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3,476 results for “Parkinsons Disease”
Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
Monipar Database: smartwatch movement data to monitor motor competency in subjects with Parkinson's disease
<p>Movement data was collected through smartwatches to monitor motor competence in subjects with Parkinson's Disease (PD). The data set collected for the Monipar study consists of triaxial acceleration data from 21 subjects with PD and 7 healthy control subjects when performing a set of physical exercises while wearing an off-the-shelf smartwatch. Each participant performed the complete set of eight exercises once a week, commonly on the same day and at a similar time. Three Matlab files are provided that contain the raw data of the experimental subgroups: (1) Supervised, (2) Remote, and (3) Healthy control. Additionally, two Matlab files are provided containing the Tremor Labels for selected subjects in the experimental subgroups: (1) Supervised and (2) Remote.</p><p>While the implementation of the experimental protocol for collecting movement data followed a consistent approach for all participants, three distinct experimental subgroups were established:</p><p>Remote group: This subgroup consisted of individuals diagnosed with Parkinson's disease (PD) who completed the experimental protocol at their regular PD association.</p><p>Supervised group: This subgroup comprised PD patients who underwent the experimental protocol under circumstances similar to the remote group. Additionally, clinical scoring (MDS-UPDRS) is reported for this group in the file "MONIPAR SUBJECTS DATA.xlsx"</p><p>Healthy control group: This subgroup consisted of healthy participants who performed exercises under the supervision of research project team members.</p><p>Data was collected using a sample rate of 50Hz and expressed in m/s^2.</p><p>Check the "Monipar_README.txt" file for details about this dataset. Further details are contained in the following reference -- if you use this dataset, please cite:</p><p>Sigcha, L., Polvorinos-Fernández, C., Costa, N., Costa, S., Arezes, P., Gago, M., ... & Pavón, I. "<strong>Monipar: Movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones</strong>". <i>Frontiers in Neurology</i>, <i>14</i>, 1326640. <a href="https://doi.org/10.3389/fneur.2023.1326640">https://doi.org/10.3389/fneur.2023.1326640</a></p><p>References:</p><p>Sigcha, L. et al. (2022). Bradykinesia Detection in Parkinson's Disease Using Smartwatches' Inertial Sensors and Deep Learning Methods. Sensors 11, 3879</p><p>Sigcha, L. et al. (2021). Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks. Sensors 21, 291.</p><p><strong>Funding:</strong></p><p>This research was funded by the following projects:</p><p>(1) "Tecnologías Capacitadoras para la Asistencia, Seguimiento y Rehabilitación de Pacientes con Enfermedad de Parkinson". Centro Internacional sobre el envejecimiento, CENIE (código 0348_CIE_6_E) Interreg V-A España-Portugal (POCTEP).</p><p>(2) FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.</p>
IMU data captured unobtrusively and in-the-wild by Parkinson's disease patients and healthy controls
<p><strong>DATASET</strong></p> <p>The dataset contains IMU signals captured in-the-wild via the accelerometer sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). A group of 31 PD patients and 14 Healthy controls contributed accelerometer data using their personal smartphones, for a period spanning many months.Tri-axial acceleration values were recorded automatically whenevera phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article.</p> <p>The data is presented as a list of python dictionaries, stored in a pickle file. Each dictionary in the list, corresponds to one subject and containes the following fields:</p> <p>1. subject_id: scalar<br> A numerical value that uniquely identifies the subject.</p> <p>2. subject_sessions: list of numpy.array<br> A list of numpy arrays of shape (N, 4) that contains the tri-axial accelerometer sessions that the subject contributed. N denotes the total length of the session in samples (which varies from session to session) Column 0 of the array contains the timestamps of the accelerometer samples. Columns 1-3 contain the acceleration values across the x,y,z directions.</p> <p>3. session_datetimes: list of datetime objects <br> A list of datetime objects that denote the capturing date and time of the corresponding entries in the subject_sessions field.</p> <p>4. annotation: dict<br> A dictionary containing the following tremor-related annotation values:<br> * updrs16: scalar int<br> The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject.</p> <p>* updrs20_right: scalar int in range [0, 4]<br> The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs20_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* updrs21_right: scalar int in range [0, 4]<br> The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs21_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* sp_expert: scalar int in range [0, 1]<br> A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article.<br> A '1' value indicates that the subject has tremor.<br> A '0' value indicates that the subject doesn't have tremor.</p> <p>* pd_status: scalar int in range [0, 1]<br> A '1' value indicates that the subject is a PD patient.<br> A '0' value indicates that the subject is a Healthy Control</p> <p>Note: Each annotation value refers to the subject as a whole, and not in any one session.<br> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><strong>LICENSE</strong></p> <p>This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p> <p> </p> <p><strong>WARRANTY</strong></p> <p>This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset. </p>
Smartwatch gait dataset in simulated Parkinson's disease restricted arm swing conditions
<p>Movement data was collected through smartwatches to monitor gait impairments in healthy subjects. </p> <p>The dataset collected for this study consists of triaxial acceleration and triaxial gyroscope data from 24 subjects when performing a set of gait activities while wearing a smartwatch in their preferred wrist. Each participant performed three gait activities twice, 30 meters straight walk while carrying progressively heavier loads (0 kg, 2 kg, and 4 kg) to simulate restricted arm swing. So, considering that there were 24 participants, 3 different activities and each activity performed twice, a total of 144 data files were obtained.</p> <p>Data was collected using a sample rate of 50Hz. Acceleration is expressed in m/s^2 and gyroscope data in rad/s.</p> <p>Check the " Bioclite_Restricted_Arm_Swing_Data_README.txt" file for details about this dataset.</p> <p><strong>Funding:</strong></p> <p>This research was funded by the following projects:</p> <div> <p>(1) Proyectos de Generación de Conocimiento 2021. PID2021-123708OB-I00, funded by MCIN/AEI/10.13039/ 501100011033/ FEDER, EU</p> </div>
A disease-specific functional connectome of Parkinson's disease patients.
<p>The contribution of the presented work is a data record featuring a disease-specific dataset of resting state functional magnetic resonance imaging (rs-fMRI) acquisitions of 75 Parkinson's disease (PD) patients prior to deep brain stimulation (DBS) implantation - the Tor-PD connectome. Specifically, the dataset comprises 77 matrices (in the folder entitled 'vol'), each containing blood-oxygen-level-dependent-signal (BOLD) signal values of every voxel in 77 corresponding rs-fMRI acquisitions in standard MNI152 NLIN 2009b space. BOLD signal time-series matrices are given as .mat files and are viewable in MATLAB. In addition to the matrices, we have also provided a mask in NIfTI-1 format corresponding to voxels in standard space where a BOLD signal could be calculated in >80% of the 77 matrices (NaN mask). The data records derived from this work can be obtained through Zenodo (https://zenodo.org/) and Lead DBS (<a href="http://lead-dbs.org/">http://lead-dbs.org/</a>). Crucially, the format of choice can be directly used without further conversion or preprocessing steps with openly available software (<a href="http://lead-dbs.org/">http://lead-dbs.org/</a>).</p>
Transcutaneous Kilohertz High-Frequency Alternating Current at 10 kHz for Upper-Limb Tremor in People with Parkinson's Disease: A double-blind, randomized, crossover study.
<p><strong><span>Abstract: <span>Background/Objectives:</span></span></strong><span> Preclinical studies have evidenced a peripheral nerve blockade with kilohertz high-frequency alternating current (KHFAC) stimulation. It could have a potential effect on aberrant nerve hyperactivity, such as tremor in people with Parkinson’s disease (PwPD). The objective was to investigate the effects of transcutaneous KHFAC at 10 kHz compared with sham intervention on tremor modulation, upper limb motor function, and adverse events in PwPD. <strong>Methods:</strong> This randomized, double-blind, crossover trial included PwPD, who received transcutaneous KHFAC and sham interventions, within a 48h washout period. Measurements were taken pre-intervention, during, immediately after, and 10 minutes post-intervention. The main outcomes were rest, postural, and kinetic tremor acceleration. Secondary outcomes were handgrip strength, nine-hole peg test (NHPT), movement onset time, and adverse events.<strong> </strong></span></p>
Principles of gait encoding in the subthalamic nucleus of people with Parkinson's disease
<p>Disruption of subthalamic nucleus dynamics in Parkinson’s disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson’s disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson’s disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson’s disease.</p>
Functional Near-Infrared Spectroscopy Reveals Delayed Hemodynamic Changes in the Primary Motor Cortex During Fine Motor Tasks and Decreased Interhemispheric Connectivity in Parkinson's Disease Patients
<p>This dataset contains functional near-infrared spectroscopy (fNIRS) data from 20 patients with Parkinson’s disease and 20 age- and sex-matched healthy subjects without movement disorders. There are 3 folders, each corresponding to a different task: a 10-second finger-tapping task, a 2-minute walking task, and a 6-minute resting-state. When using this dataset, please cite our work:</p> <div> <div>Guevara, E., Rivas-Ruvalcaba, F. J., Kolosovas-Machuca, E. S., Ramírez-Elías, M., Zapata, R. D. de L., Ramirez-GarciaLuna, J. L., & Rodríguez-Leyva, I. (2024). Parkinson’s disease patients show delayed hemodynamic changes in primary motor cortex in fine motor tasks and decreased resting-state interhemispheric functional connectivity: A functional near-infrared spectroscopy study. <em>Neurophotonics</em>, <em>11</em>(2), 025004. <a href="https://doi.org/10.1117/1.NPh.11.2.025004">https://doi.org/10.1117/1.NPh.11.2.025004</a></div> <div> <div> <div>Guevara, E., Solana-Lavalle, G., & Rosas-Romero, R. (2024). Integrating fNIRS and machine learning: Shedding light on Parkinson’s disease detection. <em>EXCLI Journal</em>, <em>23</em>, 763–771. <a href="https://doi.org/10.17179/excli2024-7151">https://doi.org/10.17179/excli2024-7151</a></div> <div> <div> <div> <div> <div>Guevara, E., Kolosovas-Machuca, E. S., & Rodríguez-Leyva, I. (2024). Exploring motor cortex functional connectivity in Parkinson’s disease using fNIRS. <em>Brain Organoid and Systems Neuroscience Journal</em>, <em>2</em>, 23–30. <a href="https://doi.org/10.1016/j.bosn.2024.04.001">https://doi.org/10.1016/j.bosn.2024.04.001</a></div> </div> </div> </div> </div> </div> </div> </div>
Dautan et al 2024 " Gut-Initiated Alpha Synuclein Fibrils Drive Parkinson's Disease Phenotypes: Temporal Mapping of non-Motor Symptoms and REM Sleep Behavior Disorder"
<p><span>Parkinson’s disease (PD) is characterized by progressive motor as well as less recognized non-motor symptoms that arise often years before motor manifestation, including sleep and gastrointestinal disturbances. Despite the heavy burden on the patient’s quality of life, these non-motor manifestations are poorly understood. To elucidate the temporal dynamics of the disease, we employed a mice model involving injection of alpha-synuclein (αSyn) pre-formed fibrils (PFF) in the duodenum and antrum as a gut-brain model of Parkinsonism. Using anatomical mapping of αSyn PFF propagation and behavioral and physiological characterizations, we unveil a correlation between post-injection time the temporal dynamics of αSyn propagation and non-motor/motor manifestations of the disease. We highlight the concurrent presence of aggregates in key brain regions, expressing acetylcholine or dopamine and their functions in sleep duration, wakefulness, and particularly REM-associated atonia corresponging to REM behavioral disorder-like symptoms. This study presents a novel and in-depth exploration into the multifaceted nature of PD, unraveling the complex connections between α-synucleinopathies, gut-brain connectivity, and the emergence of non-motor phenotypes.</span></p>
Keystroke timing and pressure data captured during touchscreen typing by early Parkinson's disease patients and healthy controls
<p><strong>DATASET</strong></p> <p>The present dataset comprises keystroke timing and pressure data that correspond to short text excerpts typed by early Parkinson’s disease (PD) patients (n=18) and healthy controls (n=15) on a common touchscreen-equipped smartphone (LG Nexus 5X with a screen of 5.2 inches in diagonal and a resolution of 1080 × 1920 pixels, running native Android 7.0). Subjects were asked to transcribe up to 11 short text excerpts, with the initial one being 200 characters-long and common for all subjects, while the rest were 40-115 characters-long, pseudorandomly drawn from the fairy tale 'The Little Prince'. Data were recorded using a custom Android Operating System input method (keyboard), developed for the purposes of the study. Additional details on exepriment design, material and methods can be found in the related research article mentioned below. </p> <p>Data consist of sequences of raw press and release timestamps (in milliseconds), as well as of values of normalized pressure (0.000-1.000) applied to initiate keystrokes, corresponding to the consecutive keys tapped during the transcription of each text excerpt. Data included in the 'Data' folder are organised in sub-folders per subject. Each sub-folder contains a number of .txt files with each one corresponding to a text excerpt typed by the particular subject. Files are named using the format S##_TEX##.txt, with S## denoting the subject's coded ID and TEX## the serial number of the transcribed text excerpt. For all subjects, file S##_TEX01.txt corresponds to the initial and common 200 characters-long text excerpt. Each file contains the sequences of raw key press/release timestamps (Tp#, Tp#) and normalized pressure (NP#), applied to initiate each keystroke, in the following format:</p> <p>{<br> Press, Tp1, Release, Tr1, NP1<br> Press, Tp2, Release, Tr2, NP2<br> .<br> .<br> . <br> Press, Tpn, Release, Trn, NPn<br> }</p> <p>where 1,2,...,n denote the serial index of the key tapped during typing.</p> <p><em>Note:</em> Out of 33 subjects, 32 managed to transcribe 8 to 11 text excerpts, while the remaining one (Subject ID: 16) typed only 5. Ten subjects (Subject IDs: 6, 14, 16, 17, 25, 27, 29, 31, 32, 33) did not manage to type the initial 200 characters-long excerpt in its entirety.</p> <p>The dataset also includes a record, in Microsoft Excel format (Demographics_Clinical_Characteristics.xlsx), of the demographic and clinical characteristics (with respect to PD) of subjects. Entries of the Excel file are linked to subjects' sub-folders and individual keystroke data text files via the coded ID of the subject.</p> <p>Demographic characteristics included:</p> <p>Age; Gender; Education level; Years of smartphone usage; Dominant hand<sup>1</sup></p> <p>Clinical characteristics included:</p> <p>Group (PD, Control); Years from diagnosis; Hoehn-Yahr disease stage; Most affected side<sup>2</sup>; Levodopa Equivalent Daily Dose; UPDRS_III<sup>3</sup> total score; UPDRS_III Item 21 Tremor-Right hand; UPDRS_III Item 21 Tremor-Left hand; UPDRS_III Item 22 Rigidity-Right hand; UPDRS_III Item 22 Rigidity-Left hand; UPDRS_III Item 23 Finger taps-Right hand; UPDRS_III Item 23 Finger taps-Left hand; UPDRS_III Item 31 Body bradykinesia/ Hypokinesia</p> <p><sup>1</sup>Dominant hand: (Relating to handedness) the operant hand generally used for performing fine motor-skills tasks.<br> <sup>2</sup>Most affected body side by Parkinson's disease<br> <sup>3</sup>UPDRS_III: Unified Parkinson's Disease Rating Scale Part III (Motor section)</p> <p> </p> <p><strong>RELATED RESEARCH</strong></p> <p>This dataset was originally used and described in the OPEN ACCESS publication: </p> <p>[1] Iakovakis, D., Hadjidimitriou, S., Charisis, V., Bostantzopoulou, S., Katsarou, Z., & Hadjileontiadis, L. J. (2018). Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson’s disease. Scientific reports, 8(1), 7663. <a href="http://doi.org/10.1038/s41598-018-25999-0">https://doi.org/10.1038/s41598-018-25999-0</a> </p> <p>All documents and papers that report on research that uses this dataset will acknowledge this by citing the above publication.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected was approved by the Aristotle University of Thessaloniki Bioethics Committee of Medical School (approval no. 359/3.4.17), Thessaloniki, Greece. Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Dimitrios Iakovakis (Electrical & Computer Engineer, PhD candidate)</p> <p>Signal Processing & Biomedical Technology Unit<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building D, 6th floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996319<br> Fax: +30 2310 996312<br> E-mail: dimiiako12@gmail.com</p> <p> </p> <p><strong>LICENSE</strong></p> <p>This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p> <p> </p> <p><strong>WARRANTY</strong></p> <p>This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset. </p>
Visual perturbation of balance suggests impaired motor control but intact visuomotor processing in Parkinson's disease, J Neurophysiol (2021): Data
<p>Data set accompanying the publication:</p> <p>Engel, D., Student, J., Schwenk, J., Morris, A. P., Waldthaler, J., Timmermann, L., & Bremmer, F. (2021). Visual perturbation of balance suggests impaired motor control but intact visuomotor processing in Parkinson's disease. <em>Journal of neurophysiology</em>, <em>126</em>(4), 1076–1089. https://doi.org/10.1152/jn.00183.2021</p>
Peripheral MC1R activation modulates immune responses and confers neuroprotection in a mouse model of Parkinson's disease
<p>Raw data sets for the manuscripts</p> <p>This work was supported by NIH grants R01NS102735 and R01NS110879, the Farmer Family Foundation Initiative for Parkinson’s Disease Research and the MJFF and ASAP [ASAP-000312].</p>
Atrophy Pattern Maps of Alzheimer's Disease, Mild Cognitive Impairment, Parkinson's Disease, and Frontotemporal Dementia
<p>The files contain voxel-wise t-statistics maps contrasting deformation based morphometry (DBM) measurements of Alzheimer's disease (AD), Parkinson's disease (PD), mild cognitive impairment (MCI), and fronto-temporal dementia (FTD) patients against matched normal controls.</p> <p>AD and MCI maps are based on ADNI data, available at:</p> <p>PD map is based on PPMI data, available at:</p> <p>FTD map is based on NIFD data, available at:</p> <p>For more information regarding the participants and method details, see:</p> <p>Dadar, Mahsa, et al. "White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer's disease and frontotemporal dementia." <em>Neurobiology of aging</em> 111 (2022): 54-63.</p>
Alterations in RNA editing in skeletal muscle following exercise training in individuals with Parkinson's disease
<p>Parkinson’s Disease (PD) is the second most common neurodegenerative disease behind Alzheimer’s Disease, currently affecting more than 10 million people worldwide. The progression of PD results in the loss of function due to neurodegeneration and neuroinflammation. The etiology of PD is multifactorial, including both genetic and environmental origins. We explored changes in RNA editing, specifically editing through the actions of the Adenosine Deaminases Acting on RNA (ADARs), in the progression of PD. Analysis of ADAR editing of skeletal muscle transcriptomes from PD patients and controls, including those that engaged in a rehabilitative exercise training program revealed significant differences in ADAR editing patterns based on age, disease status, and following rehabilitative exercise. Further, deleterious editing events in protein coding regions were identified in multiple genes with known associations to PD pathogenesis. Our findings of differential ADAR editing complement findings of changes in transcriptional network identified by a recent Lavin et al. 2020 (<a href="https://doi.org/10.3389/fphys.2020.00653">https://doi.org/10.3389/fphys.2020.00653)</a> study and offer insights into dynamic ADAR editing changes associated with PD pathogenesis. VCF files were generated using AIDD (Plonski et al., 2020) (<a href="https://doi.org/10.1186/s12859-020-03888-6">https://doi.org/10.1186/s12859-020-03888-6</a>).</p>
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>
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>
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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.
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.