Clinical criteria for subtyping Parkinson's disease: biomarkers and longitudinal progression

MRI subtypes in Parkinson's disease across diverse populations and clustering approaches

Parkinson's disease (PD) is clinically heterogeneous, which suggests the existence of subtypes; however, there has been no consensus regarding their characteristics. This study included 633 PD individuals across distinct cohorts: unmedicated de novo PD, medicated PD, mild-moderate PD, and a cohort based on diagnostic work-up in clinical practice. Additionally, 233 controls were included. Clustering based on cortical and subcortical gray matter measures was conducted with and without adjusting for global atrophy in the entire PD sample and validated within each cohort. Subtypes were characterized using baseline and longitudinal demographic and clinical data. Unadjusted results identified three clusters showing a gradient of neurodegeneration and symptom severity across the entire sample and the individual cohorts. When adjusting for global atrophy eight clusters were identified in the entire sample, lacking consistency in individual cohorts. This study identified atrophy-based subtypes in PD, emphasizing the significant impact of global atrophy on subtype number, patterns, and interpretation in cross-sectional analyses.

Contrastive machine learning reveals Parkinson's disease specific features associated with disease severity and progression

Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to diverse neuroanatomical alterations. This study employs a contrastive deep learning approach to analyze Magnetic Resonance Imaging (MRI) data from 932 PD patients and 366 controls, aiming to disentangle PD-specific neuroanatomical alterations. The results reveal that these neuroanatomical alterations in PD are correlated with individual differences in dopamine transporter binding deficit, neurodegeneration biomarkers, and clinical severity and progression. The correlation with clinical severity is verified in an external cohort. Notably, certain proteins in the cerebrospinal fluid are strongly associated with PD-specific features, particularly those involved in the immune function. The most notable neuroanatomical alterations are observed in both subcortical and temporal regions. Our findings provide deeper insights into the patterns of brain atrophy in PD and potential underlying molecular mechanisms, paving the way for earlier patient stratification and the development of treatments to slow down neurodegeneration.

Distinct Clinical Implications of Patient- Versus Clinician-Rated Motor Symptoms in Parkinson's Disease

BACKGROUND

Patient-rated motor symptoms (PRMS) and clinician-rated motor symptoms (CRMS) often differ in Parkinson's disease (PD).

OBJECTIVE

Our goal was to investigate the determinants and clinical implications of PRMS compared with CRMS in PD.

METHODS

This retrospective, observational cohort study analyzed the cross-sectional associations and longitudinal impacts of PRMS as assessed by the Movement Disorders Society-sponsored Unified PD Rating Scale (MDS-UPDRS) part 2, while controlling for CRMS measured by MDS-UPDRS part 3. Longitudinal analyses used Cox proportional hazards models and multiple linear mixed-effects random intercepts/slope models, adjusting for many clinical predictors. We conducted propensity score matching (PSM) to reinforce our analyses' robustness and surface-based morphometry to investigate neural correlates.

RESULTS

We enrolled 442 patients with early-stage PD. At baseline, regardless of CRMS, PRMS were associated with the severity of postural instability and gait disturbance (PIGD). Notably, PRMS independently and more accurately predicted faster long-term deterioration in motor function than CRMS (Hoehn and Yahr 4, adjusted hazard ratio per +1 point = 1.19 [95% confidence intervals, 1.08-1.32]), particularly in PIGD (PIGD subscore, β-interaction = 0.052 [95% confidence intervals, 0.018-0.086]). PSM confirmed these findings' robustness. Surface-based morphometry suggested that enhanced sensory processing was distinctively associated with PRMS.

CONCLUSIONS

In early-stage PD, PRMS weighed different aspects of symptoms and more effectively predicted motor deterioration compared to CRMS, with distinctive brain structural characteristics. The superior sensitivity of PRMS to subtle declines in drug-refractory symptoms like PIGD likely underlie our results, highlighting the importance of understanding the differential clinical implications of PRMS to prevent long-term motor deterioration. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Developing disease-modifying interventions in idiopathic REM sleep behavior disorder and early synucleinopathy

Alpha-synucleinopathies are prevalent neurological disorders that cause significant disability, leading to progressive clinical deterioration that is currently managed solely through symptomatic treatment. Efforts to evaluate disease-modifying therapies during the established stage of the disease have not yielded positive outcomes in terms of clinical or imaging efficacy endpoints. However, alpha-synucleinopathies have a long prodromal phase that presents a promising opportunity for intervention with disease-modifying therapies. The presence of polysomnography-confirmed REM sleep behavior disorder (RBD) is the most reliable risk factor for identifying individuals in the prodromal stage of alpha-synucleinopathy. This paper discusses the rationale behind targeting idiopathic/isolated RBD in disease-modifying trials and outlines possible study designs, including strategies for patient stratification, selection of biomarkers to assess disease progression and patient eligibility, as well as the identification of suitable endpoints. Additionally, the potential targets for disease-modifying treatment in alpha-synucleinopathies are summarized.

Impact of autonomic symptoms on the clinical course of Parkinson's disease

PURPOSE

Patients with Parkinson's disease (PD) exhibit various degrees of autonomic symptoms, which may be associated with Lewy body pathology distributed extensively in the autonomic nervous system. We hypothesized that the severity of autonomic symptoms reflects the severity of PD-related pathology, resulting in poor outcomes. The purpose of this study was to evaluate the impact of autonomic symptoms on PD progression.

METHODS

We conducted a follow-up study among consecutive patients with PD at Dokkyo Medical University Hospital. Patients underwent comprehensive baseline evaluations and were classified into high and low autonomic symptom groups using the Scale for Outcomes in Parkinson's Disease-Autonomic (SCOPA-AUT). The Kaplan‒Meier survival curves were used to analyze the time to discontinuation of their visits because of PD-related endpoints and to evaluate the association with high SCOPA-AUT scores.

RESULTS

Of the 101 patients, 74 (73%) met the inclusion criteria. During the follow-up period (mean 1654 days), 22/74 patients reached PD-related endpoints (death, 4; hospitalization, 9; nursing home institutionalization, 9). PD patients with high SCOPA-AUT scores reached the endpoints faster than those with low SCOPA-AUT scores. A high SCOPA-AUT score, including gastrointestinal, urinary, and thermoregulation domains; high motor symptom scores; and low specific binding ratios (SBRs) on 123I FP-CIT-SPECT (DAT-SPECT) were associated with reaching PD-related endpoints. A high SCOPA-AUT score was associated with reaching the endpoints even after adjustment for covariates.

CONCLUSIONS

Patients with high autonomic symptom scores had a greater risk of reaching PD-related endpoints than patients with low autonomic symptom scores.

Microstructural alterations in white matter and related neurobiology based on the new clinical subtypes of Parkinson's disease

Background and objectives

The advent of new clinical subtyping systems for Parkinson's disease (PD) has led to the classification of patients into distinct groups: mild motor predominant (PD-MMP), intermediate (PD-IM), and diffuse malignant (PD-DM). Our goal was to evaluate the efficacy of diffusion tensor imaging (DTI) in the early diagnosis, assessment of clinical progression, and prediction of prognosis of these PD subtypes. Additionally, we attempted to understand the pathological mechanisms behind white matter damage using single-photon emission computed tomography (SPECT) and cerebrospinal fluid (CSF) analyses.

Methods

We classified 135 de novo PD patients based on new clinical criteria and followed them up after 1 year, along with 45 healthy controls (HCs). We utilized tract-based spatial statistics to assess the microstructural changes of white matter at baseline and employed multiple linear regression to examine the associations between DTI metrics and clinical data at baseline and after follow-up.

Results

Compared to HCs, patients with the PD-DM subtype demonstrated reduced fractional anisotropy (FA), increased axial diffusivity (AD), and elevated radial diffusivity (RD) at baseline. The FA and RD values correlated with the severity of motor symptoms, with RD also linked to cognitive performance. Changes in FA over time were found to be in sync with changes in motor scores and global composite outcome measures. Furthermore, baseline AD values and their rate of change were related to alterations in semantic verbal fluency. We also discovered the relationship between FA values and the levels of α-synuclein and β-amyloid. Reduced dopamine transporter uptake in the left putamen correlated with RD values in superficial white matter, motor symptoms, and autonomic dysfunction at baseline as well as cognitive impairments after 1 year.

Conclusions

The PD-DM subtype is characterized by severe clinical symptoms and a faster progression when compared to the other subtypes. DTI, a well-established technique, facilitates the early identification of white matter damage, elucidates the pathophysiological mechanisms of disease progression, and predicts cognitively related outcomes. The results of SPECT and CSF analyses can be used to explain the specific pattern of white matter damage in patients with the PD-DM subtype.

Predictors associated with the rate of progression of nigrostriatal degeneration in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) manifests as a wide variety of clinical phenotypes and its progression varies greatly. However, the factors associated with different disease progression remain largely unknown.

METHODS

In this retrospective cohort study, we enrolled 113 patients who underwent 18F-FP-CIT PET scan twice. Given the negative exponential progression pattern of dopamine loss in PD, we applied the natural logarithm to the specific binding ratio (SBR) of two consecutive 18F-FP-CIT PET scans and conducted linear mixed model to calculate individual slope to define the progression rate of nigrostriatal degeneration. We investigated the clinical and dopamine transporter (DAT) availability patterns associated with the progression rate of dopamine depletion in each striatal sub-region.

RESULTS

More symmetric parkinsonism, the presence of dyslipidemia, lower K-MMSE total score, and lower anteroposterior gradient of the mean putaminal SBR were associated with faster progression rate of dopamine depletion in the caudate nucleus. More symmetric parkinsonism and lower anteroposterior gradient of the mean putaminal SBR were associated with faster depletion of dopamine in the anterior putamen. Older age at onset, more symmetric parkinsonism, the presence of dyslipidemia, and lower anteroposterior gradient of the mean putaminal SBR were associated with faster progression rate of dopamine depletion in the posterior putamen. Lower striatal mean SBR predicted the development of LID, while lower mean SBR in the caudate nuclei predicted the development of dementia.

DISCUSSION

Our results suggest that the evaluation of baseline clinical features and patterns of DAT availability can predict the progression of PD and its prognosis.

Anticipating Tomorrow: Tailoring Parkinson's Symptomatic Therapy Using Predictors of Outcome

BACKGROUND

Although research into Parkinson's disease (PD) subtypes and outcome predictions has continued to advance, recommendations for using outcome prediction to guide current treatment decisions remain sparse.

OBJECTIVES

To provide expert opinion-based recommendations for individually tailored PD symptomatic treatment based on knowledge of risk prediction and subtypes.

METHODS

Using a modified Delphi approach, members of the Movement Disorders Society (MDS) Task Force on PD subtypes generated a series of general recommendations around the question: "Using what you know about genetic/biological/clinical subtypes (or any individual-level predictors of outcome), what advice would you give for selecting symptomatic treatments for an individual patient now, based on what their subtype or individual characteristics predict about their future disease course?" After four iterations and revisions, those recommendations with over 75% endorsement were adopted.

RESULTS

A total of 19 recommendations were endorsed by a group of 13 panelists. The recommendations primarily centered around two themes: (1) incorporating future risk of cognitive impairment into current treatment plans; and (2) identifying future symptom clusters that might be forestalled with a single medication.

CONCLUSIONS

These recommendations provide clinicians with a framework for integrating future outcomes into patient-specific treatment choices. They are not prescriptive guidelines, but adaptable suggestions, which should be tailored to each individual. They are to be considered as a first step of a process that will continue to evolve as additional stakeholders provide new insights and as new information becomes available. As individualized risk prediction advances, the path to better tailored treatment regimens will become clearer.

Motor progression trajectories and risk of mild cognitive impairment in Parkinson's disease: A latent class trajectory model from PPMI cohort

AIMS

Rare studies have investigated the association between heterogeneity of motor progression and risk of early cognitive impairment in Parkinson's disease (PD). In this study, we aim to identify distinct trajectories of motor progression longitudinally and investigate their impact on predicting mild cognitive impairment (MCI).

METHODS

A 5-year cohort including 415 PD patients at baseline was collected from the Parkinson's Progression Markers Initiative. The severity of motor symptoms was evaluated using the Movement Disorder Society Unified Parkinson's Disease Rating Scale part III. The latent class trajectory model and nonlinear mixed-effects model were used to analyze and delineate the longitudinal changes in motor symptoms. Propensity score matching (PSM) was used to minimize the impact of potential confounders. Cox proportional hazard models were applied to calculate hazard ratios for MCI, and a Kaplan-Meier curve was generated using the occurrence of MCI during the follow-up as the time-to-event.

RESULTS

Two latent trajectories were identified: a mild and remitting motor symptoms class (Class 1, 33.01%) and a severe and progressive motor symptom class (Class 2, 66.99%). Patients in Class 2 initially exhibited severe motor symptoms that worsened progressively despite receiving anti-PD medications. In comparison, patients in Class 1 exhibited milder symptoms that improved following drug therapy and a slower progression. During a 5-year follow-up, patients in Class 2 showed a higher risk of developing MCI compared to those in Class 1 before PSM (Log-Rank 28.58, p < 0.001) and after PSM (Log-Rank 8.20, p = 0.004).

CONCLUSIONS

PD patients with severe and progressive motor symptoms are more likely to develop MCI than those with mild and stable motor symptoms.

A causal effect study of cortical morphology and related covariate networks in classical trigeminal neuralgia patients

Structural covariance networks and causal effects within can provide critical information on gray matter reorganization and disease-related hierarchical changes. Based on the T1WI data of 43 classical trigeminal neuralgia patients and 45 controls, we constructed morphological similarity networks of cortical thickness, sulcal depth, fractal dimension, and gyrification index. Moreover, causal structural covariance network analyses were conducted in regions with morphological abnormalities or altered nodal properties, respectively. We found that patients showed reduced sulcal depth, gyrification index, and fractal dimension, especially in the salience network and the default mode network. Additionally, the integration of the fractal dimension and sulcal depth networks was significantly reduced, accompanied by decreased nodal efficiency of the bilateral temporal poles, and right pericalcarine cortex within the sulcal depth network. Negative causal effects existed from the left insula to the right caudal anterior cingulate cortex in the gyrification index map, also from bilateral temporal poles to right pericalcarine cortex within the sulcal depth network. Collectively, patients exhibited impaired integrity of the covariance networks in addition to the abnormal gray matter morphology in the salience network and default mode network. Furthermore, the patients may experience progressive impairment in the salience network and from the limbic system to the sensory system in network topology, respectively.

Spiking Laguerre Volterra networks-predicting neuronal activity from local field potentials

Objective.Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations.Approach.Here, we fill this gap by proposing novel spiking Laguerre-Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics.Main results.The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features.Significance.These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

Resting-State Functional MRI Approaches to Parkinsonisms and Related Dementia

PURPOSE OF THE REVIEW

In this review, we attempt to summarize the most updated studies that applied resting-state functional magnetic resonance imaging (rs-fMRI) in the field of Parkinsonisms and related dementia.

RECENT FINDINGS

Over the past decades, increasing interest has emerged on investigating the presence and pathophysiology of cognitive symptoms in Parkinsonisms and their possible role as predictive biomarkers of neurodegenerative brain processes. In recent years, evidence has been provided, applying mainly three methodological approaches (i.e. seed-based, network-based and graph-analysis) on rs-fMRI data, with promising results. Neural correlates of cognitive impairment and dementia have been detected in patients with Parkinsonisms along the diseases course. Interestingly, early functional connectivity signatures were proposed to track and predict future progression of neurodegenerative processes. However, longitudinal studies are still sparce and further investigations are needed to overcome this knowledge gap.

Unsupervised clustering identified clinically relevant metabolic syndrome endotypes in UK and Taiwan Biobanks

Metabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede effective clinical management. We conducted unsupervised clustering on individuals from UK Biobank to reveal endotypes. Five MetS subgroups were identified: Cluster 1 (C1): non-descriptive, Cluster 2 (C2): hypertensive, Cluster 3 (C3): obese, Cluster 4 (C4): lipodystrophy-like, and Cluster 5 (C5): hyperglycemic. For all of the endotypes, we identified the corresponding cardiometabolic traits and their associations with clinical outcomes. Genome-wide association studies (GWASs) were conducted to identify associated genotypic traits. We then determined endotype-specific genotypic traits and constructed polygenic risk score (PRS) models specific to each endotype. GWAS of each MetS clusters revealed different genotypic traits. C1 GWAS revealed novel findings of TRIM63, MYBPC3, MYLPF, and RAPSN. Intriguingly, C1, C3, and C4 were associated with genes highly expressed in brain tissues. MetS clusters with comparable phenotypic and genotypic traits were identified in Taiwan Biobank.

Data-Driven Subtypes of Multiple System Atrophy and Their Implications for Prognosis

Background

While multiple system atrophy (MSA) presents with high heterogeneous motor and nonmotor symptoms, the associations between clinical phenotypes and prognosis are unclear.

Objective

We aimed to evaluate clinical phenotypes of MSA using data-driven approach and measure the impact of phenotypes on survival and bedbound status.

Methods

193 MSA patients were recruited from Xuanwu Hospital Capital Medical University, whose history, motor and non-motor symptoms were examined using cluster analysis. Ninety-five participants were followed-up via telephone after a mean of 31.87 months. We employed Kaplan- Meier analysis to examine survival and performed Cox and logistic regression analyses to identify factors associated with survival and bedbound status.

Results

We identified four clinical profiles of MSA: cerebellar symptom-dominant, sleep and mood disorder-dominant, rigid akinetic-dominant, and malignant diffuse. The overall median survival was 7.75 years (95% CI 7.19-8.31). After adjusting for years from symptom onset to diagnosis, age and sex, patients in the malignant diffuse and rigid akinetic-dominant clusters had greater risk of death than sleep and mood disorder-dominant cluster. Furthermore, patients in the malignant diffuse and rigid akinetic-dominant clusters had higher risk of being bedbound than cerebellar symptom-dominant cluster.

Conclusions

The malignant diffuse and sleep and mood disorder-dominant were identified besides the two classical subtypes, parkinsonism, and cerebellar symptom-variant. Patients with rigid-akinetic motor profiles have a worse prognosis than cerebellar symptom-dominant profiles in general. Diffuse symptoms, especially postural instability, and cognitive alterations at diagnosis, indicate rapid functional loss and disease progression. The different profiles and prognoses might indicate varied underlying pathological mechanisms.

Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data

Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.

Emerging perspectives on precision therapy for Parkinson's disease: multidimensional evidence leading to a new breakthrough in personalized medicine

PD is a prevalent and progressive neurodegenerative disorder characterized by both motor and non-motor symptoms. Genes play a significant role in the onset and progression of the disease. While the complexity and pleiotropy of gene expression networks have posed challenges for gene-targeted therapies, numerous pathways of gene variant expression show promise as therapeutic targets in preclinical studies, with some already in clinical trials. With the recognition of the numerous genes and complex pathways that can influence PD, it may be possible to take a novel approach to choose a treatment for the condition. This approach would be based on the symptoms, genomics, and underlying mechanisms of the disease. We discuss the utilization of emerging genetic and pathological knowledge of PD patients to categorize the disease into subgroups. Our long-term objective is to generate new insights for the therapeutic approach to the disease, aiming to delay and treat it more effectively, and ultimately reduce the burden on individuals and society.

Replication and reliability of Parkinson's disease clinical subtypes

BACKGROUND

We recently identified three distinct Parkinson's disease subtypes: "motor only" (predominant motor deficits with intact cognition and psychiatric function); "psychiatric & motor" (prominent psychiatric symptoms and moderate motor deficits); "cognitive & motor" (cognitive and motor deficits).

OBJECTIVE

We used an independent cohort to replicate and assess reliability of these Parkinson's disease subtypes.

METHODS

We tested our original subtype classification with an independent cohort (N = 100) of Parkinson's disease participants without dementia and the same comprehensive evaluations assessing motor, cognitive, and psychiatric function. Next, we combined the original (N = 162) and replication (N = 100) datasets to test the classification model with the full combined dataset (N = 262). We also generated 10 random split-half samples of the combined dataset to establish the reliability of the subtype classifications. Latent class analyses were applied to the replication, combined, and split-half samples to determine subtype classification.

RESULTS

First, LCA supported the three-class solution - Motor Only, Psychiatric & Motor, and Cognitive & Motor- in the replication sample. Next, using the larger, combined sample, LCA again supported the three subtype groups, with the emergence of a potential fourth group defined by more severe motor deficits. Finally, split-half analyses showed that the three-class model also had the best fit in 13/20 (65%) split-half samples; two-class and four-class solutions provided the best model fit in five (25%) and two (10%) split-half replications, respectively.

CONCLUSIONS

These results support the reproducibility and reliability of the Parkinson's disease behavioral subtypes of motor only, psychiatric & motor, and cognitive & motor groups.

Incidence and predictors of postural abnormalities in Parkinson's disease: a PPMI cohort study

BACKGROUND

Axial postural abnormalities (PA) are invalidating symptoms of Parkinson's disease (PD). Risk factors for PA are unknown.

OBJECTIVES

We sought to evaluate PA incidence and risk factors over the first 4-6 years of PD.

METHODS

We included 441 PD patients from the Parkinson's Progression Markers Initiative (PPMI) cohort with data at diagnosis and after 4-year follow-up. PA was defined according to a posture item ≥ 2 at the Movement Disorder Society-sponsored-revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) in Off therapeutic condition. The Kruskal-Wallis test was used to compare characteristics of patients without PA ('no-PA'), with PA at disease onset ('baseline-PA'), and PA developed during follow-up ('develop-PA'). To identify predictors of PA development, univariate and multivariate Cox regression analyses were performed considering demographic, clinical and therapeutic variables.

RESULTS

10.9% of patients showed PA at baseline and 23.7% developed PA within the first 4-6 years since diagnosis. Older age, malignant phenotype, higher MDS-UPDRS part III, Hoehn & Yahr, and dysautonomia (SCOPA-AUT) score, and lower levels of physical activity were predictors of PA development at the univariate analysis. Older age (Hazard ratio [HR] per year: 1.041) and higher MDS-UPDRS part III score (HR per point: 1.035) survived as PA development predictors in the multivariate analysis.

CONCLUSIONS

PPMI cohort data show that > 30% of PD patients present PA within the first 4-6 years of disease. Older age at onset and higher motor burden are associated with a higher risk for PA development. The protective role of physical activity merits to be further investigated.

New Perspectives of Deep Brain Stimulation Indications for Parkinson's Disease: A Critical Review

Deep Brain Stimulation (DBS) is an effective treatment option for patients with dopaminergic complications of Parkinson's disease (PD) and drug-refractory PD tremor. However, DBS and its indications can be challenging, and they are not often debated in the medical community. Through a critical narrative review, the objective of this paper is to improve the comprehension of DBS indications and help to solve the puzzle that this process can be. Proper patient selection is the first step for a good surgical outcome. In this review, then, relevant considerations are discussed, involving PD genes, PD phenotypes, indications of early stages, non-motor symptoms, neuroimaging predictors, comorbidities, and age. Individualized approaches are encouraged, including clinical and radiological factors. Social support during the whole follow-up and expectations alignment are necessary through this process and are also debated.

Advantages and Challenges of Platform Trials for Disease Modifying Therapies in Parkinson's Disease

Traditional drug development in Parkinson's disease (PD) faces significant challenges because of its protracted timeline and high costs. In response, innovative master protocols are emerging and designed to address multiple research questions within a single overarching protocol. These trials may offer advantages such as increased efficiency, agility in adding new treatment arms, and potential cost savings. However, they also present organizational, methodological, funding, regulatory, and sponsorship challenges. We review the potential of master protocols, focusing on platform trials, for disease modifying therapies in PD. These trials share a common control group and allow for the termination or addition of treatment arms during a trial with non-predetermined end. Specific issues exist for a platform trial in the PD field considering the heterogeneity of patients in terms of phenotype, genotype and staging, the confounding effects of symptomatic treatments, and the choice of outcome measures with no consensus on a non-clinical biomarker to serve as a surrogate and the slowness of PD progression. We illustrate these aspects using the examples of the main PD platform trials currently in development with each one targeting distinct goals, populations, and outcomes. Overall, platform trials hold promise in expediting the evaluation of potential therapies for PD. However, it remains to be proven whether these theoretical benefits will translate into increased production of high-quality trial data. Success also depends on the willingness of pharmaceutical companies to engage in such trials and whether this approach will ultimately hasten the identification and licensing of effective disease-modifying drugs. © 2024 International Parkinson and Movement Disorder Society.

Understanding the role of cerebellum in early Parkinson's disease: a structural and functional MRI study

Increasing evidence suggests that the cerebellum may have a role in the pathophysiology of Parkinson's disease (PD). Hence, the scope of this study was to investigate whether there are structural and functional alterations of the cerebellum and whether they correlate with motor and non-motor symptoms in early PD patients. Seventy-six patients with early PD and thirty-one age and sex-matched healthy subjects (HS) were enrolled and underwent a 3 T magnetic resonance imaging (MRI) protocol. The following MRI analyses were performed: (1) volumes of 5 cerebellar regions of interest (sensorimotor and cognitive cerebellum, dentate, interposed, and fastigial nuclei); (2) microstructural integrity of the cerebellar white matter connections (inferior, middle, and superior cerebellar peduncles); (3) functional connectivity at rest of the 5 regions of interest already described in point 1 with the rest of brain. Compared to controls, early PD patients showed a significant decrease in gray matter volume of the dentate, interposed and fastigial nuclei, bilaterally. They also showed abnormal, bilateral white matter microstructural integrity in all 3 cerebellar peduncles. Functional connectivity of the 5 cerebellar regions of interest with several areas in the midbrain, basal ganglia and cerebral cortex was altered. Finally, there was a positive correlation between abnormal functional connectivity of the fastigial nucleus with the volume of the nucleus itself and a negative correlation with axial symptoms severity. Our results showed that structural and functional alterations of the cerebellum are present in PD patients and these changes contribute to the pathophysiology of PD in the early phase.

Brain network centrality and connectivity are associated with clinical subtypes and disease progression in Parkinson's disease

To investigate brain network centrality and connectivity alterations in different Parkinson's disease (PD) clinical subtypes using resting-state functional magnetic resonance imaging (RS-fMRI), and to explore the correlation between baseline connectivity changes and the clinical progression. Ninety-two PD patients were enrolled at baseline, alongside 38 age- and sex-matched healthy controls. Of these, 85 PD patients underwent longitudinal assessments with a mean of 2.75 ± 0.59 years. Two-step cluster analysis integrating comprehensive motor and non-motor manifestations was performed to define PD subtypes. Degree centrality (DC) and secondary seed-based functional connectivity (FC) were applied to identify brain network centrality and connectivity changes among groups. Regression analysis was used to explore the correlation between baseline connectivity changes and clinical progression. Cluster analysis identified two main PD subtypes: mild PD and moderate PD. Two different subtypes within the mild PD were further identified: mild motor-predominant PD and mild-diffuse PD. Accordingly, the disrupted DC and seed-based FC in the left inferior frontal orbital gyrus and left superior occipital gyrus were severe in moderate PD. The DC and seed-based FC alterations in the right gyrus rectus and right postcentral gyrus were more severe in mild-diffuse PD than in mild motor-predominant PD. Moreover, disrupted DC were associated with clinical manifestations at baseline in patients with PD and predicted motor aspects progression over time. Our study suggested that brain network centrality and connectivity changes were different among PD subtypes. RS-fMRI holds promise to provide an objective assessment of subtype-related connectivity changes and predict disease progression in PD.

CAP2 contributes to Parkinson's disease diagnosed by neutrophil extracellular trap-related immune activity

Introduction

Neutrophil extracellular traps (NETs) constitute a crucial element of the immune system, and dysfunction in immune responses is implicated in the susceptibility and progression of Parkinson's disease (PD). Nevertheless, the mechanism connecting PD and NETs remains unclear. This study aims to uncover potential NETs-related immune biomarkers and elucidate their role in PD pathogenesis.

Methods

Through differential gene analysis of PD and NETs in GSE7621 datasets, we identified two PD subtypes and explored potential biological pathways. Subsequently, using ClusterWGCNA, we pinpointed pertinent genes and developed clinical diagnostic models. We then optimized the chosen model and evaluated its association with immune infiltration. Validation was conducted using the GSE20163 dataset. Screening the single-cell dataset GSE132758 revealed cell populations associated with the identified gene.

Results

Our findings identified XGB as the optimal diagnostic model, with CAP2 identified as a pivotal gene. The risk model effectively predicted overall diagnosis rates, demonstrating a robust correlation between infiltrating immune cells and genes related to the XGB model.

Discussion

In conclusions, we identified PD subtypes and diagnostic genes associated with NETs, highlighting CAP2 as a pivotal gene. These findings have significant implications for understanding potential molecular mechanisms and treatments for PD.

Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis

The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting counseling and inflating the number of patients needed to test potential neuroprotective treatments. Moreover, disease subtypes might require different therapies. This work uses a data-driven approach to investigate how observed heterogeneity in PD can be explained by the existence of distinct PD progression subtypes. To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts and performed extensive cross-cohort validation. A latent time joint mixed-effects model (LTJMM) was used to align patients on a common disease timescale. Progression subtypes were identified by variational deep embedding with recurrence (VaDER). In each cohort, we identified a fast-progressing and a slow-progressing subtype, reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response, features extracted from DaTSCAN imaging and digital gait assessments, education, and Alzheimer's disease pathology. Progression subtypes could be predicted with ROC-AUC up to 0.79 for individual patients when a one-year observation period was used for model training. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on these predictions can reduce the required cohort size by 43%. Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. Our predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients.

The Relationship Between Autonomic Dysfunction and Mood Symptoms in De Novo Parkinson's Disease Patients Over Time

BACKGROUND

Autonomic dysfunction is prevalent in Parkinson's disease (PD) and can worsen quality of life. We examined: (a) whether specific autonomic symptoms were more strongly associated with anxiety or depression in PD and (b) whether overall autonomic dysfunction predicted mood trajectories over a 5-year period.

METHODS

Newly diagnosed individuals with PD (N = 414) from the Parkinson's Progression Markers Initiative completed self-report measures of depression, anxiety, and autonomic symptoms annually. Cross-sectional linear regressions examined relationships between specific autonomic subdomains (gastrointestinal, cardiovascular, thermoregulatory, etc.) and mood. Multilevel modeling examined longitudinal relationships with total autonomic load.

RESULTS

Gastrointestinal symptoms were associated with both higher anxiety (b = 1.04, 95% CI [.55, 1.53], P < .001) and depression (b = .24, 95% CI [.11, .37], P = .012), as were thermoregulatory symptoms (anxiety: b = 1.06, 95% CI [.46, 1.65], P = .004; depression: b = .25, 95% CI [.09, .42], P = .013), while cardiovascular (b = .36, 95% CI [.10, .62], P = .012) and urinary symptoms (b = .10, 95% CI [.01, .20], P = .037) were associated only with depression. Longitudinally, higher total autonomic load was associated with increases in both depression (b = .01, 95% CI [.00, .02], P = .015) and anxiety (b = .04, 95% CI [.01, .06], P < .001) over time, as well as occasion-to-occasion fluctuations (depression: b = .08, 95% CI [.05, .10], P < .001; anxiety: b = .24, 95% CI [.15, .32], P < .001).

CONCLUSION

Findings suggest autonomic dysfunction, particularly gastrointestinal and thermoregulatory symptoms, may be an indicator for elevated anxiety/depression and a potential treatment target early on in PD.

Eukaryotic translation initiation factor EIF4G1 p.Ser637Cys mutation in a family with Parkinson's disease with antecedent essential tremor

Essential tremor (ET) and Parkinson's disease (PD) are common chronic movement disorders that can cause a substantial degree of disability. However, the etiology underlying these two conditions remains poorly understood. In the present study, Whole-exome sequencing of peripheral blood samples from the proband and Sanger sequencing of the other 18 family members, and pedigree analysis of four generations of 29 individuals with both ET and PD in a nonconsanguineous Chinese family were performed. Specifically, family members who had available medical information, including historical documentation and physical examination records, were included. A novel c.1909A>T (p.Ser637Cys) missense mutation was identified in the eukaryotic translation initiation factor 4γ1 (EIF4G1) gene as the candidate likely responsible for both conditions. In total, 9 family members exhibited tremor of the bilateral upper limbs and/or head starting from ages of ≥40 years, 3 of whom began showing evidence of PD in their 70s. Eukaryotic initiation factor 4 (eIF4)G1, a component of the translation initiation complex eIF4F, serves as a scaffold protein that interacts with many initiation factors and then binds to the 40S ribosomal subunit. The EIF4G1 (p.Ser637Cys) might inhibit the recruitment of the mRNA to the ribosome. In conclusion, the results from the present study suggested that EIF4G1 may be responsible for the hereditary PD with 'antecedent ET' reported in the family assessed.

Trends in parkinson's disease mortality in China from 2004 to 2021: a joinpoint analysis

BACKGROUND

This study aimed to analyze the trends of Parkinson's disease (PD) mortality rates among Chinese residents from 2004 to 2021, provide evidence for the formulation of PD prevention and control strategies to improve the quality of life among PD residents.

METHODS

Demographic and sociological data such as gender, urban or rural residency and age were obtained from the National Cause of Death Surveillance Dataset from 2004 to 2021. We then analyzed the trends of PD mortality rates by Joinpoint regression.

RESULTS

The PD mortality and standardized mortality rates in China showed an overall increasing trend during 2004-2021 (average annual percentage change [AAPC] = 7.14%, AAPCASMR=3.21%, P < 0.001). The mortality and standardized mortality rate in male (AAPC = 7.65%, AAPCASMR=3.18%, P < 0.001) were higher than that of female (AAPC = 7.03%, AAPCASMR=3.09%, P < 0.001). The PD standardized mortality rates of urban (AAPC = 5.13%, AAPCASMR=1.76%, P < 0.001) and rural (AAPC = 8.40%, AAPCASMR=4.29%, P < 0.001) residents both increased gradually. In the age analysis, the mortality rate increased with age. And the mortality rates of those aged > 85 years was the highest. Considering gender, female aged > 85 years had the fastest mortality trend (annual percentage change [APC] = 5.69%, P < 0.001). Considering urban/rural, rural aged 80-84 years had the fastest mortality trend (APC = 6.68%, P < 0.001).

CONCLUSIONS

The mortality rate of PD among Chinese residents increased from 2004 to 2021. Male sex, urban residence and age > 85 years were risk factors for PD-related death and should be the primary focus for PD prevention.

Striato-cortical functional connectivity changes in mild cognitive impairment with Lewy bodies

BACKGROUND

Functional connectivity changes in clinically overt neurodegenerative diseases such as dementia with Lewy bodies have been described, but studies on connectivity changes in the pre-dementia phase are scarce.

OBJECTIVES

We concentrated on evaluating striato-cortical functional connectivity differences between patients with Mild Cognitive Impairment with Lewy bodies and healthy controls and on assessing the relation to cognition.

METHODS

Altogether, we enrolled 77 participants (47 patients, of which 35 met all the inclusion criteria for the final analysis, and 30 age- and gender-matched healthy controls, of which 28 met all the inclusion criteria for the final analysis) to study the seed-based connectivity of the dorsal, middle, and ventral striatum. We assessed correlations between functional connectivity in the regions of between-group differences and neuropsychological scores of interest (visuospatial and executive domains z-scores).

RESULTS

Subjects with Mild Cognitive Impairment with Lewy Bodies, as compared to healthy controls, showed increased connectivity from the dorsal part of the striatum particularly to the bilateral anterior part of the temporal cortex with an association with executive functions.

CONCLUSIONS

We were able to capture early abnormal connectivity within cholinergic and noradrenergic pathways that correlated with cognitive functions known to be linked to cholinergic/noradrenergic deficits. The knowledge of specific alterations may improve our understanding of early neural changes in pre-dementia stages and enhance research of disease modifying therapy.

Relationship between plasma NFL and disease progression in Parkinson's disease: a prospective cohort study

OBJECTIVE

We aimed to examine the longitudinal change of plasma neurofilament light chain (NFL) level and explore its diagnostic and prognostic implications in Parkinson's disease (PD).

METHODS

A total of 184 patients with early PD who completed 5-year annually repeated clinical assessments were included. Plasma NFL at baseline, 1 year, and 2 year were examined, which were quantified using the ultrasensitive Simoa technology. At baseline, blood from 86 sex- and age-matched healthy controls (HC) were obtained for comparison.

RESULTS

Plasma NFL in PD patients at baseline was significantly higher than those in HC (P = 0.046), and significantly increased after 2 years (P = 0.046). Receiver operating characteristic curve indicated that a plasma NFL cut-off value of 10.79 pg/mL resulted in 39.7% sensitivity and 84.0% specificity, with an area under the curve of 0.635, to distinguish PD from HC (P < 0.001). Linear mixed-effect models indicated that baseline plasma NFL (> 9.24 pg/mL) correlated with a greater increase in the Unified Parkinson's Disease Rating Scale III (estimate = 0.651, P = 0.001) and Hoehn & Yahr stage (estimate = 0.072, P < 0.001), and also correlated with a greater decrease in the Montreal Cognitive Assessment (estimate = - 0.387, P < 0.001) during follow-up visits.

CONCLUSIONS

Plasma NFL exhibits a tendency to increase with disease progression, and elevated baseline plasma NFL can serve as a predictor for accelerated motor deterioration and cognitive decline in PD. However, plasma NFL does not have high accuracy to distinguish individuals with early-stage PD from HC.

Opportunities and Pitfalls of REM Sleep Behavior Disorder and Olfactory Dysfunction as Early Markers in Parkinson's Disease

During its pre-motor stage, Parkinson's disease (PD) presents itself with a multitude of non-motor symptoms with different degrees of specificity and sensitivity. The most important among them are REM sleep behavior disorder (RBD) and olfactory dysfunction. RBD is a parasomnia characterized by the loss of REM sleep muscle atonia and dream-enacting behaviors. Olfactory dysfunction in individuals with prodromal PD is usually described as hyposmia (reduced sense of smell) or anosmia (complete loss of olfactory function). These symptoms can precede the full expression of motor symptoms by decades. A close comprehension of these symptoms and the underlying mechanisms may enable early screening as well as interventions to improve patients' quality of life. Therefore, these symptoms have unmatched potential for identifying PD patients in prodromal stages, not only allowing early diagnosis but potentially opening a window for early, possibly disease-modifying intervention. However, they come with certain challenges. This review addresses some of the key opportunities and pitfalls of both RBD and olfactory dysfunction as early markers of PD.

Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study

BACKGROUND

The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD.

OBJECTIVE

The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis.

METHODS

The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers.

RESULTS

The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy).

CONCLUSIONS

The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.

Peripheral immune cell traits and Parkinson's disease: A Mendelian randomization study

BACKGROUND

The peripheral immune system is altered in Parkinson's disease (PD), but the causal relationship between the two remains controversial. In this study, we aimed to estimate the causal relationship between peripheral immune features and PD using a two-sample Mendelian randomization (MR) approach.

METHODS

Genome-wide association study (GWAS) data of peripheral blood immune signatures from European populations were used for exposure and PD summary statistics were used as results. We conducted a two-sample MR study using the inverse-variance weighted (IVW), MR-Egger, and weighted median methods to evaluate the causal association between these factors. MR-Egger and MR-PRESSO were used for sensitivity analysis to test and correct horizontal pleiotropy.

RESULTS

A total of 731 immune traits were analyzed for association with PD using three MR methods. After adjustment for FDR, we observed four peripheral immunological features associated with PD using the IVW method, including expression of CX3CR1 on monocytes [OR: 0.85, 95% CI: (0.81, 0.91), P = 6.56E-07] and CX3CR1 on CD14+CD16+ monocytes [OR: 0.87, 95% CI: (0.82, 0.93), P = 9.95E-06].

CONCLUSIONS

Our study further revealed the important role of monocytes in PD and indicated that CX3CR1 expression on monocytes is associated with a reduced risk of PD.

Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes

AIMS

Huntington's disease (HD) is a progressive neurodegenerative disorder with heterogeneous clinical manifestations. Identifying distinct clinical clusters and their relevant biomarkers could elucidate the underlying disease pathophysiology.

METHODS

Following the Enroll-HD program initiated in 2018.09, we have recruited 104 HD patients (including 21 premanifest) and 31 health controls at Beijing Tiantan Hospital. Principal components analysis and k-means cluster analysis were performed to determine HD clusters. Chi-square test, one-way ANOVA, and covariance were used to identify features among these clusters. Furthermore, plasma cytokines levels and brain structural imaging were used as biomarkers to delineate the clinical features of each cluster.

RESULTS

Three clusters were identified. Cluster 1 demonstrated the most severe motor and nonmotor symptoms except for chorea, the lowest whole brain volume, the plasma levels of IL-2 were higher and significantly associated with cluster 1. Cluster 2 was characterized with the most severe chorea and the largest pallidum volume. Cluster 3 had the most benign motor symptoms but moderate psychiatric problems.

CONCLUSION

We have identified three HD clusters via clinical manifestations with distinct biomarkers. Our data shed light on better understanding about the pathophysiology of HD.

Spoken Language Alterations can Predict Phenoconversion in Isolated Rapid Eye Movement Sleep Behavior Disorder: A Multicenter Study

OBJECTIVE

This study assessed the relationship between speech and language impairment and outcome in a multicenter cohort of isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD).

METHODS

Patients with iRBD from 7 centers speaking Czech, English, German, French, and Italian languages underwent a detailed speech assessment at baseline. Story-tale narratives were transcribed and linguistically annotated using fully automated methods based on automatic speech recognition and natural language processing algorithms, leading to the 3 distinctive linguistic and 2 acoustic patterns of language deterioration and associated composite indexes of their overall severity. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow-up. The Cox proportional hazard was performed to evaluate the predictive value of language patterns for phenoconversion over a follow-up period of 5 years.

RESULTS

Of 180 patients free of parkinsonism or dementia, 156 provided follow-up information. After a mean follow-up of 2.7 years, 42 (26.9%) patients developed neurodegenerative disease. Patients with higher severity of linguistic abnormalities (hazard ratio [HR = 2.35]) and acoustic abnormalities (HR = 1.92) were more likely to develop a defined neurodegenerative disease, with converters having lower content richness (HR = 1.74), slower articulation rate (HR = 1.58), and prolonged pauses (HR = 1.46). Dementia-first (n = 16) and parkinsonism-first with mild cognitive impairment (n = 9) converters had higher severity of linguistic abnormalities than parkinsonism-first with normal cognition converters (n = 17).

INTERPRETATION

Automated language analysis might provide a predictor of phenoconversion from iRBD into synucleinopathy subtypes with cognitive impairment, and thus can be used to stratify patients for neuroprotective trials. ANN NEUROL 2024;95:530-543.

Cognitive dysfunction in de novo Parkinson disease: Remitting vs. progressive cognitive impairment

INTRODUCTION

Parkinson's disease (PD) exhibits divergent cognitive trajectories; however, the factors contributing to these variations remain elusive. This study aimed to examine the clinical features of patients with different long-term cognitive trajectories in de novo PD over a five-year follow-up.

METHODS

We analyzed 258 patients who completed every annual evaluation for five years. According to the Montreal Cognitive Assessment (MoCA) scores, we classified patients into three groups: cognitively normal (n = 118, CN), remitting MoCA decline (n = 74, RMD), and progressive MoCA decline (n = 66, PMD).

RESULTS

The RMD group was associated with lower olfactory scores (Odds Ratio (OR) = 0.958, p = 0.040), whereas PMD was associated with higher depression scores (OR = 1.158, p = 0.045), probable RBD (OR = 3.169, p = 0.002), older age (OR = 1.132, p < 0.001) and lower educational attainment (OR = 0.828, p = 0.004). PMD had higher neurofilament light chain protein values than CN and RMD (p = 0.006, 0.015, respectively). Longitudinally, PMD showed a greater decline in all cognitive scores and hippocampus volumes (p = 0.004). Meanwhile, RMD exhibited intermediate cognitive and volumetric trajectories between CN and PMD and displayed worse score changes in memory tasks than CN.

CONCLUSIONS

While PMD exhibited known risk factors for cognitive impairment, along with worse cognitive performance and hippocampal volume decline, RMD displayed baseline lower olfactory scores and intermediate cognitive and hippocampal volume decline between the two groups. These findings suggest individuals in RMD may still be at risk for cognitive deficits. However, further long-term follow-up data are needed to unravel the determinants and dynamics of cognitive functions.

Transitioning from Subtyping to Precision Medicine in Parkinson's Disease: A Purpose-Driven Approach

The International Parkinson and Movement Disorder Society (MDS) created a task force (TF) to provide a critical overview of the Parkinson's disease (PD) subtyping field and develop a guidance on future research in PD subtypes. Based on a literature review, we previously concluded that PD subtyping requires an ultimate alignment with principles of precision medicine, and consequently novel approaches were needed to describe heterogeneity at the individual patient level. In this manuscript, we present a novel purpose-driven framework for subtype research as a guidance to clinicians and researchers when proposing to develop, evaluate, or use PD subtypes. Using a formal consensus methodology, we determined that the key purposes of PD subtyping are: (1) to predict disease progression, for both the development of therapies (use in clinical trials) and prognosis counseling, (2) to predict response to treatments, and (3) to identify therapeutic targets for disease modification. For each purpose, we describe the desired product and the research required for its development. Given the current state of knowledge and data resources, we see purpose-driven subtyping as a pragmatic and necessary step on the way to precision medicine. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Clinical severity in Parkinson's disease is determined by decline in cortical compensation

Dopaminergic dysfunction in the basal ganglia, particularly in the posterior putamen, is often viewed as the primary pathological mechanism behind motor slowing (i.e. bradykinesia) in Parkinson's disease. However, striatal dopamine loss fails to account for interindividual differences in motor phenotype and rate of decline, implying that the expression of motor symptoms depends on additional mechanisms, some of which may be compensatory in nature. Building on observations of increased motor-related activity in the parieto-premotor cortex of Parkinson patients, we tested the hypothesis that interindividual differences in clinical severity are determined by compensatory cortical mechanisms and not just by basal ganglia dysfunction. Using functional MRI, we measured variability in motor- and selection-related brain activity during a visuomotor task in 353 patients with Parkinson's disease (≤5 years disease duration) and 60 healthy controls. In this task, we manipulated action selection demand by varying the number of possible actions that individuals could choose from. Clinical variability was characterized in two ways. First, patients were categorized into three previously validated, discrete clinical subtypes that are hypothesized to reflect distinct routes of α-synuclein propagation: diffuse-malignant (n = 42), intermediate (n = 128) or mild motor-predominant (n = 150). Second, we used the scores of bradykinesia severity and cognitive performance across the entire sample as continuous measures. Patients showed motor slowing (longer response times) and reduced motor-related activity in the basal ganglia compared with controls. However, basal ganglia activity did not differ between clinical subtypes and was not associated with clinical scores. This indicates a limited role for striatal dysfunction in shaping interindividual differences in clinical severity. Consistent with our hypothesis, we observed enhanced action selection-related activity in the parieto-premotor cortex of patients with a mild-motor predominant subtype, both compared to patients with a diffuse-malignant subtype and controls. Furthermore, increased parieto-premotor activity was related to lower bradykinesia severity and better cognitive performance, which points to a compensatory role. We conclude that parieto-premotor compensation, rather than basal ganglia dysfunction, shapes interindividual variability in symptom severity in Parkinson's disease. Future interventions may focus on maintaining and enhancing compensatory cortical mechanisms, rather than only attempting to normalize basal ganglia dysfunction.

Distinct serum GDNF coupling with brain structural and functional changes underlies cognitive status in Parkinson's disease

AIM

Aberrations in brain connections are implicated in the pathogenesis of Parkinson's disease (PD). We previously demonstrated that Glial cell-derived neurotrophic factor (GDNF) reduction is associated with cognition decline. Nonetheless, it is elusive if the pattern of brain topological connectivity differed across PD with divergent serum GDNF levels, and the accompanying profile of cognitive deficits has yet to be determined.

METHODS

We collected data on the participants' cognition, demographics, and serum GDNF levels. Participants underwent 3.0T magnetic resonance imaging, and we assessed the degree centrality, brain network topology, and cortical thickness of the healthy control (HC) (n = 25), PD-high-GDNF (n = 19), and PD-low-GDNF (n = 19) groups using graph-theoretic measures of resting-state functional MRI to reveal how much brain connectivity varies and its clinical correlates, as well as to determine factors predicting the cognitive status in PD.

RESULTS

The results show different network properties between groups. Degree centrality abnormalities were found in the right inferior frontal gyrus and right parietal lobe postcentral gyrus, linked with cognition scores. The two aberrant clusters serve as a potentially powerful signal for determining whether a patient has PD and the patient's cognition level after integrating with GDNF, duration, and dopamine dosage. Moreover, we found a significant positive relationship between the thickness of the left caudal middle frontal lobe and a plethora of cognitive domains. Further discriminant analysis revealed that the cortical thickness of this region could distinguish PD patients from healthy controls. The mental state evaluation will also be more precise when paired with GDNF and duration.

CONCLUSION

Our findings reveal that the topological features of brain networks and cortical thickness are altered in PD patients with cognitive deficits. The above change, accompanied by the serum GDNF, may have merit as a diagnosis marker for PD and, arguably, cognition status.

Identification of Parkinson's disease subtypes with distinct brain atrophy progression and its association with clinical progression

Background

Parkinson's disease (PD) patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear.

Objective

This study aims to identify PD subtypes with different rates of GMV loss and assess their association with clinical progression.

Methods

This study included 107 PD patients (mean age: 60.06 ± 9.98 years, 70.09% male) with baseline and ≥ 3-year follow-up structural MRI scans. A linear mixed-effects model was employed to assess the rates of regional GMV loss. Hierarchical cluster analysis was conducted to explore potential subtypes based on individual rates of GMV loss. Clinical score changes were then compared across these subtypes.

Results

Two PD subtypes were identified based on brain atrophy rates. Subtype 1 (n = 63) showed moderate atrophy, notably in the prefrontal and lateral temporal lobes, while Subtype 2 (n = 44) had faster atrophy across the brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS-Part Ⅰ, β = 1.26 ± 0.18, P = 0.016) and motor (MDS-UPDRS-Part Ⅱ, β = 1.34 ± 0.20, P = 0.017) symptoms, autonomic dysfunction (SCOPA-AUT, β = 1.15 ± 0.22, P = 0.043), memory (HVLT-Retention, β = -0.02 ± 0.01, P = 0.016) and depression (GDS, β = 0.26 ± 0.083, P = 0.019) compared to subtype 1.

Conclusion

The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.

Detection of skin α-synuclein using RT-QuIC as a diagnostic biomarker for Parkinson's disease in the Chinese population

BACKGROUND

Several studies have indicated that skin holds promise as a potential sample for detecting pathological α-Syn and serving as a diagnostic biomarker for α-synucleinopathies. Despite reports in Chinese PD patients, comprehensive research on skin α-Syn detection using RT-QuIC is lacking.

OBJECTIVE

This study aimed to evaluate the diagnostic performance of skin samples using RT-QuIC from PD patients in the Chinese population.

METHODS

Patients with sporadic PD and controls were included according to the British PD Association Brain Bank diagnostic criteria. The seeding activity of misfolded α-Syn in these skin samples was detected using the RT-QuIC assay after protein extraction. Biochemical and morphological analyses of RT-QuIC products were conducted by atomic force microscopy, transmission electron microscopy, Congo red staining, and dot blot analysis.

RESULT

30 patients clinically diagnosed with PD and 28 controls with non-α-synucleinopathies were included in this study. 28 of 30 PD patients demonstrated positive α-Syn seeding activity by RT-QuIC assay. In contrast, no α-Syn seeding activity was detected in the 28 control samples, with an overall sensitivity and specificity of 93.3% and 100%, respectively (P < 0.001). Biochemical characterization of the RT-QuIC product indicated fibrillary α-Syn species in PD-seeded reactions, while control samples failed in the conversion of recombinant α-Syn substrate.

CONCLUSION

This study applied RT-QuIC technology to identify misfolded α-Syn seeding activity in skin samples from Chinese PD patients, demonstrating high specificity and sensitivity. Skin α-Syn RT-QuIC is expected to be a reliable approach for the diagnosis of PD.

Exploring Traditional Medicine Diagnostic Classification for Parkinson's Disease Using Hierarchical Clustering

INTRODUCTION

Personalized diagnosis and therapy for Parkinson's disease (PD) are needed due to the clinical heterogeneity of PD. Syndrome differentiation (SD) in traditional medicine (TM) is a diagnostic method for customized therapy that comprehensively analyzes various symptoms and systemic syndromes. However, research identifying PD classification based on SD is limited.

METHODS

Ten electronic databases were systematically searched from inception to August 10, 2021. Clinical indicators, including 380 symptoms, 98 TM signs, and herbal medicine for PD diagnosed with SD, were extracted from 197 articles; frequency statistics on clinical indicators were conducted to classify several subtypes using hierarchical clustering.

RESULTS

Four distinct cluster groups were identified, each characterized by significant cluster-specific clinical indicators with 95% confidence intervals of distribution. Subtype 2 had the most severe progression, longest progressive duration, and highest association with greater late-stage PD-associated motor symptoms, including postural instability and gait disturbance. The action properties of the herbal formula and original SD presented in the data sources for subtype 2 were associated with Yin deficiency syndrome.

DISCUSSION/CONCLUSION

Hierarchical clustering analysis distinguished various symptoms and TM signs among patients with PD. These newly identified PD subtypes may optimize the diagnosis and treatment with TM and facilitate prognosis prediction. Our findings serve as a cornerstone for evidence-based guidelines for TM diagnosis and treatment.

Einleitung

Eine personalisierte Diagnose und Therapie des Morbus Parkinson (MP) ist angesichts der ausgeprägten klinischen Heterogenität des MP unerlässlich. Die Syndromdifferenzierung (SD) ist in der traditionellen Medizin (TM) eine diagnostische Methode für eine maßgeschneiderte Therapie, bei der verschiedene Symptome und systemische Syndrome umfassend analysiert werden. Es liegen jedoch nur begrenzt Forschungsergebnisse in Bezug auf eine SD-basierte Klassifikation des MP vor.

Methoden

Zehn elektronische Datenbanken wurden systematisch durchsucht, von der Einrichtung bis zum 10. August 2021. Klinische Indikatoren einschließlich von 380 Symptomen, 98 TM-Zeichen sowie pflanzlichen Heilmitteln für mittels SD diagnostiziertem MP wurden aus 197 Artikeln extrahiert, und Häufigkeitsstatistiken der klinischen Indikatoren wurden erstellt, um mittels hierarchischem Clustering eine Reihe von Subtypen zu klassifizieren.

Ergebnisse

Vier verschiedene Cluster-Gruppen wurden identifiziert, die jeweils durch signifikante, Cluster-spezifische klinische Indikatoren mit 95% Konfidenzintervall der Verteilung gekennzeichnet waren. Subtyp 2 hatte den schwersten Verlauf, die längste Progressionsdauer und die stärkste Assoziation mit einem höheren Ausmaß von motorischen Symptomen des MP im Spätstadium, darunter Haltungsinstabilität und Gangstörungen. Die Wirkungseigenschaften der pflanzlichen Formulierung sowie die ursprüngliche SD, die in den Datenquellen für Subtyp 2 genannt wurden, waren mit Yin-Mangel-Syndrom assoziiert.

Diskussion/Schlussfolgerung

Die hierarchische Clustering-Analyse hob verschiedene Symptome und TM-Zeichen bei Patienten mit MP hervor. Die neu identifizierten MP-Subtypen könnten die Diagnose und Behandlung mittels TM optimieren und zur Prognoseerstellung beitragen. Unsere Ergebnisse sind ein Fundament für eine evidenzbasierte Leitlinie für die TM-Diagnostik und -Therapie.

Imaging Biomarkers in Prodromal and Earliest Phases of Parkinson's Disease

Assessing imaging biomarker in the prodromal and early phases of Parkinson's disease (PD) is of great importance to ensure an early and safe diagnosis. In the last decades, imaging modalities advanced and are now able to assess many different aspects of neurodegeneration in PD. MRI sequences can measure iron content or neuromelanin. Apart from SPECT imaging with Ioflupane, more specific PET tracers to assess degeneration of the dopaminergic system are available. Furthermore, metabolic PET patterns can be used to anticipate a phenoconversion from prodromal PD to manifest PD. In this regard, it is worth mentioning that PET imaging of inflammation will gain significance. Molecular imaging of neurotransmitters like serotonin, noradrenaline and acetylcholine shed more light on non-motor symptoms. Outside of the brain, molecular imaging of the heart and gut is used to measure PD-related degeneration of the autonomous nervous system. Moreover, optical coherence tomography can noninvasively detect degeneration of retinal fibers as a potential biomarker in PD. In this review, we describe these state-of-the-art imaging modalities in early and prodromal PD and point out in how far these techniques can and will be used in the future to pave the way towards a biomarker-based staging of PD.

Characterization of de novo Dementia with Lewy Body with different duration of rapid eye movement sleep behavior disorder

BACKGROUND

Cognitive disorder, parkinsonism, autonomic dysfunction (AuD) and rapid eye movement sleep behavior disorder (RBD) can occur prior to or simultaneously with Dementia with Lewy Body (DLB) onset. RBD is generally linked with progressive neurodegenerative traits. However, associations between RBD with DLB, RBD without DLB, and RBD duration effects on DLB symptoms remain unclear.

OBJECTIVES

To examine DLB symptom frequency and subtypes in RBD, and explore the effects of different RBD onset times on symptoms in de novo DLB patients.

METHODS

In this multicenter investigation, we consecutively recruited 271 de novo DLB patients. All had standardized clinical and comprehensive neuropsychological evaluations. Subgroup analyses, performed based on the duration of RBD confirmed by polysomnography before the DLB diagnosis, we compared the proportion of patients with cognitive impairment, parkinsonism, and AuD features between groups.

RESULTS

Parkinsonism and AuD incidences were significantly elevated in DLB patients with RBD when compared with patients without RBD. Subgroup analyses indicated no significant differences in parkinsonism between DLB patients who developed RBD ≥10 years prior to the DLB diagnosis and DLB patients without RBD. The incidence of non-tremor-predominant parkinsonism and AuD was significantly higher in DLB patients whose RBD duration before the DLB diagnosis was <10 years when compared with DLB patients without RBD.

CONCLUSIONS

We identified significant symptom and phenotypic variability between DLB patients with and without RBD. Also, different RBD duration effects before the DLB diagnosis had a significant impact on symptomatic phenotypes, suggesting the existence of a slowly progressive DLB neurodegenerative subtype.

Reorganization of intrinsic functional connectivity in early-stage Parkinson's disease patients with probable REM sleep behavior disorder

REM sleep behavior disorder (RBD) symptoms in Parkinson's disease (PD) suggest both a clinically and pathologically malignant subtype. However, whether RBD symptoms are associated with alterations in the organization of whole-brain intrinsic functional networks in PD, especially at early disease stages, remains unclear. Here we use resting-state functional MRI, coupled with graph-theoretical approaches and network-based statistics analyses, and validated with large-scale network analyses, to characterize functional brain networks and their relationship with clinical measures in early PD patients with probable RBD (PD+pRBD), early PD patients without probable RBD (PD-pRBD) and healthy controls. Thirty-six PD+pRBD, 57 PD-pRBD and 71 healthy controls were included in the final analyses. The PD+pRBD group demonstrated decreased global efficiency (t = -2.036, P = 0.0432) compared to PD-pRBD, and decreased network efficiency, as well as comprehensively disrupted nodal efficiency and whole-brain networks (all eight networks, but especially in the sensorimotor, default mode and visual networks) compared to healthy controls. The PD-pRBD group showed decreased nodal degree in right ventral frontal cortex and more affected edges in the frontoparietal and ventral attention networks compared to healthy controls. Furthermore, the assortativity coefficient was negatively correlated with Montreal cognitive assessment scores in the PD+pRBD group (r = -0.365, P = 0.026, d = 0.154). The observation of altered whole-brain functional networks and its correlation with cognitive function in PD+pRBD suggest reorganization of the intrinsic functional connectivity to maintain the brain function in the early stage of the disease. Future longitudinal studies following these alterations along disease progression are warranted.

Charting Disease Trajectories from Isolated REM Sleep Behavior Disorder to Parkinson's Disease

BACKGROUND

Clinical presentation and progression dynamics are variable in patients with Parkinson's disease (PD). Disease course mapping is an innovative disease modelling technique that summarizes the range of possible disease trajectories and estimates dimensions related to onset, sequence, and speed of progression of disease markers.

OBJECTIVE

To propose a disease course map for PD and investigate progression profiles in patients with or without rapid eye movement sleep behavioral disorders (RBD).

METHODS

Data of 919 PD patients and 88 isolated RBD patients from three independent longitudinal cohorts were analyzed (follow-up duration = 5.1; 95% confidence interval, 1.1-8.1] years). Disease course map was estimated by using eight clinical markers (motor and non-motor symptoms) and four imaging markers (dopaminergic denervation).

RESULTS

PD course map showed that the first changes occurred in the contralateral putamen 13 years before diagnosis, followed by changes in motor symptoms, dysautonomia, sleep-all before diagnosis-and finally cognitive decline at the time of diagnosis. The model showed earlier disease onset, earlier non-motor and later motor symptoms, more rapid progression of cognitive decline in PD patients with RBD than PD patients without RBD. This pattern was even more pronounced in patients with isolated RBD with early changes in sleep, followed by cognition and non-motor symptoms and later changes in motor symptoms.

CONCLUSIONS

Our findings are consistent with the presence of distinct patterns of progression between patients with and without RBD. Understanding heterogeneity of PD progression is key to decipher the underlying pathophysiology and select homogeneous subgroups of patients for precision medicine. © 2023 International Parkinson and Movement Disorder Society.

Prodromal dementia with Lewy bodies in REM sleep behavior disorder: A multicenter study

INTRODUCTION

Isolated/idiopathic rapid eye movement sleep behavior disorder (iRBD) is a powerful early predictor of dementia with Lewy bodies (DLB) and Parkinson's disease (PD). This provides an opportunity to directly observe the evolution of prodromal DLB and to identify which cognitive variables are the strongest predictors of evolving dementia.

METHODS

IRBD participants (n = 754) from 10 centers of the International RBD Study Group underwent annual neuropsychological assessment. Competing risk regression analysis determined optimal predictors of dementia. Linear mixed-effect models determined the annual progression of neuropsychological testing.

RESULTS

Reduced attention and executive function, particularly performance on the Trail Making Test Part B, were the strongest identifiers of early DLB. In phenoconverters, the onset of cognitive decline began up to 10 years prior to phenoconversion. Changes in verbal memory best differentiated between DLB and PD subtypes.

DISCUSSION

In iRBD, attention and executive dysfunction strongly predict dementia and begin declining several years prior to phenoconversion.

HIGHLIGHTS

Cognitive decline in iRBD begins up to 10 years prior to phenoconversion. Attention and executive dysfunction are the strongest predictors of dementia in iRBD. Decline in episodic memory best distinguished dementia-first from parkinsonism-first phenoconversion.

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment

INTRODUCTION

Cognitive impairment (CI) is a significant non-motor symptoms in Parkinson's disease (PD) that often precedes the emergence of motor symptoms by several years. Patients with PD hypothetically progress from stages without CI (PD-normal cognition [NC]) to stages with Mild CI (PD-MCI) and PD dementia (PDD). CI symptoms in PD are linked to different brain regions and neural pathways, in addition to being the result of dysfunctional subcortical regions. However, it is still unknown how functional dysregulation correlates to progression during the CI. Neuroimaging techniques hold promise in discriminating CI stages of PD and further contribute to the biomarker formation of CI in PD. In this study, we explore disparities in the clinical assessments and resting-state functional connectivity (FC) among three CI stages of PD.

METHODS

We enrolled 88 patients with PD and 26 healthy controls (HC) for a cross sectional clinical study and performed intra- and inter-network FC analysis in conjunction with comprehensive clinical cognitive assessment.

RESULTS

Our findings underscore the significance of several neural networks, namely, the default mode network (DMN), frontoparietal network (FPN), dorsal attention network, and visual network (VN) and their inter-intra-network FC in differentiating between PD-MCI and PDD. Additionally, our results showed the importance of sensory motor network, VN, DMN, and salience network (SN) in the discriminating PD-NC from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal networks for further differential diagnosis of CI stages of PD.

CONCLUSION

We propose that resting-state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD-NC to MCI or PDD. The integration of clinical and neuroimaging data may enhance the early detection of PD in clinical settings and potentially prevent the disease from advancing to more severe stages.

Disease Progression of Data-Driven Subtypes of Parkinson's Disease: 5-Year Longitudinal Study from the Early Parkinson's Disease Longitudinal Singapore (PALS) Cohort

Background

The detailed trajectory of data-driven subtypes in Parkinson's disease (PD) within Asian cohorts remains undisclosed.

Objective

To evaluate the motor, non-motor symptom (NMS) progression among the data-driven PD clusters.

Methods

In this 5-year longitudinal study, NMS scale (NMSS), Hospital Anxiety Depression Scale (HADS), and Epworth sleepiness scale (ESS) were carried out annually to monitor NMS progression. H& Y staging scale, MDS-UPDRS part III motor score, and postural instability gait difficulty (PIGD) score were assessed annually to evaluate disease severity and motor progression. Five cognitive standardized scores were used to assess detailed cognitive progression. Linear mixed model was performed to assess the annual progression rates of the longitudinal outcomes.

Results

Two hundred and six early PD patients, consisting of 43 patients in cluster A, 98 patients in cluster B and 65 subjects in cluster C. Cluster A (severe subtype) had significantly faster progression slope in NMSS Domain 3 (mood/apathy) score (p = 0.01), NMSS Domain 4 (perceptual problems) score (p = 0.02), NMSS Domain 7 (urinary) score (p = 0.03), and ESS Total Score (p = 0.04) than the other two clusters. Cluster A also progressed significantly in PIGD score (p = 0.04). For cognitive outcomes, cluster A deteriorated significantly in visuospatial domain (p = 0.002), while cluster C (mild subtype) deteriorated significantly in executive domain (p = 0.04).

Conclusions

The severe cluster had significantly faster progression, particularly in mood and perceptual NMS domains, visuospatial cognitive performances, and postural instability gait scores. Our findings will be helpful for clinicians to stratify and pre-emptively manage PD patients by developing intervention strategies to counter the progression of these domains.

Oscillatory vs. non-oscillatory subthalamic beta activity in Parkinson's disease

Parkinson's disease is characterized by exaggerated beta activity (13-35 Hz) in cortico-basal ganglia motor loops. Beta activity includes both periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and excitation/inhibition balance (i.e. non-oscillatory activity). However, the relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. We recorded, modelled and analysed subthalamic local field potentials in parkinsonian patients at rest while off or on medication. Autoregressive modelling with additive 1/f noise clarified the relationships between measures of beta activity in the time domain (i.e. amplitude and duration of beta bursts) or in the frequency domain (i.e. power and sharpness of the spectral peak) and oscillatory vs. non-oscillatory activity: burst duration and spectral sharpness are specifically sensitive to oscillatory activity, whereas burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Our experimental data confirmed the model predictions and assumptions. We subsequently analysed the effect of levodopa, obtaining strong-to-extreme Bayesian evidence that oscillatory beta activity is reduced in patients on vs. off medication, with moderate evidence for absence of modulation of the non-oscillatory component. Finally, specifically the oscillatory component of beta activity correlated with the rate of motor progression of the disease. Methodologically, these results provide an integrative understanding of beta-based biomarkers relevant for adaptive deep brain stimulation. Biologically, they suggest that primarily the oscillatory component of subthalamic beta activity is dopamine dependent and may play a role not only in the pathophysiology but also in the progression of Parkinson's disease. KEY POINTS: Beta activity in Parkinson's disease includes both true periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and synaptic balance (i.e. non-oscillatory activity). The relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. Burst duration and spectral sharpness are specifically sensitive to oscillatory activity, while burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Only the oscillatory component of subthalamic beta activity is dopamine-dependent. Stronger beta oscillatory activity correlates with faster motor progression of the disease.

Eye movements in Parkinson's disease: from neurophysiological mechanisms to diagnostic tools

Movement disorders such as Parkinson's disease (PD) impact oculomotor function - the ability to move the eyes accurately and purposefully to serve a multitude of sensory, cognitive, and secondary motor tasks. Decades of neurophysiological research in monkeys and behavioral studies in humans have characterized the neural basis of healthy oculomotor control. This review links eye movement abnormalities in persons living with PD to the underlying neurophysiological mechanisms and pathways. Building on this foundation, we highlight recent progress in using eye movements to gauge symptom severity, assess treatment effects, and serve as potential precision biomarkers. We conclude that whereas eye movements provide insights into PD mechanisms, based on current evidence they appear to lack sufficient sensitivity and specificity to serve as a standalone diagnostic tool. Their full potential may be realized when combined with other disease indicators in big datasets.