted from raw force data. In total, it includes 79 columns:</span></p> <ol> <li> <p><span>The first two columns (ID, Datetime) correspond to the ID of PD patient and Date of the recording.</span></p> </li> <li> <p><span>The next 76 columns correspond to features extracted from raw force data collected with loadsol insoles throughout a short walk assessment during in-clinic visits. The features were extracted from both time and frequency domains of the recorded timeseries. The naming of the features contains information on the channel used to extract each feature. More specifically </span><span><em>l1 </em></span><span>corresponds to left heel, </span><span><em>r1 </em></span><span>to right heel, </span><span><em>l8 </em></span><span>to left forefoot and </span><span><em>r8 </em></span><span>to right forefoot. Features extracted from the frequency domain are denoted by the inclusion of the term </span><span><em>freq </em></span><span>in their name. The full list of the extracted features used to predict Hoehn & Yahr stage is demonstrated in the table below.</span></p> </li> <li> <div> <div> <div> <p><span>The final column, HoehnYahr, indicates the Hoehn & Yahr stage of the patients.</span></p> </div> </div> </div> <div> <div> </div> </div> </li> </ol>
Structural basis for Parkinson's Disease-linked LRRK2's binding to microtubules
<p>This dataset includes all of the tabular data used in the figures in the article. Original article is available at: https://doi.org/10.1101/2022.01.21.477284</p> <p>Leucine Rich Repeat Kinase 2 (<em>LRRK2</em>) is one of the most commonly mutated genes in familial Parkinson’s Disease (PD). Under some circumstances, LRRK2 co-localizes with microtubules in cells, an association enhanced by PD mutations. We report a cryo-electron microscopy structure of the catalytic half of LRRK2, containing its kinase, which is in a closed conformation, and GTPase domains, bound to microtubules. We also report a structure of the catalytic half of LRRK1, which is closely related to LRRK2, but is not linked to PD. LRRK1’s structure is similar to LRRK2, but LRRK1 does not interact with microtubules. Guided by these structures, we identify amino acids in LRRK2’s GTPase domain that mediate microtubule binding; mutating them disrupts microtubule binding in vitro and in cells, without affecting LRRK2’s kinase activity. Our results have implications for the design of therapeutic LRRK2 kinase inhibitors.</p>
A bibliometric study on Parkinson's Disease based on the open access data of the Michael J. Fox Foundation
<h1>Description</h1> <p>This repository contains a comprehensive dataset focused on Parkinson's Disease. We provide data extracted via web scraping, along with metadata resulting from the extraction process using the NCBI API. The data pertains to the article titled 'A bibliometric study on Parkinson's Disease based on the open access data of the Michael J. Fox Foundation'.</p> <h2>Metadata Description</h2> <ul> <li> <h3>Analisys_MJFF_05_04_2024.xlsx</h3> </li> </ul> <table> <tbody> <tr> <th>Field</th> <th>Description</th> <th>Data Type</th> </tr> </tbody> <tbody> <tr> <td>AU</td> <td>List of authors in abbreviated format.</td> <td>Text</td> </tr> <tr> <td>AF</td> <td>List of authors with full names.</td> <td>Text</td> </tr> <tr> <td>TI</td> <td>Full title of the article.</td> <td>Text</td> </tr> <tr> <td>SO</td> <td>Name of the journal or publication.</td> <td>Text</td> </tr> <tr> <td>SO_CO</td> <td>Country of origin of the publication.</td> <td>Text</td> </tr> <tr> <td>LA</td> <td>Language of the article.</td> <td>Text</td> </tr> <tr> <td>DT</td> <td>Type of document, such as "Journal Article".</td> <td>Text</td> </tr> <tr> <td>DE</td> <td>Keywords or descriptors associated with the article.</td> <td>Text</td> </tr> <tr> <td>MESH</td> <td>MeSH terms that describe the content of the article.</td> <td>Text</td> </tr> <tr> <td>DI</td> <td>Digital Object Identifier (DOI).</td> <td>Text</td> </tr> <tr> <td>PG</td> <td>Number of pages or page range.</td> <td>Numeric</td> </tr> <tr> <td>GRANT_ID</td> <td>Identification of funding, when available.</td> <td>Text</td> </tr> <tr> <td>GRANT_ORG</td> <td>Organization that provided the funding.</td> <td>Text</td> </tr> <tr> <td>UT, PMID</td> <td>Unique identifiers of the article.</td> <td>Numeric</td> </tr> <tr> <td>DB</td> <td>Name of the database where the article is indexed.</td> <td>Text</td> </tr> <tr> <td>AU_UN</td> <td>Information about the academic unit or institution of the authors.</td> <td>Text</td> </tr> </tbody> </table> <ul> <li> <h3>References_MJFF_v2_Final_Corrected.csv</h3> </li> </ul> <table> <tbody> <tr> <th>Field</th> <th>Description</th> <th>Data Type</th> </tr> </tbody> <tbody> <tr> <td>Title</td> <td>Name of the article or publication.</td> <td>Text</td> </tr> <tr> <td>Authors</td> <td>List of authors who contributed to the article.</td> <td>Text</td> </tr> <tr> <td>Journal Name</td> <td>Name of the journal or periodical where the article was published.</td> <td>Text</td> </tr> <tr> <td>Publisher</td> <td>Name of the publisher who published the article.</td> <td>Text</td> </tr> <tr> <td>Volume</td> <td>Volume number of the journal in which the article appears.</td> <td>Numeric or Text</td> </tr> <tr> <td>Edition Number</td> <td>Number of the edition of the journal in which the article is found.</td> <td>Numeric or Text</td> </tr> <tr> <td>Starting Page</td> <td>Number of the first page of the article in the publication.</td> <td>Numeric</td> </tr> <tr> <td>Ending Page</td> <td>Number of the last page of the article.</td> <td>Numeric</td> </tr> <tr> <td>Publication Date</td> <td>Date on which the article was published.</td> <td>Date</td> </tr> <tr> <td>Open Access Status</td> <td>Indicates whether the article is available in open access.</td> <td>Text</td> </tr> <tr> <td>License</td> <td>Type of license under which the article was published.</td> <td>Text</td> </tr> <tr> <td>DOI (Digital Object Identifier)</td> <td>Unique identifier for the article that provides a permanent link to the online access.</td> <td>Text</td> </tr> <tr> <td>OA Location URL</td> <td>Direct URL to the article, if available in open access.</td> <td>Text</td> </tr> <tr> <td>Citation Count</td> <td>Number of times the article has been cited by other publications.</td> <td>Numeric</td> </tr> </tbody> </table> <p> </p>
Phosphoglycerate kinase is a central leverage point in Parkinson's Disease driven neuronal metabolic deficits
<p><span>Although certain drivers of familial Parkinson’s Disease (PD) compromise mitochondrial integrity, whether metabolic deficits underly other idiopathic or genetic origins of PD is unclear. Here, we demonstrate that PGK1, a gene in the PARK12 susceptibility locus, <span> </span>is rate limiting in neuronal glycolysis and that modestly increasing PGK1 expression significantly boosts <span> </span>neuronal ATP production kinetics that is sufficient to suppress PARK20-driven synaptic dysfunction. We found that his activity enhancement depends on the molecular chaperone PARK7/DJ-1, whose loss of function significantly disrupts axonal bioenergetics. <em>In-vivo, </em>viral expression of PGK1 confers protection of striatal DA axons against metabolic lesions. These data support the notion that bioenergetic deficits may underpin PD associated pathologies and point to improving neuronal ATP production kinetics as a promising path forward in PD therapeutics.</span></p>
Standing repetitive pointing task in individuals with and without Parkinson's disease
<p>[ These data refer to a manuscript currently under revision in PlosOne. In this MS we aimed to determine the effects of levodopa medication on the performance of a repetitive pointing task while standing, and to investigate the optimal trial duration in individuals with Parkinson’s disease, and older adults. Seventeen individuals with Parkinson’s disease (5 freezers) and 9 older adults stood on force platforms for 30 s and 120 s while performing a bilateral repetitive pointing task, tracked by motion capture. Participants with Parkinson’s disease were assessed on and off medication and older adults were also assessed on separate days. The main findings were that: 1) on medication, participants with Parkinson’s exhibited greater center of pressure root mean square in the medial-lateral direction, greater velocity in the medial-lateral and anterior-posterior directions, and greater range in the medial-lateral direction than off medication; 2) longer trial durations resulted in greater center of pressure range in the medial-lateral and anterior-posterior directions and greater coefficient of variation in finger pointing on the least affected side; 3) Parkinson’s participants exhibited larger range in the medial-lateral direction compared to older adults; 4) off medication, freezers presented with less range and root mean square in the anterior-posterior direction than non-freezers; and 5) a correlation emerged between the freezing of gait questionnaire and pointing asymmetry and the coefficient of variation of pointing on the most affected side. Therefore, Parkinson’s medication may increase instability during a repetitive pointing task. Longer trials may provide a better depiction of sway by discriminating between those with and without neurological impairment. Individuals with Parkinson’s were less stable than older adults, supporting that they are at a greater risk for falls. The greater restrictive postural strategy in freezers compared to non-freezers is likely a factor that augments fall-risk. Lastly, the link between freezing of gait and upper-limb movement indicates that freezing may manifest first in the lower-limbs. Add description ]</p>
Literature Review 13/02/2018: Genetic risk of Parkinson's disease dementia due to APOE4 or MAPT
<p>Literature review assessing genetic risk of dementia due to <em>APOE4</em> or <em>MAPT </em>in Parkinson's disease, performed on the 13<sup>th</sup> February 2018. All studies had to fulfil three<em> a priori </em>inclusion criteria:</p> <p>1) Case control studies using clinically diagnosed or pathologically confirmed PD and PDD.</p> <p>2) Time between motor diagnosis and experimental assessment could be defined or estimated.</p> <p>3) Genotype information supplied, allowing the odds ratio (OR) and confidence intervals (CI) to be calculated that aligned with the genotype categories used in this work.</p> <p>For <em>MAPT</em>, a PubMed search for the term “<em>MAPT Parkinson’s dementia</em>” identified 105 potential matches, of which 9 met the inclusion criteria. For <em>APOE4</em>, a PubMed search for the term “<em>APOE Parkinson’s dementia</em>” identified 188 potential matches, of which 19 met the inclusion criteria. Note, the review includes several publications arising from the CamPaIGN cohort; As we were interested in genetic risk as a function of time from diagnosis, we included each unique study time-point. </p>
Mobile Device Voice Recordings at King's College London (MDVR-KCL) from both early and advanced Parkinson's disease patients and healthy controls
<p><strong>Dataset description</strong></p> <p>The dataset description will start with describing the local conditions and other metadata, then will continue with describing the recording procedure and annotation methodology. Finally, a brief description of the dataset deployment and publication will be given.</p> <p><strong>Meta Information</strong></p> <p>The dataset was recorded at King's College London (KCL) Hospital, Denmark Hill, Brixton, London SE5 9RS in the period from 26 to 29 September 2017. We used a typical examination room with about ten square meters area and a typical reverberation tome of approx. 500ms to perform the voice recordings. Due to the fact, that the voice recordings are performed in the realistic situation of doing a phone call (i.e. participant holds the phone to the preferred ear and microphone is in direct proximity to the mouth), one can assume that all recordings were performed within the reverberation radius and thus can be considered as “clean”.</p> <p><strong>Recording Procedure</strong></p> <p>We used a Motorola Moto G4 Smartphone as recording device. To perform the voice recordings on the device, we developed a “Toggle Recording App”, which uses the same functionalities as the voice recording module used within the i-PROGNOSIS Smartphone application, but deployed as a standalone android application. This means, that the voice capturing service runs as a standalone background service on the recording device and triggers voice recordings via on- and off-hook signals of the Smartphone. Due to the fact, that we directly record the microphone signal, and not the GSM (“Global System for Mobile Communications”) compressed stream, we end up with high quality recordings with a sample rate of 44.1 kHz and a bit depth of 16 Bit (audio CD quality). The raw, uncompressed data is directly written to the external storage of the Smartphone (SD-card) using the well-known WAVE file format (.wav). We used the following workflow to perform a voice recording:</p> <ul> <li>Ask the participant to relax a bit and then to make a phone call to the test executor (off-hook signal triggered).}</li> <li>Ask the participant to read out “The North Wind and the Sun”</li> <li>Depending on the constitution of the participant either ask to read out “Tech. Engin. Computer applications in geography snippet”</li> <li>Start a spontaneous dialog with the participant, the test executor starts asking random questions about places of interest, local traffic, or personal interests if acceptable.</li> <li>Test executor ends call by farewell (on-hook signal triggered).</li> </ul> <p><strong>Annotation Scheme</strong></p> <p>For each HC and PD participant, we labeled the data regarding scores on the Hoehn & Yahr (H&Y), as well as the UPDRS II part 5 and UPDRS III part 18 scale. The voice recordings are labeled in the following scheme:</p> <p>SI_ HS_ HYR_ UPDRS II-5_UPDRS III-18</p> <p>with</p> <ul> <li>SI as subject identification in the form ID<em>NN</em>, <em>N</em> in [0, 9]</li> <li>HS as the health status label (hc or pd accordingly)</li> <li>HYR as the expert assessed H&Y scale rating</li> <li>UPDRS II-5 as the according expert peer-reviewed score</li> <li>UPDRS III-18 as the according expert assessed score</li> </ul> <p>For example, an audio recording with the file name “ID02_pd_1_2_1.wav” represents a recording of the third participant (First participant was anonymized as ID00), which has PD and a H&Y rating of 1, a UPDRS II-5 score of 2 and a UPDRS III-18 score of 1. At this point, it should be noted, that also all healthy controls were evaluated with regard to the introduced scales, because Parkinson's disease and voice degradation correlate, but don't match exactly. This means, that the data set includes one HC participant (ID31) with UPDRS II-5 and III-18 rating of 1, and also includes PD patients with UPDRS II-5 and III-18 ratings of 0. It should be emphasized, that this does not mean the data set includes ambiguous information, but that an expert was not able to hear voice degradation that would end up in a UPDRS rating greater than zero. Machine learning approaches may be able to nevertheless classify correctly, or at least learn to correlate, but not match PD and voice degradation at any time.</p> <p><strong>Appendix</strong></p> <p>North Wind and the Sun (Orthographic Version):</p> <p>“The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak. They agreed that the one who first succeeded in making the traveler take his cloak off should be considered stronger than the other. Then the North Wind blew as hard as he could, but the more he blew the more closely did the traveler fold his cloak around him; and at last the North Wind gave up the attempt. Then the Sun shone out warmly, and immediately the traveler took off his cloak. And so the North Wind was obliged to confess that the Sun was the stronger of the two.”</p> <p>BNC – Tech. Engin. Computer applications in geography snippet:</p> <p>“[...] This is because there is less scattering of blue light as the atmospheric path length and consequently the degree of scattering of the incoming radiation is reduced. For the same reason, the sun appears to be whiter and less orange-coloured as the observer's altitude increases; this is because a greater proportion of the sunlight comes directly to the observer's eye. Figure 5.7 is a schematic representation of the path of electromagnetic energy in the visible spectrum as it travels from the sun to the Earth and back again towards a sensor mounted on an orbiting satellite. The paths of waves representing energy prone to scattering (that is, the shorter wavelengths) as it travels from sun to Earth are shown. To the sensor it appears that all the energy has been reflected from point P on the ground whereas, in fact, it has not, because some has been scattered within the atmosphere and has never reached the ground at all. [...]”</p>
Analyses of metabolite profiling of Drosophila Parkinson's Disease model for identifying novel glial-based therapeutic targets
<p>Analysis for genetic screening and metabolomics identify glial adenosine metabolism as a therapeutic target in Parkinson’s disease</p> <p>This project contains the analysis of metabolite abundance measurements obtained with four different liquid chromatography mass spectrometry methods of synuclein expressing or control or fly brains in a wilde type or Adk1 knockout background.</p> <p> </p> <div> <h2>Table of contents</h2> <a href="https://github.com/jravilap/Olsen_Analyses#table-of-contents"></a></div> <div> <h3>Prerequisites</h3> <a href="https://github.com/jravilap/Olsen_Analyses#prerequisites"></a></div> <ul> <li>R (version 4.3.1 or higher)</li> <li>RStudio (optional, but recommended)</li> </ul> <div> <h3>R Packages</h3> <a href="https://github.com/jravilap/Olsen_Analyses#r-packages"></a></div> <p>The following R packages are required. You can install them using the commands below:</p> <div> <pre>install.packages(c(<span><span>"</span>readxl<span>"</span></span>, <span><span>"</span>calibrate<span>"</span></span>, <span><span>"</span>dplyr<span>"</span></span>, <span><span>"</span>ggplot2<span>"</span></span>))</pre> <div> </div> </div> <div> <h3>Package versions</h3> <a href="https://github.com/jravilap/Olsen_Analyses#package-versions"></a></div> <ul> <li>ggplot2_3.5.1</li> <li>dplyr_1.1.4</li> <li>yaml_2.3.8</li> <li>calibrate_1.7.7</li> <li>readxl_1.4.3</li> </ul> <div> <h2>Project Structure</h2> <a href="https://github.com/jravilap/Olsen_Analyses#project-structure"></a></div> <ul> <li><code>code/</code>: Contains the R scripts for the analysis.</li> <li><code>data/</code>: Processed data files. <ul> <li><code>22_0322_alphaSyn_fly_pilot_Classes.xlsx</code>: metabolite profiling data</li> <li><code>dup_metabs_decision.csv</code>: Table defining which metabolites profiled in more than one method should be used.</li> </ul> </li> <li><code>results/</code>: Output files, including plots and tables.</li> <li><code>common_functions/</code>: Custom R functions used in the analysis.</li> <li><code>config.yml</code>: Configuration file for setting paths.</li> </ul>
Elevated urine BMP phospholipids in LRRK2 and VPS35 mutation carriers with and without Parkinson's disease
<p><strong>Participant demographic and clinical characteristics, and urine BMP phospholipid levels. </strong></p> <p>For each participant, sample collection site is provided as: BCN (Barcelona), VIE (Vienna), DND (Dundee), or SSB (San Sebastian). Also provided are age at study participation, age at PD diagnosis (where applicable), sex (M, for male, and F, for female), experimental group (control, iPD –idiopathic PD –, LRRK2 G2019S, LRRK2 R1441G/C, VPS35 D620N, GBA, or other), and PD status (NMC for non-manifesting mutation carriers, or PD). Values for all measured BMP species presented as ng of BMP per mg of creatinine are provided. Additionally, urine creatinine (mg/ml) and non-normalized BMP levels are provided. BQL designates BMP levels that were below quantification level and NM designates values that were not measured for a particular individual.</p>
Dopamine transporter and synaptic vesicle sorting defects underlie auxilin-associated Parkinson's disease
<p>Auxilin participates in clathrin uncoating to facilitate presynaptic endocytosis. Loss-of-function mutations of auxilin (<em>PARK19</em>) cause Parkinson’s disease. Using auxilin KO mice, Vidyadhara et al. (2023) show that synaptic vesicle sorting deficits, cytoplasmic dopamine accumulation, dopamine transporter mistrafficking, and synaptic autophagic overload may lead to pathogenesis of Parkinson’s disease in <em>PARK19</em> patients. This file contains the data set used to generate all the main figures.</p>
Polygenic risk scores validated in patient-derived cells stratify for mitochondrial subtypes of Parkinson's disease
<p><strong>Background</strong> Parkinson’s disease (PD) is the fastest growing neurodegenerative disorder, with affected individuals expected to double during the next 20 years. This raises the urgent need to better understand the genetic architecture and downstream cellular alterations underlying PD pathogenesis, in order to identify more focused therapeutic targets. While only ∼10% of PD cases can be clearly attributed to monogenic causes, there is mounting evidence that additional genetic factors could play a role in idiopathic PD (iPD). In particular, common variants with low to moderate effect size in multiple genes regulating key neuroprotective activities may act as risk factors for PD. In light of the well-established involvement of mitochondrial dysfunction in PD, we hypothesized that a fraction of iPD cases may harbour a pathogenic combination of common variants in nuclear-encoded mitochondrial genes, ultimately resulting in neurodegeneration.</p> <p><strong>Methods</strong> to capture this mitochondria-related “missing heritability”, we leveraged on existing data from previous genome-wide association studies (GWAS) – i.e., the large PD GWAS from Nalls and colleagues. We then used computational approaches based on mitochondria-specific polygenic risk scores (mitoPRSs) for imputing the genotype data obtained from different iPD case-control datasets worldwide, including the Luxembourg Parkinson’s Study (412 iPD patients and 576 healthy controls) and the COURAGE-PD cohorts (7270 iPD cases and 6819 healthy controls).</p> <p><strong>Results</strong> applying this approach to gene sets controlling mitochondrial pathways potentially relevant for neurodegeneration in PD, we demonstrated that common variants in genes regulating <em>Oxidative Phosphorylation (OXPHOS</em>-PRS<em>)</em> were significantly associated with a higher PD risk both in the Luxembourg Parkinson’s Study (odds ratio, OR=1.31[1.14-1.50], <em>p</em>=5.4e-04) and in COURAGE-PD (OR=1.23[1.18-1.27], <em>p</em>=1.5e-29). Functional analyses in primary skin fibroblasts and in the corresponding induced pluripotent stem cells-derived neuronal progenitor cells from Luxembourg Parkinson’s Study iPD patients stratified according to the <em>OXPHOS</em>-PRS, revealed significant differences in mitochondrial respiration between high and low risk groups (<em>p</em> < 0.05). Finally, we also demonstrated that iPD patients with high <em>OXPHOS</em>-PRS have a significantly earlier age at disease onset compared to low-risk patients.</p> <p><strong>Conclusions</strong> our findings suggest that OXPHOS-PRS may represent a promising strategy to stratify iPD patients into pathogenic subgroups – in which the underlying neurodegeneration is due to a genetically defined mitochondrial burden – potentially eligible for future, more tailored mitochondrially targeted treatments.</p>
Genome-wide determinants of mortality and clinical progression in Parkinson's disease - Summary statistics
<p>Summary statistics from "Genome-wide determinants of mortality and clinical progression in Parkinson’s disease".</p>
CyTOF and Flow Cytometry dataset assocaited with "Early-to-mid stage idiopathic Parkinson's disease shows enhanced cytotoxicity and differentiation in CD8 T-cells in females"
<p>This dataset contains all the raw mass cytometry (CyTOF) and flow cytometry fcs files associated with Capelle <i>et al</i>. '<i>Early-to-mid stage idiopathic Parkinson's disease shows enhanced cytotoxicity and differentiation in CD8 T-cells in females',</i> <i><strong>Nature Communications</strong>, <strong>2023</strong>,</i> In Press.</p><p>The dataset contains the following information:</p><p>1, The folder " CoPImmunoPD Flow Zenodo V2.zip " contains all the raw fcs files of flow cytometry analysis and the excel table with marker information of five staining panels in the initial discovery analysis using fresh blood samples. The folder also includes the fcs files of analyzing cytotoxicity potential within CD8 T cells and of validation analyses using cryopreserved samples. Single-color/fluorochrome staining files have also been provided for the relevant experiments in the given subfolders for compensation.</p><p>2, The folder "<a href="https://zenodo.org/api/files/75c910aa-3615-4eff-a201-9d2d46e33ea0/CoPImmunoPD_CyTOF_Zenodo.zip">CoPImmunoPD_CyTOF_Zenodo.zip</a>" contains all the raw fcs files generated from the CyTOF measurements in the initial discovery analysis.</p><p><strong>To reproduce our published Figures, please be assure to first read all the accompanied readme/excel information annotation files deposited in the corresponding folders within the zip files, all the Source Data files of different main and supplementary Figure subpanels, Methods and/or any other relevant sections in our manuscript.</strong></p>
A Phase 3, Long-term, Extension Study of TVP-1012 (1 mg) in Early Parkinson's Disease Participants
ClinicalTrials.gov study NCT02337751. IPD Sharing: YES. Countries: 1. Publications: 1.
A Global Study to Assess the Drug Dynamics, Efficacy, and Safety of Venglustat (GZ/SAR402671) in Parkinson's Disease Patients Carrying a Glucocerebrosidase (GBA) Gene Mutation
ClinicalTrials.gov study NCT02906020. IPD Sharing: YES. Countries: 16. Publications: 3.
A Phase 2/3 Study of TVP-1012 at 0.5 mg or 1 mg in Levodopa Treated Parkinson's Disease Participants
ClinicalTrials.gov study NCT02337738. IPD Sharing: YES. Countries: 1. Publications: 1.
Study Comparing Continuous Subcutaneous Infusion Of ABBV-951 With Oral Carbidopa/Levodopa Tablets For Treatment Of Motor Fluctuations In Adult Participants With Advanced Parkinson's Disease
ClinicalTrials.gov study NCT04380142. IPD Sharing: YES. Countries: 2. Publications: 3.
Neuroplasticity in Parkinson´s Disease After Training
ClinicalTrials.gov study NCT03213873. IPD Sharing: NO. Countries: 1. Publications: 10.
Behavioral or Solifenacin Therapy for Urinary Symptoms in Parkinson Disease
ClinicalTrials.gov study NCT03149809. IPD Sharing: YES. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.