The diagnosis of Parkinson's disease

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.

Electrochemical biosensors for the detection and study of α-synuclein related to Parkinson's disease - A review

Parkinson's disease (PD) is a long-term degenerative disorder that affects predominately dopaminergic neurons in the substantia nigra, which mainly control movement. Alpha-synuclein (α-syn) is a major constituent of Lewy bodies that are reported to be the most important toxic species in the brain of PD patients. In this critical review, we highlight novel electrochemical biosensors that have been recently developed utilizing aptamers and antibodies in connection with various nanomaterials to study biomarkers related to PD such as α-syn. We also review several research articles that have utilized electrochemical biosensors to study the interaction of α-syn with biometals as well as small molecules such as clioquinol, (-)-epigallocatechin-3-gallate (EGCG) and baicalein. Due to the significant advances in nanomaterials in the past decade, electrochemical biosensors capable of detecting multiple biomarkers in clinically relevant samples in real-time have been achieved. This may facilitate the path towards commercialization of electrochemical biosensors for clinical applications and high-throughput screening of small molecules for structure-activity relationship (SAR) studies.

Vibrational Spectroscopy Fingerprinting in Medicine: from Molecular to Clinical Practice

In the last two decades, Fourier Transform Infrared (FTIR) and Raman spectroscopies turn out to be valuable tools, capable of providing fingerprint-type information on the composition and structural conformation of specific molecular species. Vibrational spectroscopy's multiple features, namely highly sensitive to changes at the molecular level, noninvasive, nondestructive, reagent-free, and waste-free analysis, illustrate the potential in biomedical field. In light of this, the current work features recent data and major trends in spectroscopic analyses going from in vivo measurements up to ex vivo extracted and processed materials. The ability to offer insights into the structural variations underpinning pathogenesis of diseases could provide a platform for disease diagnosis and therapy effectiveness evaluation as a future standard clinical tool.

Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI

Purpose

This study aimed to evaluate the accuracy and diagnostic test performance of the U-net-based segmentation method in neuromelanin magnetic resonance imaging (NM-MRI) compared to the established manual segmentation method for Parkinson’s disease (PD) diagnosis.

Methods

NM-MRI datasets from two different 3T-scanners were used: a “principal dataset” with 122 participants and an “external validation dataset” with 24 participants, including 62 and 12 PD patients, respectively. Two radiologists performed SNpc manual segmentation. Inter-reader precision was determined using Dice coefficients. The U-net was trained with manual segmentation as ground truth and Dice coefficients used to measure accuracy. Training and validation steps were performed on the principal dataset using a 4-fold cross-validation method. We tested the U-net on the external validation dataset. SNpc hyperintense areas were estimated from U-net and manual segmentation masks, replicating a previously validated thresholding method, and their diagnostic test performances for PD determined.

Results

For SNpc segmentation, U-net accuracy was comparable to inter-reader precision in the principal dataset (Dice coefficient: U-net, 0.83 ± 0.04; inter-reader, 0.83 ± 0.04), but lower in external validation dataset (Dice coefficient: U-net, 079 ± 0.04; inter-reader, 0.85 ± 0.03). Diagnostic test performances for PD were comparable between U-net and manual segmentation methods in both principal (area under the receiver operating characteristic curve: U-net, 0.950; manual, 0.948) and external (U-net, 0.944; manual, 0.931) datasets.

Conclusion

U-net segmentation provided relatively high accuracy in the evaluation of the SNpc in NM-MRI and yielded diagnostic performance comparable to that of the established manual method.

Electronic supplementary material

The online version of this article (10.1007/s00234-019-02279-w) contains supplementary material, which is available to authorized users.

Lipid and Lipid Raft Alteration in Aging and Neurodegenerative Diseases: A Window for the Development of New Biomarkers

Lipids in the brain are major components playing structural functions as well as physiological roles in nerve cells, such as neural communication, neurogenesis, synaptic transmission, signal transduction, membrane compartmentalization, and regulation of gene expression. Determination of brain lipid composition may provide not only essential information about normal brain functioning, but also about changes with aging and diseases. Indeed, deregulations of specific lipid classes and lipid homeostasis have been demonstrated in neurodegenerative disorders such as Alzheimer's disease (AD) and Parkinson's disease (PD). Furthermore, recent studies have shown that membrane microdomains, named lipid rafts, may change their composition in correlation with neuronal impairment. Lipid rafts are key factors for signaling processes for cellular responses. Lipid alteration in these signaling platforms may correlate with abnormal protein distribution and aggregation, toxic cell signaling, and other neuropathological events related with these diseases. This review highlights the manner lipid changes in lipid rafts may participate in the modulation of neuropathological events related to AD and PD. Understanding and characterizing these changes may contribute to the development of novel and specific diagnostic and prognostic biomarkers in routinely clinical practice.

Cerebrospinal fluid neurofilament light and tau protein as mortality biomarkers in parkinsonism

BACKGROUND

Mortality is increased in parkinsonian disorders, moderately in Parkinson's disease (PD) but markedly in atypical parkinsonian disorders (APD), including multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD). Still, there are no reliable quantitative biomarkers for mortality. The cerebrospinal fluid (CSF) neurodegeneration biomarkers such as neurofilament light chain (NF-L), total tau (t-tau), and the tau pathology marker phosphorylated tau (p-tau) are related to mortality in other neurological disorders (eg, amyotrophic lateral sclerosis, Alzheimer's disease), but have not been investigated in this respect in parkinsonian disorders.

AIMS

To investigate the CSF biomarkers' (NF-L, t-tau, and p-tau) relationship to mortality in parkinsonian disorders.

METHODS

Demographic, mortality, and CSF data were collected from 68 PD and 83 APD patients. Survival analysis was conducted using Cox regression, with age at lumbar puncture, gender, diagnosis, and levels of CSF biomarkers as predictors.

RESULTS

NF-L in CSF was associated with increased mortality in synucleinopathies (PD, MSA; HR 3.698 [2.196-6.228, 95% confidence interval (CI)], P < 0.001), in PSP (HR 2.767 [1.126-6.802 95% CI], P = 0.027), and in the entire cohort (HR 1.661 [1.082-2.55, 95% CI], P = 0.02). t-Tau in CSF was associated with increased mortality in PSP (HR 9.587 [1.143-80.418], P = 0.037). p-Tau in CSF was associated with decreased mortality in synucleinopathies (HR 0.196 [0.041-0.929, 95% CI], P = 0.040). Atypical parkinsonian disorders and tauopathies were associated with higher mortality (HR 8.798 [4.516-17.14, 95% CI] and HR 3.040 [1.904-4.854], respectively, P < 0.001).

CONCLUSION

NF-L and tau protein in CSF might be useful for mortality prognosis in patients with parkinsonian disorders and should be investigated in larger studies.

Differentiating Parkinson's disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load

Background

Periventricular leukoaraiosis may be an important pathological change in postural instability gait disorder (PIGD), a motor subtype of Parkinson's disease (PD). Clinical diagnosis of PIGD may be challenging for the general neurologist.

Purpose

To evaluate 1) the utility of a fully automated volume‐based morphometry (Vol‐BM) in characterizing imaging diagnostic markers in PD and PIGD, including, 2) novel deep gray nuclear lesion load (GMab), and 3) discriminatory performance of a Vol‐BM model construct in classifying the PIGD subtype.

Study Type

Prospective.

Subjects

In all, 23 PIGD, 21 PD, and 20 age‐matched healthy controls (HC) underwent MRI brain scans and clinical assessments.

Field Strength/Sequence

3.0T, sagittal 3D‐magnetization‐prepared rapid gradient echo (MPRAGE), and fluid‐attenuated inversion recovery imaging (FLAIR) sequences.

Assessment

Clinical assessment was conducted by a movement disorder neurologist. The MR brain images were then segmented using an automated multimodal Vol‐BM algorithm (MorphoBox) and reviewed by two authors independently.

Statistical Testing

Brain segmentation and clinical parameter differences and dependence were assessed using analysis of variance (ANOVA) and regression analysis, respectively. Logistic regression was performed to differentiate PIGD from PD, and discriminative reliability was evaluated using receiver operating characteristic (ROC) analysis.

Results

Significantly higher white matter lesion load (WMab) (P < 0.01), caudate GMab (P < 0.05), and lateral and third ventricular (P < 0.05) volumetry were found in PIGD, compared with PD and HC. WMab, caudate and putamen GMab, and caudate, lateral, and third ventricular volumetry showed significant coefficients (P < 0.005) in linear regressions with balance and gait assessments in both patient groups. A model incorporating WMab, caudate GMab, and caudate GM discriminated PIGD from PD and HC with a sensitivity = 0.83 and specificity = 0.76 (AUC = 0.84).

Data Conclusion

Fast, unbiased quantification of microstructural brain changes in PD and PIGD is feasible using automated Vol‐BM. Composite lesion load in the white matter and caudate, and caudate volumetry discriminated PIGD from PD and HC, and showed potential in classification of these disorders using supervised machine learning.

Level of Evidence: 1

Technical Efficacy: Stage 1

J. Magn. Reson. Imaging 2020;51:748–756.

Systematic data analysis and data mining in CatWalk gait analysis by heat mapping exemplified in rodent models for neurodegenerative diseases

BACKGROUND

Motor impairment appears as a characteristic symptom of several diseases and injuries. Therefore, tests for analyzing motor dysfunction are widely applied across preclinical models and disease stages. Among those, gait analysis tests are commonly used, but they generate a huge number of gait parameters. Thus, complications in data analysis and reporting raise, which often leads to premature parameter selection.

NEW METHODS

In order to avoid arbitrary parameter selection, we present here a systematic initial data analysis by utilizing heat-maps for data reporting. We exemplified this approach within an intervention study, as well as applied it to two longitudinal studies in rodent models related to Parkinson's disease (PD) and Huntington disease (HD).

RESULTS

The systematic initial data analysis (IDA) is feasible for exploring gait parameters, both in experimental and longitudinal studies. The resulting heat maps provided a visualization of gait parameters within a single chart, highlighting important clusters of differences.

COMPARISON WITH EXISTING METHOD

Often, premature parameter selection is practiced, lacking comprehensiveness. Researchers often use multiple separated graphs on distinct gait parameters for reporting. Additionally, negative results are often not reported.

CONCLUSIONS

Heat mapping utilized in initial data analysis is advantageous for reporting clustered gait parameter differences in one single chart and improves data mining.

Reliability of visual assessment by non-expert nuclear medicine physicians and appropriateness of indications of [123I]FP-CIT SPECT imaging by neurologists in patients with early drug-naive Parkinson's disease

Purpose

To determine the reliability of visual assessment of [¹²³I]FP-CIT SPECT imaging by non-experts in dopamine transporter (DAT) SPECT imaging in patients with early drug-naive Parkinson’s disease (PD). Also, we explored the indications of DAT SPECT imaging in clinical practice by neurologists.

Methods

We collected [¹²³I]FP-CIT SPECT scans of the Levodopa in EArly Parkinson’s disease (LEAP) trial participants that were made prior to recruitment, as part of routine clinical work-up. All scans were reassessed by an expert in DAT imaging. A survey on the use of DAT SPECT imaging was sent to all referring neurologists.

Results

The concordance of the initial local assessment and the expert reassessment was 98.7%. The survey showed that neurologists requested DAT SPECT imaging in only 73.6% of patients to differentiate between a neurodegenerative disease and non-neurodegenerative parkinsonism.

Conclusions

Visual assessment of [¹²³I]FP-CIT SPECT imaging by community nuclear medicine physicians in patients with early PD is reliable. Neurologists who request DAT SPECT scans are not always aware that the high accuracy is limited only to the differentiation between neurodegenerative and non-neurodegenerative parkinsonism. A significant portion of neurologists who request DAT SPECT scans is not always aware that the high accuracy is limited to the differentiation between neurodegenerative and non-neurodegenerative parkinsonism as DAT SPECT cannot reliably distinguish the various Parkinsonian syndromes.

Social perceptual function in parkinson's disease: A meta-analysis

Social perceptual impairment is a common presenting feature of Parkinson's disease (PD) that has the potential to contribute considerably to disease burden. The current study reports a meta-analytic integration of 79 studies which shows that, relative to controls, PD is associated with a moderate emotion recognition deficit (g = -0.57, K = 73), and that this deficit is robust and almost identical across facial and prosodic modalities. However, the magnitude of this impairment does appear to vary as a function of task and emotion type, with deficits generally greatest for identification tasks (g = -0.65, K = 54), and for negative relative to other basic emotions. With respect to clinical variables, dopaminergic medication, deep brain stimulation, and a predominant left side onset of motor symptoms are each associated with greater social perceptual difficulties. However, the magnitude of social perceptual impairment seen for the four atypical parkinsonian conditions is broadly comparable to that associated with PD. The theoretical and practical implications of these findings are discussed.

Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson's Disease Patients

Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson’s disease and to increase the therapy options for a better quality of life for patients. This work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson’s disease by the Unified Parkinson’s Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands; the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region. Texture features are computed using sum and difference of histograms (SDH) to characterize the dataset, varying the window size; however, only the most fundamental elements are used in the classification stage. A machine learning classifier provides the final classification results of the tremor level. The effectiveness of our approach is obtained by a set of performance metrics, which are also used to show a comparison between different proposed designs.