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Parkinson’s Disease (PD) is a neurodegenerative disorder, within this categoryof diseases it is among the most prevalent worldwide. The etiology of PD isbased in progressive deterioration of neural tissue in the basal ganglia (neuronalnuclei located at the base of the cerebrum) and their related structures. Current research is focusing on treatment approaches to either enhance or replaceexisting pharmaceutical treatment approaches, such as dopamine replacementtherapy. In this project, the focus was on finding correlates between movementdata and neurological signals to provide insight into potential biomarkers forcomplex motor symptoms of PD. This will in turn provide a starting point forspecifically targeted closed-loop neural stimulation that alleviates these symptoms. Although the data available at the time of this thesis did not providesufficient insight to derive a conclusion on the neural correlates, a pipeline wasdeveloped, which analyzes and synchronizes kinematic and neural data and willenable further exploration as additional data is obtained.

Yes / Despite a wealth of activity across the globe in the area of longitudinal population cohorts, surprisingly little information is available on the natural biomedical history of a number of age-related neurodegenerative diseases (ND), and the scope for intervention studies based on these cohorts is only just beginning to be explored. The Joint Programming Initiative on Neurodegenerative Disease Research (JPND) recently developed a novel funding mechanism to rapidly mobilise scientists to address these issues from a broad, international community perspective. Ten expert Working Groups, bringing together a diverse range of community members and covering a wide ND landscape (Alzheimer’s, Parkinson’s, frontotemporal degeneration, amyotrophic lateral sclerosis, Lewy-body and vascular dementia) were formed to discuss and propose potential approaches to better exploiting and coordinating cohort studies. The purpose of this work is to highlight the novel funding process along with a broad overview of the guidelines and recommendations generated by the ten groups, which include investigations into multiple methodologies such as cognition/functional assessment, biomarkers and biobanking, imaging, health and social outcomes, and pre-symptomatic ND. All of these were published in reports that are now publicly available online.

Yes / Spinal muscular atrophy, the leading genetic cause of infant mortality, is a motor neuron disease caused by low levels of survival motor neuron (SMN) protein. SMN is a multifunctional protein that is implicated in numerous cytoplasmic and nuclear processes. Recently, increasing attention is being paid to the role of SMN in the maintenance of DNA integrity. DNA damage and genome instability have been linked to a range of neurodegenerative diseases. The ribosomal DNA (rDNA) represents a particularly unstable locus undergoing frequent breakage. Instability in rDNA has been associated with cancer, premature ageing syndromes, and a number of neurodegenerative disorders. Here, we report that SMN-deficient cells exhibit increased rDNA damage leading to impaired ribosomal RNA synthesis and translation. We also unravel an interaction between SMN and RNA polymerase I. Moreover, we uncover an spinal muscular atrophy motor neuron-specific deficiency of DDX21 protein, which is required for resolving R-loops in the nucleolus. Taken together, our findings suggest a new role of SMN in rDNA integrity.

According to the National Institute of Neurological Disorders and Stroke, there are more than 600 neurologic disorders that affect approximately 50 million Americans each year. The $91 billion dollars spent by Medicare on Alzheimer's disease and other dementias in 2005 is projected to increase to $189 billion by 2015 [4]. The existence of neural stem cells (NSC's) and neurogenesis makes neural regeneration a viable option. The ethical barriers of using embryonic stem cells, rejection of the transplanted cells, and possible tumor formation, are only a few of the problems that face stem cell transplantation, a widely considered option to repopulate the brain with cells. A noninvasive pharmaceutical approach that can promote neuron regeneration and recovery would be the key to curing many neurodegenerative diseases. The development of MS-818 as a non-invasive enhancer of the proliferation process of NS Cs is revolutionary for the treatment of neurodegenerative diseases. Due to the fact that its mechanism of action remains unknown, the full pharmacological potential of MS- 818 has not been fully exploited [8]. Isolating protein targets of MS-818 is key in identifying the molecular pathways responsible for its mechanism of action. UV-Vis analysis of MS-818 showed absorbance at 275-nm, and this data was used to calculate coupling yield. MS-818 coupled to the NHS-activated sepharose beads of the affinity column with 83% efficiency. Proteins were isolated from human embryonic kidney cells (HEK 293 cells) and applied to the column. Bradford assay confirmed that bound proteins eluted in a concentration dependent manner when an MS-818 gradient was applied to the column. Polyacrylamide gel electrophoresis in the presence of sodium dodecyl sulfate of the eluate revealed two sets of polypeptides migrating at 37-75 kDa and 100-150 kDa. In addition, some trace polypeptides in the sub-35 kDa region could be seen. Although we have not yet identified specific proteins that MS-818 interacts with, we were able to successfully to isolate such proteins.

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In patients with Parkinson’s disease (PD), the molecularly misfolded form of α-synuclein was recently identified in cutaneous autonomic nerve fibers which displayed increased accumulation even in early disease stages. However, the underlying mechanisms of synucleinopathic nerve damage and its implication for brain pathology in later life remain to be elucidated. To date, specific diagnostic tools to evaluate small fiber pathology and to discriminate neurodegenerative proteinopathies are rare. Recently, research has indicated that deposition of α-synuclein in cutaneous nerve fibers quantified via immunohistochemistry in superficial skin biopsies might be a valid marker of PD which could facilitate early diagnosis and monitoring of disease progression. However, lack of standardization of techniques to quantify neural α-synuclein deposition limits their utility in clinical practice. Additional challenges include the identification of potential distinct morphological patterns of intraneural α-synuclein deposition among synucleinopathies to facilitate diagnostic discrimination and determining the degree to which structural damage relates to dysfunction of nerve fibers targeted by α-synuclein. Answering these questions might improve our understanding of the pathophysiological role of small fiber neuropathy in Parkinson’s disease, help identify new treatment targets, and facilitate assessment of response to neuroprotective treatment.

In patients with Parkinson’s disease (PD), the molecularly misfolded form of α-synuclein was recently identified in cutaneous autonomic nerve fibers which displayed increased accumulation even in early disease stages. However, the underlying mechanisms of synucleinopathic nerve damage and its implication for brain pathology in later life remain to be elucidated. To date, specific diagnostic tools to evaluate small fiber pathology and to discriminate neurodegenerative proteinopathies are rare. Recently, research has indicated that deposition of α-synuclein in cutaneous nerve fibers quantified via immunohistochemistry in superficial skin biopsies might be a valid marker of PD which could facilitate early diagnosis and monitoring of disease progression. However, lack of standardization of techniques to quantify neural α-synuclein deposition limits their utility in clinical practice. Additional challenges include the identification of potential distinct morphological patterns of intraneural α-synuclein deposition among synucleinopathies to facilitate diagnostic discrimination and determining the degree to which structural damage relates to dysfunction of nerve fibers targeted by α-synuclein. Answering these questions might improve our understanding of the pathophysiological role of small fiber neuropathy in Parkinson’s disease, help identify new treatment targets, and facilitate assessment of response to neuroprotective treatment.

There has in recent years been interdisciplinary research on utilizing machine learning for detecting and classifying neurodegenerative disorders with the sole goal of outperforming state-of-the-art models in terms of metrics such as accuracy, specificity, and sensitivity. Specifically, these studies have been conducted using existing networks on ”novel” methods of pre-processing data or by developing new convolutional neural networks. As of now, no work has looked into how different normalization techniques affect a deep or shallow convolutional neural network in terms of numerical stability, its performance, explainability, and interpretability. This work delves into what normalization technique is most suitable for deep and shallow convolutional neural networks. Two baselines were created, one shallow and one deep, and applied eight different normalization techniques to these model architectures. Conclusions were drawn based on our analysis of numerical stability, performance (metrics), and methods of Explainable Artificial Intelligence. Our findings indicate that normalization techniques affect models differently regarding the mentioned aspects of our analysis, especially numerical stability and explainability. Moreover, we show that there should indeed be a preference to select one method over the other in future studies of this interdisciplinary field.