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Synaptic impairment might precede neuronal degeneration in Parkinson’s disease. However, the intimate mechanisms altering synaptic function by the accumulation of presynaptic α-synuclein in striatal dopaminergic terminals before dopaminergic death occurs, have not been elucidated. Our aim is to unravel the sequence of synaptic functional and structural changes preceding symptomatic dopaminergic cell death. As such, we evaluated the temporal sequence of functional and structural changes at striatal synapses before parkinsonian motor features appear in a rat model of progressive dopaminergic death induced by overexpression of the human mutated A53T α-synuclein in the substantia nigra pars compacta, a protein transported to these synapses. Sequential window acquisition of all theoretical mass spectra proteomics identified deregulated proteins involved first in energy metabolism and later, in vesicle cycling and autophagy. After protein deregulation and when α-synuclein accumulated at striatal synapses, alterations to mitochondrial bioenergetics were observed using a Seahorse XF96 analyser. Sustained dysfunctional mitochondrial bioenergetics was followed by a decrease in the number of dopaminergic terminals, morphological and ultrastructural alterations, and an abnormal accumulation of autophagic/endocytic vesicles inside the remaining dopaminergic fibres was evident by electron microscopy. The total mitochondrial population remained unchanged whereas the number of ultrastructurally damaged mitochondria increases as the pathological process evolved. We also observed ultrastructural signs of plasticity within glutamatergic synapses before the expression of motor abnormalities, such as a reduction in axospinous synapses and an increase in perforated postsynaptic densities. Overall, we found that a synaptic energetic failure and accumulation of dysfunctional organelles occur sequentially at the dopaminergic terminals as the earliest events preceding structural changes and cell death. We also identify key proteins involved in these earliest functional abnormalities that may be modulated and serve as therapeutic targets to counterbalance the degeneration of dopaminergic cells to delay or prevent the development of Parkinson’s disease., This study was funded by the Instituto de Salud Carlos III through the projects PI14/00763 and PI19/01915 (co-funded by ERDF/ESF, ‘Investing in your future’). L.M.-G. held a Predoctoral Research Fellowship from the University of the Basque Country (UPV/EHU). T.R.-C. and A.Q.-V. were funded by CIBERNED. T.R.-C. held a Fundación Jesús de Gangoiti Barrera Foundation grant (Bilbao, Spain). H.J.-U. and A.B.-I. held a Predoctoral Research Fellowship from the Government of the Basque Country. Israel Science Foundation (536/19) and the Spanish Ministry of Science (SAF2016-78071-R) funded the contribution of S.K. and A.O.

Lewy body dementias (LBDs) consist of dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD), which are clinically similar syndromes that share neuropathological findings with widespread cortical Lewy body deposition, often with a variable degree of concomitant Alzheimer pathology. The objective of this article is to provide an overview of the neuropathological and clinical features, current diagnostic criteria, biomarkers, and management of LBD. Literature research was performed using the PubMed database, and the most pertinent articles were read and are discussed in this paper. The diagnostic criteria for DLB have recently been updated, with the addition of indicative and supportive biomarker information. The time interval of dementia onset relative to parkinsonism remains the major distinction between DLB and PDD, underpinning controversy about whether they are the same illness in a different spectrum of the disease or two separate neurodegenerative disorders. The treatment for LBD is only symptomatic, but the expected progression and prognosis differ between the two entities. Diagnosis in prodromal stages should be of the utmost importance, because implementing early treatment might change the course of the illness if disease-modifying therapies are developed in the future. Thus, the identification of novel biomarkers constitutes an area of active research, with a special focus on α-synuclein markers.

Background Parkinson’s Disease (PD) is a chronic neurodegenerative disease associated with motor problems such as gait impairment. Different systems based on 3D cameras, accelerometers or gyroscopes have been used in related works in order to study gait disturbances in PD. Kinect Ⓡ has also been used to build these kinds of systems, but contradictory results have been reported: some works conclude that Kinect does not provide an accurate method of measuring gait kinematics variables, but others, on the contrary, report good accuracy results. Methods In this work, we have built a Kinect-based system that can distinguish between different PD stages, and have performed a clinical study with 30 patients suffering from PD belonging to three groups: early PD patients without axial impairment, more evolved PD patients with higher gait impairment but without Freezing of Gait (FoG), and patients with advanced PD and FoG. Those patients were recorded by two Kinect devices when they were walking in a hospital corridor. The datasets obtained from the Kinect were preprocessed, 115 features identified, some methods were applied to select the relevant features (correlation based feature selection, information gain, and consistency subset evaluation), and different classification methods (decision trees, Bayesian networks, neural networks and K-nearest neighbours classifiers) were evaluated with the goal of finding the most accurate method for PD stage classification. Results The classifier that provided the best results is a particular case of a Bayesian Network classifier (similar to a Naïve Bayesian classifier) built from a set of 7 relevant features selected by the correlation-based on feature selection method. The accuracy obtained for that classifier using 10-fold cross validation is 93.40%. The relevant features are related to left shin angles, left humerus angles, frontal and lateral bents, left forearm angles and the number of steps during spin. Conclusions In this paper, it is shown that using Kinect is adequate to build a inexpensive and comfortable system that classifies PD into three different stages related to FoG. Compared to the results of previous works, the obtained accuracy (93.40%) can be considered high. The relevant features for the classifier are: a) movement and position of the left arm, b) trunk position for slightly displaced walking sequences, and c) left shin angle, for straight walking sequences. However, we have obtained a better accuracy (96.23%) for a classifier that only uses features extracted from slightly displaced walking steps and spin walking steps. Finally, the obtained set of relevant features may lead to new rehabilitation therapies for PD patients with gait problems.

Rasagiline is effective in the early stages of the disease and has shown a possible effect of modifying disease progression at a dose of 1 mg. The accurate management of dopaminergic drugs in Parkinson's disease is able to delay the appearance of motor fluctuations and dyskinesias. The combination of different drugs that provide a more continuous dopaminergic stimulation (rasagiline, dopamine agonists) not only exerts a benefit through diminishing the impact of pulsatile stimulation on post-synaptic dopamine receptors, but allows to decrease total daily levodopa requirements. The combination of rasagiline with other dopaminergic drugs has demonstrated to be as efficacious as entacapone for improving both the frequency and severity of motor fluctuations. Likewise, new evidences have shown that the earlier introduction of rasagiline is associated with a delay in introducing other dopaminergic drugs, thus indicating that the symptomatic benefit of rasagiline on daily motor function is not only present in the early Parkinson's disease stages, but is maintained over time.