In 2022, there were 4,289 deaths from Parkinson's disease in the state of California, the highest number of any state. This statistic presents the number of Parkinson's disease deaths in the U.S. in 2022, by state.

In 2022, there were over 12 deaths from Parkinson's disease per 100,000 population in the state of Utah, the highest rate of death from Parkinson's disease among all U.S. states. This statistic presents the death rate from Parkinson's disease in the U.S. in 2022, by state.

The death rate from Parkinson’s disease has gradually increased over the last decade. Parkinson’s disease is a nervous system disorder that progressively impacts movement. Symptoms include tremors, slowed movement, rigid muscles, speech changes and the loss of automatic movements. These symptoms start mildly but become more severe as the disease progresses. Parkinson’s disease death Parkinson’s disease is usually not considered a deadly disease, and many people with the disease have relatively normal life expectancies. Death from Parkinson’s disease results from complications arising from advanced symptoms, such as problems swallowing or falls. Therefore, the death rate from Parkinson’s disease increases significantly with age. Living with Parkinson’s disease It is predicted that by the year 2030 there will be around *** million people living with Parkinson’s disease in the United States. A survey of Parkinson’s disease patients showed that some of the most common symptoms from the disease included fatigue, tremors or shaking, trouble sleeping, and changes in handwriting. Common cognitive changes involved concentration and attention span, verbal fluency, information processing, and memory.

Welcome to the resting state EEG dataset collected at the University of San Diego and curated by Alex Rockhill at the University of Oregon.

Please email arockhil@uoregon.edu before submitting a manuscript to be published in a peer-reviewed journal using this data, we wish to ensure that the data to be analyzed and interpreted with scientific integrity so as not to mislead the public about findings that may have clinical relevance. The purpose of this is to be responsible stewards of the data without an "available upon reasonable request" clause that we feel doesn't fully represent the open-source, reproducible ethos. The data is freely available to download so we cannot stop your publication if we don't support your methods and interpretation of findings, however, in being good data stewards, we would like to offer suggestions in the pre-publication stage so as to reduce conflict in published scientific literature. As far as credit, there is precedent for receiving a mention in the acknowledgements section for reading and providing feedback on the paper or, for more involved consulting, being included as an author may be warranted. The purpose of asking for this is not to inflate our number of authorships; we take ethical considerations of the best way to handle intellectual property in the form of manuscripts very seriously, and, again, sharing is at the discretion of the author although we strongly recommend it. Please be ethical and considerate in your use of this data and all open-source data and be sure to credit authors by citing them.

An example of an analysis that we could consider problematic and would strongly advice to be corrected before submission to a publication would be if a machine learning approach was used and a high accuracy was achieved for classifying Parkinson's disease patient EEG data from healthy control participant EEG data without cross-validation and only using this dataset. This could potentially mislead patients or people who are interested in knowing if they have Parkinson's disease, because without cross-validation and validation with another dataset, there would not be good evidence that the classifier would apply to a novel EEG recording from a novel setup, and reporting such a high accuracy number could be seriously misleading to the public and Parkinson's patients in particular.

Note that UPDRS rating scales were collected by laboratory personnel who had completed online training and not a board-certified neurologist. Results should be interpreted accordingly, especially that analyses based largely on these ratings should be taken with the appropriate amount of uncertainty.

In addition to contacting the aforementioned email, please cite the following papers:

Nicko Jackson, Scott R. Cole, Bradley Voytek, Nicole C. Swann. Characteristics of Waveform Shape in Parkinson's Disease Detected with Scalp Electroencephalography. eNeuro 20 May 2019, 6 (3) ENEURO.0151-19.2019; DOI: 10.1523/ENEURO.0151-19.2019.

Swann NC, de Hemptinne C, Aron AR, Ostrem JL, Knight RT, Starr PA. Elevated synchrony in Parkinson disease detected with electroencephalography. Ann Neurol. 2015 Nov;78(5):742-50. doi: 10.1002/ana.24507. Epub 2015 Sep 2. PMID: 26290353; PMCID: PMC4623949.

George JS, Strunk J, Mak-McCully R, Houser M, Poizner H, Aron AR. Dopaminergic therapy in Parkinson's disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control. Neuroimage Clin. 2013 Aug 8;3:261-70. doi: 10.1016/j.nicl.2013.07.013. PMID: 24273711; PMCID: PMC3814961.

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8.

Note: see this discussion on the structure of the json files that is sufficient but not optimal and will hopefully be changed in future versions of BIDS: https://neurostars.org/t/behavior-metadata-without-tsv-event-data-related-to-a-neuroimaging-data/6768/25.

Parkinson’s disease is the second-most common neurodegenerative condition after Alzheimer’s disease, affecting approximately 1 million people in the United States. The incidence of Parkinson’s disease is reported to increase with age, with men 1.5 times more likely to develop the condition than women.

This statistic shows the projected number of people in the U.S. with Parkinson's disease in 2020 and 2030. In 2020, there are expected to be around 930,000 people with Parkinson's disease. This number is expected to reach over one million by the year 2030.

Dataset Description

This dataset is part of a longitudinal study investigating Parkinson's Disease (PD) and its associated cognitive impairments. Resting-state fMRI data were collected from participants, including healthy controls (HC) and Parkinson's Disease patients with normal cognition (PD-NC) or mild cognitive impairment (PD-MCI). The dataset is organized following the Brain Imaging Data Structure (BIDS) specifications.

License

This dataset is shared under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. Please acknowledge this dataset in publications by citing it appropriately.

Study Information

• Title: Resting State MRI data from healthy control (HC), Parkinson's disease with normal cognition (PD-NC), and Parkinson's disease with mild cognitive impairment (PD-MCI) cohorts
• Principal Investigator: James E. Galvin
• Ethics Approval: This study was approved by the Institutional Review Board (IRB) at New York University (NYU). All participants provided informed consent.

Data Acquisition

MRI Details

• Scanner Manufacturer: Siemens
• Model: TrioTim
• Magnetic Field Strength: 3 Tesla
• Sequence: Echo Planar Imaging (EPI)
• Repetition Time (TR): 2000 ms
• Echo Time (TE): 29 ms
• Flip Angle: 90°
• Slice Thickness: 3.5 mm
• Spacing Between Slices: 3.5 mm

Dataset Structure

The dataset contains the following main directories and files:

• /participants.json: Contains metadata describing participant demographics and group information.
• /sub-<label>/: Subdirectories for each participant containing:
  • anat/: Anatomical MRI data (T1-weighted images).
  • func/: Functional MRI data (resting-state).

Key Files

participants.tsv

A tab-separated file containing participant demographics, group, and other relevant information.

task-rest_bold.json

Metadata file describing the resting-state functional MRI task. Key fields include: - TaskName: "rest" - Modality: "MR" - TR: 2000 ms - EchoTime: 29 ms

Usage Notes

• Please refer to the associated publication for detailed study design and analysis pipelines.

Acknowledgments

We acknowledge the contributions of all study participants and research team members. Special thanks to the Micheal J. Fox Foundation for supporting this research.

In 2022, about 273,600 people were treated for Parkinson's disease in France. That year, with roughly 170,900 patients, the vast majority of Parkinson's patients were aged 75 and older. Nonetheless, 68,400 people treated for Parkinson's disease were aged 65 to 74 during that year.

United States Deep Brain Stimulation In Parkinson’s Disease Market was valued at USD 522.01 million in 2023 and is anticipated to project impressive growth in the forecast period with a CAGR of 7.10% through 2029.

The data are comprised of 20 PD patients and 20 age-matched controls which include both T1 and resting-state scans.

BackgroundDeep brain stimulation (DBS) improves motor and non-motor symptoms in patients with Parkinson’s disease (PD). Researchers mainly investigated the motor networks to reveal DBS mechanisms, with few studies extending to other networks. This study aimed to investigate multi-network modulation patterns using DBS in patients with PD.MethodsTwenty-four patients with PD underwent 1.5 T functional MRI (fMRI) scans in both DBS-on and DBS-off states, with twenty-seven age-matched healthy controls (HCs). Default mode, sensorimotor, salience, and left and right frontoparietal networks were identified by using the independent component analysis. Power spectra and functional connectivity of these networks were calculated. In addition, multiregional connectivity was established from 15 selected regions extracted from the abovementioned networks. Comparisons were made among groups. Finally, correlation analyses were performed between the connectivity changes and symptom improvements.ResultsCompared with HCs, PD-off showed abnormal power spectra and functional connectivity both within and among these networks. Some of the abovementioned abnormalities could be corrected by DBS, including increasing the power spectra in the sensorimotor network and modulating the parts of the ipsilateral functional connectivity in different regions centered in the frontoparietal network. Moreover, the DBS-induced functional connectivity changes were correlated with motor and depression improvements in patients with PD.ConclusionDBS modulated the abnormalities in multi-networks. The functional connectivity alterations were associated with motor and psychiatric improvements in PD. This study lays the foundation for large-scale brain network research on multi-network DBS modulation.

The data are comprised of 27 PD patients and 16 age-matched normal controls which include both T1 and resting-state scans.

The size of the U.S. Deep Brain Stimulation In Parkinson’s Disease Market market was valued at USD 556.79 million in 2023 and is projected to reach USD 911.18 million by 2032, with an expected CAGR of 7.29 % during the forecast period. The U.S deep brain stimulation (DBS) market for Parkinson’s disease concerns the implantation of electrodes into target zones in the brain for purpose of controlling symptoms such as shaking, rigidity, and bradykinesia. DBS is most commonly applied in the treatment of patients who have Parkinson’s disease that cannot be controlled by medication anymore. Due to its effectiveness in stabilizing motor fluctuations and lessened dependency on medication, it plays a large role helping enhance the quality of life. These applications include dystonia, and essential tremor which are other neurological complications. The two emerging trends present in the market include innovative technologies like directional leads and adaptive stimulation in addition to increase demand for less invasive treatment like DBS. Market segmentation In addition to, rise in Parkinson’s disease occurrence and growing use of neurostimulation therapies evidentially fuels the growth of the market. Recent developments include: In March 2023, Michigan Tech researchers successfully advanced the field of DBS systems for Parkinson's patients by integrating neuromorphic computing. This innovative approach aimed to enhance the efficiency and energy utilization of DBS systems, crucial for managing Parkinson's disease. Neuromorphic computing, often referred to as brain-inspired or neuroscience-powered artificial intelligence (AI), employs microchips and algorithms to replicate the functionality of a nervous system. The application of this technology marks a significant evolution in optimizing the therapeutic capabilities of DBS systems while concurrently addressing energy consumption concerns , In April 2022, Boston Scientific Corp. introduced the company’s latest image-guided programming software, Vercise Neural Navigator with STIMVIEW XT, marking a significant development in the field of DBS. The software was designed to provide clinicians with real-time visualization capabilities for lead placement and stimulation modeling in patients living with Parkinson's disease. This advancement was expected to have a notable impact on the U.S. DBS market , In January 2021, Medtronic initiated the ADAPT-PD trial, assessing the safety and efficacy of Adaptive DBS (aDBS) for Parkinson's disease. This investigational feature, part of the Percept PC device, was expected to enable automated brain stimulation adjustments based on a patient's clinical state, potentially revolutionizing personalized therapy if approved , In January 2020, Medtronic’s Percept PC neurostimulator received CE mark approval. This DBS system was the device to be launched in Europe with BrainSense technology that can record and sense brain signals while delivering therapy to patients with Parkinson’s disease .

IntroductionParkinson’s disease (PD) heterogeneity poses challenges to the current development of discovering the best therapeutic targets.MethodsHere, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. The features were specifically the gray matter volume and dopaminergic features of the neostriatum, i.e., the caudate, putamen, and anterior putamen. We use machine learning (ML) algorithms, including Random Forest, Logistic Regression, and Support Vector Machine, to evaluate the diagnostic power of the brain features and network patterns in differentiating the PD subtypes and distinguishing PD from HC. Finally, we assessed whether PD subtypes described through network-specific patterns are dependent on the APOE genotype.ResultsUsing data from 2396 subjects, we show that PD (n=2037) is highly associated with APOE ϵ2/ϵ4. Our findings reveal a significant DAT deficit in the left and right structures of the caudate, putamen, and anterior putamen in subjects with PD compared to subjects with SWEDD(n=137) or HC(n=222), and that APOE ϵ2/ϵ4 may accelerate DAT deficits and brain alterations in both PD and SWEDD. Furthermore, clinical symptoms of PD in subjects (SWEDD), which hardly validated by DAT scan data, can be explained by variations in APOE genotypes and other brain features beyond DAT. We show the existence of three networks states for the whole data, with the first network state describing the subjects in HC, while the remaining two network states describing the two PD subtypes—one network state typified by a mildly sparsely connected network (patterns) and the other network state characterized by a more intensified sparsity in their network. We also show that the two subtypes of PD are characterized by distinctly different levels of total gray matter volume and DAT deficit. ML models show that features extracted from brain structure and network patterns can serve as reliable biomarkers for PD and its subtypes, with the highest performance (100% AUC, 99.3% accuracy, 0.993 F1) demonstrated by the fine-tuned SVM model.ConclusionOur findings suggest that, while PD is generally associated with a larger DAT deficit in specific brain structures of the neostriatum, it exhibits intrinsic heterogeneity across individuals, which may stem from genetic factors. Such heterogeneity can be characterized by ML models and optimally mapped into network states, providing new insights to consider when developing personalized drugs.

Objective: The purpose of this cross-sectional study was to investigate the contributions of altered cerebellar resting-state functional connectivity (FC) to cognitive impairment in Parkinson disease (PD).

Methods: We conducted morphometric and resting-state functional connectivity MRI (FC-MRI) analyses contrasting 81 PD and 43 age-matched healthy controls using rigorous quality assurance measures. To investigate the relationship of cerebellar FC to cognitive status, we compared PD participants without cognitive impairment (Clinical Dementia Rating scale, CDR=0; n=47) to PD participants with impaired cognition (CDR≥0.5; n=34). Comprehensive measures of cognition across the five cognitive domains were assessed for behavioral correlations.

Results: The PD participants had significantly weaker FC between the vermis and peristriate visual association cortex compared to controls, and the strength of this FC correlated with visuospatial function and global cognition. In contrast, weaker...

Resting-state functional magnetic resonance imaging (RS-fMRI) has frequently been used to investigate local spontaneous brain activity in Parkinson’s disease (PD) in a whole-brain, voxel-wise manner. To quantitatively integrate these studies, we conducted a coordinate-based meta-analysis (CB meta-analysis), using the seed-based d mapping (SDM) method, on 15 studies that used amplitude of low frequency fluctuation (ALFF) and 11 studies that used regional homogeneity (ReHo). All these ALFF and ReHo studies have compared PD patients with healthy controls. We also performed a validation RS-fMRI study of ALFF and ReHo in a frequency-dependent manner for a novel dataset consisting of 49 PD and 49 healthy controls. Decreased ALFF was found in the left putamen in PD by meta-analysis. This finding was replicated in our independent validation dataset in the 0.027 - 0.073 Hz band, but not in the conventional frequency band of 0.01 - 0.08 Hz. Findings from the current study suggested that decreased ALFF in the putamen of PD is the most consistent finding. RS-fMRI is a promising technique for the precise localization of abnormal spontaneous activity in PD. However, more frequency-dependent studies using the same analytical methods are needed to replicate that results.

Please note that as of June 2023, Sarah D. Gunnery, PhD is the current Principal Investigator of this data collection. The Emergence and Evolution of Social Self-Management of Parkinson's Disease study (SocM-PD) is a mixed-method (quantitative-qualitative) prospective cohort study of how people with Parkinson's disease and their primary caregiver (as available) naturalistically manage chronic disease, wellness and social life in their home and community. Researchers define social self-management as the practices and experiences that ensure personal social comfort while supporting mental and physical well-being. Articulating this model will guide research to identify social factors that are deleterious to or protective of quality of life when living with chronic disease. Parkinson's Disease offers a model for studying the effect of physical disease on the social self management of daily life when physical symptoms affect fundamental social capacities. The overall objective is to understand the emergence and evolution of the trajectories of the self-management of the social lives of people living with Parkinson's disease. The central hypothesis is that expressive capacity predicts systematic change in the pattern of social self-management and quality of life outcomes. Demographic variables include age, gender, ethnicity, income, marital status, education, and employment.

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

FSL5.0

Collection description

Link to full text:
https://www.ncbi.nlm.nih.gov/pubmed/26386348

Subject species

homo sapiens

Modality

fMRI-BOLD

Cognitive paradigm (task)

rest eyes open

Map type

T

The Parkinson's Disease Handwriting Database (PaHaW) consists of multiple handwriting samples from 37 parkinsonian patients (19 men/18 women) and 38 gender and age-matched healthy controls (20 men/18 women). The database was acquired in cooperation with the Movement Disorders Center at the First Department of Neurology, Masaryk University and St. Anne's University Hospital in Brno, Czech Republic.

Each subject was asked to complete a handwriting/drawing protocol according to the prepared filled template at a comfortable speed. The protocol contains an Archimedean spiral, a grapheme, several words and a sentence. The completed template was shown to the subjects; no restrictions about the number of repetitions of syllables/words in tasks or their height were given.

A tablet was overlaid with an empty paper template (containing only printed lines and a square box specifying area for the Archimedean spiral), and a conventional ink pen was held in a normal fashion, allowing for immediate full visual feedback. The signals were recorded using the Intuos 4M (Wacom technology) digitizing tablet with 133 Hz sampling frequency.

Digitized signals were acquired during the movements executed while exerting pressure on the writing surface and during the movement above the writing surface. We denote these signals as on-surface movement and in-air movement, respectively. The perpendicular pressure exerted on the tablet surface was also recorded. The recordings started when the pen touched the surface of the digitizer and finished when the task was completed. The tablet captured the following signals (time series): x-coordinate; y-coordinate; timestamp; button status; pressure; tilt; and elevation. Button status is a binary variable, being 0 for pen-up state (in-air movement) and 1 for pen-down state (on-surface movement).