Parkinson’s disease (PD), dementia with Lewy body (DLB), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD) and multiple system atrophy (MSA) belong to a group of neurodegenerative diseases called parkinsonian syndromes. They share several clinical, neuropathological and genetic features. Neurodegenerative diseases are characterized by the progressive dysfunction of specific populations of neurons, determining clinical presentation. Neuronal loss is associated with extra- and intracellular accumulation of misfolded proteins. The parkinsonian diseases affect distinct areas of the brain. PD and MSA belong to a group of synucleinopathies that are characterized by the presence of fibrillary aggregates of α-synuclein protein in the cytoplasm of selected populations of neurons and glial cells. PSP is a tauopathy associated with the pathological aggregation of the microtubule associated tau protein. Although PD is common in the world’s aging population and has been extensively studied, the exact mechanisms of the neurodegeneration are still not fully understood. Growing evidence indicates that parkinsonian disorders to some extent share a genetic background, with two key components identified so far: the microtubule associated tau protein gene (MAPT) and the α-synuclein gene (SNCA). The main pathways of parkinsonian neurodegeneration described in the literature are the protein and mitochondrial pathways. The factors that lead to neurodegeneration are primarily environmental toxins, inflammatory factors, oxidative stress and traumatic brain injury.

Humans are a vision-dominated species; what we perceive depends on where we look. Therefore, eye movements (EMs) are essential to our interactions with the environment, and experimental findings show EMs are affected in neurodegenerative disorders (ND). This could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to establish whether changes in EM-evoked responses can tell us about the progression of ND, such as Alzheimer’s (AD) and Parkinson’s diseases (PD), in different stages. In the present review, we have analyzed the results of psychological, neurological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with machine learning (ML) methods. Thanks to ML algorithms, from the high-dimensional parameter space, we were able to find significant EM changes related to ND symptoms that gave us insights into ND mechanisms. The predictive algorithms described use various approaches, including granular computing, Naive Bayes, Decision Trees/Tables, logistic regression, C-/Linear SVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are navigation problems in 3D space in both diseases. Consequently, we investigated EM experiments in the virtual space and how they may help find neurodegeneration-related brain changes, e.g., related to place or/and orientation problems. In conclusion, EM parameters with clinical symptoms are powerful precision instruments that, in addition to their potential for predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases.

Parkinson’s disease (PD) is generally considered a sporadic disorder, but a strong genetic background is often found. The aim of this study was to identify the underlying genetic cause of PD in two affected siblings and to subsequently assess the role of mutations in Cathepsin B (CTSB) in susceptibility to PD. A typical PD family was identified and whole-exome sequencing was performed in two affected siblings. Variants of interest were validated using Sanger sequencing. CTSB p.Gly284Val was genotyped in 2077 PD patients and 615 unrelated healthy controls from the Czech Republic, Ireland, Poland, Ukraine, and the USA. The gene burden analysis was conducted for the CTSB gene in an additional 769 PD probands from Mayo Clinic Florida familial PD cohort. CTSB expression and activity in patient-derived fibroblasts and controls were evaluated by qRT-PCR, western blot, immunocytochemistry, and enzymatic assay. The CTSB p.Gly284Val candidate variant was only identified in affected family members. Functional analysis of CTSB patient-derived fibroblasts under basal conditions did not reveal overt changes in endogenous expression, subcellular localization, or enzymatic activity in the heterozygous carrier of the CTSB variant. The identification of the CTSB p.Gly284Val may support the hypothesis that the CTSB locus harbors variants with differing penetrance that can determine the disease risk.

Parkinson’s disease (PD) is a heterogenous neurodegenerative disorder. Genetic factors play a significant role, especially in early onset and familial cases. Mutations are usually found in the LRRK2 gene, but their importance varies. Some mutations, such as p.Arg1441Cys or other alterations in the 1441 codon, show clear correlation with PD, whereas others are risk factors found also in healthy populations or have neglectable consequences. They also exhibit various prevalence among different populations. The aim of this paper is to sum up the current knowledge regarding the epidemiology and pathogenicity of LRRK2 mutations, other than the well-established p.Gly2019Ser. We performed a review of the literature using PubMed database. 103 publications met our inclusion criteria. p.Arg1441Cys, p.Arg1441Gly, p.Arg1441His, p.Arg1441Ser are the most common pathogenic mutations in European populations, especially Hispanic. p.Asn1437His is pathogenic and occurs mostly in the Scandinavians. p.Asn1437Ser and p.Asn1437Asp have been reported in German and Chinese cohorts respectively. p.Ile2020Thr is a rare pathogenic mutation described only in a Japanese cohort. p.Met1869Thr has only been reported in Caucasians. p.Tyr1699Cys, p.Ile1122Val have only been found in one family each. p.Glu1874Ter has been described in just one patient. We found no references concerning mutation p.Gln416Ter. We also report the first case of a Polish PD family whose members carried p.Asn1437His.

Parkinson’s disease results in motor impairment that deteriorates patients’ quality of life. One of the symptoms negatively interfering with daily activities is kinetic tremor which should be measured to monitor the outcome of therapy. A new instrumented method of quantification of the kinetic tremor is proposed, based on the analysis of circles drawn on a digitizing tablet by a patient. The aim of this approach is to obtain a tremor scoring equivalent to that performed by trained clinicians. Models are trained with the least absolute shrinkage and selection operator (LASSO) method to predict the tremor scores on the basis of the parameters computed from the patients’ drawings. Signal parametrization is derived from both expert knowledge and the response of an artificial neural network to the raw data, thus the approach was named multimodal. The fitted models are eventually combined into model ensembles that provide aggregated scores of the kinetic tremor captured in the drawings. The method was verified with a set of clinical data acquired from 64 Parkinson’s disease patients. Automated and objective quantification of the kinetic tremor with the presented approach yielded promising results, as the Pearson’s correlations between the visual ratings of tremor and the model predictions ranged from 0.839 to 0.890 in the best-performing models.