Characterization of Vesicular Monoamine Transporter 2 and its role in Parkinson\u27s Disease Pathogenesis using Drosophila

Parkinson’s disease (PD) is a progressive neurodegenerative disorder caused by the selective loss of the dopaminergic neurons in the Substantia nigra pars compacta region of the brain. PD is also the most common neurodegenerative disorder and the second most common movement disorder. PD patients exhibit the cardinal symptoms, including tremor of the extremities, rigidity, slowness of movement, and postural instability, after 70-80% of DA neurons degenerate. It is, therefore, imperative to elucidate the underlying mechanisms involved in the selective degeneration of DA neurons. Although increasing numbers of PD genes have been identified, why these largely widely expressed genes induce selective loss of DA neurons is still not known. Notably, dopamine (DA) itself is a chemically labile molecule and can become oxidized to toxic by-products while induce the accumulation of harmful molecules such as Reactive Oxygen Species (ROS). Accordingly, DA toxicity has long been suspected to play a role in selective neuronal loss in PD. Vesicular Monoamine Transporter (VMAT) is essential for proper vesicular storage of monoamines such as DA and their regulated release. Increasing evidence have linked VMAT dysfunction with Parkinson’s disease. In this study, we re-examine the gain- and loss-of-function phenotypes of the sole VMAT homologue in Drosophila. Our results suggest that the C-terminal sequences in the two encoded VMAT isoforms not only determine their differential subcellular localizations, but also their activities in content release. In particular, VMAT2 orthologue potentially poses a unique, previously unexplored activity in promoting DA release. On the other hand, by examining DA distribution in wildtype and VMAT mutant animals, we find that there exists intrinsic difference in the dynamics of intracellular DA handling among DA neurons clustered in different brain regions. Furthermore, loss of VMAT causes severe loss of total DA levels and a redistribution of DA in Drosophila brain. Lastly, removal of both VMAT and another PD gene parkin, which is also conserved in Drosophila, results in the selective loss of DA neurons, primarily in the protocerebral anterior medial (PAM) clusters of the brain. Our results suggest a potential involvement of cytoplasmic DA in selective degeneration of DA neurons and also implicating a role for a differential intracellular DA handling mechanism underlying the regional specificity of neuronal loss in PD patients

Wearable Platform for Automatic Recognition of Parkinson Disease by Muscular Implication Monitoring

The need for diagnostic tools for the characterization of progressive movement disorders - as the Parkinson Disease (PD) - aiming to early detect and monitor the pathology is getting more and more impelling. The parallel request of wearable and wireless solutions, for the real-time monitoring in a non-controlled environment, has led to the implementation of a Quantitative Gait Analysis platform for the extraction of muscular implications features in ordinary motor action, such as gait. The here proposed platform is used for the quantification of PD symptoms. Addressing the wearable trend, the proposed architecture is able to define the real-time modulation of the muscular indexes by using 8 EMG wireless nodes positioned on lower limbs. The implemented system “translates” the acquisition in a 1-bit signal, exploiting a dynamic thresholding algorithm. The resulting 1-bit signals are used both to define muscular indexes both to drastically reduce the amount of data to be analyzed, preserving at the same time the muscular information. The overall architecture has been fully implemented on Altera Cyclone V FPGA. The system has been tested on 4 subjects: 2 affected by PD and 2 healthy subjects (control group). The experimental results highlight the validity of the proposed solution in Disease recognition and the outcomes match the clinical literature results

ESTIMATION OF STRETCH REFLEX CONTRIBUTIONS OF WRIST USING SYSTEM IDENTIFICATION AND QUANTIFICATION OF TREMOR IN PARKINSON'S DISEASE PATIENTS

"The brain's motor control can be studied by characterizing the activity of spinal motor nuclei to brain control, expressed as motor unit activity recordable by surface electrodes". When a specific area is under consideration, the first step in investigation of the motor control system pertinent to it is the system identification of that specific body part or area. The aim of this research is to characterize the working of the brain's motor control system by carrying out system identification of the wrist joint area and quantifying tremor observed in Parkinson's disease patients. We employ the ARMAX system identification technique to gauge the intrinsic and reflexive components of wrist stiffness, in order to facilitate analysis of problems associated with Parkinson's disease. The intrinsic stiffness dynamics comprise majority of the total stiffness in the wrist joint and the reflexive stiffness dynamics contribute to the tremor characteristic commonly found in Parkinson's disease patients. The quantification of PD tremor entails using blind source separation of convolutive mixtures to obtain sources of tremor in patients suffering from movement disorders. The experimental data when treated with blind source separation reveals sources exhibiting the tremor frequency components of 3-8 Hz. System identification of stiffness dynamics and assessment of tremor can reveal the presence of additional abnormal neurological signs and early identification or diagnosis of these symptoms would be very advantageous for clinicians and will be instrumental to pave the way for better treatment of the disease

Screening for Parkinson’s Disease with Response Time Barriers: A Pilot Study

Background: Although significant response time deficits (both reaction time and movement time) have been identified in numerous studies of patients with Parkinson’s disease (PD), few attempts have been made to evaluate the use of these measures in screening for PD. Methods: Receiver operator characteristic curves were used to identify cutoff scores for a unitweighted composite of two choice response tasks in a sample of 40 patients and 40 healthy participants. These scores were then cross-validated in an independent sample of 20 patients and 20 healthy participants. Results: The unit-weighted movement time composite demonstrated high sensitivity (90%) and specificity (90%) in the identification of PD. Movement time was also significantly correlated (r = 0.59, p \u3c 0.025) with the motor score of the Unified Parkinson’s Disease Rating Scale (UPDRS). Conclusions: Measures of chronometric speed, assessed without the use of biomechanically complex movements, have a potential role in screening for PD. Furthermore, the significant correlation between movement time and UPDRS motor score suggests that movement time may be useful in the quantification of PD severity

Oscillation-specific nodal alterations in early to middle stages Parkinsons disease.

Background: Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinsons disease (PD) across early stage to middle stage by using graph theory-based analysis. Methods: Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; slow-3: 0.073-0.198 Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated. Results: Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity. Conclusions: Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages

Motor symptoms in Parkinson's disease: A unified framework

Parkinson’s disease (PD) is characterized by a range of motor symptoms. Besides the cardinal symptoms (akinesia and bradykinesia, tremor and rigidity), PD patients show additional motor deficits, including: gait disturbance, impaired handwriting, grip force and speech deficits, among others. Some of these motor symptoms (e.g., deficits of gait, speech, and handwriting) have similar clinical profiles, neural substrates, and respond similarly to dopaminergic medication and deep brain stimulation (DBS). Here, we provide an extensive review of the clinical characteristics and neural substrates of each of these motor symptoms, to highlight precisely how PD and its medical and surgical treatments impact motor symptoms. In conclusion, we offer a unified framework for understanding the range of motor symptoms in PD. We argue that various motor symptoms in PD reflect dysfunction of neural structures responsible for action selection, motor sequencing, and coordination and execution of movement

Timetable of Gait Cycle Events in Parkinson's Disease.

The study used an algorithmic method to measure fluctuations in the timetable of gait cycle events in patients with Parkinson's disease (PD). Subjects with severe PD (n=10; age 63.6 ± 10.1 years; Hoehn & Yahr [H & Y] disability score 3 or 4), mild PD (n=10; age 65.5 ± 4.3; H & Y ≦ 2), and normal controls (n=10; age 65.1 ± 13.3) were studied. A camera was mounted on the trunk, and the subjects walked in a self-selected manner. Overhead images of the foot path were analyzed to geometrically describe motion in terms of displacement and velocity. The timing of three gait events, i.e.,¹⁾ feet adjacent,²⁾ maximum speed of swinging foot, and³⁾ the trunk climbing to its highest point in mid-stance, was determined for extracted steps during steady-state gait. In severe PD, 74.9 ± 21.7% of steps was timetabled so that the swinging leg and the stance-phase leg became side by side before the trunk rose to its highest point to achieve 'foot clearance'. This pattern was significantly less prevalent in mild PD and controls. An altered timetable of gait cycle events may provide quantitative indices of gait disability during steady-state walking in patients with PD

On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing

Objective: While Parkinson’s disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing. Method: EEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli. Results: Behaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli. Conclusion: These distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients