The diagnosis of Parkinson's disease

Compensatory neuritogenesis of serotonergic afferents within the striatum of a transgenic rat model of Parkinson's disease

The majority of patients with Parkinson's disease (PD) suffer from L-DOPA-induced dyskinesia (LID). Besides a dysfunctional dopaminergic system, changes of the serotonergic network may be linked to this severe and adverse symptom. Particularly, serotonergic neurons have the potential to synthesize dopamine, likely associated with a disproportional dopamine release within the striatum. We hypothesized that the serotonergic system is adaptively altered in the striatum due to the reduced dopaminergic input. To answer this question, we analyzed a transgenic rat PD model ubiquitously expressing human α-synuclein using a bacterial artificial chromosome. Neurite analysis showed a profound loss of dopaminergic fibers by ~30-40% within the dorsal striatum paralleled by a ~50% reduction of dopaminergic neurons in the substantia nigra pars compacta. In contrast, serotonergic fibers showed an increased fiber density in the dorsal striatum by ~100%, while the number of serotonergic neurons within the raphe nuclei (RN) and its proximal neuritic processes were unaffected. Furthermore, both the dopaminergic and serotonergic fiber density remained unchanged in the neighboring motor cortex M1/M2. Interestingly, essential enzymes required for L-DOPA turnover and dopamine release were expressed in serotonergic neurons of the RN. In parallel, the serotonergic autoreceptor levels involved in a serotonergic negative feedback loop were reduced within the striatum, suggesting a dysfunctional neurotransmitter release. Overall, the increased serotonergic fiber density with its capacity for dopamine release within the striatum suggests a compensatory, site-specific serotonergic neuritogenesis. This maladaptive serotonergic plasticity may be linked to adverse symptoms such as LIDs in PD.

Is There Any Clinical Value of Adding 123I-Metaiodobenzylguanidine Myocardial Scintigraphy to 123I-Ioflupane (DaTscan) in the Differential Diagnosis of Parkinsonism?

PURPOSE

The aim of the study is to evaluate the impact of myocardial I-metaiodobenzylguanidine (MIBG) in the diagnosis, clinical management, and differential diagnosis of Parkinson disease (PD) and non-PD parkinsonism.

METHODS

The study enrolled 41 patients with parkinsonism. An initial diagnosis was reached after thorough clinical and imaging evaluation. After 2 to 5 years of follow-up, a final diagnosis was established. All patients underwent, soon after their initial visit, presynaptic striatal DaT scintigraphy with I-FP-CIT (DaTscan) and I-MIBG myocardial scintigraphy. DaTscan is not specific to distinguish among different types of neurodegenerative parkinsonism. I-MIBG myocardial scintigraphy displays the functional status of cardiac sympathetic nerves, which is reduced in PD/dementia with Lewy bodies (DLB) and normal in atypical parkinsonian syndromes and secondary or nondegenerative parkinsonism.

RESULTS

No patients showed adverse effects during or after both scintigraphies. A positive DaTscan was found in all patients in the PD/DLB group (17/17) and in 15 of 24 patients in the non-PD group. Myocardial I-MIBG scintigraphy was associated with lower sensitivity (82% vs 100%) but higher specificity than DaTscan (79% vs 38%) in diagnosis PD/DLB from non-PD parkinsonism. A positive scan result on both techniques, to confirm diagnosis of PD/DLB, significantly improved the specificity of DaTscan, from 38% to 75%, with no reduction in sensitivity.

CONCLUSIONS

Myocardial I-MIBG imaging provides complementary value to I-FP-CIT in the proper diagnosis, treatment plan, and differential diagnosis between PD and other forms of parkinsonism.

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer's disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders-focusing on Alzheimer's disease, Parkinson's disease and schizophrenia-from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

Trimetazidine Use and the Risk of Parkinsonism: A Nationwide Population-Based Study

An association between trimetazidine (TMZ), an anti-anginal drug, and parkinsonism has been reported in a number of studies. However, evidence from studies with long-term follow-up and better validity is lacking. We investigated the risk of TMZ-associated parkinsonism, specifically the incidence rate, cumulative dose–response relationship, and combined effects with other parkinsonism-inducing medications. This propensity score-matched retrospective cohort study was conducted using 14-year health insurance claims data in South Korea. The risk of parkinsonism was evaluated using multivariate Cox proportional hazard regression analysis, adjusted for comorbidities and concurrent medications. A total of 9712 TMZ users and 29,116 matched non-TMZ users were included. TMZ users had a significantly higher incidence rate of parkinsonism than non-TMZ users (9.34 vs. 6.71 per 1000 person-years; p < 0.0001). TMZ use significantly increased the risk of parkinsonism (adjusted hazard ratio = 1.38; 95% confidence interval = 1.26–1.51). Increased risks were observed with accumulated doses of TMZ, as well as concurrent use of other parkinsonism-inducing medications. The findings indicate that TMZ use significantly increases the risk of parkinsonism in the South Korean population. Closer monitoring should be considered for TMZ users, especially for those who are older, using TMZ at high cumulative doses and other parkinsonism-inducing medications.

Blood neurofilament light chain in Parkinson disease and atypical parkinsonisms: A protocol for systematic review and meta-analysis

BACKGROUND

Neurofilament light chain (NfL), an index of neuroaxonal injury, is a promising diagnostic and prognostic fluid biomarker with high translational value in many neurodegenerative disorders. Blood NfL measurement has been an exciting and active field of research in idiopathic Parkinson disease (PD) and atypical parkinsonisms. However, blood NfL levels in these parkinsonisms from existing literature were inconsistent. No comprehensive meta-analysis has ever been conducted.

METHODS

Three major biomedical electronic databases PubMed, Embase, and Web of Science were comprehensively searched from inception to July 10, 2020. This protocol will be prepared based on the guidelines recommended by the statement of Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Original observational studies that measured blood (serum/plasma) NfL concentrations in patients with parkinsonisms (multiple system atrophy [MSA], progressive supranuclear palsy [PSP], corticobasal syndrome [CBS], and dementia with Lewy bodies [DLB]), and healthy controls (HCs) will be included. Quality assessment of the included studies will be performed using the Newcastle Ottawa Scale (NOS). Meta-analyses will be conducted using the STATA software version 13.0. The standardized mean differences as the measure of effect size and 95% confidence intervals were calculated for each comparison of blood NfL levels. Heterogeneity analysis, sensitivity analysis, publication bias, subgroup analysis, and meta-regression analysis will be carried out to test the robustness of the results.

RESULTS

The meta-analysis will obtain the effect sizes of blood NfL levels in the following comparisons: PD versus HC, MSA versus HC, PSP versus HC, CBS versus HC, DLB versus HC, MSA versus PD, PSP versus PD, CBS versus PD, and DLB versus PD.

CONCLUSIONS

The present meta-analysis will provide the quantitative evidence of NfL levels in idiopathic PD and atypical parkinsonisms, hoping to facilitate differential diagnoses in clinical practice.

REGISTRATION NUMBER

INPLASY202070091.

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.

Evaluation of Wearable Sensor Devices in Parkinson's Disease: A Review of Current Status and Future Prospects

Parkinson's disease (PD) decreases the quality of life of the affected individuals. The incidence of PD is expected to increase given the growing aging population. Motor symptoms associated with PD render the patients unable to self-care and function properly. Given that several drugs have been developed to control motor symptoms, highly sensitive scales for clinical evaluation of drug efficacy are needed. Among such scales, the objective and continuous evaluation of wearable devices is increasingly utilized by clinicians and patients. Several electronic technologies have revolutionized the clinical monitoring of PD development, especially its motor symptoms. Here, we review and discuss the recent advances in the development of wearable devices for bradykinesia, tremor, gait, and myotonia. Our aim is to capture the experiences of patients and clinicians, as well as expand our understanding on the application of wearable technology. In so-doing, we lay the foundation for further research into the use of wearable technology in the management of PD.

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model

Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aβ42), Aβ40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aβ40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders.

Prevalence of Parkinson Disease in Italy: a systematic review and meta-analysis

Introduction:

Parkinson’s disease (PD) is a common disease of unknown etiology. Even though accurate information on the epidemiology of PD is critical for defining appropriate health policies, epidemiological data on Parkinson’s disease (PD) in Italy are often defined as scant or conflicting. Our study attempted to provide an overview on the prevalence of (PD) by means of a systematic review and metanalysis of existing data.

Material and methods:

We searched into two different databases (PubMed and EMBASE), focusing on studies reporting the prevalence of PD in Italy. Data were extracted using a standardized assessment form, and results of such analyses were systematically reported, summarized and compared.

Results:

A total of 16 studies were eventually included in the analyses, with a prevalence rate of 193.7/100,000. Available reports were heterogeneous both in design and in eventual figures, and also prevalence estimates were affected by substantial heterogeneity. Interestingly, prevalence rates ranged from 37.8/100,000 inhabitants in subjects aged 0 to 64 years, to 578.7 in age group 65 to 75 years, and 1235.7 in age group 75 years or older. PD was significantly associated with male sex, but only in older age groups (i.e. Odds Ratio, OR 1.37 95%CI 1.22-1.53, and OR 1.31, 95%CI 1.21-1.42 for age groups 65-74 years and 75 years or more, respectively).

Discussion and conclusion:

While the observed variations in prevalence rates may result from environmental or genetic factors, differences in methodologies for case ascertainment and diagnostic criteria may have significantly affected our estimates. As a consequence, the comparability of existing studies is limited.

Postural instability and neuropsychiatric disturbance in the overlapping phenotype of essential tremor and Parkinson's Disease

OBJECTIVES

The aim of this study was to perform a comparative analysis of postural and neuropsychiatric features in patients with essential tremor (ET), various Parkinson's disease (PD) phenotypes, and the phenotype of combined ET and PD (ET-PD).

METHODS

169 PD patients with early (1.0-2.0) and 111 PD patients with advanced (2.5-3.0) stages based on the Hoehn and Yahr scale, 55 patients with ET and 26 patients with ET-PD were enrolled in the study. Motor and non-motor symptoms of patients with PD, ET and ET-PD were studied using standardized scales and stabilometry.

RESULTS

Patients with ETPD had milder manifestations of parkinsonism compared to PD patients at the same stages of the disease. Patients in the advanced PD group showed more pronounced posture and balance impairment compared to all other groups assessed by standardized walking and balance scales. No difference using scales for postural assessment was found between ETPD, ET and early stage PD. Using stabilometry, we discovered that indexes of stabilometric parameters were lower in ETPD patients compared to ET and advanced PD, although no difference between ETPD and early PD was found. PD patients in early stages had lower results in most of the indexes compared to ET.

CONCLUSION

Here, we conducted for the first time a stabilometric examination of ET-PD patients, which could be helpful in differential diagnosis. This analysis helps expand understanding of the clinical manifestations of PD, ET and ET-PD.

Immune profiling of plasma-derived extracellular vesicles identifies Parkinson disease

Objective

To develop a diagnostic model based on plasma-derived extracellular vesicle (EV) subpopulations in Parkinson disease (PD) and atypical parkinsonism (AP), we applied an innovative flow cytometric multiplex bead-based platform.

Methods

Plasma-derived EVs were isolated from PD, matched healthy controls, multiple system atrophy (MSA), and AP with tauopathies (AP-Tau). The expression levels of 37 EV surface markers were measured by flow cytometry and correlated with clinical scales. A diagnostic model based on EV surface markers expression was built via supervised machine learning algorithms and validated in an external cohort.

Results

Distinctive pools of EV surface markers related to inflammatory and immune cells stratified patients according to the clinical diagnosis. PD and MSA displayed a greater pool of overexpressed immune markers, suggesting a different immune dysregulation in PD and MSA vs AP-Tau. The receiver operating characteristic curve analysis of a compound EV marker showed optimal diagnostic performance for PD (area under the curve [AUC] 0.908; sensitivity 96.3%, specificity 78.9%) and MSA (AUC 0.974; sensitivity 100%, specificity 94.7%) and good accuracy for AP-Tau (AUC 0.718; sensitivity 77.8%, specificity 89.5%). A diagnostic model based on EV marker expression correctly classified 88.9% of patients with reliable diagnostic performance after internal and external validations.

Conclusions

Immune profiling of plasmatic EVs represents a crucial step toward the identification of biomarkers of disease for PD and AP.

Cellular senescence in aging and age-related diseases: Implications for neurodegenerative diseases

Aging is the major predictor for developing multiple neurodegenerative diseases, including Alzheimer's disease (AD) other dementias, and Parkinson's disease (PD). Senescent cells, which can drive aging phenotypes, accumulate at etiological sites of many age-related chronic diseases. These cells are resistant to apoptosis and can cause local and systemic dysfunction. Decreasing senescent cell abundance using senolytic drugs, agents that selectively target these cells, alleviates neurodegenerative diseases in preclinical models. In this review, we consider roles of senescent cells in neurodegenerative diseases and potential implications of senolytic agents as an innovative treatment.

Unsupervised clustering of dopamine transporter PET imaging discovers heterogeneity of parkinsonism

Parkinsonism has heterogeneous nature, showing distinctive patterns of disease progression and prognosis. We aimed to find clusters of parkinsonism based on ¹⁸F‐fluoropropyl‐carbomethoxyiodophenylnortropane (FP‐CIT) PET as a data‐driven approach to evaluate heterogenous dopaminergic neurodegeneration patterns. Two different cohorts of patients who received FP‐CIT PET were collected. A labeled cohort (n = 94) included patients with parkinsonism who underwent a clinical follow‐up of at least 3 years (mean 59.0 ± 14.6 months). An unlabeled cohort (n = 813) included all FP‐CIT PET data of a single‐center. All PET data were clustered by a dimension reduction method followed by hierarchical clustering. Four distinct clusters were defined according to the imaging patterns. When the diagnosis of the labeled cohort of 94 patients was compared with the corresponding cluster, parkinsonism patients were mostly included in two clusters, cluster “0” and “2.” Specifically, patients with progressive supranuclear palsy were significantly more included in cluster 0. The two distinct clusters showed significantly different clinical features. Furthermore, even in PD patients, two clusters showed a trend of different clinical features. We found distinctive clusters of parkinsonism based on FP‐CIT PET‐derived heterogeneous neurodegeneration patterns, which were associated with different clinical features. Our results support a biological underpinning for the heterogeneity of neurodegeneration in parkinsonism.

Robust Feature Engineering for Parkinson Disease Diagnosis: New Machine Learning Techniques

Background

Parkinson disease (PD) is a common neurodegenerative disorder that affects between 7 and 10 million people worldwide. No objective test for PD currently exists, and studies suggest misdiagnosis rates of up to 34%. Machine learning (ML) presents an opportunity to improve diagnosis; however, the size and nature of data sets make it difficult to generalize the performance of ML models to real-world applications.

Objective

This study aims to consolidate prior work and introduce new techniques in feature engineering and ML for diagnosis based on vowel phonation. Additional features and ML techniques were introduced, showing major performance improvements on the large mPower vocal phonation data set.

Methods

We used 1600 randomly selected /aa/ phonation samples from the entire data set to derive rules for filtering out faulty samples from the data set. The application of these rules, along with a joint age-gender balancing filter, results in a data set of 511 PD patients and 511 controls. We calculated features on a 1.5-second window of audio, beginning at the 1-second mark, for a support vector machine. This was evaluated with 10-fold cross-validation (CV), with stratification for balancing the number of patients and controls for each CV fold.

Results

We showed that the features used in prior literature do not perform well when extrapolated to the much larger mPower data set. Owing to the natural variation in speech, the separation of patients and controls is not as simple as previously believed. We presented significant performance improvements using additional novel features (with 88.6% certainty, derived from a Bayesian correlated t test) in separating patients and controls, with accuracy exceeding 58%.

Conclusions

The results are promising, showing the potential for ML in detecting symptoms imperceptible to a neurologist.

Association of inflammatory factors and aging in Parkinson's disease

BACKGROUND

Parkinson's disease as a common neurodegenerative disease, has been found to be related to inflammation. So we observed the characteristics of inflammatory indexes in patients with Parkinson's disease and investigated the relationship between inflammatory cytokines and clinical characteristics. Emerging data may reveal novel neuroinflammatory pathways and identify new targets for treatment of Parkinson's disease.

METHODS

We examined the inflammatory indexes in 183 patients and 89 healthy controls in association with clinical characteristics.

RESULTS

Patients had significantly higher levels of monocytes, neutrophils, high-sensitivity C-reactive protein, and monocyte-to-high-density lipoprotein ratios (p < 0.01) and lower levels of lymphocytes (p = 0.02) than the controls. There were no significant differences in age, leukocytes, high-density lipoprotein, or neutrophil-lymphocyte ratios between the two groups (p > 0.05). Multivariate logistic regression analysis of these indicators revealed that lymphocyte level was a protective factor (p = 0.025, OR=-0.679), while high-sensitivity C-reactive protein level was a risk factor (p = 0.000, OR=1.168) for Parkinson's disease. High-sensitivity C-reactive protein levels were higher in older Parkinson's disease patients.

CONCLUSION

High-sensitivity C-reactive protein is positively related to the risk of Parkinson's disease, especially in aging patients. High-sensitivity C-reactive protein is a potential biomarker for disease progression and treatment response for Parkinson's disease.

Lrrk2 modulation of Wnt signaling during zebrafish development

Mutations in leucine-rich repeat kinase 2 (lrrk2) are the most common genetic cause of Parkinson's disease. Difficulty in elucidating the pathogenic mechanisms resulting from disease-associated Lrrk2 variants stems from the complexity of Lrrk2 function and activities. Lrrk2 contains multiple protein-protein interacting domains, a GTPase domain, and a kinase domain. Lrrk2 is implicated in many cellular processes including vesicular trafficking, autophagy, cytoskeleton dynamics, and Wnt signaling. Here, we generated a zebrafish lrrk2 allelic series to study the requirements for Lrrk2 during development and to dissect the importance of its various domains. The alleles are predicted to encode proteins that either lack all functional domains (lrrk2^sbu304 ), the GTPase, and kinase domains (lrrk2^sbu71 ) or the kinase domain (lrrk2^sbu96 ). All three lrrk2 mutants are viable, morphologically normal, and display wild-type-like locomotion. Because Lrrk2 modulates Wnt signaling in some contexts, we assessed Wnt signaling in all three mutant lines. Analysis of Wnt signaling by studying the expression of target genes using whole mount RNA in situ hybridization and a transgenic Wnt reporter revealed wild-type domains of Wnt activity in each of the mutants. However, we found that Wnt pathway activation is attenuated in lrrk2^{sbu304/sbu304} , which lacks both scaffolding and catalytic domains, but not in the other alleles during late embryogenesis. This supports a model in which Lrrk2 scaffolding functions are key to a context-dependent role in promoting canonical Wnt signaling.

Local White Matter Fiber Clustering Differentiates Parkinson's Disease Diagnoses

Scans without evidence of dopaminergic deficit (SWEDD) patients are often misdiagnosed with Parkinson's disease (PD) but have normal dopamine transporter scans. We hypothesised that white matter tracts associated with motor and cognition functions may be affected differently by SWEDD and PD. Automatically annotated fibre clustering (AAFC) is a novel clustering method based on diffusion magnetic resonance imaging (dMRI) tractography that enables highly robust reconstruction of white matter tracts that are composed of corresponding clusters. This study aimed to investigate the white matter properties in the subdivisions of white matter tracts among SWEDD and PD groups. We applied AAFC to identify white matter tracts related to motion and cognition functions in the dataset consisting of SWEDD (n = 22), PD (n = 30) and normal control (NC) (n = 30). Then, we resampled 200 nodes along fibres of cluster, and the diffusion metric values corresponding to each node were calculated and used for comparison. Compared with NC, PD showed significant difference (p < 0.05) in two clusters in thalamo-frontal (TF), one cluster in thalamo-parietal (TP) and one cluster in thalamo-occipital (TO), whereas SWEDD presented no significant difference. Three clusters in cingulum bundle (CB) commonly exhibited significant differences in PD versus SWEDD and NC versus SWEDD. The support vector machine classifier achieved high accuracies in PD-NC, PD-SWEDD and NC-SWEDD classifications. This outcome validated these local white matter differences were useful to separate the three groups. These results suggest that PD exerts more significant effects on thalamo tracts than SWEDD, and unique microstructural changes occur in CB tract in SWEDD.

Progression of grey and white matter brain damage in Parkinson's disease: a critical review of structural MRI literature

The current review summarizes the current knowledge on longitudinal cortical and subcortical grey and white matter MRI findings assessed using T1-weighted and one-tensor diffusion-weighted MRI in Parkinson's disease (PD) patients. Results were reviewed according to disease duration, disease severity and cognitive impairment. The most consistent findings are those showing a progressive cortical atrophy accumulation in caudate, putamen, temporal/hippocampal, frontal and parietal areas in de novo PD cases and in the early/middle phase of the disease, with the achievement of a plateau in the later stage. Analyzing results according to the patient cognitive status, only a few studies used longitudinal MRI metrics to predict mild cognitive impairment or dementia conversion in PD patients, suggesting that atrophy of the hippocampus, fronto-temporal areas, caudate, thalamus and accumbens might play a role in this process. Stratifying patients according to disease severity, findings appear partially controversial, although showing a progressive atrophy of basal ganglia over 1 year of follow up and a widespread cortical thinning over 3-6 years in mild to moderate PD patients. Finally, microstructural damage of the main motor and associative WM tracts seems to be present, and rapidly progress, even in the early phase of PD. The utility of structural MRI metrics as biomarkers of PD progression and their role in improving the accuracy of disease progression prediction is still debated.

Peripheral neuropathy in Parkinson's disease

Peripheral neuropathy (PN) is a common neurological problem defined as a dysfunction of sensory, motor, and autonomic nerves. The presence of peripheral neuropathy has recently been noticed in Parkinson's disease (PD) This comorbidity is concerning as it increases the burden on patients whose motor functions are previously compromised. A comprehensive computer-based literature review utilizing multiple peer-reviewed databases (e.g., Embase, PsycINFO, CINAHL, etc.) was conducted. There is evidence for the utility of robust diagnostic criteria to distinguish between large fiber neuropathy (LFN) and small fiber neuropathy (SFN). Some studies have established links between prolonged L-DOPA exposure and prevalence with increased levels of homocysteine (HCY) and methylmalonic acid (MMA) as pathological underlying mechanisms. PN in PD patients with relatively truncated exposure to L-DOPA therapy may have underlying mutations in the Parkin and MHTFR gene or separate mitochondrial disorders. Vitamin B12 and cobalamin deficiencies have also been implicated as drivers of PN. Accumulation of phosphorylated α-synuclein is another central feature in PN and deems urgent exploration via large cohort studies. Importantly, these underlying mechanisms have been linked to peripheral denervation. This review delves into the potential treatments for PN targeting B12 deficiencies and the use of COMT inhibitors along with other novel approaches. Avenues of research with powerful randomized controlled and long-term cohort studies exploring genetic mechanisms and novel treatment pathways is urgently required to alleviate the burden of disease exerted by PN on PD.

Essential tremor vs idiopathic Parkinson disease: Utility of transcranial sonography

Substantia nigra (SN) hyperechogenicity measured by transcranial sonography (TCS) is a promising biomarker for Parkinson disease (PD). The aim of this study was to explore the diagnostic accuracy of SN hyperechogenicity (SN) for differentiating PD from essential tremor (ET). A total of 119 patients with PD, 106 ET patients and 112 healthy controls that underwent TCS from November 2016 to February 2019 were included in this single-center retrospective case-control study. Two reviewers who were blinded to clinical information independently measured the SN by TCS imaging. The diagnostic sensitivity, specificity, and accuracy of TCS imaging were evaluated between the PD and healthy controls and between patients with PD and ET. Interrater agreement was assessed with the Cohen κ statistic. TCS imaging of the SN allowed to differentiate between patients with PD and ET with a sensitivity (91.6% and 90.8%) and specificity (91.5% and 89.6%) for readers 1 and 2, respectively. Interobserver agreement was excellent (к = 0.87). In addition, measurement of the SN allowed to differentiate between patients with PD and healthy subjects with a sensitivity (91.6% and 90.8%) and specificity (88.4% and 89.3%) for readers 1 and 2, respectively. Interobserver agreement was excellent (к = 0.91). Measurement of SN on TCS images could be a useful tool to distinguishing patients with PD from those with ET.

Patients' views on the ethical challenges of early Parkinson disease detection

Objective

To evaluate the point of view of patients with Parkinson disease (PD) on early detection and risk disclosure in the prodromal phase of PD and to derive recommendations for an ethical framework for the recruitment of prodromal PD cohorts.

Methods

A standardized questionnaire to evaluate the patients' perception on early diagnosis in PD was designed by an interdisciplinary study group. After testing in a preliminary feasibility study (n = 20), the survey was performed retrospectively with patients from our clinic.

Results

A total of 101 patients with PD answered the questions. The majority of patients reported that time from onset of motor symptoms to diagnosis was burdensome, including false diagnoses and many consultations of various medical specialists. However, most of the patients evaluated early risk disclosure with skepticism. Freedom of choice and the potential of changes in lifestyle were rated as important.

Conclusion

Although patients with PD reported the time to diagnosis retrospectively as burdensome, the majority was skeptical regarding early disclosure of risk, especially with regard to the lack of pharmacologic options. Circumstances under which early detection and disclosure would have been approved by the majority of patients were (1) advice on lifestyle changes (exercise, nutrition) as potentially disease course–modifying therapy; (2) the establishment of an early diagnosis “culture,” including early clarification of the patients' wish to know; and (3) regular support and follow-up of individuals after risk disclosure.

Usability Issues of Clinical and Research Applications of Virtual Reality in Older People: A Systematic Review

Aging is a condition that may be characterized by a decline in physical, sensory, and mental capacities, while increased morbidity and multimorbidity may be associated with disability. A wide range of clinical conditions (e.g., frailty, mild cognitive impairment, metabolic syndrome) and age-related diseases (e.g., Alzheimer's and Parkinson's disease, cancer, sarcopenia, cardiovascular and respiratory diseases) affect older people. Virtual reality (VR) is a novel and promising tool for assessment and rehabilitation in older people. Usability is a crucial factor that must be considered when designing virtual systems for medicine. We conducted a systematic review with Preferred Reporting Items for Systematic reviews and Meta-analysis (PRISMA) guidelines concerning the usability of VR clinical systems in aging and provided suggestions to structure usability piloting. Findings show that different populations of older people have been recruited to mainly assess usability of non-immersive VR, with particular attention paid to motor/physical rehabilitation. Mixed approach (qualitative and quantitative tools together) is the preferred methodology; technology acceptance models are the most applied theoretical frameworks, however senior adapted models are the best within this context. Despite minor interaction issues and bugs, virtual systems are rated as usable and feasible. We encourage usability and user experience pilot studies to ameliorate interaction and improve acceptance and use of VR clinical applications in older people with the aid of suggestions (VR-USOP) provided by our analysis.

Self-reported rates of impulsivity in Parkinson's Disease

OBJECTIVE

Impulsive decision-making is characterized by actions taken without considering consequences. Patients with Parkinson's disease (PD) who receive dopaminergic treatment, especially dopamine agonists, are at risk of developing impulsive-compulsive behaviors (ICBs). We assessed impulse-related changes across a large heterogeneous PD population using the Barratt impulsivity scale (BIS-11) by evaluating BIS-11 first- and second-order factors.

METHODS

We assessed a total of 204 subjects: 93 healthy controls (HCs), and 68 ICB- and 43 ICB + PD patients who completed the BIS-11. Using a general linear model and a least absolute shrinkage and selection operation regression, we compared BIS-11 scores between the HC, ICB- PD, and ICB + PD groups.

RESULTS

Patients with PD rated themselves as more impulsive than HCs in the BIS-11 total score, second-order attention domain, and first-order attention and self-control domains. ICB + patients recorded higher total scores as well as higher scores in the second-order non-planning domain and in self-control and cognitive complexity than ICB- patients.

INTERPRETATION

These results indicate that the patients with PD show particular problems with attentional control, whereas ICB + patients show a distinct problem in cognitive control and complexity. Additionally, it appears that all patients with PD are more impulsive than their age- and sex-matched healthy peers. Increased impulsivity may be a result of the disease course, or attributed to dopaminergic medication use, but these results emphasize the importance of the cognitive components of impulsivity in patients with PD.

Neuropathological correlates of parkinsonian disorders in a large Dutch autopsy series

The clinical diagnosis in patients with parkinsonian disorders can be challenging, and a definite diagnosis requires neuropathological confirmation. The aim of this study was to examine whether a clinical diagnosis of Parkinson’s disease (PD) and atypical parkinsonian disorders predict the presence of Lewy pathology (LP) and concomitant neuropathological lesions.

We included 293 donors with a history of parkinsonism without dementia at disease onset, collected by the Netherlands Brain Bank (NBB) from 1989 to 2015. We retrospectively categorized donors according the International Parkinson and Movement Disorder Society clinical diagnostic criteria for PD (MDS-PD criteria) as ‘not PD’, ‘probable PD’ or ‘established PD’. We compared the final clinical diagnosis to presence of neuropathological lesions as defined by BrainNet Europe and National Institute on Aging – Alzheimer's Association guidelines.

LP was present in 150 out of 176 donors (85%) with a clinical diagnosis of PD, in 8 out of 101 donors (8%) with atypical parkinsonian disorders and in 4 out of 16 donors (25%) without a definite clinical diagnosis. Independent from age at death, stages of amyloid-β, but not neurofibrillary tau or neuritic plaques, were higher in donors with LP compared to other types of pathology (p = 0.009). The MDS-PD criteria at a certainty level of ‘probable PD’ predicted presence of LP with a diagnostic accuracy of 89.3%. Among donors with LP, ‘established PD’ donors showed similar Braak α-synuclein stages and stages of amyloid-β, neurofibrillary tau and neuritic plaques compared to ‘not PD’ or ‘probable PD’ donors.

In conclusion, both a clinical diagnosis of PD as well as MDS-PD criteria accurately predicted presence of LP in NBB donors. LP was associated with more widespread amyloid-β pathology, suggesting a link between amyloid-β accumulation and LP formation.

GBA1 mutations: Prospects for exosomal biomarkers in α-synuclein pathologies

The discovery that patients with Gaucher Disease (GD), a rare lysosomal storage disorder, were developing symptoms similar to Parkinson's disease (PD) led to investigation of the relationship between the two seemingly unrelated pathologies. GD, an autosomal recessive disorder, is the result of a biallelic mutation in the gene GBA1, which encodes for the enzyme glucocerebrosidase (GCase). Since the observation of its relation to PD, GBA1 mutations have become recognized as the most common genetic risk factor for development of synucleinopathies such as PD and dementia with Lewy bodies. Although the exact mechanism by which GBA1 mutations promote PD is unknown, current understanding suggests that impaired GCase inhibits lysosomal activity and decreases the overall ability of the cell to degrade proteins, specifically the neuronal protein α-synuclein. Decreased elimination of α-synuclein can lead to its abnormal accumulation and aggregation, an important component of PD development. Further understanding of how decreased GCase activity increases risk for α-synuclein pathology can assist with the development of clinical biomarkers for early detection of synucleinopathies, as well as promote novel treatments tailored for people with a GBA1 mutation. Historically, α-synuclein has not been a reliable biomarker for PD. However, recent research on α-synuclein content within exosomes, which are small vesicles released by cells that carry specific cellular cargo, has yielded encouraging results. Moreover, decreased GCase activity has been shown to influence exosomal contents. Exosomes have emerged as a promising new avenue for the identification of novel biomarkers and therapeutic targets aimed at improving neuronal GCase function and limiting the development of synucleinopathies.

Discriminating α-synuclein strains in Parkinson's disease and multiple system atrophy

Synucleinopathies are neurodegenerative diseases that are associated with the misfolding and aggregation of α-synuclein, including Parkinson's disease, dementia with Lewy bodies and multiple system atrophy1. Clinically, it is challenging to differentiate Parkinson's disease and multiple system atrophy, especially at the early stages of disease2. Aggregates of α-synuclein in distinct synucleinopathies have been proposed to represent different conformational strains of α-synuclein that can self-propagate and spread from cell to cell3-6. Protein misfolding cyclic amplification (PMCA) is a technique that has previously been used to detect α-synuclein aggregates in samples of cerebrospinal fluid with high sensitivity and specificity7,8. Here we show that the α-synuclein-PMCA assay can discriminate between samples of cerebrospinal fluid from patients diagnosed with Parkinson's disease and samples from patients with multiple system atrophy, with an overall sensitivity of 95.4%. We used a combination of biochemical, biophysical and biological methods to analyse the product of α-synuclein-PMCA, and found that the characteristics of the α-synuclein aggregates in the cerebrospinal fluid could be used to readily distinguish between Parkinson's disease and multiple system atrophy. We also found that the properties of aggregates that were amplified from the cerebrospinal fluid were similar to those of aggregates that were amplified from the brain. These findings suggest that α-synuclein aggregates that are associated with Parkinson's disease and multiple system atrophy correspond to different conformational strains of α-synuclein, which can be amplified and detected by α-synuclein-PMCA. Our results may help to improve our understanding of the mechanism of α-synuclein misfolding and the structures of the aggregates that are implicated in different synucleinopathies, and may also enable the development of a biochemical assay to discriminate between Parkinson's disease and multiple system atrophy.

Parkinson patients without tremor show changed patterns of mechanical muscle oscillations during a specific bilateral motor task compared to controls

The pathophysiology of Parkinson's disease (PD) is still not understood. There are investigations which show a changed oscillatory behaviour of brain circuits or changes in variability of, e.g., gait parameters in PD. The aim of this study was to investigate whether or not the motor output differs between PD patients and healthy controls. Thereby, patients without tremor are investigated in the medication off state performing a special bilateral isometric motor task. The force and accelerations (ACC) were recorded as well as the Mechanomyography (MMG) of the biceps brachii, the brachioradialis and of the pectoralis major muscles using piezoelectric-sensors during the bilateral motor task at 60% of the maximal isometric contraction. The frequency, a specific power ratio, the amplitude variation and the slope of amplitudes were analysed. The results indicate that the oscillatory behaviour of motor output in PD patients without tremor deviates from controls: thereby, the 95%-confidence-intervals of power ratio and of amplitude variation of all signals are disjoint between PD and controls and show significant differences in group comparisons (power ratio: p = 0.000-0.004, r = 0.441-0.579; amplitude variation: p = 0.000-0.001, r = 0.37-0.67). The mean frequency shows a significant difference for ACC (p = 0.009, r = 0.43), but not for MMG. It remains open, whether this muscular output reflects changes of brain circuits and whether the results are reproducible and specific for PD.

Rate of change in maximum 18F-FDOPA PET uptake and non-enhancing tumor volume predict malignant transformation and overall survival in low-grade gliomas

PURPOSE

To examine whether the rate of change in maximum ¹⁸F-FDOPA PET uptake and the rate of change in non-enhancing tumor volume could predict malignant transformation and residual overall survival (OS) in low grade glioma (LGG) patients who received serial ¹⁸F-FDOPA PET and MRI scans.

METHODS

27 LGG patients with ≥ 2 ¹⁸F-FDOPA PET and MRI scans between 2003 and 2016 were included. The rate of change in FLAIR volume (uL/day) and maximum normalized ¹⁸F-FDOPA specific uptake value (nSUV_max/month), were compared between histological and molecular subtypes. General linear models (GLMs) were used to integrate clinical information with MR-PET measurements to predict malignant transformation. Cox univariate and multivariable regression analyses were performed to identify imaging and clinical risk factors related to OS.

RESULTS

A GLM using patient age, treatment, the rate of change in FLAIR and ¹⁸F-FDOPA nSUV_max could predict malignant transformation with > 67% sensitivity and specificity (AUC = 0.7556, P = 0.0248). A significant association was observed between OS and continuous rates of change in PET uptake (HR = 1.0212, P = 0.0034). Cox multivariable analysis confirmed that continuous measures of the rate of change in PET uptake was an independent predictor of OS (HR = 1.0242, P = 0.0033); however, stratification of patients based on increasing or decreasing rate of change in FLAIR (HR = 2.220, P = 0.025), PET uptake (HR = 2.148, P = 0.0311), or both FLAIR and PET (HR = 2.354, P = 0.0135) predicted OS.

CONCLUSIONS

The change in maximum normalized ¹⁸F-FDOPA PET uptake, with or without clinical information and rate of change in tumor volume, may be useful for predicting the risk of malignant transformation and estimating residual survival in patients with LGG.

Improvement of classification performance of Parkinson's disease using shape features for machine learning on dopamine transporter single photon emission computed tomography

OBJECTIVE

To assess the classification performance between Parkinson's disease (PD) and normal control (NC) when semi-quantitative indicators and shape features obtained on dopamine transporter (DAT) single photon emission computed tomography (SPECT) are combined as a feature of machine learning (ML).

METHODS

A total of 100 cases of both PD and normal control (NC) from the Parkinson's Progression Markers Initiative database were evaluated. A summed image was generated and regions of interests were set to the left and right striata. Area, equivalent diameter, major axis length, minor axis length, perimeter and circularity were calculated as shape features. Striatum binding ratios (SBRputamen and SBRcaudate) were used as comparison features. The classification performance of the PD and NC groups according to receiver operating characteristic analysis of the shape features was compared in terms of SBRs. Furthermore, we compared the classification performance of ML when shape features or SBRs were used alone and in combination.

RESULTS

The shape features (except minor axis length) and SBRs indicated significant differences between the NC and PD groups (p < 0.05). The top five areas under the curves (AUC) were as follows: circularity (0.972), SBRputamen (0.972), major axis length (0.945), SBRcaudate (0.928) and perimeter (0.896). When classification was done using ML, AUC was as follows: circularity and SBRs (0.995), circularity alone (0.990), and SBRs (0.973). The classification performance was significantly improved by combining SBRs and circularity than by SBRs alone (p = 0.018).

CONCLUSION

We found that the circularity obtained from DAT-SPECT images could help in distinguishing NC and PD. Furthermore, the classification performance of ML was significantly improved using circularity in SBRs together.

Inflammation biomarker discovery in Parkinson's disease and atypical parkinsonisms

Background

Parkinson’s disease (PD) and atypical parkinsonisms (APD) have overlapping symptoms challenging an early diagnosis. Diagnostic accuracy is important because PD and APD have different prognosis and response to treatment. We aimed to identify diagnostic inflammatory biomarkers of PD and APD in cerebrospinal fluid (CSF) using the multiplex proximity extension assay (PEA) technology and to study possible correlations of biomarkers with disease progression.

Methods

CSF from a longitudinal cohort study consisting of PD and APD patients (PD, n = 44; multiple system atrophy (MSA), n = 14; vascular parkinsonism (VaP), n = 9; and PD with VaP, n = 7) and controls (n = 25) were analyzed.

Results

Concentrations of CCL28 were elevated in PD compared to controls (p = 0.0001). Five other biomarkers differentiated both MSA and PD from controls (p < 0.05) and 10 biomarkers differentiated MSA from controls, of which two proteins, i.e. beta nerve growth factor (β-NGF) and Delta and Notch like epidermal growth factor-related receptor (DNER), were also present at lower levels in MSA compared to PD (both p = 0.032). Two biomarkers (MCP-1 and MMP-10) positively correlated with PD progression (rho > 0.650; p < 0.01).

Conclusions

PEA technique identified potential new CSF biomarkers to help to predict the prognosis of PD. Also, we identified new candidate biomarkers to distinguish MSA from PD.

Nanotechnology-based Targeting of Neurodegenerative Disorders: A Promising Tool for Efficient Delivery of Neuromedicines

Traditional drug delivery approaches remained ineffective in offering better treatment to various neurodegenerative disorders (NDs). In this context, diverse types of nanocarriers have shown their great potential to cross the blood-brain barrier (BBB) and have emerged as a prominent carrier system in drug delivery. Moreover, nanotechnology-based methods usually involve numerous nanosized carrier platforms, which potentiate the effect of the therapeutic agents in the therapy of NDs especially in diagnosis and drug delivery with negligible side effects. In addition, nanotechnology-based techniques have offered several strategies to cross BBB to intensify the bioavailability of drug moieties in the brain. In the last few years, diverse kinds of nanoparticles (NPs) have been developed by incorporating various biocompatible components (e.g., polysaccharide-based NPs, polymeric NPs, selenium NPs, AuNPs, protein-based NPs, gadolinium NPs, etc.), that showed great therapeutic benefits against NDs. Eventually, this review provides deep insights to explore recent applications of some innovative nanocarriers enclosing active molecules for the efficient treatment of NDs.

Neurofilament Light Chain as a Biomarker, and Correlation with Magnetic Resonance Imaging in Diagnosis of CNS-Related Disorders

The search for diagnostic and prognostic biomarkers for neurodegenerative conditions is of high importance, since these disorders may present difficulties in differential diagnosis. Biomarkers with high sensitivity and specificity are required. Neurofilament light chain (NfL) is a unique biomarker related to axonal damage and neural cell death, which is elevated in a number of neurological disorders, and can be detected in cerebrospinal fluid (CSF), as well as blood, serum, or plasma samples. Although the NfL concentration in CSF is higher than that in blood, blood measurement may be easier in practice due to its lesser invasiveness, reproducibility, and convenience. Many studies have investigated NfL in both CSF and serum/plasma as a potential biomarker of neurodegenerative disorders. Neuroimaging biomarkers can also potentially improve detection of CNS-related disorders at an early stage. Magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) are sensitive techniques to visualize neuroaxonal loss. Therefore, investigating the combination of NfL levels with indices extracted from MRI and DTI scans could potentially improve diagnosis of CNS-related disorders. This review summarizes the evidence for NfL being a reliable biomarker in the early detection and disease management in several CNS-related disorders. Moreover, we highlight the correlation between MRI and NfL and ask whether they can be combined.

Technology-Based Objective Measures Detect Subclinical Axial Signs in Untreated, de novo Parkinson's Disease

BACKGROUND

Technology-based objective measures (TOMs) recently gained relevance to support clinicians in the assessment of motor function in Parkinson's disease (PD), although limited data are available in the early phases.

OBJECTIVE

To assess motor performances of a population of newly diagnosed, drug free PD patients using wearable inertial sensors and to compare them to healthy controls (HC) and differentiate different PD subtypes [tremor dominant (TD), postural instability gait disability (PIGD), and mixed phenotype (MP)].

METHODS

We enrolled 65 subjects, 36 newly diagnosed, drug-free PD patients and 29 HCs. PD patients were clinically defined as tremor dominant, postural instability-gait difficulties or mixed phenotype. All 65 subjects performed seven MDS-UPDRS III motor tasks wearing inertial sensors: rest tremor, postural tremor, rapid alternating hand movement, foot tapping, heel-to-toe tapping, Timed-Up-and-Go test (TUG) and pull test. The most relevant motor tasks were found combining ReliefF ranking and Kruskal- Wallis feature-selection methods. We used these features, linked to the relevant motor tasks, to highlight differences between PD from HC, by means of Support Vector Machine (SVM) classifier. Furthermore, we adopted SVM to support the relevance of each motor task on the classification accuracy, excluding one task at time.

RESULTS

Motion analysis distinguished PD from HC with an accuracy as high as 97%, based on SVM performed with measured features from tremor and bradykinesia items, pull test and TUG. Heel-to-toe test was the most relevant, followed by TUG and Pull Test.

CONCLUSIONS

In this pilot study, we demonstrate that the SVM algorithm successfully distinguishes de novo drug-free PD patients from HC. Surprisingly, pull test and TUG tests provided relevant features for obtaining high SVM classification accuracy, differing from the report of the experienced examiner. The use of TOMs may improve diagnostic accuracy for these patients.

Immunotherapy Targeting Neurodegenerative Proteinopathies: α-Synucleinopathies and Tauopathies

α-Synuclein and tau deposition in the central nervous system is responsible for various parkinsonian syndromes, including Parkinson’s disease, multiple system atrophy, dementia with Lewy bodies, progressive supranuclear palsy and corticobasal degeneration. Emerging evidence has suggested that pathologic α-synuclein and tau are transmitted from cell to cell and further accelerate the aggregation of pathologic proteins in neighboring cells. Furthermore, extracellular pathologic proteins have also been reported to provoke inflammatory responses that lead to neurodegeneration. Therefore, immunotherapies targeting extracellular α-synuclein and tau have been proposed as potential disease-modifying strategies. In this review, we summarize completed phase I trials and ongoing phase II trials of immunotherapies against α-synuclein and tau and further discuss concerns and hurdles to overcome in the future.

Neuroimaging in Vascular Parkinsonism

PURPOSE OF REVIEW

Being a disease with heterogeneous presentations and unclear consensus on its diagnostic criteria, it is difficult to differentiate vascular parkinsonism (VaP) from other neurodegenerative parkinsonism variants. Ongoing research on structural and functional neuroimaging targeting dopaminergic pathway provides us more insight into the pathophysiology of VaP to improve diagnostic accuracy. The aim of this article is to review how the emerging imaging modalities help the diagnostic process and treatment decision in VaP.

RECENT FINDINGS

Dopamine transporter imaging is a promising tool in differentiating presynaptic parkinsonism and VaP. It also predicts the levodopa responders in VaP. Advanced MRI techniques including volumetry, diffusion tensor imaging and sequences visualising substantia nigra are under development, and they are complementary to each other in detecting structural and functional changes in VaP, which is crucial to ensure the quality of future therapeutic trials for VaP. Dopamine transporter imaging is recommended to patients with suspected VaP. Multimodal MRI in VaP would be an important area to be investigated in the near future.

'Atypical' Parkinson's disease - sporadic

Parkinson's disease still is a clinical diagnosis. Also, the MDS Clinical Diagnostic Criteria for Parkinson's disease published in 2015 are based on clinical characteristics and were designed codifying the diagnostic process of an expert. The purpose was to support less experienced neurologists to achieve the diagnostic procedure up to the level of an expert. The criteria include both negative and positive properties. However, some features exclude patients with typical Parkinson's disease mainly during their early or late stages. These includes symptoms such as the absence of the combination of typical motor symptoms, the insufficient response to dopaminergic treatment, autonomic dysfunction, dystonia, postural instability or cognitive impairment. This chapter discusses those "atypical" symptom constellations that complicate the differential diagnosis of PD versus atypical parkinsonism and illustrates additional considerations that might be helpful to achieve a correct diagnosis.

Computer mouse use captures ataxia and parkinsonism, enabling accurate measurement and detection

Background

Objective assessments of movement impairment are needed to support clinical trials and facilitate diagnosis. The objective of the current study was to determine if a rapid web‐based computer mouse test (Hevelius) could detect and accurately measure ataxia and parkinsonism.

Methods

Ninety‐five ataxia, 46 parkinsonism, and 29 control participants and 229,017 online participants completed Hevelius. We trained machine‐learning models on age‐normalized Hevelius features to (1) measure severity and disease progression and (2) distinguish phenotypes from controls and from each other.

Results

Regression model estimates correlated strongly with clinical scores (from r = 0.66 for UPDRS dominant arm total to r = 0.83 for the Brief Ataxia Rating Scale). A disease change model identified ataxia progression with high sensitivity. Classification models distinguished ataxia or parkinsonism from healthy controls with high sensitivity (≥0.91) and specificity (≥0.90).

Conclusions

Hevelius produces a granular and accurate motor assessment in a few minutes of mouse use and may be useful as an outcome measure and screening tool. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson's Disease

The increment of the prevalence of neurological diseases due to the trend in population aging demands for new strategies in disease management. In Parkinson's disease (PD), these strategies should aim at improving diagnosis accuracy and frequency of the clinical follow-up by means of decentralized cost-effective solutions. In this context, a system suitable for the remote monitoring of PD subjects is presented. It consists of the integration of two approaches investigated in our previous works, each one appropriate for the movement analysis of specific parts of the body: low-cost optical devices for the upper limbs and wearable sensors for the lower ones. The system performs the automated assessments of six motor tasks of the unified Parkinson's disease rating scale, and it is equipped with a gesture-based human machine interface designed to facilitate the user interaction and the system management. The usability of the system has been evaluated by means of standard questionnaires, and the accuracy of the automated assessment has been verified experimentally. The results demonstrate that the proposed solution represents a substantial improvement in PD assessment respect to the former two approaches treated separately, and a new example of an accurate, feasible and cost-effective mean for the decentralized management of PD.

Silhouette-Length-Scaled Gait Parameters for Motor Functional Analysis in Mice and Rats

Gait analysis of transgenic mice and rats modeling human diseases often suffers from the condition that those models exhibit genotype-driven differences in body size, weight, and length. Thus, we hypothesized that scaling by the silhouette length improves the reliability of gait analysis allowing normalization for individual body size differences. Here, we computed video-derived silhouette length and area parameters from a standard markerless gait analysis system using image-processing techniques. By using length- and area-derived data along with body weight and age, we systematically scaled individual gait parameters. We compared these different scaling approaches and report here that normalization for silhouette length improves the validity and reliability of gait analysis in general. The application of this silhouette length scaling to transgenic Huntington disease mice and Parkinson´s disease rats identifies the remaining differences reflecting more reliable, body length-independent motor functional differences. Overall, this emphasizes the need for silhouette-length-based intra-assay scaling as an improved standard approach in rodent gait analysis.

Autophagic- and Lysosomal-Related Biomarkers for Parkinson's Disease: Lights and Shadows

Parkinson’s disease (PD) is a neurodegenerative disorder that currently affects 1% of the population over the age of 60 years, for which no disease-modifying treatments exist. This lack of effective treatments is related to the advanced stage of neurodegeneration existing at the time of diagnosis. Thus, the identification of early stage biomarkers is crucial. Biomarker discovery is often guided by the underlying molecular mechanisms leading to the pathology. One of the central pathways deregulated during PD, supported both by genetic and functional studies, is the autophagy-lysosomal pathway. Hence, this review presents different studies on the expression and activity of autophagic and lysosomal proteins, and their functional consequences, performed in peripheral human biospecimens. Although most biomarkers are inconsistent between studies, some of them, namely HSC70 levels in sporadic PD patients, and cathepsin D levels and glucocerebrosidase activity in PD patients carrying GBA mutations, seem to be consistent. Hence, evidence exists that the impairment of the autophagy-lysosomal pathway underlying PD pathophysiology can be detected in peripheral biosamples and further tested as potential biomarkers. However, longitudinal, stratified, and standardized analyses are needed to confirm their clinical validity and utility.

The early diagnosis of Parkinson's disease through combined biomarkers

OBJECTIVE

This study primarily aims to explore the value of combining the measurement of plasma α-synuclein oligomer levels with enhanced T2 star-weighted angiography (ESWAN) in the early diagnosis of Parkinson's disease.

METHODS

Sixty patients with early Parkinson's disease and 30 normal adults, with similar ages and genders, were enrolled in the study. Their levels of plasma α-synuclein oligomers were measured, and ESWAN was performed. The amplitudes, phases and R2* values of the head, body and tail of the ipsilateral and contralateral substantia nigra pars compacta (SNc) were measured, at the side of the limb with severe symptoms or early symptoms. The receiver operating characteristic (ROC) curve was used to explore the value of these indexes in the early diagnosis of Parkinson's disease.

RESULTS

The plasma level of α-synuclein oligomer was significantly higher in the experimental group than in the control group (P < 0.05). The amplitude values of the head and tail of contralateral SNcs were significantly lower in the experimental group than in the control group (P < 0.05). In the single-index assessment, the serum α-synuclein oligomer had the highest specificity (70%), while the sensitivity of the amplitude of the head and tail of the contralateral SNc was 75% and 80%, respectively. The area under the curve, for the combination of these three indicators, was 0.827, diagnostic efficiency was particularly high, and sensitivity and specificity both reached 80%.

CONCLUSION

The combined detection of plasma α-synuclein oligomer and amplitude of the head and tail of the SNc has high diagnostic specificity and sensitivity.