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3,476 results for “Parkinson disease”
Brain regional and cell type-specific chromatin changes among sporadic, GBA1 variant, and APOE E4 Parkinson’s Disease
GEO Series GSE273511. Homo sapiens. 426 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Serum microRNA Profiling at an Early and Late Stages of Rotenone-Induced Parkinson’s Disease in Rats
GEO Series GSE301539. Rattus norvegicus. 9 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Methodology for objective assessment of Bradykinesia in Parkinson's disease
<p>Parkinson's disease (PD) is a potentially, devastating and multifactorial neurodegenerative disorder, linked to genetic and environmental factors, which compromises motor and non-motor functions. One difficulty in evaluating the PD progress is related to the prolonged time for clinical assessments and questionnaires application, the complexity and heterogeneity of motor impairments and the need for a specialized analysis. Bradykinesia is a Parkinson's disease symptoms, characterized as the slowness in the execution of movements, such as the reaction time to start the movements, the increase the range of movement during the execution of the tasks time for the duration of the motor pattern, the rapid fatigue in performing prolonged tasks and changing rhythm during activities. The assessment of bradykinesia severity can be influenced by the examiner's time of experience, by the patient's cooperation, as by the variability of inter-examiner results. The Movement Disorder Society (MDS) highlighted the need to develop and validate technologies for remote assessment of motor condition in PD. In this sense, this research presents two distinct strategies for the bradykinesia assessment, which are: (i) intra and inter-rater remote assessment of bradykinesia in Parkinson's disease and (ii) objective assessment, using inertial sensors, with the aim of discriminating the bradykinesia components: slowness, amplitude and rhythm. To the first objective (i), a mediation team performed and recorded the clinical evaluation MDS-UPDRS (Movement Disorder Society – Unified Parkinson's Disease Rating Scale) and edited all the videos, to later available the videos to 14 evaluators with varying experience, between 3 and 10 years, in Parkinson's disease evaluation. For intra and inter-rater analysis, statistical methods were applied between evaluations, Cohen's Kappa, intraclass coefficient correlation (ICC) and the Bland-Altman method. The correlation results obtained between experience raters are strong. Difficulty in clinical assessments can be influenced by lack of training and difficulty using clinical scales. Correlation matches also revealed low agreement due inexperience and poorly trained examiners. Despite flaws in clinical assessments, a large number of clinical raters, using blinded methods and gold standard rating scales, provide a more robust "fundamental truth" of bradykinesia assessment. It is important to highlight that the entire study execution was only possible due the mediator group compliance with the protocol. The effectiveness and feasibility of telemedicine during the COVID-19 pandemic is considered a breakthrough in medical care, enabling remote access, even outside the pandemic context. In the second objective of thesis (ii), the aim was to evaluate the bradykinesia components: slowness, amplitude and rhythm, extracted from inertial signals. The experimental protocol consisted of four tasks perform: finger tapping, open and close the hand, pronation and supination and flexion and extension. For that, inertial sensors were positioned at the upper limb for movements analysis. Fifteen individuals with Parkinson's disease and fifteen healthy individuals with matched sex and age performed this protocol. Signal processing was performed in R software and three feature extraction methods were used. The algorithm SFA (Slow Feature Analysis) was applied to extract the slowness characteristic. An index was created to extract the amplitude, based on the total amplitude and interquartile amplitude, and the rhythm was quantified by the variation coefficient. The component indices were correlated to the clinical assessment and statistical metrics for inter-group comparison. The obtained results indicate strong correlations between the bradykinesia components and clinical assessments, between -0.9 and -0.8 for amplitude and slowness, respectively. And for intergroup comparisons, the three indices showed a statistically significant difference. With this study, it can be concluded that the processing tools chosen to extract the characteristics of the inertial sensors were adequate, innovated and efficient in the discrimination of the components of bradykinesia.</p>
TULIP Dataset (CVPR 2024): Multi-Camera Videos and Clinician Ratings of the MDS-UPDRS Part III Motor Exam for Parkinson's Disease Assessment
<div> <p><strong>TULIP Dataset (Version 1.0.3)</strong></p> </div> <div> <p>** Updates! We include one more subject, so total we have 12 subjects in this dataset.</p> </div> <div> <p><strong>Overview:</strong><br>The TULIP (Three-dimensional Understanding and Learning of Impairments in Parkinson’s) dataset provides high-resolution RGB data from multi-camera setups, supporting research on precision motor assessments for Parkinson’s Disease (PD). Version 1.0.0 features synchronized RGB data from six cameras, capturing multiple angles of PD and healthy participants performing clinically relevant motor tasks. We chose the name TULIP, a nod to the floral emblem of PD research and advocacy, to symbolize our goal for this dataset, to foster transformative new machine learning approaches for PD understanding and treatment.</p> </div> <div>This dataset was published as part of our <a title="https://openaccess.thecvf.com/content/CVPR2024/html/Kim_TULIP_Multi-camera_3D_Precision_Assessment_of_Parkinsons_Disease_CVPR_2024_paper.html" href="https://openaccess.thecvf.com/content/CVPR2024/html/Kim_TULIP_Multi-camera_3D_Precision_Assessment_of_Parkinsons_Disease_CVPR_2024_paper.html" target="_blank" rel="noopener noreferrer">CVPR 2024 Paper.</a></div> <div> </div> <div>Kyungdo Kim, Sihan Lyu, Sneha Mantri, Timothy W. Dunn; <em>TULIP: Multi-camera 3D Precision Assessment of Parkinson's Disease</em>; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 22551-22562</div> <p> </p> <p><strong>Data Contents:</strong><br>RGB videos are organized by subject ID and activity, with synchronized recordings from six camera perspectives per activity. Each video documents motor tasks such as gait and finger tapping, in line with the UPDRS standards for PD, allowing for a detailed study of joint angles, tremors, and other movements key to understanding PD progression.</p> <p>· <em><strong> </strong>Multi-Camera Video Data:</em> RGB videos from six cameras enable robust 3D pose extraction.</p> <p>· <em>Metadata: </em>Includes camera parameters (intrinsic and extrinsic matrices for 3D reconstruction) and task descriptions.</p> <div> <p>· <em>Clinical Examination Labels:</em> Task-specific labels aligned with clinical motor assessments, with annotations from three clinicians to aid in automated scoring models. We also provided the labels in the csv file format.</p> </div> <div> <p> </p> </div> <p><strong>File Structure:</strong><br>Data is organized by Subject ID > Activities > Camera Perspective, with each folder containing RGB video files. Annotations and activity labels are available in a CSV file for easy correlation of tasks with motor patterns. For the camera parameters (pickle file), each subject has its own set of parameters. When you open the pickle file, the order of the elements is as follows: [proj_matrices, cam_matrices, extrinsic_matrices, rmatrices, rvecs, tvecs, distcoeffs]. Here’s a brief description of each:</p> <p>· <em>proj_matrices: </em>Camera projection matrix (3x4 format)</p> <p>· <em>cam_matrices:</em> Intrinsic camera matrix</p> <p>· <em>extrinsic_matrices:</em> Extrinsic camera matrix</p> <p>· <em>rmatrices:</em> Rotation matrix</p> <p>· <em>rvecs:</em> Rotation vector</p> <p>· <em>tvecs:</em> Translation vector</p> <div> <p>· <em>distcoeffs:</em> Distortion coefficients, which is a zero matrix in our case.</p> </div> <div> <p> </p> </div> <p><strong>Privacy and Consent:</strong><br>Faces are blurred to ensure privacy. This initial version of the dataset contains data from 12 participants, with data from the remaining 3 participants expected to be released in the near future.</p> <p><strong>Code Availability:</strong><br>Behavioral feature extraction demo code for 3D poses will be available on our github (github link can be found on the <a title="https://www.tulipproject.net/" href="https://www.tulipproject.net/" target="_blank" rel="noopener noreferrer">TULIP Project</a> page). For further details and access to our publication on TULIP data and baseline projects, please visit <a title="https://www.tulipproject.net/" href="https://www.tulipproject.net/" target="_blank" rel="noopener noreferrer">TULIP Project</a> or <a title="https://openaccess.thecvf.com/content/CVPR2024/html/Kim_TULIP_Multi-camera_3D_Precision_Assessment_of_Parkinsons_Disease_CVPR_2024_paper.html" href="https://openaccess.thecvf.com/content/CVPR2024/html/Kim_TULIP_Multi-camera_3D_Precision_Assessment_of_Parkinsons_Disease_CVPR_2024_paper.html" target="_blank" rel="noopener noreferrer">CVPR 2024 Paper</a>.</p> <p><strong>Citing this dataset:</strong></p> <p>Please cite our CVPR paper if use this dataset in your work. Citation:</p> <p>Kyungdo Kim, Sihan Lyu, Sneha Mantri, Timothy W. Dunn; <em>TULIP: Multi-camera 3D Precision Assessment of Parkinson's Disease</em>; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 22551-22562</p>
Dataset related to the article: Exercise Intolerance and Oxygen Desaturation in Patients with Parkinson's Disease: Triggers for Respiratory Rehabilitation?
<p>Dataset related to the article:</p> <p>Vitacca, M.; Olivares, A.; Comini, L.; Vezzadini, G.; Langella, A.; Luisa, A.; Petrolati, A.; Frigo, G.; Paneroni, M. Exercise Intolerance and Oxygen Desaturation in Patients with Parkinson’s Disease: Triggers for Respiratory Rehabilitation? <em>Int. J. Environ. Res. Public Health</em> <strong>2021</strong>, <em>18</em>, 12298.</p> <p>Abstract: The role that oxygen desaturation plays in exercise tolerance and its rehabilitative implications<br> in patients with Parkinson’s disease (PD) are unclear. We aimed to test exercise tolerance and<br> oxygen saturation levels both during exercise and at night in PD patients to better define their rehabilitative<br> needs. In clinically stable PD patients, undergoing inpatient rehabilitation, and in “ON” phase,<br> we prospectively assessed clinical data, sleepiness, comorbidities, PD severity (Hoehn&Yahr, HY),<br> motor function (ADLs, UPDRSII and UPDRSIII, Barthel Index, Functional Independence Measure),<br> balance, spirometry, respiratory muscles (MIP/MEP), peak cough expiratory flow (PCEF), continuous<br> night oxygen monitoring, and meters at 6MWT. Of 55 patients analyzed (28 with moderate–severe PD,<br> HY 2.5), 37% and 23% showed moderate–severe impairment on UPDRSII and UPDRSIII, respectively;<br> 96% had reduced exercise tolerance and severe respiratory muscles impairment (MIP/MEP<br> < 45% pred.); 21.8% showed desaturations during exercise; and 12.7% showed nocturnal desaturations.<br> At multiple regression, low exercise tolerance and low mean nocturnal and exercise-induced<br> saturation correlated with several respiratory and motor function and disability indices (all p <<br> 0.03). Exercise tolerance, exercise-induced desaturations, and nocturnal desaturations were extremely<br> frequent in PD patients and were worse in more severe PD patients. This suggests considering a<br> combined role for motor and respiratory rehabilitation in these patients.</p>
Dataset related to: Voice alterations, dysarthria and respiratory derangements in patients with Parkinson's disease
<p>Dataset related to the article:</p> <p>Di Pietro DA, Olivares A, Comini L, Vezzadini G, Luisa A, Petrolati A, Boccola S, Boccali E, Pasotti M, Danna L, Vitacca M. Voice Alterations, Dysarthria, and Respiratory Derangements in Patients With Parkinson's Disease. J Speech Lang Hear Res. 2022 17;65(10):3749-3757.</p> <p>Purpose: Almost 90% of people with Parkinson’s disease (PD) develop voice and speech disorders during the course of the disease. Ventilatory dysfunction is one of the main causes. We aimed to evaluate relationships between respiratory impairments and speech/voice changes in PD.<br> Method: At Day 15 from admission, in consecutive clinically stable PD patients in a neurorehabilitation unit, we collected clinical data as follows: comorbidities, PD severity, motor function and balance, respiratory function at rest (including muscle strength and cough ability), during exercise-induced desaturation and at night, voice function (Voice Handicap Index [VHI] and acoustic analysis [Praat]),<br> speech disorders (Robertson Dysarthria Profile [RDP]), and postural abnormalities. Based on an arbitrary RDP cutoff, two groups with different dysarthria degree were identified—moderate–severe versus no–mild dysarthria—and compared.<br> Results: Of 55 patients analyzed (median value Unified Parkinson’s Disease Rating Scale Part II 9 and Part III 17), we found significant impairments in inspiratory and expiratory muscle pressure (> 90%, both), exercise tolerance at 6-min walking distance (96%), nocturnal (12.7%) and exercise-induced (21.8%) desaturation, VHI (34%), and Praat Shimmer% (89%). Patients with moderate–severe dysarthria (16% of total sample) had more comorbidities/disabilities and worse respiratory pattern and postural abnormalities (camptocormia) than those with no–mild dysarthria. Moreover, the risk of presenting nocturnal desaturation,<br> reduced peak expiratory flow, and cough ability was about 11, 13, and 8 times higher in the moderate–severe group.<br> Conclusions: Dysarthria and respiratory dysfunction are closely associated in PD patients, particularly nocturnal desaturation and reduced cough ability. In addition, postural condition could be at the base of both respiratory and voice impairments.</p>
Dataset related to: "Perceptual and qualitative voice alterations detected by GIRBAS in patients with Parkinson's disease: is there a relation with lung function and oxygenation?"
<p>We provide the raw data used for the following article:</p> <p>Olivares A, Comini L, Di Pietro DA, Vezzadini G, Luisa A, Boccali E, Boccola S, Vitacca M. <strong>Perceptual and qualitative voice alterations detected by GIRBAS in patients with Parkinson's disease: is there a relation with lung function and oxygenation?</strong> Aging Clin Exp Res. 2023 Mar;35(3):633-638. doi: 10.1007/s40520-022-02324-4. </p> <p>Abstract</p> <p><strong>Background: </strong>Impairments in respiration, voice and speech are common in people with Parkinson's disease (PD).</p> <p><strong>Aims: </strong>To evaluate the prevalence of dysphonia, assessed by a specific acoustic evaluation and description of the voice by the speech therapist (GIRBAS), and its relation with lung function and oxygenation, in particular cough ability and during the night or exercise desaturation.</p> <p><strong>Methods: </strong>This is a posthoc analysis of a prospective cross-sectional observational study on PD patients collecting anthropometric and clinical data, comorbidities, PD severity, motor function and balance, respiratory function at rest, during exercise and at night, voice function with acoustic analysis and presence of speech disorders, in addition to the GIRBAS scale. Based on GIRBAS Global dysphonia ('G') score, we divided patients into dysphonic (moderate-to-severe deviance from the euphonic condition) vs. no/mild dysphonic and analyzed the relations with respiratory impairments.</p> <p><strong>Results: </strong>We analyzed 55 patients and found significant impairments in both respiratory and voice/speech functions. Most patients (85.5%) presented mild-to-severe deviance from the euphonic condition in at least one GIRBAS perceptual element (80% of cases for Global dysphonia) and only 14.5% did not show deviance in all elements simultaneously. At Odds Ratio analysis, the risk of presenting nocturnal desaturation and reduced peak cough expiratory flow was approximately 24 and 8 times higher, respectively, in dysphonic patients vs. those with no/mild dysphonia.</p> <p><strong>Conclusion: </strong>Perceptual and qualitative evaluation of the voice with GIRBAS showed that mild-to-severe dysphonia was highly prevalent in PD patients, and associated with nocturnal oxygen desaturation and poor cough ability.</p>
Compassionate Use Study of Pergolide in Patients With Parkinson's Disease
ClinicalTrials.gov study NCT00624741. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Intermediate Size Expanded Access Protocol Using Autologous HB-adMSCs for the Treatment of Parkinson's Disease
ClinicalTrials.gov study NCT06056427. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Parkinson's disease (PD) genetic asymmetry in neuronal nuclei from paired hemisphere samples of prefrontal cortex (ATAC-Seq)
GEO Series GSE264549. Homo sapiens. 69 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
SNP genotype data from induced Pluripotent Stem cells from Parkinson's Disease patients harboring mutations in the GBA1 gene, and from healthy control donors
GEO Series GSE99471. Homo sapiens. 13 samples. Type: SNP genotyping by SNP array; Genome variation profiling by SNP array.
Preclinical quality, safety and efficacy of a human embryonic stem cell-derived product for treatment of people with moderate Parkinson’s Disease (STEM-PD)
GEO Series GSE229769. Homo sapiens. 9 samples. Type: Genome variation profiling by SNP array; SNP genotyping by SNP array; Other.
Integrating network pharmacology, transcriptomics to reveal neuroprotective of curcumin activate PI3K / AKT pathway in Parkinson’s disease
GEO Series GSE243886. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
GPNMB confers risk for Parkinson’s disease through interaction with alpha-synuclein
GEO Series GSE206327. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
Systematic characterization of LUHMES cell-based Parkinson’s disease models reveals potential novel drug targets.
GEO Series GSE287941. Homo sapiens. 22 samples. Type: Expression profiling by high throughput sequencing.
Parkinson Disease Frontal Cortex Transcriptomic analysis
GEO Series GSE156928. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Gut microbiome dysregulation is associated with elevated toxic bile acids in Parkinson’s disease
GEO Series GSE156647. mouse gut metagenome. 40 samples. Type: Other.
Gene expression changes in multiple brain regions of a mouse MPTP model of Parkinson's disease
GEO Series GSE7707. Mus musculus. 18 samples. Type: Expression profiling by array.
Differential 5-methylcytosine and 5-hydroxymethylcytosine in Parkinson’s and Alzheimer’s Disease.
GEO Series GSE138597. Homo sapiens. 36 samples. Type: Methylation profiling by array.
Single-Cell Atlas of Neuroglial Dynamics in SNCA-A53T Parkinson's Disease Model
GEO Series GSE306642. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
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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)
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.
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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.
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