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

PPMI-PD is designed to identify clinical, imaging, genetic and biospecimen markers of Parkinson's disease (PD) progression. At baseline, participants were at least 30 years old, had a clinical diagnosis of Parkinson's Disease (PD), and had a positive dopamine transporter single photon emission tomography (SPECT) result. They were recruited within 7 years of diagnosis. Over 700 participants were recruited for the study. Among the participants, over 400 were not being treated for PD despite having received a diagnosis, and almost 300 had rare genetic variants.

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.

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.

The dataset consists of functional MRI images of healthy controls and Parkinson's disease patients. Consists of DICOM images. This images can be used to train a classification model to detect parkinson's disease from fMRI

A human skin proteome MS spectral library, generated from tissue punch biopsies of the C8 paravertebral region in Parkinson's disease patients. This data and associated protocol (https://www.protocols.io/view/proteomics-sample-preparation-of-human-skin-punch-14egn9e46l5d/v1) 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.

Objective

To identify biotypes in newly diagnosed Parkinson’s disease patients and test whether these biotypes could explain inter-individual differences in longitudinal progression.

Methods

In this longitudinal analysis, we use a data-driven approach clustering PD patients from the Parkinson’s Progression Markers Initiative (PPMI) (n = 314, age = 61.0 ± 9.5, 34.1% female, 5 years follow-up). Voxel-level neuroanatomical features were estimated using deformation-based morphometry (DBM) of T1-weighted MRI. Voxels whose deformation values were significantly correlated (P < 0.01) with clinical scores (MDS-UPDRS-Parts I-III, MDS-UPDRS-total, tremor score, and postural instability and gait difficulty score) at baseline were selected. Then, these neuroanatomical features were subjected to hierarchical cluster analysis. Changes in the longitudinal progression and neuroanatomical pattern were compared between different biotypes.

Results

Two neuroanatomical biotypes were identified: (i...

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.

ObjectivesThe aim of this study was to determine whether neurofifilament light (NfL) could reflect motor decline and compare the predictive values of cerebrospinal fluid (CSF) and serum NfL in individuals with PD.MethodsCSF/serum samples were collected from patients with PD and healthy controls (HCs) with motor assessments at baseline and after three years of follow-up from the Parkinson’s Progression Markers Initiative (PPMI). Multiple linear regression models and linear mixed-effects models were used to investigate the associations of motor assessments with baseline and longitudinal CSF/serum NfL. Associations between the change rates of motor assessments and CSF/serum NfL were further investigated via multiple linear regression models. Mediating effect analysis was used to research whether CSF alpha-synuclein (α-syn) acts as the mediator between NfL and motor assessments.ResultsWe found patients with PD had higher baseline CSF/serum NfL levels than HCs. Both baseline CSF/serum NfLs and their change rates predicted measurable motor decline in PD assessed by different motor scores. Baseline serum NfL and its rate of change were strongly associated with CSF NfL levels in patients with PD (P < 0.001). Besides, there were also significant differences in CSF/serum NfL levels and predicted values of motor decline between men and women with PD. Mediating effect analysis showed CSF α-syn mediated the effect of CSF NfL on total Unified Parkinson’s Disease Rating Scale (UPDRS) scores and UPDRSIII with 30.6 and 20.2% mediation, respectively.ConclusionOur results indicated that NfL, especially serum NfL concentration, could serve as an easily accessible biomarker to monitor the severity and progression of motor decline in individuals with PD, especially in men with PD. Besides, CSF α-syn acts as a mediator between NfL and motor progression.

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.

PurposeThis study aimed to develop machine learning models for the diagnosis of Parkinson’s disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance.MethodsBrain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson’s Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson’s correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets.ResultsAfter feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong’s test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models.ConclusionThe combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.

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...

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.

Objective: Despite significant progress in understanding the factors influencing cognitive function in Parkinson’s disease (PD), there is a notable gap in data representation for the Latinx population. This study aims to evaluate the contributors to and disparities in cognitive performance among Latinx patients with PD. Methods: A retrospective analysis was conducted based on cross-sectional data encompassing demographic, environmental, motor, and non-motor disease characteristics from the Latin American Research Consortium on the Genetics of PD (LARGE-PD) and the Parkinson’s Progression Markers Initiative (PPMI) cohorts. Linear regression multivariable models were applied to identify variables affecting Montreal Cognitive Assessment (MoCA) scores, accounting for age, sex, and years of education. Results: The analysis comprised of 3,054 PD patients (2,041 from LARGE-PD and 1,013 from PPMI) and 1,303 Latinx-controls. Latinx-PD patients (mean age 63.0 ± 11.8, 56.8% male) exhibited a significantly lower average MoCA score (p < .001) compared to white Non-Hispanic PD patients from PPMI (mean age 67.5 ± 9.9, 61.7% male). This difference persisted when comparing the Latinx-PD to the Latinx-controls (mean age 58.7 ± 9.3, 33.2% male; p < .001). Factors significantly associated with better MoCA scores in Latinx-PD included unilateral symptom onset (p = .009), and higher educational attainment (p < .001). Conversely, those associated with worse scores included the use of dopamine agonists (p = .01), previous tobacco use (p = .01), older age (p < .001), and a higher Hoehn and Yahr scale score (p < .001). Conclusions: Latinx-PD patients demonstrated significantly lower cognitive scores compared to their white non-Hispanic PD counterparts and Latinx-controls. These results highlight the importance of interpreting MoCA scores in a nuanced manner within diverse populations.

BackgroundIron deposition has been observed in Parkinsonism and is emerging as a diagnostic marker for movement disorders. Brain functional network disruption has also been detected in parkinsonism, and is believed to be accountable for specific symptoms in parkinsonism. However, how iron deposition influences brain network remains to be elucidated.MethodsWe recruited 16 Parkinson’s disease (PD), 8 multiple system atrophy (MSA) and 7 progressive supranuclear palsy (PSP) patients. T1-weighted, susceptibility weighted images and resting-state functional MRI (rs-fMRI) were acquired. Quantitative susceptibility mapping (QSM) analysis was performed to quantify iron deposition in substantia nigra, putamen and dentate nucleus. Cerebellar network, sensorimotor network, default mode network and language networks were segregated using independent analysis. Network and iron deposition status were evaluated in relation to diagnostic groups, motor and non-motor symptoms. The relationship between quantitative iron deposition and brain network status was further interrogated. To further validate the findings, 13 healthy controls and 37 PD patients who had available T1 and rs-fMRI scans were selected from Parkinson’s progression markers initiative (PPMI) database, and network analysis was performed.ResultsIn local cohort, compared to PD, MSA patients showed greater iron deposition in putamen, while PSP patients had greater iron deposition in caudate nucleus and thalamus. Cerebellar and language networks showed significant difference across diagnostic groups, while default mode network and sensorimotor network did not. MSA patients had significantly impaired cerebellar network and language networks compared to PD patients. Cerebellar network was positively associated with motor symptom scores while language network was positively associated with MoCA scores in the patients. Iron deposition was negatively associated with both networks’ activity in the patients. In PPMI cohort, impairment was found in both cerebellar and language networks in PD. Cerebellar and language networks correlated with motor and cognitive impairment, respectively.ConclusionCerebellar network and language networks are differently influenced in MSA, PD and PSP, which can serve as potential diagnostic marker. Impairment of cerebellar network and language network are associated with motor symptoms and cognitive impairment, respectively. Moreover, dysfunction of the networks is associated with iron deposition in deep nuclei (SN, DN, Putamen).

BackgroundCurrently available treatment options for Parkinson's disease are symptomatic and do not alter the course of the disease. Recent studies have raised the possibility that cardiovascular risk management may slow the progression of the disease.ObjectivesWe estimated the effect of baseline cardiovascular risk factors on the progression of Parkinson's disease, using measures for PD-specific motor signs and cognitive functions.MethodsWe used data from 424 de novo Parkinson's disease patients and 199 age-matched controls from the observational, multicenter Parkinson's Progression Markers Initiative (PPMI) study, which included follow-up of up to 9 years. The primary outcome was the severity of PD-specific motor signs, assessed with the MDS-UPDRS part III in the “OFF”-state. The secondary outcome was cognitive function, measured with the Montreal Cognitive Assessment, Symbol Digit Modalities Test, and Letter-Number Sequencing task. Exposures of interest were diabetes mellitus, hypertension, body mass index, cardiovascular event history and hypercholesterolemia, and a modified Framingham risk score, measured at baseline. The effect of each of these exposures on disease progression was modeled using linear mixed models, including adjustment for identified confounders. A secondary analysis on the Tracking Parkinson's cohort including 1,841 patients was performed to validate our findings in an independent patient cohort.ResultsMean age was 61.4 years, and the average follow-up was 5.5 years. We found no statistically significant effect of any individual cardiovascular risk factor on the MDS-UPDRS part III progression (all 95% confidence intervals (CIs) included zero), with one exception: in the PD group, the estimated effect of a one-point increase in body mass index was 0.059 points on the MDS-UPDRS part III per year (95% CI: 0.017 to 0.102). We found no evidence for an effect of any of the exposures on the rate of change in cognitive functioning in the PD group. Similar results were observed for the Tracking Parkinson's cohort (all 95% CIs overlapped with PPMI), but the 95% CI of the effect of body mass index on the MDS-UPDRS part III progression included zero.ConclusionsBased on this analysis of two large cohorts of de novo PD patients, we found no evidence to support clinically relevant effects of cardiovascular risk factors on the clinical progression of Parkinson's disease.

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.

The 3 PD datasets compared in the present study (PD = Parkinson’s disease, NC = normal controls).

Excel tables with differentially expressed genes in blood at the initial visit and at a 36 months follow up in Parkinson's disease patients from the PPMI cohort. DEG are presented uncorrected and corrected for medications and levodopa equivalent daily dose.