This is a small test sample data for Parkinson's Progression Markers Initiative (PPMI) data derivatives. It includes white matter tracts in common space (MNI space) and tracts in the subject's original space. It also includes anatomical measures extracted from diffusion data such as FA, RD, MD, AD for 5 patient subjects and 5 control subjects.Data was processed using DIPY.This data set was generated by Chandio, B.Q., et al. Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci Rep 10, 17149 (2020) paper.Read paper here: https://rdcu.be/b8rUr

IntroductionThe extensive clinical variations observed in Parkinson's disease (PD) pose challenges in early diagnosis and treatment initiation. However, genetic research in PD has significantly transformed the clinical approach to its treatment. Moreover, researchers have adopted a subtyping strategy based on homogeneous clinical symptoms to improve clinical diagnosis and treatment approaches. We conducted a study to explore clinical characteristics in genetic PD groups with motor symptom subtyping.MethodsData was driven from the Parkinson's Progression Markers Initiative (PPMI) database. The sporadic PD (sPD) group and the genetic PD group including patients with leucine-rich kinase 2 (LRRK2) or glucosylceramidase β (GBA) mutations were analyzed. Motor subtyping was performed using Movement Disorder Society-Unified Parkinson's disease rating scale (MDS-UPDRS) scores. I-123 FP-CIT SPECT scans were used to calculate specific binding ratios (SBRs) in the caudate and putamen. Clinical symptoms of each group were also compared.ResultsMDS-UPDRS III scores were lower in the LRRK2 group, compared with the GBA and sPD group (P < 0.001), but no significant differences in striatal SBRs. The putaminal SBR value of the LRRK2 group was higher than the sPD group (P < 0.05). Within the GBA group, we observed lower SBR values in the postural instability/gait difficulty (PIGD) subtype GBA group compared to the tremor-dominant (TD) subtype GBA group (P < 0.05). The TD subtype GBA group exhibited superior putaminal SBRs compared to the TD subtype sPD group (P < 0.05). The TD subtype LRRK2 group had better putaminal SBR values (P < 0.001) and MDS-UPDRS Part III scores (P < 0.05) compared to the TD sPD group.DiscussionsOur subtyping approach offers valuable insights into the clinical characteristics and progression of different genetic PD subtypes. To further validate and expand these findings, future research with larger groups and long-term follow-up data is needed. The subtyping strategy based on motor symptoms holds promise in enhancing the diagnosis and treatment of genetic PD.

Original PPMI diffusion MRI data of 64 subjects (32 patients, 32 healthy controls) was processed using DIPY.PPMI data derivatives include white matter tracts in common space (MNI space) and tracts in subject's original space. It includes anatomical measures extracted from diffusion data such as FA, RD, MD, and AD and it also contains CSA peaks file for 32 patient subjects and 32 control subjects.This data set was generated by Chandio, B.Q., et al. Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci Rep 10, 17149 (2020) paper.Read paper here: https://rdcu.be/b8rUr

BackgroundAutonomic dysfunction, including gastrointestinal, cardiovascular, and urinary dysfunction, is often present in early Parkinson's Disease (PD). However, the knowledge of the longitudinal progression of these symptoms, and the connection between different autonomic domains, is limited. Furthermore, the relationship between the presence of autonomic symptoms in early-stage PD and olfactory dysfunction, a possible marker of central nervous system involvement, has not been fully investigated.ObjectivesWe aimed to investigate the occurrence and progression of autonomic dysfunction in recently diagnosed (< 2 years) untreated PD patients and determine any coexistence of symptoms in individual patients. We also investigated the relationship between autonomic symptoms, olfactory dysfunction, and motor impairment.MethodsData were obtained from the Parkinson's Progression Markers Initiative (PPMI) database. Autonomic dysfunction was measured using the Scales for Outcomes in Parkinson's Disease (SCOPA-AUT). Symptom frequency and mean scores over 7 years were determined. The simultaneous occurrence of different autonomic symptoms was also examined. Finally, the relationships between SCOPA-AUT scores, olfactory dysfunction, and motor impairment were investigated using the University of Pennsylvania Smell Identification Test (UPSIT) and the Movement Disorder Society—Unified Parkinson's Disease Rating Scale (MDS-UPDRS), respectively.ResultsFollow-up data were available for 7 years for 171 PD patients and for 5 years for 136 HCs. Mean SCOPA-AUT score increased significantly from baseline to the 7-year follow-up for each autonomic domain, except for female sexual dysfunction. Most patients reported three or more autonomic symptoms. Common clusters of symptoms were composed of combinations of gastrointestinal, urinary, thermoregulatory, and sexual dysfunction. At baseline, greater SCOPA-AUT total score was associated with lower UPSIT scores (r = −0.209, p = 0.006) and with greater total MDS-UDPRS III score (r = 0.218, p = 0.004).ConclusionsAutonomic dysfunction, often with coexistence of autonomic manifestations, is common in early PD and progressively worsens over the first 7 years of disease, suggesting that these symptoms should be addressed with appropriate treatments early in the disease. The association between greater autonomic dysfunction and greater olfactory impairment, coupled with the association with more severe motor scores at baseline, indicates that patients who show more severe autonomic dysfunction could also have more severe involvement of the central nervous system at the time of diagnosis.

BackgroundDopaminergic denervation and motor symptoms are usually asymmetric at the onset of Parkinson's disease (PD). In this study, we estimated the asymmetry of specific binding ratio (SBR) of I-123 FP-CIT SPECT images during 4-years of follow up, to demonstrate the pattern of serial changes of asymmetry.MethodsClinical and I-123 FP-CIT SPECT image data of 301 PD patients and 141 normal controls were reviewed from the Parkinson's Progression Markers Initiative cohort. I-123 FP-CIT SPECT images were taken at baseline, 1-, 2-, and 4-year follow up periods for PD patients, and at baseline for normal controls. Asymmetry index were calculated by two methods. Method 1, by using the ratio of absolute difference of right and left SBRs to the average SBR. Method 2, by using the ratio of absolute difference of right and left SBRs to the SBR values of age-matched normal controls.ResultsAsymmetry index by method 2 revealed a more significant decrease during the 4-year follow up period, compared with method 1. The baseline asymmetry index of the putamen by method 2 showed significant correlation with the non-dominant putamen SBRs. However, there were no significant correlation with the baseline asymmetry index by method 2 and motor symptoms, cognition, nor autonomic symptoms.ConclusionWe suggest a novel asymmetry index in association to age-matched normal SBR values. This novel index could be adopted in predicting and evaluating the natural course of PD.

Cortical connectivity predicts cognition across time in Parkinson's disease Hunter P. Twedt, Brooke E. Yeager, Jacob Simmering, Jordan L. Schultz, & Nandakumar S. Narayanan

Dataset description:

27 participants were selected from the PPMI dataset. These data are found within the folder 'BIDS'. Within 'BIDS' are each participant's folders containing their respective MRI data. Within each of these participant folders are three sessions corresponding to Visits 1, 2, and 3 that are discussed in the manuscript. In this regard, 'ses-01' corresponds to 'Visit 1', 'ses-02' corresponds to 'Visit 2', and 'ses-03' corresponds to 'Visit 3'. Within each 'ses-' folder are 'anat' and 'func' folders containing respective MRI data in .nii format. Raw DICOMs from the PPMI database were converted to NIFTI using MRIcroGL.

Objective: To investigate the effect of polygenic load on the progression of striatal dopaminergic dysfunction in patients with Parkinson's disease (PD). Methods: Using data from 335 PD patients in the Parkinson's Progression Markers Initiative (PPMI) database, we investigated the longitudinal association of PD-associated polygenic load with changes in striatal dopaminergic activity as measured by 123I-N-3-fluoropropyl-2-beta-carboxymethoxy-3beta-(4-iodophenyl) nortropane (123I-FP-CIT) single photon emission computed tomography (SPECT) over 4 years. PD-associated polygenic load was estimated by calculating weighted genetic risk scores (GRS) using: i) all available 27 PD-risk single nucleotide polymorphisms (SNPs) in the PPMI database (GRS1); and ii) 23 SNPs with minor allele frequency > 0.05 (GRS2). Results: GRS1 and GRS2 were correlated with younger age-at-onset in PD patients (GRS1, Spearman's rho = -0.128, p = 0.019; GRS2, Spearman's rho = -0.109, p = 0.047). Although GRS1 did not...

To facilitate a larger proteomic study of human skin from Parkinson's disease patient cohorts, a spectral library of the human skin proteome was generated through mass spectrometry (MS) analysis. For this purpose, twenty-two 3mm skin tissue punch biopsies from the C8 paravertebral region collected from a single Parkinson's disease patient cohort were analysed by data-independent acquisition mass spectrometry (DIA-MS) proteomics.

The protocol for sample preparation, DIA-MS proteomics and data analysis can be found here: https://www.protocols.io/view/proteomics-sample-preparation-of-human-skin-punch-14egn9e46l5d/v1

Raw mass spectrometry data generated from the analysis were deposited to the PRIDE database and can be accessed here: https://www.ebi.ac.uk/pride/archive/projects/PXD060089 or searching using project identifier: PXD060089

This data and protocol were generated as part of a pilot study in collaboration with the Michael J. Fox Foundation. The biospecimens were obtained from the Parkinson’s Progression Marker Initiative (PPMI) (RRID:SCR_006431). For up-to-date information on the study, visit www.ppmi-info.org.

These data files support figures found in "Variations in ADAR editing of nonsense-mediated decay targets in PD males and females". Original RNA-seq data is freely available upon request from the Parkinson’s Progression Markers Initiative (PPMI), https://www.ppmi-info.org/access-data-specimens/download-data (Marek et al., 2011).

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.

AD and MCI maps are based on ADNI data, available at:

PD map is based on PPMI data, available at:

FTD map is based on NIFD data, available at:

For more information regarding the participants and method details, see:

Dadar, Mahsa, et al. "White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer's disease and frontotemporal dementia." Neurobiology of aging 111 (2022): 54-63.

BackgroundThe association between body mass index (BMI) and rapid eye-movement (REM) sleep-related behavioral disorder (RBD) in Parkinson’s disease (PD) remains unknown. Our study was to investigate the association of BMI with RBD in PD patients.MethodsIn this cross-sectional study, a total of 1,115 PD participants were enrolled from Parkinson’s Progression Markers Initiative (PPMI) database. BMI was calculated as weight divided by height squared. RBD was defined as the RBD questionnaire (RBDSQ) score with the cutoff of 5 or more assessed. Univariable and multivariable logistic regression models were performed to examine the associations between BMI and the prevalence of RBD. Non-linear correlations were explored with use of restricted cubic spline (RCS) analysis. And the inflection point was determined by the two-line piecewise linear models.ResultsWe identified 426 (38.2%) RBD. The proportion of underweight, normal, overweight and obese was 2.61, 36.59, 40.36, and 20.44%, respectively. In the multivariate logistic regression model with full adjustment for confounding variables, obese individuals had an odds ratio of 1.77 (95% confidence interval: 1.21 to 2.59) with RBD compared with those of normal weight. In the RCS models with three knots, BMI showed a non-linear association with RBD. The turning points of BMI estimated from piecewise linear models were of 28.16 kg/m2, 28.10 kg/m2, and 28.23 kg/m2 derived from univariable and multivariable adjusted logistic regression models. The effect modification by depression on the association between BMI and RBD in PD was also found in this study. Furthermore, the sensitivity analyses linked with cognition, education, and ethnic groups indicated the robustness of our results.ConclusionThe current study found a significant dose–response association between BMI and RBD with a depression-based difference in the impact of BMI on RBD in PD patients.

In last years, several approaches to develop an effective Computer-Aided-Diagnosis (CAD) system for Parkinson's Disease (PD) have been proposed. Most of these methods have focused almost exclusively on brain images through the use of Machine-Learning algorithms suitable to characterize structural or functional patterns. Those patterns provide enough information about the status and/or the progression at intermediate and advanced stages of Parkinson's Disease. Nevertheless this information could be insufficient at early stages of the pathology. The Parkinson's Progression Markers Initiative (PPMI) database includes neurological images along with multiple biomedical tests. This information opens up the possibility of comparing different biomarker classification results. As data come from heterogeneous sources, it is expected that we could include some of these biomarkers in order to obtain new information about the pathology. Based on that idea, this work presents an Ensemble Classification model with Performance Weighting. This proposal has been tested comparing Healthy Control subjects (HC) vs. patients with PD (considering both PD and SWEDD labeled subjects as the same class). This model combines several Support-Vector-Machine (SVM) with linear kernel classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF), RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features and a list of defined Morphological Features) from PPMI database subjects. The proposed methodology makes use of all data sources and selects the most discriminant features (mainly from neuroimages). Using this performance-weighted ensemble classification model, classification results up to 96% were obtained.

DIPY workshop 2021 Bundle Analytics (BUAN) demo data consisting of Parkinson's Progression Markers Initiative (PPMI) data derivatives. It includes Model Arcuate Fasciculus Left (AF_L), extracted AF_L tracts in common space (MNI space) and tracts in the subject's original space. It also includes anatomical measure Fractional Anisotropy (FA) for 5 patient subjects and 5 control subjects.Data was processed using DIPY.This data set was generated by Chandio, B.Q., et al. Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Sci Rep 10, 17149 (2020) paper.Read paper here: https://rdcu.be/b8rUr

PPMI Group, UAB financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Both neuroimaging and genomics datasets are often gathered for the detection of neurodegenerative diseases. Huge dimensionalities of neuroimaging data as well as omics data pose tremendous challenge for methods integrating multiple modalities. There are few existing solutions that can combine both multi-modal imaging and multi-omics datasets to derive neurological insights. We propose a deep neural network architecture that combines both structural and functional connectome data with multi-omics data for disease classification. A graph convolution layer is used to model functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data simultaneously to learn compact representations of the connectome. A separate set of graph convolution layers are then used to model multi-omics datasets, expressed in the form of population graphs, and combine them with latent representations of the connectome. An attention mechanism is used to fuse these outputs and provide insights on which omics data contributed most to the model's classification decision. We demonstrate our methods for Parkinson's disease (PD) classification by using datasets from the Parkinson's Progression Markers Initiative (PPMI). PD has been shown to be associated with changes in the human connectome and it is also known to be influenced by genetic factors. We combine DTI and fMRI data with multi-omics data from RNA Expression, Single Nucleotide Polymorphism (SNP), DNA Methylation and non-coding RNA experiments. A Matthew Correlation Coefficient of greater than 0.8 over many combinations of multi-modal imaging data and multi-omics data was achieved with our proposed architecture. To address the paucity of paired multi-modal imaging data and the problem of imbalanced data in the PPMI dataset, we compared the use of oversampling against using CycleGAN on structural and functional connectomes to generate missing imaging modalities. Furthermore, we performed ablation studies that offer insights into the importance of each imaging and omics modality for the prediction of PD. Analysis of the generated attention matrices revealed that DNA Methylation and SNP data were the most important omics modalities out of all the omics datasets considered. Our work motivates further research into imaging genetics and the creation of more multi-modal imaging and multi-omics datasets to study PD and other complex neurodegenerative diseases.

Magnetic resonance imaging data phenotypes for the Parkinson's Progression Markers Initiative.see: preprint https://doi.org/10.1101/2024.09.23.24313179 submitted to "Scientific Data"

BackgroundNon-alcoholic fatty liver disease (NAFLD) or liver fibrosis may share similar pathophysiological features with Parkinson’s disease (PD), yet their correlation was unclear. This study aimed to explore their correlation between PD and liver fibrosis using the fibrosis-4 score (FIB-4) as a surrogate marker.MethodsWe analyzed Parkinson’s Progression Markers Initiative (PPMI) data and enrolled PD patients with comprehensive baseline and 5-year follow-up time-point clinical data. Participants were categorized based on FIB-4 levels to assess the association between FIB-4 scores and various clinical scales, controlling for potential confounders. Differences in the progression of clinical scales over five years were compared using generalized linear mixed models (GLMM).ResultsBaseline FIB-4 levels positively correlated to scores of baseline section III of the Unified-Parkinson Disease Rating Scale (UPDRS III) (r = 0.145, p = 0.017), Epworth Sleepiness Scale (EPSS) (r = 0.140, P = 0.022), Hopkins Verbal Learning Test (HVLT)-delayed recall (r = 0.128, P = 0.036) and HVLT-retention (r = 0.128, p = 0.036). GLMM analysis revealed an independent correlation between FIB-4 subgroup*time and several clinical scales including the State-trait Anxiety Inventory (STAI), Symbol Digit Modalities Test (SDMT), Semantic Fluency Test (SF), HVLT-total recall, and HVLT-delayed recall, with the high FIB-4 subgroup exhibiting a greater decline in these scores compared to the low FIB-4 subgroup (all p<0.05).ConclusionElevated baseline FIB-4 correlated to more severe baseline daytime sleepiness, motor symptoms, and memory function in PD patients, along with a more rapid decline in cognitive functions such as executive function, information processing ability, and memory. Additionally, a high FIB-4 might confer a protective effect against anxiety.

ObjectiveParkinson’s disease (PD) is a common neurodegenerative disorder, and the early and accurate differentiation of its motor subtypes is of significant importance for clinical diagnosis and treatment planning. Research has shown that deep brain nuclei such as the thalamus, caudate nucleus, putamen, and globus pallidus play a critical role in the pathogenesis of different motor subtypes of Parkinson’s disease. This study aims to utilize deep learning and radiomics technology to establish an automated method for differentiating motor subtypes of Parkinson’s disease.MethodsThe data for this study were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database, including a total of 135 Parkinson’s disease patients, comprising 43 cases of the Postural Instability/Gait Difficulty (PIGD) subtype and 92 cases of the Tremor Dominant (TD) subtype. High-resolution MRI scans were used to extract 2,264 radiomics features from 8 deep brain nuclei, including bilateral thalamus, caudate nucleus, putamen, and globus pallidus. After dimensionality reduction, five independent machine learning classifiers [AdaBoost, Bagging Decision Tree (BDT), Gaussian Process (GP), Logistic Regression (LR), and Random Forest (RF)] were trained on the training set and validated on the test set. Model performance was evaluated using the Area Under the Curve (AUC) metric.ResultsAfter feature selection, 17 most discriminative radiomics features were retained. Among the models, the BDT-based diagnostic model demonstrated the best performance, achieving AUC values of 1.000 and 0.962 on the training and test sets, respectively. DeLong’s test results indicated that the BDT model significantly outperformed other models. Calibration curve analysis showed that the Parkinson’s disease subtype classification model based on MRI radiomics features exhibited good calibration and stability. Clinical decision curve analysis revealed that the BDT model demonstrated significant clinical net benefits across a wide probability range, indicating high clinical utility.ConclusionThe BDT model based on MRI radiomics features constructed in this study exhibited excellent performance in differentiating motor subtypes of Parkinson’s disease and can serve as an effective tool for clinical auxiliary diagnosis. This fully automated model is capable of processing MRI data and providing results within 3 min, offering an efficient and reliable solution for the early differentiation of Parkinson’s disease motor subtypes, with significant clinical application value.

Genetic and environmental factors lead to the manifestation of Parkinson’s disease (PD) but related mechanisms are only rudimentarily understood. Cytochromes P450 (P450s) are involved in the biotransformation of toxic compounds and in many physiological processes and thus predestinated to be involved in PD. However, so far only SNPs (single nucleotide polymorphisms) in CYP2D6 and CYP2E1 have been associated with the susceptibility of PD. Our aim was to evaluate the role of all 57 human P450s and their redox partners for the etiology and pathophysiology of PD and to identify novel potential players which may lead to the identification of new biomarkers and to a causative treatment of PD. The PPMI (Parkinson’s Progression Markers Initiative) database was used to extract the gene sequences of all 57 P450s and their three redox partners to analyze the association of SNPs with the occurrence of PD. Applying statistical analyses of the data, corresponding odds ratios (OR) and confidence intervals (CI) were calculated. We identified SNPs significantly over-represented in patients with a genetic predisposition for PD (GPD patients) or in idiopathic PD (IPD patients) compared to HC (healthy controls). Xenobiotic-metabolizing P450s show a significant accumulation of SNPs in PD patients compared with HC supporting the role of toxic compounds in the pathogenesis of PD. Moreover, SNPs with high OR values (>5) in P450s catalyzing the degradation of cholesterol (CYP46A1, CY7B1, CYP39A1) indicate a prominent role of cholesterol metabolism in the brain for PD risk. Finally, P450s participating in the metabolism of eicosanoids show a strong over-representation of SNPs in PD patients underlining the effect of inflammation on the pathogenesis of PD. Also, the redox partners of P450 show SNPs with OR > 5 in PD patients. Taken together, we demonstrate that SNPs in 26 out of 57 P450s are at least 5-fold over-represented in PD patients suggesting these P450s as new potential players in the pathogenesis of PD. For the first time exceptionally high OR values (up to 12.9) were found. This will lead to deeper insight into the origin and development of PD and may be applied to develop novel strategies for a causative treatment of this disease.

BackgroundIn the last decades, the association of household pesticide usage with Parkinson's disease (PD) has been poorly explored, with discordant results. Based on the Parkinson's Progression Markers Initiative (PPMI) cohort study, we analyzed (1) the association of household pesticide exposure with the development of PD and (2) the effect of household pesticides on progression of PD.MethodsData from participants of the “FOllow Up persons with Neurologic Disease” (FOUND study) included in the PPMI cohort database were analyzed. The PPMI FOUND study applied the Parkinson's Disease Risk Factor Questionnaire to collect information regarding the use of pesticides in non-work settings during periods of life, and the lifetime pesticide exposure for each participant was estimated. We defined a high use of pesticides if the exposure estimate had a z-score higher than one standard deviation from the mean. Also, we evaluated longitudinal data of people with PD to analyze the effect of high use of household pesticides on disease progression according to motor impairment, cognitive dysfunction, depressive symptoms, and modification of motor clinical phenotype.ResultsWe analyzed data from 206 people with PD and 64 healthy controls, almost all from the USA. High use of household pesticides was not associated with the odds of developing PD. Regarding PD progression, only cognitive dysfunction was associated with the high use of household fungicides (HR 5.64 per standard deviation increase in exposure estimate, 95% CI 1.41–22.6).ConclusionsChronic exposure to household pesticides may impact the clinical progression of PD, especially cognitive symptoms.