Multiple modality biomarker prediction of cognitive impairment in prospectively followed de novo Parkinson disease

Impact of dopamine deficiency and REM sleep behavior disorder on cognition in early neuronal synuclein disease with hyposmia

Objectives

To determine the impact of dopamine deficiency and isolated REM sleep behavior disorder (iRBD) on cognitive performance in early neuronal alpha-synuclein disease (NSD) with hyposmia.

Methods

Using Parkinson's Progression Markers Initiative baseline data, cognitive performance was assessed with a cognitive summary score (CSS) developed by applying regression-based internal norms derived from a robust healthy control (HC) group. Performance was examined for participants with hyposmia classified as NSD-Integrated Staging System (NSD-ISS) Stage 2, either Stage 2A (CSF alpha-synuclein seed amplification assay [SAA]+, SPECT dopamine transporter scan [DaTscan]-) or 2B (SAA+, DaTscan+).

Results

Participants were Stage 2A (N=101), Stage 2B (N=227) and HCs (N=158). Although Stage 2 overall had intact Montreal Cognitive Assessment scores (mean (SD) =27.0 (2.3)), Stage 2A had a numerically worse CSS (z-score mean difference =0.05, p-value NS; effect size=0.09) and Stage 2B had a statistically worse CSS (z-score mean difference =0.23, p-value <0.05; effect size=0.40) compared with HCs. In Stage 2A participants with hyposmia alone had normal cognition, but presence of comorbid iRBD was associated with significantly worse cognition (z-score mean difference =0.33, p-value <0.05, effect size =0.50). In Stage 2B participants with hyposmia had abnormal cognition (z-score mean difference =0.18, p-value =.0078, effect size =0.29), and superimposed iRBD had a non-statistically significant additive effect.

Interpretation

Using a CSS, early NSD with hyposmia is associated with measurable cognitive deficits compared with robust HCs, particularly in presence of dopamine system impairment or comorbid iRBD, highlighting the importance of focusing on cognition in early-stage synuclein disease.

Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease

Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progression exist, comparing traditional probabilistic models to deep learning methods remains understudied. This study compares sequential modeling techniques to identify cognitive progression in individuals with and without PD. Using data from the Parkinson's Progression Marker Initiative, shallow Markov, deep recurrent (long short-term memory [LSTM]), and nonrecurrent (temporal fusion transformer [TFT]) models were compared to predict cognitive status over time. Cognitive status was categorized into normal cognition (NC), mild cognitive impairment (MCI), and dementia. Predictions were made annually for up to 3 years using clinical data, including demographics, cognitive assessments, PD severity, and medical history. Each approach was evaluated using inverse probability weighted (IPW-) F1 scores. An ensemble method combined outputs from the Markov, LSTM, and TFT models. The dataset included 917 individuals (53% PD; 30% at risk for PD; 17% Healthy Controls). The TFT model outperformed others across all annual periods (IPW-F1 = 0.468) compared to the Markov (IPW-F1 = 0.349) and LSTM (IPW-F1 = 0.414) models, with improved performance using an ensemble approach (IPW-F1 = 0.502). For MCI and dementia predictions, which were rarer occurrences compared to NC status (ratios: 50:8:1), the TFT model consistently outperformed competing models, achieving IPW-F1 scores of 0.496 and 0.533 for MCI and dementia, respectively. In conclusion, sequential deep learning models like TFT, which mitigate long-term memory loss and can interpret complex, high-dimensional data, perform best overall in predicting clinically important cognitive transitions. These methods should be further explored for predicting degenerative conditions.

Machine learning for predicting cognitive decline within five years in Parkinson's disease: Comparing cognitive assessment scales with DAT SPECT and clinical biomarkers

OBJECTIVE

Parkinson's disease (PD) is an age-related neurodegenerative condition characterized mostly by motor symptoms. Although a wide range of non-motor symptoms (NMS) are frequently experienced by PD patients. One of the important and common NMS is cognitive impairment, which is measured using different cognitive scales. Monitoring cognitive impairment and its decline in PD is essential for patient care and management. In this study, our goal is to identify the most effective cognitive scale in predicting cognitive decline over a 5-year timeframe initializing clinical biomarkers and DAT SPECT.

METHODS

Machine Learning has previously shown superior performance in image and clinical data classification and detection. In this study, we propose to use machine learning with different types of data, such as DAT SPECT and clinical biomarkers, to predict PD-CD based on various cognitive scales. We collected 330 DAT SPECT images and their clinical data in baseline, years 2,3,4, and 5 from Parkinson's Progression Markers Initiative (PPMI). We then designed a 3D Autoencoder to extract deep radiomic features (DF) from DAT SPECT images, and we then concatenated it with 17 clinical features (CF) to predict cognitive decline based on Montreal Cognitive Assessment (MoCA) and The Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-I).

RESULTS

The utilization of MoCA as a cognitive decline scale yielded better performance in various years compared to MDS-UPDRS-I. In year 4, the application of the deep radiomic feature resulted in the highest achievement, with a cross-validation AUC of 89.28, utilizing the gradient boosting classifier. For the MDS-UPDRS-I scale, the highest achievement was obtained by utilizing the deep radiomic feature, resulting in a cross-validation AUC of 81.34 with the random forest classifier.

CONCLUSIONS

The study findings indicate that the MoCA scale may be a more effective predictor of cognitive decline within 5 years compared to MDS-UPDRS-I. Furthermore, deep radiomic features had better performance compared to sole clinical biomarkers or clinical and deep radiomic combined. These results suggest that using the MoCA score and deep radiomic features extracted from DAT SPECT could be a promising approach for identifying individuals at risk for cognitive decline in four years. Future research is needed to validate these findings and explore their utility in clinical practice.

Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions

A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.

Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson's disease

BACKGROUND

Early detection of Parkinson's disease (PD) patients at high risk for mild cognitive impairment (MCI) can help with timely intervention. White matter structural connectivity is considered an early and sensitive indicator of neurodegenerative disease.

OBJECTIVES

To investigate whether baseline white matter structural connectivity features from diffusion tensor imaging (DTI) of de novo PD patients can help predict PD-MCI conversion at an individual level using machine learning methods.

METHODS

We included 90 de novo PD patients who underwent DTI and 3D T1-weighted imaging. Elastic net-based feature consensus ranking (ENFCR) was used with 1000 random training sets to select clinical and structural connectivity features. Linear discrimination analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN) and naïve Bayes (NB) classifiers were trained based on features selected more than 500 times. The area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN) and specificity (SPE) were used to evaluate model performance.

RESULTS

A total of 57 PD patients were classified as PD-MCI nonconverters, and 33 PD patients were classified as PD-MCI converters. The models trained with clinical data showed moderate performance (AUC range: 0.62-0.68; ACC range: 0.63-0.77; SEN range: 0.45-0.66; SPE range: 0.64-0.84). Models trained with structural connectivity (AUC range, 0.81-0.84; ACC range, 0.75-0.86; SEN range, 0.77-0.91; SPE range, 0.71-0.88) performed similar to models that were trained with both clinical and structural connectivity data (AUC range, 0.81-0.85; ACC range, 0.74-0.85; SEN range, 0.79-0.91; SPE range, 0.70-0.89).

CONCLUSIONS

Baseline white matter structural connectivity from DTI is helpful in predicting future MCI conversion in de novo PD patients.

Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features

Patients with Parkinson's Disease (PD) often suffer from cognitive decline. Accurate prediction of cognitive decline is essential for early treatment of at-risk patients. The aim of this study was to develop and evaluate a multimodal machine learning model for the prediction of continuous cognitive decline in patients with early PD. We included 213 PD patients from the Parkinson's Progression Markers Initiative (PPMI) database. Machine learning was used to predict change in Montreal Cognitive Assessment (MoCA) score using the difference between baseline and 4-years follow-up data as outcome. Input features were categorized into four sets: clinical test scores, cerebrospinal fluid (CSF) biomarkers, brain volumes, and genetic variants. All combinations of input feature sets were added to a basic model, which consisted of demographics and baseline cognition. An iterative scheme using RReliefF-based feature ranking and support vector regression in combination with tenfold cross validation was used to determine the optimal number of predictive features and to evaluate model performance for each combination of input feature sets. Our best performing model consisted of a combination of the basic model, clinical test scores and CSF-based biomarkers. This model had 12 features, which included baseline cognition, CSF phosphorylated tau, CSF total tau, CSF amyloid-beta1-42, geriatric depression scale (GDS) scores, and anxiety scores. Interestingly, many of the predictive features in our model have previously been associated with Alzheimer's disease, showing the importance of assessing Alzheimer's disease pathology in patients with Parkinson's disease.

Visual Dysfunction in Parkinson's Disease

Non-motor symptoms in Parkinson's disease (PD) include ocular, visuoperceptive, and visuospatial impairments, which can occur as a result of the underlying neurodegenerative process. Ocular impairments can affect various aspects of vision and eye movement. Thus, patients can show dry eyes, blepharospasm, reduced blink rate, saccadic eye movement abnormalities, smooth pursuit deficits, and impaired voluntary and reflexive eye movements. Furthermore, visuoperceptive impairments affect the ability to perceive and recognize visual stimuli accurately, including impaired contrast sensitivity and reduced visual acuity, color discrimination, and object recognition. Visuospatial impairments are also remarkable, including difficulties perceiving and interpreting spatial relationships between objects and difficulties judging distances or navigating through the environment. Moreover, PD patients can present visuospatial attention problems, with difficulties attending to visual stimuli in a spatially organized manner. Moreover, PD patients also show perceptual disturbances affecting their ability to interpret and determine meaning from visual stimuli. And, for instance, visual hallucinations are common in PD patients. Nevertheless, the neurobiological bases of visual-related disorders in PD are complex and not fully understood. This review intends to provide a comprehensive description of visual disturbances in PD, from sensory to perceptual alterations, addressing their neuroanatomical, functional, and neurochemical correlates. Structural changes, particularly in posterior cortical regions, are described, as well as functional alterations, both in cortical and subcortical regions, which are shown in relation to specific neuropsychological results. Similarly, although the involvement of different neurotransmitter systems is controversial, data about neurochemical alterations related to visual impairments are presented, especially dopaminergic, cholinergic, and serotoninergic systems.

Predicting dichotomised outcomes from high-dimensional data in biomedicine

In many biomedical applications, we are more interested in the predicted probability that a numerical outcome is above a threshold than in the predicted value of the outcome. For example, it might be known that antibody levels above a certain threshold provide immunity against a disease, or a threshold for a disease severity score might reflect conversion from the presymptomatic to the symptomatic disease stage. Accordingly, biomedical researchers often convert numerical to binary outcomes (loss of information) to conduct logistic regression (probabilistic interpretation). We address this bad statistical practice by modelling the binary outcome with logistic regression, modelling the numerical outcome with linear regression, transforming the predicted values from linear regression to predicted probabilities, and combining the predicted probabilities from logistic and linear regression. Analysing high-dimensional simulated and experimental data, namely clinical data for predicting cognitive impairment, we obtain significantly improved predictions of dichotomised outcomes. Thus, the proposed approach effectively combines binary with numerical outcomes to improve binary classification in high-dimensional settings. An implementation is available in the R package cornet on GitHub (https://github.com/rauschenberger/cornet) and CRAN (https://CRAN.R-project.org/package=cornet).

Dopamine Transporter Deficient Rodents: Perspectives and Limitations for Neuroscience

The key element of dopamine (DA) neurotransmission is undoubtedly DA transporter (DAT), a transmembrane protein responsible for the synaptic reuptake of the mediator. Changes in DAT's function can be a key mechanism of pathological conditions associated with hyperdopaminergia. The first strain of gene-modified rodents with a lack of DAT were created more than 25 years ago. Such animals are characterized by increased levels of striatal DA, resulting in locomotor hyperactivity, increased levels of motor stereotypes, cognitive deficits, and other behavioral abnormalities. The administration of dopaminergic and pharmacological agents affecting other neurotransmitter systems can mitigate those abnormalities. The main purpose of this review is to systematize and analyze (1) known data on the consequences of changes in DAT expression in experimental animals, (2) results of pharmacological studies in these animals, and (3) to estimate the validity of animals lacking DAT as models for discovering new treatments of DA-related disorders.

Cerebral cortical thickness and cognitive decline in Parkinson's disease

In Parkinson's disease (PD), reduced cerebral cortical thickness may reflect network-based degeneration. This study performed cognitive assessment and brain MRI in 30 PD participants and 41 controls at baseline and 18 months later. We hypothesized that cerebral cortical thickness and volume, as well as change in these metrics, would differ between PD participants who remained cognitively stable and those who experienced cognitive decline. Dividing the participant sample into PD-stable, PD-decline, and control-stable groups, we compared mean cortical thickness and volume within segments that comprise the prefrontal cognitive-control, memory, dorsal spatial, and ventral object-based networks at baseline. We then compared the rate of change in cortical thickness and volume between the same groups using a vertex-wise approach. We found that the PD-decline group had lower cortical thickness within all 4 cognitive networks in comparison with controls, as well as lower cortical thickness within the prefrontal and medial temporal networks in comparison with the PD-stable group. The PD-decline group also experienced a greater rate of volume loss in the lateral temporal cortices in comparison with the control group. This study suggests that lower thickness and volume in prefrontal, medial, and lateral temporal regions may portend cognitive decline in PD.

Cognitive Profile in Parkinson's Disease Dementia Patients with Low versus Normal Cerebrospinal Fluid Amyloid Beta

Introduction

In patients with Parkinson's disease (PD), low cerebrospinal fluid (CSF) amyloid beta 1-42 (Ab42) at baseline is the most consistent CSF biomarker as a risk factor for developing dementia. Low CSF Ab42 is, however, a typical hallmark of Alzheimer's disease (AD). Hence, low CSF Ab42 in patients with PD may indicate presence of comorbid AD pathology and may predict a more AD-like cognitive profile when they develop dementia. Our study aimed to investigate if low CSF Ab42 at baseline is associated with a more AD-like cognitive profile in PD patients with dementia.

Methods

In a prospectively followed-up, population-based cohort of newly diagnosed PD patients, we compared the cognitive profile of dementia in those with a low CSF Ab42 level at baseline with that of patients who had normal levels at the time when they developed dementia. Four different cognitive domain z-scores (memory, attention, executive, visuospatial) were calculated. Patients were subdivided into three tertiles or categorized dichotomously based on the baseline CSF Ab42 levels as measured by electrochemiluminescence and ELISA.

Results

During 10-year follow-up, 37 patients met the inclusion criteria. Memory domain composite z-scores, memory subtest z-scores, and the difference between long-delay free recall versus recognition scores were not significantly different between the groups. Composite z-scores of visuospatial functions significantly differed between the tertiles, which was not significant after Bonferroni correction. In the dichotomous group analysis, z-scores of visuospatial functions significantly differed between the two groups. The other cognitive domain z-scores were not significantly different.

Conclusions

In patients with PD dementia, low CSF Ab42 level at baseline is not associated with a specific cognitive profile.

Parkinson's Progression Markers Initiative: A Milestone-Based Strategy to Monitor Parkinson's Disease Progression

BACKGROUND

Identifying a meaningful progression metric for Parkinson's disease (PD) that reflects heterogeneity remains a challenge.

OBJECTIVE

To assess the frequency and baseline predictors of progression to clinically relevant motor and non-motor PD milestones.

METHODS

Using data from the Parkinson's Progression Markers Initiative (PPMI) de novo PD cohort, we monitored 25 milestones across six domains ("walking and balance"; "motor complications"; "cognition"; "autonomic dysfunction"; "functional dependence"; "activities of daily living"). Milestones were intended to be severe enough to reflect meaningful disability. We assessed the proportion of participants reaching any milestone; evaluated which occurred most frequently; and conducted a time-to-first-event analysis exploring whether baseline characteristics were associated with progression.

RESULTS

Half of participants reached at least one milestone within five years. Milestones within the cognitive, functional dependence, and autonomic dysfunction domains were reached most often. Among participants who reached a milestone at an annual follow-up visit and remained active in the study, 82% continued to meet criteria for any milestone at one or more subsequent annual visits and 55% did so at the next annual visit. In multivariable analysis, baseline features predicting faster time to reaching a milestone included age (p < 0.0001), greater MDS-UPDRS total scores (p < 0.0001), higher GDS-15 depression scores (p = 0.0341), lower dopamine transporter binding (p = 0.0043), and lower CSF total α-synuclein levels (p = 0.0030). Symptomatic treatment was not significantly associated with reaching a milestone (p = 0.1639).

CONCLUSION

Clinically relevant milestones occur frequently, even in early PD. Milestones were significantly associated with baseline clinical and biological markers, but not with symptomatic treatment. Further studies are necessary to validate these results, further assess the stability of milestones, and explore translating them into an outcome measure suitable for observational and therapeutic studies.

Heterozygote Dopamine Transporter Knockout Rats Display Enhanced Cocaine Locomotion in Adolescent Females

Cocaine is a powerful psychostimulant that is one of the most widely used illicit addictive. The dopamine transporter (DAT) plays a major role in mediating cocaine's reward effect. Decreases in DAT expression increase rates of drug abuse and vulnerability to comorbid psychiatric disorders. We used the novel DAT transgenic rat model to study the effects of cocaine on locomotor behaviors in adolescent rats, with an emphasis on sex. Female rats showed higher response rates to cocaine at lower acute and chronic doses, highlighting a higher vulnerability and perceived gender effects. In contrast, locomotor responses to an acute high dose of cocaine were more marked and sustained in male DAT heterozygous (HET) adolescents. The results demonstrate the augmented effects of chronic cocaine in HET DAT adolescent female rats. Knockout (KO) DAT led to a level of hyperdopaminergia which caused a marked basal hyperactivity that was unchanged, consistent with a possible ceiling effect. We suggest a role of alpha synuclein (α-syn) and PICK 1 protein expressions to the increased vulnerability in female rats. These proteins showed a lower expression in female HET and KO rats. This study highlights gender differences associated with mutations which affect DAT expression and can increase susceptibility to cocaine abuse in adolescence.

Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease

Cognitive impairment is a debilitating symptom in Parkinson's disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson's Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.

Beta amyloid deposition and cognitive decline in Parkinson's disease: a study of the PPMI cohort

The accumulation of beta amyloid in the brain has a complex and poorly understood impact on the progression of Parkinson's disease pathology and much controversy remains regarding its role, specifically in cognitive decline symptoms. Some studies have found increased beta amyloid burden is associated with worsening cognitive impairment in Parkinson's disease, especially in cases where dementia occurs, while other studies failed to replicate this finding. To better understand this relationship, we examined a cohort of 25 idiopathic Parkinson's disease patients and 30 healthy controls from the Parkinson's Progression Marker Initiative database. These participants underwent [¹⁸F]Florbetaben positron emission tomography scans to quantify beta amyloid deposition in 20 cortical regions. We then analyzed this beta amyloid data alongside the longitudinal Montreal Cognitive Assessment scores across 3 years to see how participant's baseline beta amyloid levels affected their cognitive scores prospectively. The first analysis we performed with these data was a hierarchical cluster analysis to help identify brain regions that shared similarity. We found that beta amyloid clusters differently in Parkinson's disease patients compared to healthy controls. In the Parkinson's disease group, increased beta amyloid burden in cluster 2 was associated with worse cognitive ability, compared to deposition in clusters 1 or 3. We also performed a stepwise linear regression where we found an adjusted R² of 0.495 (49.5%) in a model explaining the Parkinson's disease group's Montreal Cognitive Assessment score 1-year post-scan, encompassing the left gyrus rectus, the left anterior cingulate cortex, and the right parietal cortex. Taken together, these results suggest regional beta amyloid deposition alone has a moderate effect on predicting future cognitive decline in Parkinson's disease patients. The patchwork effect of beta amyloid deposition on cognitive ability may be part of what separates cognitive impairment from cognitive sparing in Parkinson's disease. Thus, we suggest it would be more useful to measure beta amyloid burden in specific brain regions rather than using a whole-brain global beta amyloid composite score and use this information as a tool for determining which Parkinson's disease patients are most at risk for future cognitive decline.

Path analysis of biomarkers for cognitive decline in early Parkinson’s disease

Clinical and biochemical diversity of Parkinson’s disease (PD) and numerous demographic, clinical, and pathological measures influencing cognitive function and its decline in PD create problems with the determination of effects of individual measures on cognition in PD. This is particularly the case where these measures significantly interrelate with each other producing intricate networks of direct and indirect effects on cognition. Here, we use generalized structural equation modelling (GSEM) to identify and characterize significant paths for direct and indirect effects of 14 baseline measures on global cognition in PD at baseline and at 4 years later. We consider 269 drug-naïve participants from the Parkinson’s Progression Marker Initiative database, diagnosed with idiopathic PD and observed for at least 4 years after baseline. Two GSEM networks are derived, highlighting the possibility of at least two different molecular pathways or two different PD sub-types, with either CSF p-tau181 or amyloid beta (1–42) being the primary protein variables potentially driving progression of cognitive decline. The models provide insights into the interrelations between the 14 baseline variables, and determined their total effects on cognition in early PD. High CSF amyloid concentrations (> 500 pg/ml) are associated with nearly full protection against cognitive decline in early PD in the whole range of baseline age between 40 and 80 years, and irrespectively of whether p-tau181 or amyloid beta (1–42) are considered as the primary protein variables. The total effect of depression on cognition is shown to be strongly amplified by PD, but not at the time of diagnosis or at prodromal stages. CSF p-tau181 protein could not be a reliable indicator of cognitive decline because of its significantly heterogeneous effects on cognition. The outcomes will enable better understanding of the roles of the clinical and pathological measures and their mutual effects on cognition in early PD.

The epidemiology of cognitive function in Parkinson's disease

Epidemiology is the study of the distribution of disease in human populations, which is important in evaluating burden of illness, identifying modifiable risk factors, and planning for current and projected needs of the health care system. Parkinson's disease (PD) is the second most common serious neurodegenerative illness and is expected to further increase in prevalence. Cognitive changes are increasingly viewed as an integral non-motor feature in PD, emerging even in the prodromal phase of the disease. The prevalence of PD-MCI ranges from 20% to 40% depending on the population studied. The incidence of PD-dementia increases with duration of disease, with estimates growing from 3% to 30% of individuals followed for 5 years or less to over 80% after 20 years. There are several challenges in estimating the frequency of cognitive change, including only recently standardized diagnostic criteria, variation depending on exact neuropsychological evaluations performed, and differences in population sampling. Clinical features associated with cognitive decline include older age, increased disease duration and severity, early gait dysfunction, dysautonomia, hallucinations and other neuropsychiatric features, the presence of REM behavior disorder, and posterior predominant dysfunction on neuropsychological testing. There is increasing evidence that genetic risk factors, in particular GBA and MAPT mutations, contribute to cognitive change. Possible protective factors include higher cognitive reserve and regular exercise. Important sequelae of cognitive decline in PD include higher caregiver burden, decreased functional status, and increased risk of institutionalization and mortality. Many remaining uncertainties regarding the epidemiology of cognitive change in PD require future research, with improved biomarkers and more sensitive and convenient outcome measures.

Thalamic Dorsomedial Nucleus Free Water Correlates with Cognitive Decline in Parkinson's Disease

BACKGROUND

Brain diffusion tensor imaging (DTI) has been shown to reflect cognitive changes in early Parkinson's disease (PD) but the diffusion-based measure free water (FW) has not been previously assessed.

OBJECTIVES

To assess if FW in the thalamic nuclei primarily involved with cognition (ie, the dorsomedial [DMN] and anterior [AN] nuclei), the nucleus basalis of Meynert (nbM), and the hippocampus correlates with and is associated with longitudinal cognitive decline and distinguishes cognitive status at baseline in early PD. Also, to explore how FW compares with conventional DTI, FW-corrected DTI, and volumetric assessments for these outcomes.

METHODS

Imaging data and Montreal Cognitive Assessment (MoCA) scores from the Parkinson's Progression Markers Initiative database were analyzed using partial correlations and ANCOVA. Primary outcome multiple comparisons were corrected for false discovery rate (q value).

RESULTS

Thalamic DMN FW changes over 1 year correlated with MoCA changes over both 1 and 3 years (partial correlations -0.222, q = 0.040, n = 130; and - 0.229, q = 0.040, n = 123, respectively; mean PD duration at baseline = 6.85 months). NbM FW changes over 1 year only correlated with MoCA changes over 3 years (-0.222, q = 0.040). Baseline hippocampal FW was associated with cognitive impairment at 3 years (q = 0.040) and baseline nbM FW distinguished PD-normal cognition (MoCA ≥26) from PD-cognitive impairment (MoCA ≤25), (q = 0.008). The exploratory comparisons showed FW to be the most robust assessment modality for all outcomes.

CONCLUSIONS

Thalamic DMN FW is a promising cognition progression biomarker in early PD that may assist in identifying cognition protective therapies in clinical trials. FW is a robust assessment modality for these outcomes. © 2021 International Parkinson and Movement Disorder Society.

White matter microstructure in Parkinson's disease with and without elevated rapid eye movement sleep muscle tone

People with Parkinson's disease who have elevated muscle activity during rapid eye movement sleep (REM sleep without atonia) typically have a worse motor and cognitive impairment compared with those with normal muscle atonia during rapid eye movement sleep. This study used tract-based spatial statistics to compare diffusion MRI measures of fractional anisotropy, radial, mean and axial diffusivity (measures of axonal microstructure based on the directionality of water diffusion) in white matter tracts between people with Parkinson's disease with and without rapid eye movement sleep without atonia and controls and their relationship to measures of motor and cognitive function. Thirty-eight individuals with mild-to-moderate Parkinson's disease and 21 matched control subjects underwent ultra-high field MRI (7 T), quantitative motor assessments of gait and bradykinesia and neuropsychological testing. The Parkinson's disease cohort was separated post hoc into those with and without elevated chin or leg muscle activity during rapid eye movement sleep based on polysomnography findings. Fractional anisotropy was significantly higher, and diffusivity significantly lower, in regions of the corpus callosum, projection and association white matter pathways in the Parkinson's group with normal rapid eye movement sleep muscle tone compared with controls, and in a subset of pathways relative to the Parkinson's disease group with rapid eye movement sleep without atonia. The Parkinson's disease group with elevated rapid eye movement sleep muscle tone showed significant impairments in the gait and upper arm speed compared with controls and significantly worse scores in specific cognitive domains (executive function, visuospatial memory) compared with the Parkinson's disease group with normal rapid eye movement sleep muscle tone. Regression analyses showed that gait speed and step length in the Parkinson's disease cohort were predicted by measures of fractional anisotropy of the anterior corona radiata, whereas elbow flexion velocity was predicted by fractional anisotropy of the superior corona radiata. Visuospatial memory task performance was predicted by the radial diffusivity of the posterior corona radiata. These findings show that people with mild-to-moderate severity of Parkinson's disease who have normal muscle tone during rapid eye movement sleep demonstrate compensatory-like adaptations in axonal microstructure that are associated with preserved motor and cognitive function, but these adaptations are reduced or absent in those with increased rapid eye movement sleep motor tone.

Recent advances in using diffusion tensor imaging to study white matter alterations in Parkinson's disease: A mini review

Parkinson's disease (PD) is the second most common age-related neurodegenerative disease with cardinal motor symptoms. In addition to motor symptoms, PD is a heterogeneous disease accompanied by many non-motor symptoms that dominate the clinical manifestations in different stages or subtypes of PD, such as cognitive impairments. The heterogeneity of PD suggests widespread brain structural changes, and axonal involvement appears to be critical to the pathophysiology of PD. As α-synuclein pathology has been suggested to cause axonal changes followed by neuronal degeneration, diffusion tensor imaging (DTI) as an in vivo imaging technique emerges to characterize early detectable white matter changes due to PD. Here, we reviewed the past 5-year literature to show how DTI has helped identify axonal abnormalities at different PD stages or in different PD subtypes and atypical parkinsonism. We also showed the recent clinical utilities of DTI tractography in interventional treatments such as deep brain stimulation (DBS). Mounting evidence supported by multisite DTI data suggests that DTI along with the advanced analytic methods, can delineate dynamic pathophysiological processes from the early to late PD stages and differentiate distinct structural networks affected in PD and other parkinsonism syndromes. It indicates that DTI, along with recent advanced analytic methods, can assist future interventional studies in optimizing treatments for PD patients with different clinical conditions and risk profiles.

Striatal Dopaminergic Depletion Pattern Reflects Pathological Brain Perfusion Changes in Lewy Body Diseases

PURPOSE

In Lewy body diseases (LBD), various symptoms occur depending on the distribution of Lewy body in the brain, and the findings of brain perfusion and dopamine transporter single-photon emission computed tomography (DAT-SPECT) also change accordingly. We aimed to evaluate the correlation between brain perfusion SPECT and quantitative indices calculated from DAT-SPECT in patients with LBD.

PROCEDURES

We retrospectively enrolled 35 patients with LBD who underwent brain perfusion SPECT with N-isopropyl-p-[¹²³I] iodoamphetamine and DAT-SPECT with ¹²³I-ioflupane. Mini-mental state examination (MMSE) data were also collected from 19 patients. Quantitative indices (specific binding ratio [SBR], putamen-to-caudate ratio [PCR], and caudate-to-putamen ratio [CPR]) were calculated using DAT-SPECT. These data were analysed by the statistical parametric mapping procedure.

RESULTS

In patients with LBD, decreased PCR index correlated with hypoperfusion in the brainstem (medulla oblongata and midbrain) (uncorrected p < 0.001, k > 100), while decreased CPR index correlated with hypoperfusion in the right temporoparietal cortex (family-wise error corrected p < 0.05), right precuneus (uncorrected p < 0.001, k > 100), and bilateral temporal cortex (uncorrected p < 0.001, k > 100). However, there was no significant correlation between decreased SBR index and brain perfusion. Additionally, the MMSE score was correlated with hypoperfusion in the left temporoparietal cortex (uncorrected p < 0.001).

CONCLUSIONS

This study suggests that regional changes in striatal ¹²³I-ioflupane accumulation on DAT-SPECT are related to brain perfusion changes in patients with LBD.

Longitudinal Change and Progression Indicators Using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale in Two Independent Cohorts with Early Parkinson's Disease

BACKGROUND

The MDS-Unified Parkinson's disease (PD) Rating Scale (MDS-UPDRS) is the most used scale in clinical trials. Little is known about the predictive potential of its single items.

OBJECTIVE

To systematically dissect MDS-UPDRS to predict PD progression.

METHODS

574 de novo PD patients and 305 healthy controls were investigated at baseline (BL) in the single-center DeNoPa (6-year follow-up) and multi-center PPMI (8-year follow-up) cohorts. We calculated cumulative link mixed models of single MDS-UPDRS items for odds ratios (OR) for class change within the scale. Models were adjusted for age, sex, time, and levodopa equivalent daily dose. Annual change and progression of the square roots of the MDS-UDPRS subscores and Total Score were estimated by linear mixed modeling.

RESULTS

Baseline demographics revealed more common tremor dominant subtype in DeNoPa and postural instability and gait disorders-subtype and multiethnicity in PPMI. Subscore progression estimates were higher in PPMI but showed similar slopes and progression in both cohorts. Increased ORs for faster progression were found from BL subscores I and II (activities of daily living; ADL) most marked for subscore III (rigidity of neck/lower extremities, agility of the legs, gait, hands, and global spontaneity of movements). Tremor items showed low ORs/negative values.

CONCLUSION

Higher scores at baseline for ADL, freezing, and rigidity were predictors of faster deterioration in both cohorts. Precision and predictability of the MDS-UPDRS were higher in the single-center setting, indicating the need for rigorous training and/or video documentation to improve its use in multi-center cohorts, for example, clinical trials.

Apathy Reflects Extra-Striatal Dopaminergic Degeneration in de novo Parkinson's Disease

BACKGROUND

Apathy represents a core neuropsychiatric symptom in Parkinson's disease (PD). As there is currently no established effective treatment for apathy in PD, further investigating the biological origin of this symptom is needed to design novel therapeutic strategies. Among the multiple neurotransmitter alterations that have been associated with apathy, the involvement of extra-striatal dopaminergic degeneration remains to be fully explored.

OBJECTIVE

To investigate whether apathy in PD reflects increased dopaminergic degeneration extending beyond striatal regions.

METHODS

In the de novo PD cohort of the Parkinson's Progression Markers Initiative (PPMI), we performed whole-brain I123-Ioflupane Single Photon Emission Computed Tomography (DAT-SPECT) analyses to characterize cross-sectional and longitudinal differences in DAT uptake associated with the presence of apathy. We also assessed the relationship between apathy and cognition in this sample, as apathy has been suggested to herald cognitive decline.

RESULTS

Apathetic PD patients (N = 70) had similar sociodemographic, clinical, and biomarker profiles compared to the non-apathetic group (N = 333) at baseline. However, apathy was associated with an increased risk of developing cognitive impairment after a four-year follow-up period (p = 0.006). Compared to non-apathetic patients, apathetic patients showed a widespread reduction of extra-striatal DAT uptake at baseline as well as an increased longitudinal loss of DAT uptake (corrected p < 0.05).

CONCLUSIONS

Isolated apathy in PD is associated with extra-striatal dopaminergic degeneration. As this abnormal dopamine depletion was in turn related to cognitive performance, this might explain, at least partially, the increased risk of apathetic PD patients to develop cognitive impairment or dementia.

Appropriateness of Applying Cerebrospinal Fluid Biomarker Cutoffs from Alzheimer's Disease to Parkinson's Disease

BACKGROUND

While cutoffs for abnormal levels of the cerebrospinal fluid (CSF) biomarkers amyloid-β 1-42 (Aβ142), total tau (t-tau), phosphorylated tau (p-tau), and the ratios of t-tau/Aβ142 and p-tau/Aβ142, have been established in Alzheimer's disease (AD), biologically relevant cutoffs have not been studied extensively in Parkinson's disease (PD).

OBJECTIVE

Assess the suitability and diagnostic accuracy of established AD-derived CSF biomarker cutoffs in the PD population.

METHODS

Baseline and longitudinal data on CSF biomarkers, cognitive diagnoses, and PET amyloid imaging in 423 newly diagnosed patients with PD from the Parkinson's Progression Markers Initiative (PPMI) cohort were used to evaluate established AD biomarker cutoffs compared with optimal cutoffs derived from the PPMI cohort.

RESULTS

Using PET amyloid imaging as the gold standard for AD pathology, the optimal cutoff of Aβ142 was higher than the AD cutoff, the optimal cutoffs of t-tau/Aβ142 and p-tau/Aβ142 were lower than the AD cutoffs, and their confidence intervals (CIs) did not overlap with the AD cutoffs. Optimal cutoffs for t-tau and p-tau to predict cognitive impairment were significantly lower than the AD cutoffs, and their CIs did not overlap with the AD cutoffs.

CONCLUSION

Optimal cutoffs for the PPMI cohort for Aβ142, t-tau/Aβ142, and p-tau/Aβ142 to predict amyloid-PET positivity and for t-tau and p-tau to predict cognitive impairment differ significantly from cutoffs derived from AD populations. The presence of additional pathologies such as alpha-synuclein in PD may lead to disease-specific CSF biomarker characteristics.

3D convolutional neural networks-based multiclass classification of Alzheimer's and Parkinson's diseases using PET and SPECT neuroimaging modalities

Background

Alzheimer’s disease (AD) is a neurodegenerative brain pathology formed due to piling up of amyloid proteins, development of plaques and disappearance of neurons. Another common subtype of dementia like AD, Parkinson’s disease (PD) is determined by the disappearance of dopaminergic neurons in the region known as substantia nigra pars compacta located in the midbrain. Both AD and PD target aged population worldwide forming a major chunk of healthcare costs. Hence, there is a need for methods that help in the early diagnosis of these diseases. PD subjects especially those who have confirmed postmortem plaque are a strong candidate for a second AD diagnosis. Modalities such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) can be combined with deep learning methods to diagnose these two diseases for the benefit of clinicians.

Result

In this work, we deployed a 3D Convolutional Neural Network (CNN) to extract features for multiclass classification of both AD and PD in the frequency and spatial domains using PET and SPECT neuroimaging modalities to differentiate between AD, PD and Normal Control (NC) classes. Discrete Cosine Transform has been deployed as a frequency domain learning method along with random weak Gaussian blurring and random zooming in/out augmentation methods in both frequency and spatial domains. To select the hyperparameters of the 3D-CNN model, we deployed both 5- and 10-fold cross-validation (CV) approaches. The best performing model was found to be AD/NC(SPECT)/PD classification with random weak Gaussian blurred augmentation in the spatial domain using fivefold CV approach while the worst performing model happens to be AD/NC(PET)/PD classification without augmentation in the frequency domain using tenfold CV approach. We also found that spatial domain methods tend to perform better than their frequency domain counterparts.

Conclusion

The proposed model provides a good performance in discriminating AD and PD subjects due to minimal correlation between these two dementia types on the clinicopathological continuum between AD and PD subjects from a neuroimaging perspective.

Low soluble amyloid-β 42 is associated with smaller brain volume in Parkinson's disease

INTRODUCTION

We sought to examine whether levels of soluble alpha-synuclein (α-syn), amyloid-beta (Aβ42), phosphorylated tau (p-tau), and total tau (t-tau), as measured in cerebrospinal fluid (CSF), are associated with changes in brain volume in Parkinson's disease.

METHODS

We assessed the 4-year change in total brain volume (n = 99) and baseline CSF α-syn, Aβ42, p-tau, and t-tau of Parkinson Progression Markers Initiative participants. We used linear mixed models to assess the longitudinal effect of baseline CSF biomarkers on total and regional brain volume and thickness as well as linear regression for cross-sectional analyses at baseline and year 2. All models were adjusted for age and gender; brain volume models also adjusted for baseline intracranial volume. Bonferroni correction was applied.

RESULTS

The 4-year change in total brain volume was -21.2 mm³ (95% confidence interval, -26.1, -16.3). There were no significant associations between the 4-year change in total brain volume and baseline levels of any CSF biomarker (all p-values > 0.05). On cross-sectional analyses, CSF Aβ42 was linearly associated with total brain volume at baseline (R² = 0.60, p = 0.0004) and at year 2 (R² = 0.66, p < 0.0001), with CSF Aβ42 < 1100 pg/ml, the threshold for brain amyloid pathology, associated with smaller total brain volume at baseline (p = 0.0010) and at year 2 (p = 0.0002). CSF α-syn was linearly associated with total brain volume at baseline (R² = 0.58, p = 0.0044) but not at year 2 (R² = 0.58, p = 0.1342).

CONCLUSION

Reduction in soluble Aβ42 is associated with lower total brain volume in Parkinson's disease.

Mitochondrial Dysfunction, Protein Misfolding and Neuroinflammation in Parkinson's Disease: Roads to Biomarker Discovery

Parkinson’s Disease (PD) is a highly prevalent neurodegenerative disease among older adults. PD neuropathology is marked by the progressive loss of the dopaminergic neurons of the substantia nigra pars compacta and the widespread accumulation of misfolded intracellular α-synuclein (α-syn). Genetic mutations and post-translational modifications, such as α-syn phosphorylation, have been identified among the multiple factors supporting α-syn accrual during PD. A decline in the clearance capacity of the ubiquitin-proteasome and the autophagy-lysosomal systems, together with mitochondrial dysfunction, have been indicated as major pathophysiological mechanisms of PD neurodegeneration. The accrual of misfolded α-syn aggregates into soluble oligomers, and the generation of insoluble fibrils composing the core of intraneuronal Lewy bodies and Lewy neurites observed during PD neurodegeneration, are ignited by the overproduction of reactive oxygen species (ROS). The ROS activate the α-syn aggregation cascade and, together with the Lewy bodies, promote neurodegeneration. However, the molecular pathways underlying the dynamic evolution of PD remain undeciphered. These gaps in knowledge, together with the clinical heterogeneity of PD, have hampered the identification of the biomarkers that may be used to assist in diagnosis, treatment monitoring, and prognostication. Herein, we illustrate the main pathways involved in PD pathogenesis and discuss their possible exploitation for biomarker discovery.

Biomarkers of neurodegeneration and glial activation validated in Alzheimer's disease assessed in longitudinal cerebrospinal fluid samples of Parkinson's disease

Aim

Several pathophysiological processes are involved in Parkinson’s disease (PD) and could inform in vivo biomarkers. We assessed an established biomarker panel, validated in Alzheimer’s Disease, in a PD cohort.

Methods

Longitudinal cerebrospinal fluid (CSF) samples from PPMI (252 PD, 115 healthy controls, HC) were analyzed at six timepoints (baseline, 6, 12, 24, 36, and 48 months follow-up) using Elecsys® electrochemiluminescence immunoassays to quantify neurofilament light chain (NfL), soluble TREM2 receptor (sTREM2), chitinase-3-like protein 1 (YKL40), glial fibrillary acidic protein (GFAP), interleukin-6 (IL-6), S100, and total α-synuclein (αSyn).

Results

αSyn was significantly lower in PD (mean 103 pg/ml vs. HC: 127 pg/ml, p<0.01; area under the curve [AUC]: 0.64), while all other biomarkers were not significantly different (AUC NfL: 0.49, sTREM2: 0.54, YKL40: 0.57, GFAP: 0.55, IL-6: 0.53, S100: 0.54, p>0.05) and none showed a significant difference longitudinally. We found significantly higher levels of all these markers between PD patients who developed cognitive decline during follow-up, except for αSyn and IL-6.

Conclusion

Except for αSyn, the additional biomarkers did not differentiate PD and HC, and none showed longitudinal differences, but most markers predict cognitive decline in PD during follow-up.

Disentangled and Proportional Representation Learning for Multi-View Brain Connectomes

Diffusion MRI-derived brain structural connectomes or brain networks are widely used in the brain research. However, constructing brain networks is highly dependent on various tractography algorithms, which leads to difficulties in deciding the optimal view concerning the downstream analysis. In this paper, we propose to learn a unified representation from multi-view brain networks. Particularly, we expect the learned representations to convey the information from different views fairly and in a disentangled sense. We achieve the disentanglement via an approach using unsupervised variational graph auto-encoders. We achieve the view-wise fairness, i.e. proportionality, via an alternative training routine. More specifically, we construct an analogy between training the deep network and the network flow problem. Based on the analogy, the fair representations learning is attained via a network scheduling algorithm aware of proportionality. The experimental results demonstrate that the learned representations fit various downstream tasks well. They also show that the proposed approach effectively preserves the proportionality.

Evaluation of neurotrophic factors and education level as predictors of cognitive decline in alcohol use disorder

Cognitive reserve (CR) is the capability of an individual to cope with a brain pathology through compensatory mechanisms developed through cognitive stimulation by mental and physical activity. Recently, it has been suggested that CR has a protective role against the initiation of substance use, substance consumption patterns and cognitive decline and can improve responses to treatment. However, CR has never been linked to cognitive function and neurotrophic factors in the context of alcohol consumption. The present cross-sectional study aims to evaluate the association between CR (evaluated by educational level), cognitive impairment (assessed using a frontal and memory loss assessment battery) and circulating levels of brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NT-3) in patients with alcohol use disorder (AUD). Our results indicated that lower educational levels were accompanied by earlier onset of alcohol consumption and earlier development of alcohol dependence, as well as impaired frontal cognitive function. They also suggest that CR, NT-3 and BDNF may act as compensatory mechanisms for cognitive decline in the early stages of AUD, but not in later phases. These parameters allow the identification of patients with AUD who are at risk of cognitive deterioration and the implementation of personalized interventions to preserve cognitive function.

RNA sequencing of whole blood reveals early alterations in immune cells and gene expression in Parkinson's disease

Changes in the blood-based RNA transcriptome have the potential to inform biomarkers of Parkinson's disease (PD) progression. Here we sequenced a discovery set of whole-blood RNA species in 4,871 longitudinally collected samples from 1,570 clinically phenotyped individuals from the Parkinson's Progression Marker Initiative (PPMI) cohort. Samples were sequenced to an average of 100 million read pairs to create a high-quality transcriptome. Participants with PD in the PPMI had significantly altered RNA expression (>2,000 differentially expressed genes), including an early and persistent increase in neutrophil gene expression, with a concomitant decrease in lymphocyte cell counts. This was validated in a cohort from the Parkinson's Disease Biomarkers Program (PDBP) consisting of 1,599 participants and by alterations in immune cell subtypes. This publicly available transcriptomic dataset, coupled with available detailed clinical data, provides new insights into PD biological processes impacting whole blood and new paths for developing diagnostic and prognostic PD biomarkers.

Brain Atrophy Mediates the Relationship between Misfolded Proteins Deposition and Cognitive Impairment in Parkinson's Disease

Parkinson’s disease is associated with cognitive decline, misfolded protein deposition and brain atrophy. We herein hypothesized that structural abnormalities may be mediators between plasma misfolded proteins and cognitive functions. Neuropsychological assessments including five domains (attention, executive, speech and language, memory and visuospatial functions), ultra-sensitive immunomagnetic reduction-based immunoassay (IMR) measured misfolded protein levels (phosphorylated-Tau, Amyloidβ-42 and 40, α-synuclein and neurofilament light chain) and auto-segmented brain volumetry using FreeSurfur were performed for 54 Parkinson’s disease (PD) patients and 37 normal participants. Our results revealed that PD patients have higher plasma misfolded protein levels. Phosphorylated-Tau (p-Tau) and Amyloidβ-42 (Aβ-42) were correlated with atrophy of bilateral cerebellum, right caudate nucleus, and right accumbens area (RAA). In mediation analysis, RAA atrophy completely mediated the relationship between p-Tau and digit symbol coding (DSC). RAA and bilateral cerebellar cortex atrophy partially mediated the Aβ-42 and executive function (DSC and abstract thinking) relationship. Our study concluded that, in PD, p-Tau deposition adversely impacts DSC by causing RAA atrophy. Aβ-42 deposition adversely impacts executive functions by causing RAA and bilateral cerebellum atrophy.

Parkinson disease-associated cognitive impairment

Parkinson disease (PD) is the second most common neurodegenerative disorder, affecting >1% of the population ≥65 years of age and with a prevalence set to double by 2030. In addition to the defining motor symptoms of PD, multiple non-motor symptoms occur; among them, cognitive impairment is common and can potentially occur at any disease stage. Cognitive decline is usually slow and insidious, but rapid in some cases. Recently, the focus has been on the early cognitive changes, where executive and visuospatial impairments are typical and can be accompanied by memory impairment, increasing the risk for early progression to dementia. Other risk factors for early progression to dementia include visual hallucinations, older age and biomarker changes such as cortical atrophy, as well as Alzheimer-type changes on functional imaging and in cerebrospinal fluid, and slowing and frequency variation on EEG. However, the mechanisms underlying cognitive decline in PD remain largely unclear. Cortical involvement of Lewy body and Alzheimer-type pathologies are key features, but multiple mechanisms are likely involved. Cholinesterase inhibition is the only high-level evidence-based treatment available, but other pharmacological and non-pharmacological strategies are being tested. Challenges include the identification of disease-modifying therapies as well as finding biomarkers to better predict cognitive decline and identify patients at high risk for early and rapid cognitive impairment.

A systematic review of associations between common SNCA variants and clinical heterogeneity in Parkinson's disease

There is great heterogeneity in both the clinical presentation and rate of disease progression among patients with Parkinson’s disease (PD). This can pose prognostic difficulties in a clinical setting, and a greater understanding of the risk factors that contribute to modify disease course is of clear importance for optimizing patient care and clinical trial design. Genetic variants in SNCA are an established risk factor for PD and are candidates to modify disease presentation and progression. This systematic review aimed to summarize all available primary research reporting the association of SNCA polymorphisms with features of PD. We systematically searched PubMed and Web of Science, from inception to 1 June 2020, for studies evaluating the association of common SNCA variants with age at onset (AAO) or any clinical feature attributed to PD in patients with idiopathic PD. Fifty-eight studies were included in the review that investigated the association between SNCA polymorphisms and a broad range of outcomes, including motor and cognitive impairment, sleep disorders, mental health, hyposmia, or AAO. The most reproducible findings were with the REP1 polymorphism or rs356219 and an earlier AAO, but no clear associations were identified with an SNCA polymorphism and any individual clinical outcome. The results of this comprehensive summary suggest that, while there is evidence that genetic variance in the SNCA region may have a small impact on clinical outcomes in PD, the mechanisms underlying the association of SNCA polymorphisms with PD risk may not be a major factor driving clinical heterogeneity in PD.

Positron emission computed tomography/single photon emission computed tomography in Parkinson disease

Parkinson disease (PD) is the second-most common neurodegenerative disorder. Its main pathological mechanism is the selective degeneration and deletion of dopaminergic neurons in the dense part of the substantia nigra and the damage of dopaminergic neurons caused by the abnormal deposition of a Lewy body, leading to a decreased dopamine level. Positron emission computed tomography (PET)/single photon emission computed tomography (SPECT) is a molecular imaging technology that can directly or indirectly reflect changes in molecular levels by using a specific tracer. With the research and development on the tracers of related enzymes for labeling dopamine transporter and dopamine receptor and for being involved in dopamine formation, this imaging technology has been applied to all aspects of PD research. It not only contributes to clinical work but also provides an important theoretical basis for exploring the pathological mechanism of PD at a molecular level. Therefore, this review discusses the application value of PET/SPECT in PD in terms of early diagnosis, disease severity evaluation, clinical manifestations, differential diagnosis, and pathological mechanism.

Reduced erythrocytic CHCHD2 mRNA is associated with brain pathology of Parkinson's disease

Peripheral biomarkers indicative of brain pathology are critically needed for early detection of Parkinson’s disease (PD). In this study, using NanoString and digital PCR technologies, we began by screening for alterations in genes associated with PD or atypical Parkinsonism in erythrocytes of PD patients, in which PD-related changes have been reported, and which contain ~ 99% of blood α-synuclein. Erythrocytic CHCHD2 mRNA was significantly reduced even at the early stages of the disease. A significant reduction in protein and/or mRNA expression of CHCHD2 was confirmed in PD brains collected at autopsy as well as in the brains of a PD animal model overexpressing α-synuclein, in addition to seeing a reduction of CHCHD2 in erythrocytes of the same animals. Overexpression of α-synuclein in cellular models of PD also resulted in reduced CHCHD2, via mechanisms likely involving altered subcellular localization of p300 histone acetyltransferase. Finally, the utility of reduced CHCHD2 mRNA as a biomarker for detecting PD, including early-stage PD, was validated in a larger cohort of 205 PD patients and 135 normal controls, with a receiver operating characteristic analysis demonstrating > 80% sensitivity and specificity.

Improved motor outcome prediction in Parkinson's disease applying deep learning to DaTscan SPECT images

PURPOSE

Dopamine transporter (DAT) SPECT imaging is routinely used in the diagnosis of Parkinson's disease (PD). Our previous efforts demonstrated the use of DAT SPECT images in a data-driven manner by improving prediction of PD clinical assessment outcome using radiomic features. In this work, we develop a convolutional neural network (CNN) based technique to predict clinical motor function evaluation scores directly from longitudinal DAT SPECT images and non-imaging clinical measures.

PROCEDURES

Data of 252 subjects from the Parkinson's Progression Markers Initiative (PPMI) database were used in this work. The motor part (III) score of the unified Parkinson's disease rating scale (UPDRS) at year 4 was selected as outcome, and the DAT SPECT images and UPDRS_III scores acquired at year 0 and year 1 were used as input data. The specified inputs and outputs were used to develop a CNN based regression method for prediction. Ten-fold cross-validation was used to test the trained network and the absolute difference between predicted and actual scores was used as the performance metric. Prediction using inputs with and without DAT images was evaluated.

RESULTS

Using only UPDRS_III scores at year 0 and year 1, the prediction yielded an average difference of 7.6 ± 6.1 between the predicted and actual year 4 motor scores (range [5, 77]). The average difference was reduced to 6.0 ± 4.8 when longitudinal DAT SPECT images were included, which was determined to be statistically significant via a two-sample t-test, and demonstrates the benefit of including images.

CONCLUSIONS

This study shows that adding DAT SPECT images to UPDRS_III scores as inputs to deep-learning based prediction significantly improves the outcome. Without requiring segmentation and feature extraction, the CNN based prediction method allows easier and more universial application.

Neuroprotective effect of Na+ /H+ exchangers isoform-1 inactivation against 6-hydroxydopamine-induced mitochondrial dysfunction and neuronal apoptosis in Parkinson's disease models

Parkinson's disease (PD) is a disabling neurodegenerative disease mainly caused by degeneration of mesencephalic dopaminergic neurons in the substantia nigra pars compacta (SNpc). The neuroprotective role of Na⁺ /H⁺ exchangers isoform-1 (NHE1) inactivation in cerebral ischemic damage has been elucidated. The current study aimed to investigate the impacts of NHE1 in PD. In this study, 6-hydroxydopamine (6-OHDA)-induced PD rat models were established to attempt to illuminate the role and underlying mechanisms of NHE1 in SNpc neurons of PD. Meanwhile, nerve growth factor-stimulated PC12 cells followed by 6-OHDA treatment was used to mimic PD in vitro. Results showed that the protein levels of NHE1 were significantly increased in the SNpc neurons of rats and differentiated PC12 cells after 6-OHDA treatment. Inactivation of NHE1 with chemical inhibitor HOE642 suppressed SNpc neuronal loss and NHE1 expression in PD rats. The overlays of tyrosine hydroxylase and NHE1 displayed that NHE1 expression was not colocalized but closely associated with TH. Besides, treatment with HOE642 relieved the dyskinesia, mitochondrial dysfunction, and neuronal apoptosis. Further in vitro evidence confirmed that inhibition of NHE1 by genetic-knockdown prevented mitochondrial dysfunction and apoptosis. Our study represents the first experimental evidence of a potential role for NHE1 in the pathogenesis of PD.

A Prodromal Brain-Clinical Pattern of Cognition in Synucleinopathies

OBJECTIVE

Isolated (or idiopathic) rapid eye movement sleep behavior disorder (iRBD) is associated with dementia with Lewy bodies (DLB) and Parkinson's disease (PD). Biomarkers are lacking to predict conversion to a dementia or a motor-first phenotype. Here, we aimed at identifying a brain-clinical signature that predicts dementia in iRBD.

METHODS

A brain-clinical signature was identified in 48 patients with polysomnography-confirmed iRBD using partial least squares between brain deformation and 27 clinical variables. The resulting variable was applied to 78 patients with iRBD followed longitudinally to predict conversion to a synucleinopathy, specifically DLB. The deformation scores from patients with iRBD were compared with 207 patients with PD, DLB, or prodromal DLB to assess if scores were higher in DLB compared to PD.

RESULTS

One latent variable explained 31% of the brain-clinical covariance in iRBD, combining cortical and subcortical deformation and subarachnoid/ventricular expansion to cognitive and motor variables. The deformation score of this signature predicted conversion to a synucleinopathy in iRBD (p = 0.036, odds ratio [OR] = 2.249; 95% confidence interval [CI] = 1.053-4.803), specifically to DLB (OR = 4.754; 95% CI = 1.283-17.618, p = 0.020) and not PD (p = 0.286). Patients with iRBD who developed dementia had scores similar to clinical and prodromal patients with DLB but higher scores compared with patients with PD. The deformation score also predicted cognitive performance over 1, 2, and 4 years in patients with PD.

INTERPRETATION

We identified a brain-clinical signature that predicts conversion in iRBD to more severe/dementing forms of synucleinopathy. This pattern may serve as a new biomarker to optimize patient care, target risk reduction strategies, and administer neuroprotective trials. ANN NEUROL 2021;89:341-357.

Novel PET Biomarkers to Disentangle Molecular Pathways across Age-Related Neurodegenerative Diseases

There is a need to disentangle the etiological puzzle of age-related neurodegenerative diseases, whose clinical phenotypes arise from known, and as yet unknown, pathways that can act distinctly or in concert. Enhanced sub-phenotyping and the identification of in vivo biomarker-driven signature profiles could improve the stratification of patients into clinical trials and, potentially, help to drive the treatment landscape towards the precision medicine paradigm. The rapidly growing field of neuroimaging offers valuable tools to investigate disease pathophysiology and molecular pathways in humans, with the potential to capture the whole disease course starting from preclinical stages. Positron emission tomography (PET) combines the advantages of a versatile imaging technique with the ability to quantify, to nanomolar sensitivity, molecular targets in vivo. This review will discuss current research and available imaging biomarkers evaluating dysregulation of the main molecular pathways across age-related neurodegenerative diseases. The molecular pathways focused on in this review involve mitochondrial dysfunction and energy dysregulation; neuroinflammation; protein misfolding; aggregation and the concepts of pathobiology, synaptic dysfunction, neurotransmitter dysregulation and dysfunction of the glymphatic system. The use of PET imaging to dissect these molecular pathways and the potential to aid sub-phenotyping will be discussed, with a focus on novel PET biomarkers.

Depressive symptoms are associated with worse cognitive prognosis in patients with newly diagnosed idiopathic Parkinson disease

BACKGROUND

Although depression is very common in patients with Parkinson disease (PD), only a few studies have investigated the longitudinal effects of initial depression on cognitive decline in these patients. The purpose of this study was to investigate the effect of depression on cognitive functions in patients with PD.

METHODS

We used data from the Parkinson Progression Markers Initiative (PPMI) to investigate the relationship between depression and PD. Depressive symptoms were measured in patients with PD based on the Geriatric Depression Scale (GDS) or Neuropsychiatric Inventory-Questionnaire (NPI-Q) scores obtained at baseline. We evaluated cognitive decline as whether a patient with PD progressed to PD with mild cognitive impairment (MCI) during a 4-year follow-up period. Multivariate Cox regression analysis was done to know whether depression can predict the conversion to MCI. In addition, a voxel-based morphometric analysis using volumetric brain magnetic resonance imaging was used to compare structural changes related to future cognitive decline as well as to reveal longitudinal effect of baseline depression on cortical atrophy.

RESULTS

Data from 263 patients with cognitively normal de novo PD who were available for longitudinal cognitive testing were analysed. The multivariate Cox regression analysis revealed that the depressive symptoms were independent risk factors for conversion to MCI in patients with de novo PD after adjusting for covariates (hazards ratio (95% CI)) of depression defined by the GDS (1.753 (1.084-2.835)) and the NPI (1.815 (1.083-3.042)) scores, respectively. The significant structural changes in PD with MCI as well as longitudinal effect of baseline depression on subsequent cortical atrophy were found in multiple areas on the voxel-based morphometric analysis (P < 0.001, family-wise error rate corrected).

CONCLUSIONS

Our study indicates that the presence of depressive symptoms in patients with early PD is associated with a higher risk of progression to MCI and early depression may reflect subsequent cortical atrophy.

Extrastriatal SPECT-DAT uptake correlates with clinical and biological features of de novo Parkinson's disease

Striatal dopamine transporter (DAT) uptake assessment through I¹²³-Ioflupane Single-Pphoton Emission Computed Tomography (SPECT) provides valuable information about the dopaminergic denervation occurring in Parkinson's disease (PD). However, little is known about the clinical or biological relevance of extrastriatal DAT uptake in PD. Here, from the Parkinson's Progression Markers Initiative, we studied 623 participants (431 PD and 192 healthy controls) with available SPECT data. Even though striatal denervation was undoubtedly the imaging hallmark of PD, extrastriatal DAT uptake was also reduced in patients with PD. Topographically, widespread frontal but also temporal and posterior cortical regions showed lower DAT uptake in PD patients with respect to healthy controls. Importantly, a longitudinal voxelwise analysis confirmed an active one-year loss of extrastriatal DAT uptake within the PD group. Extrastriatal DAT uptake also correlated with the severity of motor symptoms, cognitive performance, and cerebrospinal fluid α-synuclein levels. In addition, we found an association between the Catechol-O-methyltransferase val¹⁵⁸met genotype and extrastriatal DAT uptake. These results highlight the clinical and biological relevance of extrastriatal SPECT-DAT uptake in PD.

Synuclein in neurodegeneration

Neurodegenerative diseases are a heterogeneous group of disorders characterized by gradual progressive neuronal loss in the central nervous system. Unfortunately, the pathogenesis of many of these diseases remains unknown. Synucleins are a family of small, highly charged proteins expressed predominantly in neurons. Following their discovery, much has been learned about their structure, function, interaction with other proteins and role in neurodegenerative disease over the last two decades. One of these proteins, α-Synuclein (α-Syn), appears to be involved in many neurodegenerative disorders. These include Parkinson's disease (PD), dementia with Lewy bodies (DLB), Rapid Eye Movement Sleep Behavior Disorder (RBD) and Pure Autonomic Failure (PAF), i.e., collectively termed α-synucleinopathies. This review focuses on α-Syn dysfunction in neurodegeneration and assesses its role in synucleinopathies from a biochemical, genetic and neuroimaging perspective.

Management of psychiatric disorders in Parkinson's disease : Neurotherapeutics - Movement Disorders Therapeutics

Affective disorders (depression and anxiety), psychosis, impulse control disorders, and apathy are common and sometimes disabling psychiatric conditions in Parkinson disease (PD). Psychiatric aspects of PD are associated with numerous adverse outcomes, yet in spite of this and their high frequency, there remains incomplete understanding of epidemiology, presentation, risk factors, neural substrate, and management strategies. Psychiatric features are typically co- or multimorbid, and there is great intra- and interindividual variability in presentation [1]. The neuropathophysiological changes that occur in PD, as well as the association between PD treatment and particular psychiatric disorders, suggest a neurobiological contribution to many psychiatric symptoms. There is evidence that psychiatric disorders in PD are still under-recognized and undertreated, and although psychotropic medication use is common, randomized controlled trials demonstrating efficacy and tolerability are largely lacking. Future research on neuropsychiatric complications in PD should be oriented toward determining modifiable correlates or risk factors, and most importantly, establishing efficacious and well-tolerated treatment strategies.

Diffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review

Diffusion tensor imaging (DTI) allows measuring fractional anisotropy and similar microstructural indices of the brain white matter. Lower than normal fractional anisotropy as well as higher than normal diffusivity is associated with loss of microstructural integrity and neurodegeneration. Previous DTI studies in Parkinson's disease (PD) have demonstrated abnormal fractional anisotropy in multiple white matter regions, particularly in the dopaminergic nuclei and dopaminergic pathways. However, DTI is not considered a diagnostic marker for the earliest Parkinson's disease since anisotropic alterations present a temporally divergent pattern during the earliest Parkinson's course. This article reviews a majority of clinically employed DTI studies in PD, and it aims to prove the utilities of DTI as a marker of diagnosing PD, correlating clinical symptomatology, tracking disease progression, and treatment effects. To address the challenge of DTI being a diagnostic marker for early PD, this article also provides a comparison of the results from a longitudinal, early stage, multicenter clinical cohort of Parkinson's research with previous publications. This review provides evidences of DTI as a promising marker for monitoring PD progression and classifying atypical PD types, and it also interprets the possible pathophysiologic processes under the complex pattern of fractional anisotropic changes in the first few years of PD. Recent technical advantages, limitations, and further research strategies of clinical DTI in PD are additionally discussed.

Patient and Clinician Impressions of Cognitive Impairment in Parkinson's Disease

We investigated patient and clinician impressions of cognitive impairment and whether they correlated with objective measures of cognitive impairment. Cognitive categorization, neuropsychological assessment scores, and Montreal Cognitive Assessment scores were documented at baseline, 3 years, and 7 years for 388 PD patients in the Parkinson's Progression Markers Initiative (PPMI). We found that both patient and clinician impressions of cognitive decline were significantly associated with gold-standard criteria for cognitive impairment to a similar degree. Both patient and clinician perspectives should be considered in determining cognitive status and should be followed up with diagnostic testing.

Dopamine transporter imaging in neurodegenerative movement disorders: PET vs. SPECT

Purpose

The dopamine transporter (DAT) serves as biomarker for parkinsonian syndromes. DAT can be measured in vivo with single-photon emission computed tomography (SPECT) and positron emission tomography (PET). DAT-SPECT is the current clinical molecular imaging standard. However, PET has advantages over SPECT measurements, and PET radioligands with the necessary properties for clinical applications are on the rise. Therefore, it is time to review the role of DAT imaging with SPECT compared to PET.

Methods

PubMed and Web of Science were searched for relevant literature of the previous 10 years. Four topics for comparison were used: diagnostic accuracy, quantitative accuracy, logistics, and flexibility.

Results

There are a few studies directly comparing DAT-PET and DAT-SPECT. PET and SPECT both perform well in discriminating neurodegenerative from non-neurodegenerative parkinsonism. Clinical DAT-PET imaging seems feasible only recently, thanks to simplified DAT assessments and better availability of PET radioligands and systems. The higher resolution of PET makes more comprehensive assessments of disease progression in the basal ganglia possible. Additionally, it has the possibility of multimodal target assessment.

Conclusion

DAT-SPECT is established for differentiating degenerative from non-degenerative parkinsonism. For further differentiation within neurodegenerative Parkinsonian syndromes, DAT-PET has essential benefits. Nowadays, because of wider availability of PET systems and radioligand production centers, and the possibility to use simplified quantification methods, DAT-PET imaging is feasible for clinical use. Therefore, DAT-PET needs to be considered for a more active role in the clinic to take a step forward to a more comprehensive understanding and assessment of Parkinson’s disease.

Longitudinal Measurements of Glucocerebrosidase activity in Parkinson's patients

Objective

Reduction in glucocerebrosidase (GCase; encoded by GBA) enzymatic activity has been linked to Parkinson’s disease (PD). Here, we correlated GCase activity and PD phenotype in the Parkinson’s Progression Markers Initiative (PPMI) cohort.

Methods

We measured GCase activity in dried blood spots from 1559 samples of participants in the inception PPMI cohort, collected in four annual visits (from baseline visit to Year‐3). Participants (PD, n = 392; controls, n = 175) were fully sequenced for GBA variants by means of genome‐wide genotyping arrays, whole‐exome sequencing, whole‐genome sequencing, Sanger sequencing, and RNA‐sequencing.

Results

Fifty‐two PD participants (13.4%) and 13 (7.4%) controls carried a GBA variant. GBA status was strongly associated with GCase activity. Among noncarriers, GCase activity was similar between PD and controls. Among GBA p.E326K carriers (PD, n = 20; controls, n = 5), activity was significantly lower in PD carriers than control carriers (9.53 µmol/L/h vs. 11.68 µmol/L/h, P = 0.035). Glucocerebrosidase activity was moderately (r = 0.45) associated with white blood cell (WBC) count. Next, we divided the noncarriers with PD to tertiles based on WBC count‐corrected enzymatic activity. Members of the lower tertile had higher MDS‐Unified Parkinson’s Disease Rating Scale motor score in the “off” medication examination at year‐III exam. Longitudinal analyses demonstrated slight reduction of activity in samples collected earlier on in the study, likely because of longer storage time.

Interpretation

GCase activity is associated with GBA genotype, WBC count, and among p.E326K variant carriers, with PD status. Reduced activity may also be associated with worse phenotype but longer follow up is required to confirm this observation.

Multimodal dopamine transporter (DAT) imaging and magnetic resonance imaging (MRI) to characterise early Parkinson's disease

Idiopathic Parkinson's disease (PD), the second most common neurodegenerative disorder, is characterised by the progressive loss of dopaminergic nigrostriatal terminals. Currently, in early idiopathic PD, dopamine transporter (DAT)-specific imaging assesses the extent of striatal dopaminergic deficits, and conventional magnetic resonance imaging (MRI) of the brain excludes the presence of significant ischaemic load in the basal ganglia as well as signs indicative of other forms of Parkinsonism. In this article, we discuss the use of multimodal DAT-specific and MRI protocols for insight into the early pathological features of idiopathic PD, including: structural MRI, diffusion tensor imaging, nigrosomal iron imaging and neuromelanin-sensitive MRI sequences. These measures may be acquired serially or simultaneously in a hybrid scanner. From current evidence, it appears that both nigrosomal iron imaging and neuromelanin-sensitive MRI combined with DAT-specific imaging are useful to assist clinicians in diagnosing PD, while conventional structural MRI and diffusion tensor imaging protocols are better suited to a research context focused on characterising early PD pathology. We believe that in the future multimodal imaging will be able to characterise prodromal PD and stratify the clinical stages of PD progression.