Parkinson's Disease Diagnostics Research Articles

Advancing Parkinson's Disease Diagnostics: The Potential of Arylpyrazolethiazole Derivatives for Imaging α-Synuclein Aggregates.

The development of positron emission tomography (PET) tracers capable of detecting α-synuclein (α-syn) aggregates in vivo would represent a breakthrough for advancing the understanding and enabling the early diagnosis of Parkinson's disease and related disorders. It also holds the potential to assess the efficacy of therapeutic interventions. However, this remains challenging due to different structures of α-syn aggregates, the need for selectivity over other structurally similar amyloid proteins, like amyloid-β (Aβ), which frequently coexist with α-syn pathology, and the low abundance of the target in the brain that requires the development of a high-affinity ligand. To develop a successful PET tracer for the central nervous system (CNS), stringent criteria in terms of polarity and molecular size must also be considered, as the tracer must penetrate the blood-brain barrier and have low nonspecific binding to brain tissue. Here, we report a series of arylpyrazolethiazole (APT) derivatives, rationally designed from a structure-activity relationship study centered on existing ligands for α-syn fibrils, with a particular focus on the selectivity toward α-syn fibrils and control of physicochemical properties suitable for a CNS PET tracer. In vitro competition binding assays performed against [3H]MODAG-001 using recombinant α-syn and Aβ1-42 fibrils revealed APT-13 with an inhibition constant of 27.8 ± 9.7 nM and a selectivity of more than 3.3 fold over Aβ. Radiolabeled [11C]APT-13 demonstrated excellent brain penetration in healthy mice with a peak standardized uptake value of 1.94 ± 0.29 and fast washout from the brain (t 1/2 = 9 ± 1 min). This study highlights the potential of APT-13 as a lead compound for developing PET tracers to detect α-syn aggregates in vivo.

Harnessing Graphdiyne for Selective Cu2+ Detection: A Promising Tool for Parkinson's Disease Diagnostics and Pathogenesis.

Cu2+ accelerates the viral-like propagation of α-synuclein fibrils and plays a key role in the pathogenesis of Parkinson's disease (PD). Therefore, the accurate detection of Cu2+ is essential for the diagnosis of PD and other neurological diseases. The Cu2+ detection process is impeded by substances that have similar electrochemical properties. In this study, graphdiyne (GDY), a new kind of carbon allotrope with strong electron-donating ability, was utilized for the highly selective detection of Cu2+ by taking advantage of its outstanding adsorption capacity for Cu2+. Density functional theory (DFT) calculations show that Cu atoms are adsorbed in the cavity of GDY, and the absorption energy between Cu and C atoms is higher than that of graphene (GR), indicating that the cavity of GDY is favorable for the adsorption of Cu atoms and electrochemical sensing. The GDY-based electrochemical sensor can effectively avoid the interference of amino acids, metal ions and neurotransmitters and has a high sensitivity of 9.77 μA·μM-1·cm-2, with a minimum detectable concentration of 200 nM. During the investigating pathogenesis and therapeutic process of PD with α-synuclein as the diagnostic standard, the concentration of Cu2+ in cells before and after L-DOPA and GSH treatments were examined, and it was found that Cu2+ exhibits high potential as a biomarker for PD. This study not only harnesses the favorable adsorption of the GDY and Cu2+ to improve the specificity of ion detection but also provide clues for deeper understanding of the role of Cu2+ in neurobiology and neurological diseases.

Lab-made disposable screen-printed electrochemical sensors and immunosensors modified with Pd nanoparticles for Parkinson's disease diagnostics.

Anew conductive ink based on the addition of carbon black to a poly(vinyl alcohol) matrix is developed and investigated for electrochemical sensing and biosensing applications. The produced devices were characterized using morphological and electrochemical techniques and modified with Pd nanoparticles to enhance electrical conductivity and reaction kinetics. With the aid of chemometrics, the parameters for metal deposition were investigated and the sensor was applied tothe determination of Parkinson's disease biomarkers, specifically epinephrine and α-synuclein. A linear behavior was obtained in the range 0.75 to 100μmol L-1 of the neurotransmitter, and the device displayed a limit of detection (LOD) of 0.051μmol L-1. The three-electrode system was then tested using samples of synthetic cerebrospinal fluid. Afterward, the device was modified with specific antibodies to quantify α-synuclein using electrochemical impedance spectroscopy. In phosphate buffer, a linear range was obtained for α-synuclein concentrations from 1.5 to 15μgmL-1, with a calculated LOD of 0.13μgmL-1. The proposed immunosensor was also applied to blood serum samples, and, in this case, the linear range was observed from 6.0 to 100.5μgmL-1 of α-synuclein, with a LOD = 1.3µgmL-1. Both linear curves attend the range for the real diagnosis, demonstrating its potential application to complex matrices.

Rapid Metabolic Profiling of 1 μL Crude Cerebrospinal Fluid by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Can Differentiate De Novo Parkinson's Disease.

Parkinson's disease (PD) is a highly prevalent neurodegenerative disorder affecting the motor system. However, the correct diagnosis of PD and atypical parkinsonism may be difficult with high clinical uncertainty. There is an urgent need to identify reliable biomarkers using high-throughput, molecular-specific methods to improve current diagnostics. Here, we present a matrix-assisted laser desorption/ionization mass spectrometry imaging method that requires minimal sample preparation and only 1 μL of crude cerebrospinal fluid (CSF). The method enables analysis of hundreds of samples in a single experiment while simultaneously detecting numerous metabolites with subppm mass accuracy. To test the method, we analyzed CSF samples from 12 de novo PD patients (that is, newly diagnosed and previously untreated) and 12 age-matched controls. Within the identified molecules, we found neurotransmitters and their metabolites such as γ-aminobutyric acid, 3-methoxytyramine, homovanillic acid, serotonin, histamine, amino acids, and metabolic intermediates. Limits of detection were estimated for multiple neurotransmitters with high linearity (R2 > 0.99) and sensitivity (as low as 16 pg/μL). Application of multivariate classification led to a highly significant (P < 0.001) model of PD prediction with a 100% classification rate, which was further thoroughly validated with a permutation test and univariate analysis. Molecules related to the neuromelanin pathway were found to be significantly increased in the PD group, indicated by their elevated relative intensities compared to the control group. Our method enables rapid detection of PD-related biomarkers in low sample volumes and could serve as a valuable tool in the development of robust PD diagnostics.

Using 18F-AV-133 VMAT2 PET Imaging to Monitor Progressive Nigrostriatal Degeneration in Parkinson Disease.

There are limited validated biomarkers in Parkinson disease (PD) which substantially hinders the ability to monitor disease progression and consequently measure the efficacy of disease-modifying treatments. Imaging biomarkers, such as vesicular monoamine transporter type 2 (VMAT2) PET, enable enhanced diagnostic accuracy and detect early neurodegenerative changes associated with prodromal PD. This study sought to assess whether 18F-AV-133 VMAT2 PET is sensitive enough to monitor and quantify disease progression over a 2-year window. 18F-AV-133 PET scans were performed on participants with PD and REM sleep behavior disorder (RBD) and neurologic controls (NC). All participants were scanned twice ∼26 months apart. Regional tracer retention was calculated with a primary visual cortex reference region and expressed as the standard uptake volume ratio. Regions of interest included caudate, anterior, and posterior putamen. At the time of scanning, participants underwent clinical evaluation including UPDRSMOTOR test, Sniffin' Sticks, and Hospital Anxiety and Depression Score. Over the 26-month interval, a significant decline in PET signal was observed in all 3 regions in participants with PD (N = 26) compared with NC (N = 12), consistent with a decrease in VMAT2 level and ongoing neurodegeneration. Imaging trajectory calculations suggest that the neurodegeneration in PD occurs over ∼33 years [CI: 27.2-39.5], with ∼10.5 years [CI: 9.1-11.3] of degeneration in the posterior putamen before it becomes detectable on a VMAT2 PET scan, a further ∼6.5 years [CI: 1.6-12.7] until symptom onset, and a further ∼3 years [CI: 0.3-8.7] until clinical diagnosis. Over a 2-year period, 18F-AV-133 VMAT2 PET was able to detect progression of nigrostriatal degeneration in participants with PD, and it represents a sensitive tool to identify individuals at risk of progression to PD, which are currently lacking using clinical readouts. Trajectory models propose that there is nigrostriatal degeneration occurring for 20 years before clinical diagnosis. These data demonstrate that VMAT2 PET provides a sensitive measure to monitor neurodegenerative progression of PD which has implications for PD diagnostics and subsequently clinical trial patient stratification and monitoring. This study provides Class IV evidence that VMAT2 PET can detect patients with Parkinson disease and quantify progression over a 2-year window.

A Glance at Biogenesis and Functionality of MicroRNAs and Their Role in the Neuropathogenesis of Parkinson's Disease.

MicroRNAs (miRNAs) are short, noncoding RNA transcripts. Mammalian miRNA coding sequences are located in introns and exons of genes encoding various proteins. As the central nervous system is the largest source of miRNA transcripts in living organisms, miRNA molecules are an integral part of the regulation of epigenetic activity in physiological and pathological processes. Their activity depends on many proteins that act as processors, transporters, and chaperones. Many variants of Parkinson's disease have been directly linked to specific gene mutations which in pathological conditions are cumulated resulting in the progression of neurogenerative changes. These mutations can often coexist with specific miRNA dysregulation. Dysregulation of different extracellular miRNAs has been confirmed in many studies on the PD patients. It seems reasonable to conduct further research on the role of miRNAs in the pathogenesis of Parkinson's disease and their potential use in future therapies and diagnosis of the disease. This review presents the current state of knowledge about the biogenesis and functionality of miRNAs in the human genome and their role in the neuropathogenesis of Parkinson's disease (PD)-one of the most common neurodegenerative disorders. The article also describes the process of miRNA formation which can occur in two ways-the canonical and noncanonical one. However, the main focus was on miRNA's use in in vitro and in vivo studies in the context of pathophysiology, diagnosis, and treatment of PD. Some issues, especially those regarding the usefulness of miRNAs in PD's diagnostics and especially its treatment, require further research. More standardization efforts and clinical trials on miRNAs are needed.

Alternating Magnetic Field-Promoted Nanoparticle Mixing: The On-Chip Immunocapture of Serum Neuronal Exosomes for Parkinson's Disease Diagnostics.

The analysis of cargo proteins in exosome subpopulations has considerable value in diagnostics but a translatable impact has been limited by lengthy or complex exosome extraction protocols. We describe herein a scalable, fast, and low-cost exosome extraction using an alternating (AC) magnetic field to support the dynamic mixing of antibody-coated magnetic beads (MBs) with serum samples within 3D-printed microfluidic chips. Zwitterionic polymer-coated MBs are, specifically, magnetically agitated and support ultraclean exosome capture efficiencies >70% from <50 μL of neat serum in 30 min. Applied herein to the immunocapture of neuronal exosomes using anti-L1CAM antibodies, prior to the array-based assaying of α-synuclein (α-syn) content by a standard duplex electrochemical sandwich ELISA, sub pg/mL detection was possible with an excellent coefficient of variation and a sample-to-answer time of ∼75 min. The high performance and semiautomation of this approach hold promise in underpinning low-cost Parkinson's disease diagnostics and is of value in exosomal biomarker analyses more generally.

Ataxic speech disorders and Parkinson's disease diagnostics via stochastic embedding of empirical mode decomposition.

Medical diagnostic methods that utilise modalities of patient symptoms such as speech are increasingly being used for initial diagnostic purposes and monitoring disease state progression. Speech disorders are particularly prevalent in neurological degenerative diseases such as Parkinson's disease, the focus of the study undertaken in this work. We will demonstrate state-of-the-art statistical time-series methods that combine elements of statistical time series modelling and signal processing with modern machine learning methods based on Gaussian process models to develop methods to accurately detect a core symptom of speech disorder in individuals who have Parkinson's disease. We will show that the proposed methods out-perform standard best practices of speech diagnostics in detecting ataxic speech disorders, and we will focus the study, particularly on a detailed analysis of a well regarded Parkinson's data speech study publicly available making all our results reproducible. The methodology developed is based on a specialised technique not widely adopted in medical statistics that found great success in other domains such as signal processing, seismology, speech analysis and ecology. In this work, we will present this method from a statistical perspective and generalise it to a stochastic model, which will be used to design a test for speech disorders when applied to speech time series signals. As such, this work is making contributions both of a practical and statistical methodological nature.

Parametric analysis of pilot voice signals in Parkinson?s disease diagnostics

Parkinson's disease (PD) is a neurodegenerative disease of the central nervous system (CNS) characterized by the progressive loss of dopaminergic neurons in the substantia nigra. The article describes an analysis of pilot voice signal analysis in Parkinson's disease diagnostics. Frequency domain signal analysis was mainly used to assess the state of a patient's voice apparatus in order to support PD diagnostics. The recordings covered uttering the ?a? sound at least twice with extended phonation. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. Spectral speech signal coefficients may be determined based on different defined frequency scales. The authors used four frequency scales: linear, Mel, Bark and ERB . Spectral descriptors have been defined for each scales which are widely used in machine and deep learning applications, and perceptual analysis. The usefulness of extracted features was assessed taking various methods into account. The discriminatory ability of individual coefficients was evaluated using the Fisher coefficient and LDA technique.. The results of numerical experiments have shown different efficiencies of the proposed descriptors using different frequencies scales.

Generative Adversarial Networks as a Data Augmentation Tool for CNN-Based Parkinson's Disease Diagnostics

Growing research interest has arisen towards automated neurodegenerative disease diagnostics based on the information extracted from the digital drawing tests. Since the performance of modern modelling techniques (machine learning, deep learning) relies heavily on the size of training data available, data scarcity is one of the most significant problems in computer-aided diagnostics. This paper proposes using Generative Adversarial Networks to synthesise digital drawing tests acquired from Parkinson's patients and healthy controls. Four different architectures (StyleGAN2-ADA, StyleGAN2-ADA + LeCam, StyleGAN3 and ProjectedGAN) are evaluated and compared with the traditional data augmentation methods. Convolutional neural networks are utilised for Parkinson's disease diagnostics. Our results indicate that GAN-generated images’ addition outperforms the standard augmentation methods in classifying Parkinson's disease in some settings. Therefore, the proposed framework could serve as a potential decision support tool for clinicians in computer-aided fine-motor analysis for neurodegenerative disease diagnostics.

Using Graphene-Based Biosensors to Detect Dopamine for Efficient Parkinson's Disease Diagnostics.

Parkinson’s disease (PD) is a neurodegenerative disease in which the neurotransmitter dopamine (DA) depletes due to the progressive loss of nigrostriatal neurons. Therefore, DA measurement might be a useful diagnostic tool for targeting the early stages of PD, as well as helping to optimize DA replacement therapy. Moreover, DA sensing appears to be a useful analytical tool in complex biological systems in PD studies. To support the feasibility of this concept, this mini-review explores the currently developed graphene-based biosensors dedicated to DA detection. We discuss various graphene modifications designed for high-performance DA sensing electrodes alongside their analytical performances and interference studies, which we listed based on their limit of detection in biological samples. Moreover, graphene-based biosensors for optical DA detection are also presented herein. Regarding clinical relevance, we explored the development trends of graphene-based electrochemical sensing of DA as they relate to point-of-care testing suitable for the site-of-location diagnostics needed for personalized PD management. In this field, the biosensors are developed into smartphone-connected systems for intelligent disease management. However, we highlighted that the focus should be on the clinical utility rather than analytical and technical performance.

Defining Optimal Exercises for Efficient Detection of Parkinson’s Disease Using Machine Learning and Wearable Sensors

Our society exhibits a worldwide trait of a quickly growing cohort of patients with neurodegenerative diseases, such as Parkinson's disease (PD). According to the analysts, there is a plausible “PD pandemic” to occur within the next two decades. Nowadays, the research in the area focuses on how to detect, predict, or classify PD and similar diseases without addressing the point of what activities or exercises a subject should do to improve the performance of these tasks. In this article, we propose a method based on machine learning (ML) and wearable sensors to identify the optimal exercises for the efficient detection of PD in patients. We first define 15 common tasks that are typically used to diagnose PD in modern clinical practice. However, these exercises still carry a high risk of misdiagnosis and, moreover, not all of them work well in the scope of existing ML solutions to support the diagnosis. Herein, we collect the data in a real clinical setting using a compact wearable wireless sensor node entailing a board gyroscope, accelerometer, and magnetometer. Application of ML methods to the collected data reveals three “most efficient” exercises to assist diagnosticians with the highest discriminating power (0.9 ROC AUC in each task). The proposed solution can be implemented as a medical decision support system for real-time PD diagnostics.

Patterns of diffusion kurtosis changes in Parkinson's disease subtypes

BackgroundDiffusion kurtosis imaging has been applied to evaluate white matter and basal ganglia microstructure in mixed Parkinson's disease (PD) groups with inconclusive results. ObjectivesTo evaluate specific patterns of kurtosis changes in PD and to assess the utility of diffusion imaging in differentiating between healthy subjects and cognitively normal PD, and between PD with and without mild cognitive impairment. MethodsDiffusion scans were obtained in 92 participants using 3T MRI. Differences in white matter were tested by tract-based spatial statistics. Gray matter was evaluated in basal ganglia, thalamus, hippocampus, and motor and premotor cortices. Brain atrophy was also assessed. Multivariate logistic regression was used to identify a combination of diffusion parameters with the highest discrimination power between groups. ResultsDiffusion kurtosis metrics showed a significant increase in substantia nigra (p = 0.037, Hedges' g = 0.89), premotor (p = 0.009, Hedges' g = 0.85) and motor (p = 0.033, Hedges' g = 0.87) cortices in PD with normal cognition compared to healthy participants. Combined diffusion markers in gray matter reached 81% accuracy in differentiating between both groups. Significant white matter microstructural changes, and kurtosis decreases in the cortex were present in cognitively impaired versus cognitively normal PD. Diffusion parameters from white and gray matter differentiated between both PD phenotypes with 78% accuracy. ConclusionsIncreased kurtosis in gray matter structures in cognitively normal PD reflects increased hindrance to water diffusion caused probably by alpha-synuclein-related microstructural changes. In cognitively impaired PD, the changes are mostly driven by decreased white matter integrity. Our results support the utility of diffusion kurtosis imaging for PD diagnostics.

Facile Impedimetric Analysis of Neuronal Exosome Markers in Parkinson's Disease Diagnostics.

The egress of α-synuclein in neuronally derived exosomes predates the clinical presentation of Parkinson’s disease (PD), offering a means of developing a predictive or prognostic test. Here, we report the reagentless impedimetric assay of two internal exosome markers (α-synuclein and syntenin-1) from neuronal exosomes. Exosomes were efficiently extracted from patient sera using anti-L1CAM conjugated zwitterionic polymer-modified magnetic beads prior to lysis and analyzed by electrochemical impedance spectroscopy. The quantification of α-synuclein level across 40 clinical samples resolved statistically significant differences between PD patients and healthy controls (HC).

Detection and Assessment of α-Synuclein Oligomers in the Urine of Parkinson's Disease Patients.

α-Synuclein (α-syn) is a major component of Lewy bodies, a pathologic marker of Parkinson's disease (PD) in post-mortem studies. The use of α-syn as a practical PD biomarker has been investigated by numerous researchers. However, reports of differences in α-syn levels in biofluids, such as cerebrospinal fluid, plasma, and saliva, between PD patients and controls are inconsistent. Recently, the measurement of α-syn oligomer levels has emerged as a novel approach to diagnose PD. Lysates and culture media from two different types of dopaminergic neuronal cells or urine samples from 11 non-PD and 21 PD patients were collected and analyzed. We developed and performed an enzyme-linked immuno-absorbent assay (ELISA) to detect various oligomeric α-syn using distinct pairs of antibodies. We validated our ELISA using rotenone-induced alterations of α-syn levels in human dopaminergic neurons. Total urinary α-syn levels, measured using our ELISA method, showed no difference between PD and non-PD individuals, but a higher level of α-syn oligomer recognized by MJFR-14-6-5-2 in PD urine samples was observed. Levels of distinct oligomeric α-syn detected by ASyO5 were lower in PD urine samples. Three different α-syn ELISA results were analyzed with respect to the severity of PD, but only the correlation between total α-syn levels and PD index was significant. Our findings suggest that detection of distinct oligomeric formations of α-syn and measurement of their levels in urine might be feasible for use in PD diagnostics.

Concordance of Mini-Mental State Examination, Montreal Cognitive Assessment and Parkinson Neuropsychometric Dementia Assessment in the classification of cognitive performance in Parkinson's disease

BackgroundCognitive impairment (CI) is frequently observed in Parkinson's disease (PD) and negatively influences the patient's and carer's quality of life. As a first step, assessment of CI is often accomplished by using screening instruments (level I diagnosis). Three commonly used instruments are the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Parkinson Neuropsychometric Dementia Assessment (PANDA). Because different preferences regarding test selection exist between clinics, this study aims to provide evidence about the concordance of these tests. It also converts total test scores of the three instruments to assist clinical practice. MethodsBetween January and December 2018, 96 patients with idiopathic PD were examined at the University Hospital of Cologne, Germany. Comparability of MMSE, MoCA, and PANDA scores was investigated by calculating correlations, classification agreements, and percentile ranks. Additionally, we converted test scores among the three screening instruments by implementing the equipercentile equating method and log-linear smoothing. ResultsThe MMSE classified 26%, the PANDA 32.3% and the MoCA 54.2% of PD patients as having CI. The screening instruments' concordance in classifying cognition into normal cognition versus CI was 75% (AC1 = 0.62) for MMSE and PANDA, 63.5% (AC1 = 0.28) for MoCA and PANDA, and 57.3% (AC1 = 0.24) for MMSE and MoCA. The provided conversion table enables a quick and easy transformation of the three screening instruments within PD diagnostics. ConclusionThese results contribute to a better understanding of the screenings' utility and concordance in a population of PD patients. Additionally, communication between clinics may be enhanced.

Prion Properties of Alpha-Synuclein

The prion properties of alpha-synuclein, a key aggregating protein involved in the pathogenesis of so-called synucleinopathies, including Parkinson's disease (PD), dementia with Lewy bodies, multiple system atrophy, and its various conformers are discussed. It is shown that alpha-synuclein may be transferred between cells by prion-like propagation. Similarly to other prions, alpha-synuclein aggregation develops from the initial lag-phase (nucleation) to the subsequent growth phase (elongation), and to the stationary phase where the aggregates and monomers exist in equilibrium. Similarly to prions, alpha-synuclein undergoes conformational changes from an alpha-helix to its beta-folded structure. However, there is currently no evidence that alpha-synuclein-dependent PD can be transmitted from person-to-person. This review describes the prion properties of alpha-synuclein, possible ways of its intercellular propagation, and novel approaches to PD diagnostics.

Sensor Array for Detection of Early Stage Parkinson's Disease before Medication.

Early diagnosis of Parkinson's disease (PD) is important because it affects the choice of therapy and is subject to a relatively high degree of error. In addition, early detection of PD can potentially enable the start of neuroprotective therapy before extensive loss of dopaminergic neurons of the substantia nigra occurs. However, until now, studies for early detection of PD using volatile biomarkers sampled only treated and medicated patients. Therefore, there is a great need to evaluate untreated patients for establishing a real world screening and diagnostic technology. Here we describe for the first time a clinical trial to distinguish between de novo PD and control subjects using an electronic system for detection of volatile molecules in exhaled breath (sensor array). We further determine for the first time the association to other common tests for PD diagnostics as smell, ultrasound, and nonmotor symptoms. The test group consisted of 29 PD patients after initial diagnosis by an experienced neurologist, compared with 19 control subjects of similar age. The sensitivity, specificity, and accuracy values of the sensor array to detect PD from controls were 79%, 84%, and 81% respectively, in comparison with midbrain ultrasonography (93%, 90%, 92%) and smell detection (62%, 89%, 73%). The results confirm previous data showing the potential of sensor arrays to detect PD.

Transcranial Ultrasonographic Image Analysis System for Decision Support in Parkinson Disease.

Transcranial ultrasonography (US) is a relatively new neuroimaging modality proposed for early diagnostics of Parkinson disease (PD). The main limitation of transcranial US image-based diagnostics is a high degree of subjectivity caused by low quality of the transcranial images. The article presents a developed image analysis system and evaluates the potential of automated image analysis on transcranial US. The system consists of algorithms for the segmentation and assessment of informative brain regions (midbrain and substantia nigra) and a decision support subsystem, which is equipped with 64 classification algorithms. Transcranial US images of 191 participants (118 patients with a clinical PD diagnosis and 73 healthy control participants) were analyzed. The diagnostic sensitivity and specificity achieved by the proposed system were 85% and 75%, respectively. Digital transcranial US image analysis is challenging, and the application of a such system as the sole instrument for decisions in clinical practice remains inconclusive. However, the proposed system could be used as a supplementary tool for automated assessment of US parameters for decision support in PD diagnostics and to reduce observer variability.

Murine Motor and Behavior Functional Evaluations for Acute 1-Methyl-4-Phenyl-1,2,3,6-Tetrahydropyridine (MPTP) Intoxication

Acute intoxication with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induces nigrostriatal neurodegeneration that reflects Parkinson's disease (PD) pathobiology. The model is commonly used for rodent studies of PD pathogenesis and diagnostics and for developmental therapeutics. However, tests of motor function in MPTP-intoxicated mice have yielded mixed results. This unmet need reflects, in part, lesion severity, animal variability, and the overall test sensitivity and specificity. In attempts to standardize rodent motor function and behavioral tests, mice were trained on the rotarod or habituated in an open field test chamber, and baseline performance measurements were collected prior to MPTP intoxication. One week following MPTP intoxication, motor function and behavior were assessed and baseline measurements applied to post-MPTP measurements with normalization to PBS controls. Rotarod and open field tests assessed in this manner demonstrated significant differences between MPTP- and saline-treated mice, while tests of neuromuscular strength and endurance did not. We conclude that the rotarod and open field tests provide reliable measures of motor function for MPTP-intoxicated mice.