Parkinson's Disease Cohort Research Articles

Functional anatomy of the subthalamic nucleus and the pathophysiology of cardinal features of Parkinson's disease unraveled by focused ultrasound ablation.

The subthalamic nucleus (STN) modulates basal ganglia output and plays a fundamental role in the pathophysiology of Parkinson's disease (PD). Blockade/ablation of the STN improves motor signs in PD. We assessed the topography of focused ultrasound subthalamotomy (n = 39) by voxel-based lesion-symptom mapping to identify statistically validated brain voxels with the optimal effect against each cardinal feature and their respective cortical connectivity patterns by diffusion-weighted tractography. Bradykinesia and rigidity amelioration were associated with ablation of the rostral motor STN subregion connected to the supplementary motor and premotor cortices, whereas antitremor effect was explained by lesioning the posterolateral STN projection to the primary motor cortex. These findings were corroborated prospectively in another PD cohort (n = 12). This work concurs with recent deep brain stimulation findings that suggest different corticosubthalamic circuits underlying each PD cardinal feature. Our results provide sound evidence in humans of segregated anatomy of subthalamic-cortical connections and their distinct role in PD pathophysiology and normal motor control.

Decreased SNCA Expression in Whole-Blood RNA Analysis of Parkinson's Disease Adjusting for Lymphocytes.

Blood-based RNA transcriptomics offers a promising avenue for identifying biomarkers of Parkinson's Disease (PD) progression and may provide mechanistic insights into the systemic biological processes underlying its pathogenesis beyond the well-defined neurodegenerative features. Previous studies have indicated an age-dependent increase in neutrophil-enriched gene expression, alongside a reduction in lymphocyte counts, in individuals with PD. These immune cell changes can obscure disease-relevant transcriptomic signals. In this study, we performed differential expression (DE) analysis of whole-blood RNA sequencing data from PD cohorts, incorporating a correction for immune cell-enriched gene expression, particularly neutrophil-related pathways, to improve the resolution of PD-associated molecular changes. Using 1,254 Parkinson's Progression Markers Initiative (PPMI) samples with complete blood count (CBC) data, we developed a predictive model to estimate neutrophil percentages in a 6,987 PPMI and Parkinson's Disease Biomarkers Program (PDBP) samples. We mitigated the confounding effects of immune cell-enriched gene expression by integrating predicted neutrophil percentages as a covariate in DE analysis. This approach revealed a consistent and significant downregulation of SNCA across all PD cohorts, a finding previously obscured by immune cell signatures. Lowered SNCA expression was found in individuals with known predisposition genes (e.g., SNCA, GBA, LRRK2) and in non-genetic PD cohorts lacking known pathogenic mutations, suggesting it may represent a key transcriptomic hallmark of the disease.

Dopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach.

Dopamine transporter imaging is routinely used in Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) diagnosis. While [11C]CFT PET is prevalent in Asia with a large APS database, Europe relies on [123I]FP-CIT SPECT with limited APS data. Our aim was to develop a deep learning-based method to convert [11C]CFT PET images to [123I]FP-CIT SPECT images, facilitating multicenter studies and overcoming data scarcity to promote Artificial Intelligence (AI) advancements. A CycleGAN was trained on [11C]CFT PET (n = 602, 72%PD) and [123I]FP-CIT SPECT (n = 1152, 85%PD) images from PD and non-parkinsonian control (NC) subjects. The model generated synthetic SPECT images from a real PET test set (n = 67, 75%PD). Synthetic images were quantitatively and visually evaluated. Fréchet Inception Distance indicated higher similarity between synthetic and real SPECT than between synthetic SPECT and real PET. A deep learning classification model trained on synthetic SPECT achieved sensitivity of 97.2% and specificity of 90.0% on real SPECT images. Striatal specific binding ratios of synthetic SPECT were not significantly different from real SPECT. The striatal left-right differences and putamen binding ratio were significantly different only in the PD cohort. Real PET and real SPECT had higher contrast-to-noise ratio compared to synthetic SPECT. Visual grading analysis scores showed no significant differences between real and synthetic SPECT, although reduced diagnostic performance on synthetic images was observed. CycleGAN generated synthetic SPECT images visually indistinguishable from real ones and retained disease-specific information, demonstrating the feasibility of translating [11C]CFT PET to [123I]FP-CIT SPECT. This cross-modality synthesis could enhance further AI classification accuracy, supporting the diagnosis of PD and APS.

Association between serum IGF‑1 levels and non-motor symptoms in Parkinson's disease.

We aimed to measure serum insulin-like growth factor 1 (IGF-1) levels in Parkinson's disease (PD) patients and assess their correlation with non-motor symptoms (NMS). Emerging evidence suggests that abnormal levels of IGF-1 play a crucial role in the development of PD. Further systematic research is needed to explore the potential roles of abnormal IGF-1 levels in NMS of PD. The study enrolled a total of 129 PD patients and 130 healthy controls (HCs). Within the PD cohort, 74 patients were classified as being in the early stage, while 55 were in the moderate stage. This study found no significant difference in serum IGF-1 levels between PD patients and HC. Further analysis revealed no significant difference in IGF-1 levels between early-stage PD and those in the moderate stages. Linear regression analysis indicated a significant association between serum IGF-1 levels and Nonmotor Symptom Scale (NMSS) scores in PD patients. Linear regression analysis revealed significant correlations between serum IGF-1 levels and general cognitive function, information processing speed, and executive function in PD patients. Furthermore, lower serum IGF-1 levels were associated with fatigue in PD patients. In summary, our study suggests a potential association between serum IGF-1 levels and specific NMS in patients with PD. These findings highlight the importance of long-term follow-up studies to determine whether serum biomarkers can serve as valuable tools for early detection of NMS in PD.

Risk factors and evolution of weight loss in Parkinson's disease: A 9-year population-based study

IntroductionWeight loss is considered a common complication of Parkinson's disease (PD), but there are few prospective longitudinal studies on weight loss in patients followed from time of PD diagnosis. We sought to determine the frequency, evolution and risk factors of weight loss in a representative incident PD cohort. MethodsIn this prospective population-based observational study, we followed 180 newly-diagnosed, initially drug-naïve PD patients and 161 controls with repetitive weight examinations over 9 years. We used Cox regression models with adjustment for potential confounders to identify independent risk factors of clinically significant (>10 %) weight loss. ResultsMean % weight change during follow-up was −3.9 (±11.2) in patients and −1.4 (±8.1) in controls (p = 0.016). Clinically significant weight loss was observed in 26.7 % of patients and 10.6 % of controls (RR 2.53; 95 % CI 1.52–4.21; p < 0.001). Age was the only independent baseline risk factor for weight loss (HR 1.06 per year; 95 % CI 1.03–1.10; p < 0.001). Additional time-dependent risk factors were presence of olfactory impairment (HR 2.42; 95 % CI 1.14–5.15; p = 0.021), presence of dyskinesias (HR 3.14; 95 % CI 1.58–6.23; p = 0.001), and cognitive impairment (HR per MMSE unit 0.90; 95 % CI 0.82–0.99; p = 0.036). Dopamine agonist use reduced the risk of weight loss during follow-up (HR 0.44; 95 % CI 0.24–0.82; p = 0.007). ConclusionThe risk of weight loss is more than doubled in the general PD population and associated with both disease-related features and drug-related complications. This suggests a multifactorial nature of weight loss in PD, which is important to consider in research and clinical practice.

Optimal MMSE and MoCA cutoffs for cognitive diagnoses in Parkinson's disease: A data-driven decision tree model

BackgroundDetecting cognitive impairment in Parkinson's disease (PD) is challenging due to diverse manifestations and outdated diagnostic criteria. Cognitive screening tools, as Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), are adopted worldwide, but despite several cutoffs has been proposed for PD, no consensus has been reached, hindered by limited sample sizes, lack of validation, and inconsistent age- and education-adjustments. ObjectivesDetermine the optimal MMSE and MoCA cutoffs in a large PD cohort, spanning from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD), and develop a decision tree model to assist physicians in cognitive workups. MethodsOur retrospective Italian multicenter study involves 1780 PD, cognitively diagnosed with a level-II assessment: PD-NC(n = 700), PD-MCI(n = 706), and PDD(n = 374). Optimal cutoffs (for raw scores) were determined through ROC analysis. Then, a machine learning approach—decision trees—was adopted to validate and analyze the possible inclusion of other relevant clinical features. ResultsThe decision tree model selected as primary feature a MMSE cutoff ≤24 to predict dementia, and a score ≤ 27 for PD-MCI. To enhance PD-MCIvs.PD-NC accuracy, it also recommends including a MoCA score ≤ 22 for PD-MCI, and > 22 for PD-NC. Age and education were not selected as relevant features for the cognitive workup. Both MoCA and MMSE cutoffs exhibited high sensitivity and specificity in detecting PD cognitive statues. ConclusionsFor the first time, a clinical decision tree model based on robust MMSE and MoCA cutoffs has been developed, allowing to diagnose PD-MCI and/or PDD with a high accuracy and short administration time.

Application of optical coherence tomography in neurodegenerative diseases: a focus on Parkinson’s, Alzheimer’s, and Schizophrenia in Bulgarian patients

This study investigates retinal alterations in patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), and schizophrenia (SZ) using Optical Coherence Tomography (OCT). The primary objective was to determine whether these neurodegenerative diseases manifest in measurable retinal changes and to assess the impact of disease duration, medication use, and cognitive decline on these alterations. A cross-sectional observational design was employed, including 132 patients and age- and gender-matched controls. OCT imaging was performed using the Topcon 3D OCT-1 Maestro 2, focusing on the retinal nerve fibre layer (RNFL), ganglion cell complex (GCC), and macular thickness. Significant retinal thinning was observed in the AD and PD groups, correlating with disease severity and cognitive decline, and was more pronounced with longer disease duration. In contrast, no significant retinal changes were identified in the SZ group. The study also explored the effects of different drug classes, revealing correlations between specific medications and retinal parameters, particularly in the AD and PD cohorts. To our knowledge, this is the first study of its kind conducted with Bulgarian patients. These findings suggest that OCT may serve as a non-invasive biomarker for neurodegeneration in AD and PD, though its utility in SZ remains limited. Future research should aim to standardize OCT protocols, further investigate the potential of retinal imaging in tracking neurodegenerative disease progression, and explore its integration with other neuroimaging techniques for a more comprehensive diagnostic and monitoring approach.

Association of plasma neurofilament light chain and Lipoprotein-related phospholipase A2 with motor subtypes of Parkinson’s disease

Neurofilament light chain (NfL) levels were reliable biomarkers of neurodegeneration in Parkinson’s disease (PD). Lipoprotein-related Phospholipase A2(Lp-PLA2) levels have also been increasingly studied in PD. We aimed to explore the association of plasma NfL and Lp-PLA2 with the diagnosis, motor subtypes and disease severity of PD. Plasma NfL and Lp-PLA2 were assayed separately in 106 participants (74 PD and 32 healthy controls, HC). The motor subtypes of PD were classified according to the MDS-UPDRS components, and motor and non-motor manifestations of patients were also evaluated. Subsequently, correlation analyses were performed. The plasma NfL levels were higher in the PD than HC, and were positively correlated with age, UPDRS II, UPDRS III and the modified Hoehn and Yahr staging scale (H&Y stage) in the PD. Moreover, plasma Lp-PLA2 levels were lower in the PD than HC, and were positively correlated with Parkinson’s Disease Quality of Life Questionnaire (PDQ-39) in the PD. For further distinguishing tremor-dominant (TD) from postural instability and gait difficulty-dominant (PIGD), plasma Lp-PLA2 levels were higher in the TD than PIGD, but there was no significant difference in NfL. plasma Lp-PLA2 levels were positively correlated with UPDRS I, Hamilton Anxiety Rating Scale (HAMA) and PDQ-39 in the TD. These resultssuggest that NfL and Lp-PLA2 may be potential biomarkers for the diagnosis of PD. We first demonstrated the potential utility of plasma Lp-PLA2 in differentiating motor subtypes. These findings deserve further evidence in larger PD cohorts.

Advancing Parkinson's Disease Research in Canada: The Canadian Open Parkinson Network (C-OPN) Cohort.

Enhancing the interactions between study participants, clinicians, and investigators is imperative for advancing Parkinson's disease (PD) research. The Canadian Open Parkinson Network (C-OPN) stands as a nationwide endeavor, connecting the PD community with ten accredited universities and movement disorders research centers spanning, at the time of this analysis, British Columbia, Alberta, Ontario, and Quebec. Our aim is to showcase C-OPN as a paradigm for bolstering national collaboration to accelerate PD research and to provide an initial overview of already collected data sets. The C-OPN database comprises de-identified data concerning demographics, symptoms and signs, treatment approaches, and standardized assessments. Additionally, it collects venous blood-derived biomaterials, such as for analyses of DNA, peripheral blood mononuclear cells (PBMC), and serum. Accessible to researchers, C-OPN resources are available through web-based data management systems for multi-center studies, including REDCap. As of November 2023, the C-OPN had enrolled 1,505 PD participants. The male-to-female ratio was 1.77:1, with 83% (n = 1098) residing in urban areas and 82% (n = 1084) having pursued post-secondary education. The average age at diagnosis was 60.2±10.3 years. Herein, our analysis of the C-OPN PD cohort encompasses environmental factors, motor and non-motor symptoms, disease management, and regional differences among provinces. As of April 2024, 32 research projects have utilized C-OPN resources. C-OPN represents a national platform promoting multidisciplinary and multisite research that focuses on PD to promote innovation, exploration of care models, and collaboration among Canadian scientists.

Eleven Years of Change: Disease Progression in Biomarker-Defined Sporadic Parkinson's Disease.

Long-term longitudinal data on outcomes in sporadic Parkinson's Disease are limited, especially from cohorts with extensive biological characterization. Recent advances in biomarkers characterization of Parkinson's Disease necessitate an updated examination of long-term progression within contemporary cohorts like the Parkinson's Progression Markers Initiative, which enrolled individuals within 2 years of clinical diagnosis of Parkinson's Disease. Our study leverages the Neuronal Synuclein Disease framework, which defines the disease based on biomarker assessed presence of neuronal alpha-synuclein and dopamine deficit, rather than based on conventional clinical diagnostic criteria. In this study we aimed to provide a comprehensive long-term description of disease progression using the integrated biological and clinical staging system framework. We analyzed data from 344 participants from the sporadic Parkinson's Disease cohort in the Parkinson's Progression Markers Initiative, who met Neuronal Synuclein Disease criteria. We assessed 11-year progression in a spectrum of clinical measures. We used Cox proportional hazards models to assess the association between baseline stage and time to key outcomes, including survival, postural instability (Hoehn & Yahr ≥ 3), loss of independence (Schwab & England < 80%), cognitive decline, and domain-based milestones such as walking and balance, motor complications, autonomic dysfunction, and activities of daily living. Additional analyses were completed to account for death and participant dropout. Biomarker analysis included dopamine transporter binding measures, as well as serum urate, neurofilament light chain and CSF amyloid-beta, phosphorylated tau and total tau. At baseline, despite the cohort consisting of individuals within 2 years of clinical diagnosis, there was clear separation of participants in Neuronal Synuclein Disease Stages (23% Stage 2b, 67% Stage 3, 10% Stage 4). At 11 years, data were available for 153 participants; 35 participants had died over the follow up period. Of retained participants, 59% presented normal cognition, 24% had evidence of postural instability and mean Schwab & England score was 78.5. Serum neurofilament light chain consistently increased over time. No other biofluids had a consistent change in trajectory. Of importance, baseline Neuronal Synuclein Disease Stage predicted progression to clinically meaningful milestones. This study provides data on longitudinal, 11-year progression in Neuronal Synuclein Disease participants within 2 years of clinical diagnosis. We observed better long-term outcomes in this contemporary observational study cohort. It highlights the heterogeneity in the early Parkinson's Disease population as defined by clinical diagnostic criteria and underscores the importance of shifting from clinical to biologically and functionally based inclusion criteria in the design of new clinical trials.

Cerebrospinal Fluid Total, Phosphorylated and Oligomeric A-Synuclein in Parkinson's Disease: A Systematic Review, Meta-Analysis and Meta-Regression Study.

Background: The diagnostic accuracy for Parkinson's disease (PD), a synucleinopathy, based on diagnostic criteria is suboptimal. A biomarker for synucleinopathies is pivotal both from a clinical and from a research point of view. CSF a-synuclein has been extensively studied over the past two decades as a candidate biomarker of synucleinopathies. Herein, we present data on studies focusing on total, phosphorylated and oligomeric CSF a-synuclein in PD. Methods: Pubmed, Scopus and Web of Science were searched for studies with >10 PD patients and control subjects, with data (mean, SD) on total, phosphorylated or oligomeric a-synuclein. Cohen's d, as a measure of effect size, was calculated for all a-synuclein forms. Subgroup analysis and meta-regression were performed in an effort to explain between-study heterogeneity. Results: Thirty studies on total, six studies on oligomeric and one study on phosphorylated a-synuclein were included. Total a-synuclein was decreased and oligomeric a-synuclein increased in PD patients vs. controls. The effect size was medium for total and high for oligomeric a-synuclein. A-syn forms provided suboptimal combined sensitivity/specificity for the differentiation of PD from controls. There was significant between-study heterogeneity. The PD cohort characteristics (sex, age, disease duration, UPDRS, H & Y) and study characteristics (study design, healthy vs. neurological controls, control for CSF blood contamination, method of a-syn measurement) could not account for between-study heterogeneity. Publication bias was limited. Conclusions: CSF a-synuclein levels lack sufficient accuracy to be used as biomarkers for PD. The standardization of (pre)analytical variables may improve the discriminatory power of a-synuclein forms in the future.

Genetic Analysis of Neurite Outgrowth Inhibitor-Associated Genes in Parkinson's Disease: A Cross-Sectional Cohort Study.

Parkinson's disease (PD) is a neurodegenerative disease caused by a combination of aging, environmental, and genetic factors. Previous research has implicated both causative and susceptibility genes in PD development. Nogo-A, a neurite outgrowth inhibitor, has been shown to impact axon growth through ligand-receptor interactions negatively, thereby involved in the deterioration of dopaminergic neurons. However, rare genetic studies have identified the relationship between neurite outgrowth inhibitor (Nogo)-associated genes and PD from a signaling pathway perspective. We enrolled 3959 PD patients and 2931 healthy controls, categorized into two cohorts based on their family history and age at onset: sporadic early Parkinson's disease & familial Parkinson's disease (sEOPD & FPD) cohort and sporadic late Parkinson's disease (sLOPD) cohort. We selected 17 Nogo-associated genes and stratified them into three groups via their function, respectively, ligand, receptors, and signaling pathway groups. Additionally, we conducted the burden analysis in rare variants, the logistic regression analysis in common variants, and the genotype-phenotype association analysis. Last, bioinformatics analysis and functional experiments were conducted to identify the role of the MTOR gene in PD. Our findings demonstrated that the missense variants in the MTOR gene might increase PD risk, while the deleterious variants in the receptor subtype of Nogo-associated genes might mitigate PD risk. However, common variants of Nogo-associated genes showed no association with PD development in two cohorts. Furthermore, genotype-phenotype association analysis suggested that PD patients with MTOR gene variants exhibited relatively milder motor symptoms but were more susceptible developing dyskinesia. Additionally, bioinformatics analysis results showed MTOR gene was significantly decreased in PD, indicating a potential negative role of the mTOR in PD pathogenesis. Experimental data further demonstrated that MHY1485, a mTOR agonist, could rescue MPP+-induced axon inhibition, further implicating the involvement of mTOR protein in PD by regulating cell growth and axon growth. Our preliminary investigation highlights the association of Nogo-associated genes with PD onset in the Chinese mainland population and hints at the potential role of the MTOR gene in PD. Further research is warranted to elucidate the mechanistic pathways underlying these associations and their therapeutic implications.

Involvement of the basal forebrain and hippocampus in memory deficits in Parkinson's disease

IntroductionMagnetic resonance imaging (MRI)-determined atrophy of the nucleus basalis of Meynert (Ch4) predicts cognitive decline in Parkinson's disease (PD). However, interactions with other brain regions causing the decline remain unclear. This study aimed to describe how MRI-determined Ch4 atrophy leads to cognitive decline in patients with PD. MethodsWe evaluated 137 patients with PD and 39 healthy controls using neuropsychological examinations, MRI, and 123I-ioflupane single-photon emission computed tomography. First, we explored brain areas with regional gray matter loss correlated with Ch4 volume reduction using voxel-based morphometry (VBM). We then assessed the correlation between Ch4 volume reduction and cognitive impairments in PD using partial correlation coefficients (rpar). Finally, we examined whether the regional gray matter loss mediated the association between Ch4 volume reduction and cognitive impairments using mediation analysis. ResultsOur PD cohort was “advanced-stage enriched.” VBM analyses revealed that Ch4 volume loss was correlated with volume reduction in the medial temporal lobe in PD (P < 0.05, family-wise error corrected, >29 voxels). Ch4 volume reduction was significantly correlated with verbal memory deficits in PD when adjusted for age, sex, total brain volume, and 123I-ioflupane uptake in the caudate (rpar = 0.28, P < 0.001). The mediation analysis revealed that the hippocampus mediated the effects of Ch4 volumes on verbal memory (average causal mediation effect = 0.013, 95 % CI = 0.006–0.020, P < 0.001). ConclusionParticularly in advanced-stage PD, Ch4 atrophy was associated with medial temporal lobe atrophy, which played an intermediary role in the relationship between Ch4 atrophy and verbal memory impairments.

Quality of Life after Deep Brain Stimulation in Parkinson's Disease: Does the Target Matter?

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) and globus pallidus internus (GPi) is an accepted therapy for Parkinson's disease (PD) with disabling motor complications. For elderly patients with poorer cognition and postural instability, GPi has been proposed as the preferable DBS target based on expert opinion, arguing GPi-DBS may be less complicated by depression, apathy, worsened verbal fluency, and executive dysfunction, resulting in greater improvement in quality of life (QoL). However, data supporting such patient-tailored approach are lacking. The aims were to analyze whether the DBS target influences QoL in a PD cohort and a matched subgroup of frail patients with poor cognitive status and reduced postural stability, and whether other factors affect the QoL outcomes. In this retrospective study, we analyzed a single-center cohort of 138 PD patients who received bilateral STN-DBS (117) or GPi-DBS (21) using the mentioned approach for target selection. All patients underwent standardized clinical evaluations of motor- and nonmotor signs as well as QoL before and 1 year after surgery. DBS of both targets improved motor signs, dyskinesias, and pain. QoL improved without significant difference between the targets, but with a trend for greater improvement across all QoL domains in favor of the STN, even in an STN subgroup matched to the GPi group. Our results contradict the prevailing belief that GPi-DBS is superior in frail PD patients with cognitive decline and postural instability, questioning the proposed patient-tailored approach of DBS target selection. Further studies are needed for a data-driven approach.

Natural Health Products for Symptomatic Relief of Parkinson's Disease: Prevalence, Interest, and Awareness.

Natural health products have emerged as a potential symptomatic therapeutic approach for people with Parkinson's disease (PD). To determine the prevalence of natural health product use, interest in natural health products, awareness of potential herb-drug interactions, and consultation of healthcare professionals regarding natural health products use among people with PD. Cross-sectional 4-item survey embedded in the PRIME-NL study, which is a population-based cohort of PD. Of 367 people with PD, 36% reported having used natural health products to alleviate PD-related symptoms, with coffee, cannabis and turmeric being the most popular. Furthermore, 71% of people with PD were interested in learning more about natural health products. 39% of natural health products users were aware that these products could interact with PD medication and 39% had discussed their use with their healthcare professional. Natural health products are commonly used to alleviate symptoms by people with PD, but most users are unaware that these products can interact with PD medication and do not discuss their consumption with their healthcare professional.

Classification of Parkinson's disease by deep learning on midbrain MRI.

Susceptibility map weighted imaging (SMWI), based on quantitative susceptibility mapping (QSM), allows accurate nigrosome-1 (N1) evaluation and has been used to develop Parkinson's disease (PD) deep learning (DL) classification algorithms. Neuromelanin-sensitive (NMS) MRI could improve automated quantitative N1 analysis by revealing neuromelanin content. This study aimed to compare classification performance of four approaches to PD diagnosis: (1) N1 quantitative "QSM-NMS" composite marker, (2) DL model for N1 morphological abnormality using SMWI ("Heuron IPD"), (3) DL model for N1 volume using SMWI ("Heuron NI"), and (4) N1 SMWI neuroradiological evaluation. PD patients (n = 82; aged 65 ± 9 years; 68% male) and healthy-controls (n = 107; 66 ± 7 years; 48% male) underwent 3 T midbrain MRI with T2*-SWI multi-echo-GRE (for QSM and SMWI), and NMS-MRI. AUC was used to compare diagnostic performance. We tested for correlation of each imaging measure with clinical parameters (severity, duration and levodopa dosing) by Spearman-Rho or Kendall-Tao-Beta correlation. Classification performance was excellent for the QSM-NMS composite marker (AUC = 0.94), N1 SMWI abnormality (AUC = 0.92), N1 SMWI volume (AUC = 0.90), and neuroradiologist (AUC = 0.98). Reasons for misclassification were right-left asymmetry, through-plane re-slicing, pulsation artefacts, and thin N1. In the two DL models, all 18/189 (9.5%) cases misclassified by Heuron IPD were controls with normal N1 volumes. We found significant correlation of the SN QSM-NMS composite measure with levodopa dosing (rho = -0.303, p = 0.006). Our data demonstrate excellent performance of a quantitative QSM-NMS marker and automated DL PD classification algorithms based on midbrain MRI, while suggesting potential further improvements. Clinical utility is supported but requires validation in earlier stage PD cohorts.

Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data.

Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinson's disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.

Mapping brain morphology to cognitive deficits: a study on PD-CRS scores in Parkinson's disease with mild cognitive impairment.

The Parkinson's Disease-Cognitive Rating Scale (PD-CRS) is a widely used tool for detecting mild cognitive impairment (MCI) in Parkinson's Disease (PD) patients, however, the neuroanatomical underpinnings of this test's outcomes require clarification. This study aims to: (a) investigate cortical volume (CVol) and cortical thickness (CTh) disparities between PD patients exhibiting mild cognitive impairment (PD-MCI) and those with preserved cognitive abilities (PD-IC); and (b) identify the structural correlates in magnetic resonance imaging (MRI) of overall PD-CRS performance, including its subtest scores, within a non-demented PD cohort. This study involved 51 PD patients with Hoehn & Yahr stages I-II, categorized into two groups: PD-IC (n = 36) and PD-MCI (n = 15). Cognitive screening evaluations utilized the PD-CRS and the Montreal Cognitive Assessment (MoCA). PD-MCI classification adhered to the Movement Disorder Society Task Force criteria, incorporating extensive neuropsychological assessments. The interrelation between brain morphology and cognitive performance was determined using FreeSurfer. Vertex-wise analysis of the entire brain demonstrated a notable reduction in CVol within a 2,934 mm2 cluster, encompassing parietal and temporal regions, in the PD-MCI group relative to the PD-IC group. Lower PD-CRS total scores correlated with decreased CVol in the middle frontal, superior temporal, inferior parietal, and cingulate cortices. The PD-CRS subtests for Sustained Attention and Clock Drawing were associated with cortical thinning in distinct regions: the Clock Drawing subtest correlated with changes in the parietal lobe, insula, and superior temporal cortex morphology; while the PD-CRS frontal-subcortical scores presented positive correlations with CTh in the transverse temporal, medial orbitofrontal, superior temporal, precuneus, fusiform, and supramarginal regions. Additionally, PD-CRS subtests for Semantic and Alternating verbal fluency were linked to CTh changes in orbitofrontal, temporal, fusiform, insula, and precentral regions. PD-CRS performance mirrors neuroanatomical changes across extensive fronto-temporo-parietal areas, covering both lateral and medial cortical surfaces, in PD patients without dementia. The observed changes in CVol and CTh associated with this cognitive screening tool suggest their potential as surrogate markers for cognitive decline in PD. These findings warrant further exploration and validation in multicenter studies involving independent patient cohorts.

Transcriptomic changes in oligodendrocytes and precursor cells associate with clinical outcomes of Parkinson’s disease

Several prior studies have proposed the involvement of various brain regions and cell types in Parkinson’s disease (PD) pathology. Here, we performed snRNA-seq on the prefrontal cortex and anterior cingulate regions from a small cohort of post-mortem control and PD brain tissue. We found a significant association of oligodendrocytes (ODCs) and oligodendrocyte precursor cells (OPCs) with PD-linked risk loci and report several dysregulated genes and pathways, including regulation of tau-protein kinase activity, regulation of inclusion body assembly and protein processing involved in protein targeting to mitochondria. In an independent PD cohort with clinical measures (681 cases and 549 controls), polygenic risk scores derived from the dysregulated genes significantly predicted Montreal Cognitive Assessment (MoCA)-, and Beck Depression Inventory-II (BDI-II)-scores but not motor impairment (UPDRS-III). We extended our analysis of clinical outcome prediction by incorporating differentially expressed genes from three separate datasets that were previously published by different laboratories. In the first dataset from the anterior cingulate cortex, we identified an association between ODCs and BDI-II. In the second dataset obtained from the substantia nigra (SN), OPCs displayed an association with UPDRS-III. In the third dataset from the SN region, a distinct subtype of OPCs, labeled OPC_ADM, exhibited an association with UPDRS-III. Intriguingly, the OPC_ADM cluster also demonstrated a significant increase in PD samples. These results suggest that by expanding our focus to glial cells, we can uncover region-specific molecular pathways associated with PD symptoms.

Orthostatic Hypotension and Risk of Mild Cognitive Impairment and Dementia in Parkinson's Disease.

Orthostatic hypotension (OH) is a common condition in Parkinson's disease (PD) with a possible link to cognitive decline. The aim was to explore the association between OH and PD-associated mild cognitive impairment (PD-MCI) and dementia (PDD) over 9 years in a population-based incident PD cohort. We prospectively followed up patients from PD diagnosis with serial blood pressure measurements, clinical examinations, and neuropsychological assessments. We defined OH using (1) consensus-based criteria and (2) clinically significant OH by mean arterial pressure (MAP) in standing position ≤75 mmHg. PD-MCI and PDD were diagnosed according to acknowledged criteria. We applied generalized estimating equations models to investigate associations between OH measurements and cognitive impairment over time. Weibull accelerated failure time regression models were used to study if early OH (≤3 years of PD diagnosis) accelerates the time to incident PD-MCI and PDD. Of 186 enrolled patients, consensus-based OH affected 68.8%, clinically significant OH 33.9%, PD-MCI 60.8%, and PDD 31.2%. Consensus-based OH was associated with PD-MCI (odds ratio [OR]: 2.04, 95% confidence interval: 1.44-2.90, P < 0.001), whereas clinically significant OH was associated with both PD-MCI (OR: 1.95, 1.11-3.43, P = 0.020) and PDD (OR: 3.66, 1.95-6.86, P < 0.001). Early clinically significant OH, but not early consensus-based OH, reduced time to incident PD-MCI by 54% (P = 0.021) and time to PDD by 44% (P = 0.003) independently of potential confounders, including supine hypertension and cardiovascular disease. MAP in standing position emerged as a stronger predictor of cognitive decline than OH determined using consensus-based criteria. These findings have implications for both research and clinical practice.