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3,900 results for “Parkinson”
Sign-specific stimulation "hot" and "cold" spots in Parkinson's disease validated with machine learning
<p><strong>Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become a standard therapy for Parkinson’s disease (PD). Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific parkinsonian signs when informed by precise locations of stimulation.</strong></p> <p> </p> <p><strong>We studied 275 PD patients who underwent STN-DBS between 2003 and 2018. We selected pre-DBS and best available post-DBS scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to DBS. Volumes of tissue activated (VTAs) were computed and weighted by i) tremor, ii) rigidity, iii) bradykinesia, and iv) axial signs changes. Then, sign-specific sites of optimal (“hot spots”) and suboptimal efficacy (“cold spots”) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n=51 STN-DBS patients from another institution).</strong></p> <p><strong> </strong></p> <p><strong>Tremor and rigidity hot spots were largely located outside and dorsolateral to STN whereas hot spots for bradykinesia and axial signs had larger overlap with STN. Using VTA overlap with sign-specific hot and cold spots, support vector machine (SVM) classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68-98%) when only considering VTA overlap with hot spots but was markedly lower (41-72%) when only using cold spots. The model also performed poorly (44-48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using VTA overlap with the sign-specific hot and cold spots.</strong></p> <p><br> <strong>In two independent datasets, distinct brain areas could predict sign-specific clinical changes in PD patients with STN-DBS. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement. </strong></p> <p><strong>Hot and cold spots for each sign are publicly available as binary labels in NIfTI format. </strong></p>
Parkinson Research: MARG Sensor Data of the Pronation-Supination Task [old version]
<p>In this ZIP-file you find supplementary data to the manuscript "<strong>Analysis and Visualization of 3D Motion Data for UPDRS Rating of Patients with Parkinson's Disease". </strong>26 subjects (13 PD patients and 13 controls) performed Item 3.6 "Pronation-Supination Movements of Hands" of the MDS-UPDRS [1]. The ZIP-file contains anonymized subject data, 51 data features for each record, results of the different UPDRS ratings from all neurologists and the MARG sensor raw data of the pronation-supination phase in single data files (csv).</p>
Parkinson Research: MARG Sensor Data of the Pronation-Supination Task
<p>In this ZIP-file you find supplementary data to the manuscript "<strong>Analysis and Visualization of 3D Motion Data for UPDRS Rating of Patients with Parkinson's Disease". </strong>26 subjects (13 PD patients and 13 controls) performed Item 3.6 "Pronation-Supination Movements of Hands" of the MDS-UPDRS [1]. The ZIP-file contains anonymized subject data, 51 data features for each record, results of the different UPDRS ratings from six neurologists and the MARG sensor raw data of the pronation-supination phase in single data files (csv).</p>
Figure 3. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 3. - TimelineThis Gantt chart provides an estimate of the relative timing and duration for achieving each of the Aims.
Figure 1. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 1. - First pass at a VPDRS static graphicFigure 1 corresponds to the first self-administered MDS-UPDRS question:1.7 SLEEP PROBLEMS.Over the past week, have you had trouble going to sleep at night or staying asleep through the night? Consider how rested you felt after waking up in the morning.0: Normal: No problems.1: Slight: Sleep problems are present but usually do not cause trouble getting a full night of sleep.2: Mild: Sleep problems usually cause some difficulties getting a full night of sleep.3: Moderate: Sleep problems cause a lot of difficulties getting a full night of sleep, but I still usually sleep for more than half the night.4: Severe: I usually do not sleep for most of the night."
Figure 2. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 2. - Prototype for the mobile phone appThis screen shows a pre-release version of Node, which will support the VPDRS/UPDRS modules. Here we present a means by which a person administering a questionnaire can securely log into and manipulate patient information locally and through cloud services and lastly an example clinician-administered UPDRS question.
Type-II kinase inhibitors that target Parkinson's Disease-associated LRRK2
<p>This dataset includes all of the tabular data used in the tables and figures in the article. Additionally, we provide a table with key resources for data aquisition.</p> <p>Aberrant increases in kinase activity of leucine-rich repeat kinase 2 (LRRK2) are associated with Parkinson’s disease (PD). Numerous LRRK2-selective type-I kinase inhibitors have been developed and some have entered clinical trials. In this study, we present the first LRRK2-selective type-II kinase inhibitors. Targeting the inactive conformation of LRRK2 is functionally distinct from targeting the active-like conformation using type-I inhibitors. We designed these inhibitors using a combinatorial chemistry approach fusing selective LRRK2 type-I and promiscuous type-II inhibitors by iterative cycles of synthesis supported by structural biology and activity testing. Our current lead structures are selective and potent LRRK2 inhibitors. Through cellular assays, cryo-electron microscopy structural analysis, and in vitro motility assays, we show that our inhibitors stabilize the open, inactive kinase conformation. These new conformation-specific compounds will be invaluable as tools to study LRRK2’s function and regulation, and expand the potential therapeutic options for PD.</p>
Inhibition of Parkinson's Disease-related LRRK2 by type-I and type-II kinase inhibitors: activity and structures
<p>Mutations in Leucine Rich Repeat Kinase 2 (LRRK2) are a common cause of familial Parkinson’s Disease (PD), and a risk factor for the sporadic form. Increased kinase activity has been shown in both familial and sporadic PD patients. This has made LRRK2 kinase inhibitors a major focus of drug development efforts in PD. Although significant progress has been made in understanding the structural biology of LRRK2, there are no available structures of LRRK2 inhibitor complexes. To this end, we solved cryo-EM structures of LRRK2, wild-type and PD-linked mutants, bound to the LRRK2-specific type-I inhibitor MLi-2 and the broad-spectrum type-II inhibitor GZD-824. Our structures revealed LRRK2’s kinase in the active-like state, stabilized by type-I inhibitor interactions, and an inactive DYG-out type-II inhibitor complex. The structures also showed how inhibitor-induced conformational changes are affected by the N-terminal half of LRRK2. The structural models provide a template for the rational development of LRRK2 kinase inhibitors covering both canonical inhibitor binding modes.</p>
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>
Fig. 7 in The genus Pandanus Parkinson (Pandanaceae) on Halmahera Island (Moluccas, Indonesia) with descriptions of three new species and a key to the species on the island
Fig. 7. – Pandanus benstoneoides Callm., Buerki & Phillipson (staminate). A. Staminate inflorescence; B. Section of tip of inflorescence showing arrangement of single stamens; C, D. Stamens. [A-G: Phillipson et al. 6488, G] [Drawing: R. L. Andriamiarisoa]
Fig. 8 in The genus Pandanus Parkinson (Pandanaceae) on Halmahera Island (Moluccas, Indonesia) with descriptions of three new species and a key to the species on the island
Fig. 8. – Pandanus halmaherensis Callm. & A. P. Keim. A. Tip of a leaf; B, C. Side view of a drupes; D. Syncarp on peduncle; D. Cross section of a drupe; F. Basal and medium sections of a leaf. [A-F: Callmander, Haris & Mahroji 1078, G] [Drawing: R. L. Andriamiarisoa]
Fig. 5 in The genus Pandanus Parkinson (Pandanaceae) on Halmahera Island (Moluccas, Indonesia) with descriptions of three new species and a key to the species on the island
Fig. 5. – Pandanus beguinii Callm. & A. P. Keim. A. Tip of a leaf; B. Syncarp on peduncle; C. Basal section of a leaf; D. Side view of a drupe; E. Top view of the pileus; F. Cross section of a drupe. [A-G: Callmander, Haris, Lasut & Mahroji 1088, G] [Drawing: R. L. Andriamiarisoa]
Fig. 4 in The genus Pandanus Parkinson (Pandanaceae) on Halmahera Island (Moluccas, Indonesia) with descriptions of three new species and a key to the species on the island
Fig. 4. – Distribution map of the three endemic new species of Halmahera. A. Pandanus beguinii Callm. & A. P. Keim; B. Pandanus benstoneoides Callm., Buerki & Phillipson; C. Pandanus halmaherensis Callm. & A. P. Keim.
Fig. 3 in The genus Pandanus Parkinson (Pandanaceae) on Halmahera Island (Moluccas, Indonesia) with descriptions of three new species and a key to the species on the island
Fig. 3. – Field pictures of Halmahera Pandanus Parkinson species. A, C. Pandanus beguinii Callm. & A. P. Keim (pistillate plant); B. Pandanus beguinii Callm. & A. P. Keim (staminate plant); D. Pandanus dubius Spreng.;E, F. Pandanus halmaherensis Callm. & A. P. Keim; G, H. Pandanus papuanus Solms; I. Pandanus kaernbachii Warb. [photo taken in Seram]; J. Pandanus conoideus Lam. [Photos: A: M. Merello; B-G: M. Callmander; H, J: P. Phillipson; I: A. Keim]
Dataset Related to article "NEURODEVELOPMENTAL DISORDER AND LATE-ONSET DEGENERATIVE PARKINSONISM IN A PATIENT WITH A WDR45 DEFECT"
<p>NGS data (.vcf; .bam; .bam.bai; .csv; .fastq) of patient analysed and sanger sequencing (.abi) for confirm the mutation.</p>
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>
Electrophysiological data of the paper 'Serotonergic and dopaminergic neurons in the dorsal raphe are differentially altered in a mouse model for parkinsonism'
<p>This excel data set contains the electrophysiological data presented in the paper including figure 1I, 1J, figure 3, figure 5, suppl. figure 2A, suppl. figure 3, suppl. figure 6E, 6G, 6L & 6N.</p> <p> </p> <p>More information about how the data was extracted can be found in the materials and methods section of the paper. </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>
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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)
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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.