Discriminate Parkinson's Disease Patients Research Articles

Evaluating the Diagnostic Potential of Combined Salivary and Skin Biomarkers in Parkinson's Disease.

Oligomeric alpha-synuclein (α-syn) in saliva and phosphorylated α-syn deposits in the skin have emerged as promising diagnostic biomarkers for Parkinson's disease (PD). This study aimed to assess and compare the diagnostic value of these biomarkers in discriminating between 38 PD patients and 24 healthy subjects (HSs) using easily accessible biological samples. Additionally, the study sought to determine the diagnostic potential of combining these biomarkers and to explore their correlations with clinical features. Salivary oligomeric α-syn levels were quantified using competitive ELISA, while skin biopsies were analyzed through immunofluorescence to detect phosphorylated α-syn at Ser129 (p-S129). Both biomarkers individually were accurate in discriminating PD patients from HSs, with a modest agreement between them. The combined positivity of salivary α-syn oligomers and skin p-S129 aggregates differentiated PD patients from HSs with an excellent discriminative ability with an AUC of 0.9095. The modest agreement observed between salivary and skin biomarkers individually suggests that they may reflect different aspects of PD pathology, thus providing complementary information when combined. This study's results highlight the potential of utilizing a multimodal biomarker approach to enhance diagnostic accuracy in PD.

Exploring the Complex Phenotypes of Impaired Finger Dexterity in Mild-to-moderate Stage Parkinson's Disease: A Time-Series Analysis.

Impaired dexterity is an early motor symptom in Parkinson's disease (PD) that significantly impacts the daily activity of patients; however, what constitutes complex dexterous movements remains controversial. To explore the characteristics of finger dexterity in mild-to-moderate stage PD. We quantitatively assessed finger dexterity in 48 mild-to-moderate stage PD patients and 49 age-matched controls using a simple alternating two-finger typing test for 15 seconds. Time-series analyses of various kinematic parameters with machine learning were compared between sides and groups. Both the more and less affected hands of patients with PD had significantly lower typing frequency and slower typing velocity than the non-dominant and the dominant hands of controls (p = 0.019, p = 0.016, p < 0.001, p < 0.001). The slope of the typing velocity decreased with time, indicating a sequence effect in the PD group. A typing duration of 6 seconds was determined sufficient to discriminate PD patients from controls. Typing error, repetition, and repetition rate were significantly higher in the more affected hands of patients with PD than in the non-dominant hand of controls (p < 0.001, p = 0.03, p < 0.001). The error rate was constant, whereas the repetition rate was steep during the initiation of typing. A predictive model of the more affected hand demonstrated an accuracy of 70% in differentiating PD patients from controls. Our study demonstrated complex components of impaired finger dexterity in mild-to-moderate stage PD, namely bradykinesia with sequence effects, error, and repetition at the initiation of movement, suggesting that multiple neural networks may be involved in dexterity deficits in PD.

Deep Learning and Artificial Intelligence Applied to Model Speech and Language in Parkinson's Disease.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in the world, and it is characterized by the production of different motor and non-motor symptoms which negatively affect speech and language production. For decades, the research community has been working on methodologies to automatically model these biomarkers to detect and monitor the disease; however, although speech impairments have been widely explored, language remains underexplored despite being a valuable source of information, especially to assess cognitive impairments associated with non-motor symptoms. This study proposes the automatic assessment of PD patients using different methodologies to model speech and language biomarkers. One-dimensional and two-dimensional convolutional neural networks (CNNs), along with pre-trained models such as Wav2Vec 2.0, BERT, and BETO, were considered to classify PD patients vs. Healthy Control (HC) subjects. The first approach consisted of modeling speech and language independently. Then, the best representations from each modality were combined following early, joint, and late fusion strategies. The results show that the speech modality yielded an accuracy of up to 88%, thus outperforming all language representations, including the multi-modal approach. These results suggest that speech representations better discriminate PD patients and HC subjects than language representations. When analyzing the fusion strategies, we observed that changes in the time span of the multi-modal representation could produce a significant loss of information in the speech modality, which was likely linked to a decrease in accuracy in the multi-modal experiments. Further experiments are necessary to validate this claim with other fusion methods using different time spans.

Assessing of the Italian version of the Memory Strategy Test (TMS) in people with Parkinson disease: a preliminary descriptive psychometric study

BackgroundPrevious literature has shown that executive functions (EF) are related to performance in memory (M) tasks. The Test of Memory strategies (TMS) is a psychometric test that examines EF and M simultaneously and it was recently validated on an Italian healthy cohort. The first aim of the study was to apply TMS, for the first time, on a sample of patients with Parkinson's disease (PD), who are characterized by mild cognitive impairment. The second aim is to investigate whether TMS scores can discriminate PD patients from healthy controls.MethodNinety-eight subjects were enrolled, including 68 patients with PD, and 30 Italian healthy controls (HC), who also underwent a memory evaluation through well-known tests.ResultsConfirmatory factor analysis (CFA) demonstrated that TMS of PD patients had a bi-dimensional structure as previously found in healthy cohort. In detail, The TMS-1 and TMS-2 lists require greater involvement of the EF factor, while TMS-3, TMS-4 and TMS-5 the M factor. Receiver operating characteristic (ROC) curves and precision-recall (PR) curves showed that the M subscale can distinguish between HC and PD, while EF had poor discrimination power.ConclusionThe hypothesized prediction model of TMS test seems to have adequate ability to discriminate PD from HC especially for the M function.

NIMHANS Neuropsychological Battery for Elderly in Parkinson's Disease Patients: Validation and Diagnosis using MDS PD-MCI Task Force Criteria in Indian Population.

Cognitive impairment is a common non-motor feature of Parkinson's Disease (PD). Diagnosis of mild cognitive impairment is challenging and routinely missed in clinical practice. Our study aimed to study the efficacy of NIMHANS Neuropsychological Battery for Elderly (NNB-E) in diagnosing subtle cognitive deficits in PD patients. The aim of this study is to validate NNB-E and evaluate cognitive impairment in PD patients in comparison with healthy controls. We recruited 31 PD patients and 31 healthy controls in the current study. We validated NNB-E using receiver operating characteristic (ROC) curve analysis, Crohnbach's alpha, principal component analysis, and Pearson product-moment correlation, and studied the cognitive impairments using NNB-E in the non-demented PD patients and controls who scored ≥24 on HMSE. Cognitive performance of PD patients was poor compared to controls. NNB-E showed good internal consistency and construct validity with Crohnbach's alpha of 0.861 and area under the curve (AUC) of 0.878. The battery was able to detect mild cognitive impairment in 74.1% of patients and 6.4% of controls. The ROC curve showed that the overall sensitivity of the battery was 73.2% and specificity was 92.6% at an optimal cutoff score. Different cutoff values set for defining PD-MCI as per MDS task force criteria resulted in varying frequencies of MCI ranging from 25.8% to 71%. Our study established the validity of NNB-E in PD patients, and this tool was suitable for diagnosing PD-MCI and discriminating PD patients from normal controls in the Indian population. This study also showed PD-MCI at various cutoff scores with greater impairment in executive and attention domains.

Exemplar-Based Sparse Representations for Detection of Parkinson's Disease From Speech

Parkinson's disease (PD) is a progressive neurological disorder which affects the motor system. The automatic detection of PD improves the diagnosis of the disease, and it can be done in a non-invasive manner from speech. In this paper, we investigate the use of an exemplar-based sparse representation (SR) classification approach for detecting PD from speech. Exemplars are speech feature vectors extracted from the training data. The idea is to formulate the detection task as a problem of finding sparse representations of test speech feature vectors with respect to training speech exemplars. The main advantage of using the SR approach instead of conventional machine learning (ML)-based approaches is that the training step–which is time-consuming and sometimes requires unorganized hyper-parameter tuning–is not needed. Furthermore, SRs are more robust to redundancy and noise in the data. In this work, we study SR classification approaches based on two sparse coding models, namely, l1-regularized least squares ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> LS) and non-negative least squares (NNLS). We propose a strategy based on class-specific dictionaries for improving performance of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> LS- and NNLS-based SR classification. To investigate the detection performance, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> LS- and NNLS-based approaches are applied and compared with the traditional PD detection approach based on ML classification algorithms using the PC-GITA PD dataset and an openly available dataset consisting of mobile device voice recordings from healthy and PD patients. The results indicate that the proposed NNLS-based SR classification approach performs better than the traditional ML-based methods in discriminating PD patients from healthy subjects.

Evaluation of the complete Sniffin Sticks Test versus its subtests in differentiating Parkinson's disease patients from healthy controls.

Hyposmia is one of the most common, as well as the first nonmotor condition in Parkinson disease (PD). The sniffin sticks test (SST) evaluates three different aspects of olfactory function: threshold (T), discrimination (D), and identification (I). The sum of the scores of these three subtests produce a global score of olfaction, the Threshold-Discrimination-Identification (TDI) score. The aim of this study was to investigate if the TDI score or one of its subtests is better to discriminate PD patients from controls. We recruited 27 PD patients and 17 healthy age-matched controls (HC) who were evaluated through a clinical interview, the Montreal Cognitive Assessment and Movement Disorders Society - Unified Parkinson Disease Rating Scale. The olfaction was assessed using the complete SST. The performance of PD patients on the olfactory test was significantly worse when compared with the HC (T: 3.0 vs. 6.5, p < 0.001; D: 8.1 vs. 11.2, p < 0.001; I: 7.3 vs. 11.7, p < 0.001; TDI: 18.8 vs. 29.9, p < 0.001). The prevalence of olfaction impairment in our study (PD: 100%, and HC: 56%) was greater than that found in the literature. Cognition influenced the performance on TDI. The olfactory subtests were impaired proportionally between patients and controls. Furthermore, D and I were correlated, but only in PD patients. The TDI showed a tendency to better discriminate PD patients from HC, when compared with its subtests. Although the complete olfactory evaluation is time consuming, it seems to be a superior tool to identify olfaction impairment in PD patients, when compared with the isolated subtests.

Serum NFL discriminates Parkinson disease from essential tremor and reflect motor and cognition severity

ObjectiveTo investigate the diagnostic value of serum neurofilament light chain (NFL) for discriminating Parkinson disease (PD) from Essential tremor (ET) and healthy controls, and to evaluate its correlation with some clinical features of PD patients.MethodsThis cross-sectional study measured NFL levels with electrochemiluminescence immunoassay in serum of 146 PD patients, 82 ET patients and 60 age-matched healthy controls. We used multivariate regression analyses to examine whether NFL contributes to PD biomarkers. Disease severity were assessed by Unified Parkinson’s Disease Rating Scale part III (UPDRS III), Hoehn & Yahr (H-Y) stage and Mini-Mental State Examination (MMSE).ResultsSerum NFL levels were significantly higher in PD than in ET and healthy controls (16.6 ± 3.5, 12.2 ± 2.4 and 11.8 ± 2.4 pg/mL, respectively, p < 0.001). In patients with PD, serum NFL were markedly increased in patients with advanced H-Y stage and patients with dementia (both p < 0.001). The correlation analysis revealed that serum NFL was positively associated with UPDRS III score (r = 0.79, p < 0.001) and H-Y stage (r = 0.86, p < 0.001), and negatively correlated with MMSE scores (r = − 0.70, p < 0.001). Further multivariate regression analyses showed that serum NFL was an independent contributor to motor symptom and cognition severity in PD patients (all p < 0.01).ConclusionsSerum NFL levels were markedly elevated may be a useful clinical biomarker for discriminating PD patients from ET and controls. Serum NFL may serve as a potential blood biomarker for motor and cognition severity of PD.

Disrupted morphological grey matter networks in early-stage Parkinson\u2019s disease

While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.

Measurement of corneal nerve fiber parameters in patients with Parkinson's disease

Objective: To analyze the characteristic changes of corneal nerve fibers in patients with Parkinson's disease (PD) by corneal confocal microscopy (CCM) and investigate the association of corneal nerve fiber parameters with disease severity and motor symptoms. Methods: Forty-two patients with PD were recruited from the Department of Neurology, Henan University People's Hospital from June 2018 to October 2019. Meanwhile, 40 healthy controls who visited the hospital for physical examination at the same period were enrolled. Corneal nerve fibers in both eyes of all participants were detected by using CCM. The differences of corneal nerve fibers were comparatively analyzed between PD group and healthy controls. Associations of corneal nerve parameters with clinical characteristics such as course of disease, Hoehn and Yahr stage (H-Y stage), unified Parkinson disease rating scale (UPDRS), levodopa equivalent daily dosage (LEDD) were analyzed by using partial correlations. The receiver operating characteristic (ROC) curve was used to analyze the capability of corneal nerve fibers for distinguishing patients with PD from healthy controls. Results: Corneal nerve fiber density (CNFD) in PD group ((19±3)/mm2) was significantly decreased compared with healthy controls ((28±4)/mm2) (t=10.798, P<0.001). However, corneal nerve branch density (CNBD) was significantly increased in PD group ((25±11)/mm2) compared with healthy controls ((18±6)/mm2) (t=-3.427, P=0.001). Meanwhile, corneal nerve fiber length (CNFL) was decreased in PD group ((11.0±2.5) mm/mm2) in comparison with healthy controls ((12.5±1.6) mm/mm2) (t=3.139, P=0.002). ROC curve analysis revealed that CNFD could discriminate PD patients from healthy controls, with an area under the curve of 0.961 3 (95%CI: 92.42-99.84, P<0.000 1). CNFD was negatively correlated with H-Y stage and UPDRS-Ⅲ (r=-0.501 and -0.399, both P<0.05). CNBD was significantly negatively associated with H-Y stage, UPDRS-Ⅲ and UPDRS-Total (r=-0.622, -0.394 and -0.354, respectively, all P<0.05). CNFL was negatively correlated with H-Y stage, UPDRS-Ⅲ and UPDRS-total (r=-0.574, -0.484 and -0.422, respectively, all P<0.05). Conclusion: Small nerve fiber injuries exist in PD patients. Corneal nerve fibers negatively correlates with motor symptoms. CNFD have a good discriminative power to distinguish PD patients from healthy controls and may serve as a marker for PD.

Retracted: An easy-to-use deep-learning model for highly accurate diagnosis of Parkinson's disease using SPECT images

Accurate diagnosis of Parkinson's Disease (PD) at its early stages remains a challenge for modern clinicians. In this study, we utilize a convolutional neural network (CNN) approach to address this problem. In particular, we develop a CNN-based network model highly capable of discriminating PD patients based on Single Photon Emission Computed Tomography (SPECT) images from healthy controls. A total of 2723 SPECT images are analyzed in this study, of which 1364 images from the healthy control group, and the other 1359 images are in the PD group. Image normalization process is carried out to enhance the regions of interests (ROIs) necessary for our network to learn distinguishing features from them. A 10-fold cross-validation is implemented to evaluate the performance of the network model. Our approach demonstrates outstanding performance with an accuracy of 99.34 %, sensitivity of 99.04 % and specificity of 99.63 %, outperforming all previously published results. Given the high performance and easy-to-use features of our network, it can be deduced that our approach has the potential to revolutionize the diagnosis of PD and its management.

Diagnosing REM sleep behavior disorder in Parkinson's disease without a gold standard: a latent-class model study.

To ascertain whether current diagnostic criteria for REM sleep behavior disorder (RBD) are appropriate in patients with Parkinson's disease (PD) consulting a movement disorder center, to evaluate the accuracy of REM sleep without atonia (RSWA) thresholds and determine the value of screening questionnaires to discriminate PD patients with RBD. One hundred twenty-eight consecutive PD patients (M = 80; mean age: 65.6 ± 8.3 years) underwent screening questionnaires, followed by a sleep-focused interview and a full-night video-polysomnography (vPSG). Without a gold standard, latent class models (LCMs) were applied to create an unobserved ("latent") variable. Sensitivity analysis was performed using RSWA cutoff derived from two visual scoring methods. Finally, we assessed the respective diagnostic performance of each diagnostic criterion for RBD and of the screening questionnaires. According to the best LCM-derived model, patients having either "history" or "video" with RSWA or alternatively showing both "history" and "video" without RSWA were classified as having RBD. Using both SINBAR and Montreal scoring methods, RSWA criterion showed the highest sensitivity while concomitant history of RBD and vPSG-documented behaviors, regardless to presence of RSWA, displayed the highest specificity. Currently recommended diagnostic threshold of RSWA was found to be optimal in our large cohort of PD patients. Both the RBD screening questionnaire (RBDSQ) and the RBD single question (RBD1Q) showed poor sensitivity and specificity. Results of the best LCM for diagnosis of RBD in PD were consistent with the current diagnostic criteria. Moreover, RBD might be considered in those PD patients with both history and vPSG-documented dream enactment behaviors, but with RSWA values within the normal range.

Long Noncoding RNA POU3F3 and α-Synuclein in Plasma L1CAM Exosomes Combined with β-Glucocerebrosidase Activity: Potential Predictors ofParkinson's Disease.

Long noncoding RNAs (lncRNAs) are implicated in the autophagic-lysosomal pathway (ALP) and are closely linked to Parkinson's disease (PD) pathology. β-Glucocerebrosidase (GCase) has also been reported to be correlated with α-synuclein (α-syn) proteostasis. However, lncRNAs and α-syn in neural-derived L1CAM exosomes and GCase activity in the plasma of PD patients have not been studied. This study used an ultrasensitive methodology, fluorescence nanoparticle tracking analysis (NTA), to measure plasma L1CAM exosomes and Quanterix Simoa to measure α-syn concentrations in L1CAM exosomes. Eighty-five healthy controls and 93 PD patients were enrolled, and several scales were used to rate the severity of PD. Receiver operating characteristic (ROC) curves were applied to map the diagnostic accuracy of categorizing PD patients and healthy subjects. We found increased Linc-POU3F3 and α-syn concentrations in L1CAM exosomes and decreased GCase activity in PD patients compared with controls. The three biomarkers displayed obvious differences among PD patients based on gender, H-Y stage, and UPDRS-III distribution. Interestingly, Linc-POU3F3 was significantly positively correlated with α-syn in L1CAM exosomes and inversely correlated with GCase activity in PD patients. Significant correlations were observed among L1CAM exosomal Linc-POU3F3 levels, GCase activity, and PD severity, including motor/cognitive dysfunction. Additionally, the combination of Linc-POU3F3 and α-syn in L1CAM exosomes and GCase activity could discriminate PD patients from controls. These results suggest that L1CAM exosomal Linc-POU3F3, L1CAM exosomal α-syn, and GCase activity may shed light on the mechanism underlying the autophagic-lysosomal system in the pathogenesis of PD and could be used to assess the severity of PD.

Diagnostic Accuracy, Item Analysis and Age Effects of the UPSIT Spanish Version in Parkinson's Disease.

The University of Pennsylvania Smell Identification Test (UPSIT) is the most commonly used test to detect olfactory impairment in Parkinson's disease (PD), but the cut-off score for clinical purposes is often difficult to establish because of age and sex effects. The current work aims to study the sensitivity and specificity of the UPSIT Spanish version and its accuracy in discriminating PD patients at different age groups from healthy controls (HC), and to perform an item analysis. Ninety-seven non-demented PD patients and 65 HC were assessed with the UPSIT Spanish version. Sensitivity, specificity, and diagnostic accuracy for PD were calculated. Multiple regression analysis was used to define predictors of UPSIT scores. Using the normative cut-off score for anosmia (≤18), the UPSIT showed a sensitivity of 54.6% with a specificity of 100.0% for PD. We found that, using the UPSIT cut-off score of ≤25, sensitivity was 81.4% and specificity 84.6% (area under the receiver operating characteristic curve = 0.908). Diagnosis and age were good predictors of UPSIT scores (B = -10.948; p < .001; B = -0.203; p < .001). When optimal cut-off scores were considered according to age ranges (≤60, 61-70, and ≥71), sensitivity and specificity values were >80.0% for all age groups. In the Spanish UPSIT version, sensitivity and specificity are improved when specific cut-off scores for different age groups are computed.

Combined Assessment of Serum Alpha-Synuclein and Rab35 is a Better Biomarker for Parkinson's Disease.

Background and PurposeIt is essential to develop a reliable predictive serum biomarker for Parkinson's disease (PD). The accumulation of alpha-synuclein (αSyn) and up-regulated expression of Rab35 participate in the etiology of PD. The purpose of this investigation was to determine whether the combined assessment of serum αSyn and Rab35 is a useful predictive biomarker for PD.MethodsSerum levels of αSyn or Rab35 were determined in serum samples from 59 sporadic PD patients, 19 progressive supranuclear palsy (PSP) patients, 20 multiple system atrophy (MSA) patients, and 60 normal controls (NC). Receiver operating characteristics (ROC) curves were calculated to determine the diagnostic accuracy of αSyn or/and Rab35 in discriminating PD patients from NC or atypical parkinsonian patients.ResultsThe levels of αSyn and Rab35 were increased in PD patients. The serum level of Rab35 was positively correlated with that of αSyn in PD patients. Compared to analyzing αSyn or Rab35 alone, the combined analysis of αSyn and Rab35 produced a larger area under the ROC curve and performed better in discriminating PD patients from NC, MSA patients, or PSP patients. When age was dichotomized at 55, 60, 65, or 70 years, the combined assessment of αSyn and Rab35 for classifying PD was better in the group below the cutoff age than in the group above the cutoff age.ConclusionsCombined assessment of serum αSyn and Rab35 is a better biomarker for discriminating PD patients from NC or atypical parkinsonian patients, and is a useful predictive biomarker for younger sporadic PD patients.

Progression Rate Associated Peripheral Blood Biomarkers of Parkinson's Disease.

Parkinson disease (PD) is one of the most frequent neurodegenerative disorders. The aim of this study was to identify blood biomarkers capable to discriminate PD patients with different progression rates. Differentially expressed genes (DEGs) were acquired by comparing the expression profiles of PD patients with rapid and slow progression rates, using an expression dataset from Gene Expression Omnibus (GEO) under accession code of GSE80599. Altered biological processes and pathways were revealed by functional annotation. Potential biomarkers of PD were identified by protein-protein interaction (PPI) network analysis. Critical transcription factors (TFs) and miRNAs regulating DEGs were predicted by TF analysis and miRNA analysis. A total of 225 DEGs were identified between PD patients with rapid and slow progression profiles. These genes were significantly enriched in biological processes and pathways related to fatty acid metabolism. Among these DEGs, ZFAND4, SRMS, UBL4B, PVALB, DIRAS1, PDP2, LRCH1, and MYL4 were potential progression rate associated biomarkers of PD. Additionally, these DEGs may be regulated by miRNAs of the miR-30 family and TFs STAT1 and GRHL3. Our results may contribute to our understanding of the molecular mechanisms underlying different PD progression profiles.

Nigrosome Imaging and Neuromelanin Sensitive MRI in Diagnostic Evaluation of Parkinsonism.

Recent developments in magnetic resonance imaging (MRI) techniques have offered new research opportunities to visualize in vivo substantia nigra pathology in Parkinson's disease (PD). This paper summarizes the main findings of nigrosome imaging and neuromelanin sensitive MRI studies in patients with PD and other parkinsonisms. The PubMed database was searched from 2005 to 2017 using the following keywords: Parkinson's disease and parkinsonism, in combination with MRI, nigrosome, neuromelanin, and iron. Only publications in English were included. Nigrosome or dorsal nigral hyperintensity abnormalities are studied using T2* and susceptibility weighted imaging MRI sequences in most studies, whereas Neuromelanin imaging is usually performed using T1-weighted fast spin echo sequence. Nigrosome abnormalities have been consistently demonstrated in PD patients, and nigrosome imaging has high sensitivity and specificity in distinguishing PD from healthy controls, though it is unable to reliably separate PD from atypical parkinsonisms. Reduced neuromelanin-related signals and/or volume loss in neuromelanin containing structures have been found in PD patients, and neuromelanin sensitive MRI imaging can also discriminate PD patients from healthy controls with high accuracy, though there is a degree of heterogeneity in the imaging findings. Preliminary findings suggested that longitudinal change of neuromelanin signal could be detected in PD, raising the possibility of using it as a marker of disease progression. Nigrosome imaging and neuromelanin sensitive MRI are promising tools to study nigral pathology and to improve the diagnosis of PD. However, further studies are required to standardize analysis approaches, confirm longitudinal changes, and assess their generalizability.

α-Synuclein Heterocomplexes with β-Amyloid Are Increased in Red Blood Cells of Parkinson's Disease Patients and Correlate with Disease Severity.

Neurodegenerative disorders (NDs) are characterized by abnormal accumulation/misfolding of specific proteins, primarily α-synuclein (α-syn), β-amyloid1–42 (Aβ1–42) and tau, in both brain and peripheral tissues. In addition to oligomers, the role of the interactions of α-syn with Aβ or tau has gradually emerged. Nevertheless, despite intensive research, NDs have no accepted peripheral markers for biochemical diagnosis. In this respect, Red Blood Cells (RBCs) are emerging as a valid peripheral model for the study of aging-related pathologies. Herein, a small cohort (N = 28) of patients affected by Parkinson’s disease (PD) and age-matched controls were enrolled to detect the content of α-syn (total and oligomeric), Aβ1–42 and tau (total and phosphorylated) in RBCs. Moreover, the presence of α-syn association with tau and Aβ1–42 was explored by co-immunoprecipitation/western blotting in the same cells, and quantitatively confirmed by immunoenzymatic assays. For the first time, PD patients were demonstrated to exhibit α-syn heterocomplexes with Aβ1–42 and tau in peripheral tissues; interestingly, α-syn-Aβ1–42 concentrations were increased in PD subjects with respect to healthy controls (HC), and directly correlated with disease severity and motor deficits. Moreover, total-α-syn levels were decreased in PD subjects and inversely related to their motor deficits. Finally, an increase of oligomeric-α-syn and phosphorylated-tau was observed in RBCs of the enrolled patients. The combination of three parameters (total-α-syn, phosphorylated-tau and α-syn-Aβ1–42 concentrations) provided the best fitting predictive index for discriminating PD patients from controls. Nevertheless further investigations should be required, overall, these data suggest α-syn hetero-aggregates in RBCs as a putative tool for the diagnosis of PD.

A Practical Measurement of Parkinson's Patients Gait Using Simple Walker-Based Motion Sensing and Data Analysis

We present personal aid for mobility and monitoring (PAMM II), an instrumented walker for Parkinson's disease (PD) patients' gait monitoring. The objective of the walker is to aid in the diagnosis and monitoring of PD progression as well as the effects of clinical treatment and rehabilitation. In contrast to existing devices, the walker is a low-cost solution that is simple to operate and maintain, requiring no adjustments, special usage instructions, or infrastructure. This preliminary study reports on the efficiency, reliability, and accuracy of PAMM II when used to evaluate 22 PD patients and 20 control individuals. All subjects walked two prescribed paths while pushing the walker, and their kinematic motion signals were automatically collected by the walker. Feature derivation from the walker's signals was followed by combinations of two classical feature selection methods and two learning algorithms, with the objective of discriminating PD patients from control subjects. Sensitivity and specificity scores of 91% and 95% were achieved for the first walking protocol, whereas discrimination over the second walking protocol produced sensitivity and specificity scores of 96% and 100%. These preliminary results provide insight as to the usefulness of PAMM II and its data processing algorithms for the assessment of PD patients' condition.

Objective Assessment of Bradykinesia Estimated from the Wrist Extension in Older Adults and Patients with Parkinson's Disease.

Parkinson's disease (PD) presents several motor signs, including tremor and bradykinesia. However, these signs can also be found in other motor disorders and in neurologically healthy older adults. The incidence of bradykinesia in PD is relatively high in all stages of the disorder, even when compared to tremor. Thus, this research proposes an objective assessment of bradykinesia in patients with PD (G PD: 15 older adults with Parkinson's disease, 65.3±9.1years) and older adults (G HV: 12 healthy older adults, 60.1±6.1years). The severity of bradykinesia in the participants of G PD was assessed using the Unified Parkinson's Disease Rating Scale. Movement and muscular activity were detected by means of inertial (accelerometer, gyroscope, magnetometer) and electromyographic sensors while the participants performed wrist extension against gravity with the forearm on pronation. Mean and standard error of inertial and electromyographic signal parameters could discriminate PD patients from healthy older adults (p value <0.05). In discriminating patients with PD from healthy older adults, the mean sensitivity and specificity were respectively 86.67 and 83.33%. The discrimination between the groups, based on the objective evaluation of bradykinesia, may contribute to the accurate diagnosis of PD and to the monitoring of therapies to control parkinsonian bradykinesia, and opens the possibility for further comparative studies considering individuals suffering from other motor disorders.