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Cognitive impairment is considered as essential feature of non-motor symptoms in Parkinson's disease (PD). It is a result of underlying pathological processes in the brain of PD patients and it leads to decreased quality of life. In this thesis an analysis of the structure and profile of cognitive impairment is presented with special emphasis on executive functions and memory. We take diagnostic entities developed for the description of PD cognitive spectrum such as mild cognitive impairment (PD-MCI) and dementia (PD-D) as examples of heterogeneity and different severity of cognitive impairment in PD. However, neuropsychological methods in Czech version that would measure these diagnotic units were not adequatly validated. In the experimental part we test a hypothesis, if gait disorder with falls in PD is interconnected with cognitive impairment, and if PD-fallers have more severe cognitive deficit than PD-non-fallers. On the basis of nine validity or normative data studies we show psychometric properties and clinical utility of several basic neuropsychological methods in the Czech population for memory (Rey Auditory Verbal Learning Test, California Verbal Learning Test, Second Edition, Memory For Intentions Screening Test and Enhanced Cued Recall Test), sustained attention and executive functions...

Cognitive impairment is considered as essential feature of non-motor symptoms in Parkinson's disease (PD). It is a result of underlying pathological processes in the brain of PD patients and it leads to decreased quality of life. In this thesis an analysis of the structure and profile of cognitive impairment is presented with special emphasis on executive functions and memory. We take diagnostic entities developed for the description of PD cognitive spectrum such as mild cognitive impairment (PD-MCI) and dementia (PD-D) as examples of heterogeneity and different severity of cognitive impairment in PD. However, neuropsychological methods in Czech version that would measure these diagnotic units were not adequatly validated. In the experimental part we test a hypothesis, if gait disorder with falls in PD is interconnected with cognitive impairment, and if PD-fallers have more severe cognitive deficit than PD-non-fallers. On the basis of nine validity or normative data studies we show psychometric properties and clinical utility of several basic neuropsychological methods in the Czech population for memory (Rey Auditory Verbal Learning Test, California Verbal Learning Test, Second Edition, Memory For Intentions Screening Test and Enhanced Cued Recall Test), sustained attention and executive functions...

Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classification methods (Convolutional Neural Network—CNN, Support Vector Machine—SVM, K-Nearest Neighbors—KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classifiers, the automatic classification of normal and abnormal EMG patterns during gait (2 classes: “healthy” and “patient”) was possible with a high degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classification with respect to the underlying disorder even when classifiers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation.

Hereditary spastic paraplegia type 54 is a rare autosomal recessive neurological gait disorder characterized by paraplegia, muscle spasticity, and intellectual disability. This length-dependent distal axonopathy is caused by mutations in the DDHD2 gene, which encodes the intracellular phospholipase A1 DDHD2. Little is known about the molecular function of the DDHD2 protein, especially in the context of HSP54. Thus, there is a need to further investigate its molecular functions and investigate the impact of DDHD2 deficiency in disease-relevant cells. Here, lipidomic profiling of dermal fibroblasts derived from three unrelated patients has revealed 19 glycerophosphoethanolamine species at differential levels in patients relative to unaffected controls. However, patient cells appear to have an unaffected Golgi apparatus morphology and lipid droplet formation, despite DDHD2’s proposed roles in these processes. To study the gene function in neuronal cells, I transdifferentiated the fibroblasts into induced neuronal precursor cells and found all the patient cells arrested in the G0/G1 phase of upon conversion. Given that these cell lines are unsustainable, I generated a stable knockdown cell line in the highly proliferative HEK293A to study the molecular biology of DDHD2. The knockdown cells had a reduced growth, were delayed in the G2/M phase of the cell cycle, and became multinucleated. I then treated the cells with antineoplastic compounds paclitaxel and nocodazole and found more knockdown cells in G0/G1 than controls, suggesting the possible occurrence of mitotic slippage. Lastly, I report a novel subcellular localization for DDHD2 at the microtubule organization center.