The diagnosis of Parkinson’s disease (PD) follows the UK Brain Bank Criteria, which demands bradykinesia and one additional symptom, i.e. rigidity, resting tremor or postural instability. The latter is not a useful sign for the early diagnosis of PD, because it does not appear before Hoehn and Yahr stage 3. Early symptoms of PD which precede the onset of motor symptoms are hyposmia, REM sleep behavioral disorder, constipation, and depression. In addition, an increasing number of patients whose PD is related to a genetic defect are being described. Thus, genetic testing may eventually develop into a tool to identify at-risk patients. The clinical diagnosis of PD can be supported by levodopa or apomorphine tests. Imaging studies such as cranial CT or MRI are helpful to distinguish idiopathic PD from atypical or secondary PD. SPECT and PET methods are valuable to distinguish PD tremor from essential tremor if this is clinically not possible. Using all of these methods, we may soon be able to make a premotor diagnosis of PD, which will raise the question whether early treatment is possible and ethically and clinically advisable. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

Background: Parkinson’s disease (PD) is often accompanied by non-motor complications, such as dementia, depression, and psychotic symptoms, which worsen the prognosis and increase the personal and socioeconomic burden of disease. Prevalence estimates of these complications are quite variable and are lacking for the outpatient care sector. Methods: As part of a larger, nationwide, cross-sectional epidemiological study in n=315 neurological outpatient settings in Germany, this paper estimates the frequency of dementia and cognitive impairment in n=873 outpatients meeting the UK Brain Bank criteria for idiopathic PD. Assessments were based on a clinical interview and neuropsychological assessments, including the Hoehn & Yahr rating and Unified Parkinson’s Disease Rating Scale (UPDRS). Cognitive impairment was assessed by the Mini-Mental State Exam (MMSE), Clock Drawing Test (CDT) and the Parkinson Neuropsychometric Dementia Assessment (PANDA) and the clinician’s diagnosis of dementia was based on the diagnostic criteria of DSMIV. Results Using standardized cutoff scores, the prevalence of cognitive impairment in the study sample as measured by various methods was 17.5% by MMSE (≤ 24), 41.8% by CDT (≥ 3), 43.6% by PANDA (≤ 14), and 28.6% met the DSM-IV criteria for dementia. All estimates increased with age and PD severity. Gender was an inconsistent contributor while illness duration had no significant impact on cognition. Multiple regression analyses revealed PD severity to be the strongest predictor of dementia risk (OR=4.3; 95 % CI: 2.1–9.1), while neuropsychiatric syndromes had independent, although modest additional contributions (OR=2.5, 95% CI: 1.6–3.8). Conclusion: Estimates of cognitive impairment and dementia in PD patients are largely dependent on the diagnostic measure used. Using established clinical diagnostic standards for dementia the overall rate on routine outpatient neurological care is 28.6%, but using more sensitive neuropsychological measures, rates for cognitive impairment might be up to 2-fold higher. The MMSE revealed strikingly low sensitivity. Neuropsychiatric syndromes, in addition to PD severity and age, have an independent – although modest – additional contribution to patients’ risk for cognitive impairment and dementia.

Hintergrund: Die Parkinson-Krankheit (PK) ist häufig durch Demenz und Depression gekennzeichnet, die den Krankheitsverlauf erschweren und das Risiko einer Pflegebedürftigkeit zusätzlich erhöhen können. Über die genauen Zusammenhänge zwischen PK und diesen Komplikationen liegen für Ambulanzpatienten jedoch bislang keine Zahlen vor. Patienten und Methode: Bundesweit wurden 1449 Patienten mit PK von 315 niedergelassenen Fachärzten untersucht. Neben dem neurologischen Zustand und der Pflegebedürftigkeit wurden auch demenzielle Syndrome nach DSM-IV-Kritierien sowie Depressionen mit der Montgomery-Asberg Depression Rating Scale (MADRS) dokumentiert. Ergebnisse: Insgesamt 18,3% der Patienten waren pflegebedürftig, hiervon hatten 51,9% und 43,2% die Pflegestufen I und II. Auch nach Kontrolle des PK-Schweregrads hatten Patienten mit Depression (OR=2,8, 95%-KI:1,8–4,3), Demenz (OR=2,7; 95%-KI:1,8–4,1) bzw. mit beiden Störungen (OR=3,9, 95%-KI:2,5–6,0) ein höheres Risiko für Pflegebedürftigkeit als Patienten ohne diese Störungen. Patienten ≥76 Jahre hatten ein 4fach höheres Risiko für eine Pflegestufe als Patienten ≤65 Jahre (OR=3,5, 95%-KI:2,3–5,5). Über die Altersgruppen hinweg nahm das Risiko, pflegebedürftig zu werden, bei depressiven Patienten am stärksten zu (von 11,9% auf 42,0%). Schlussfolgerung: Das Risiko für eine Pflegebedürftigkeit ist bei Demenz und Depression stark erhöht. Die Daten legen insbesondere für die Depression als Einzelkomplikation eine vergleichbar hohe Krankheitslast nahe wie für die Demenz. / Background: Parkinson’s disease (PD) is frequently accompanied by dementia or depression which can aggravate the clinical picture of the disease and increase the risk of care dependency (CD). Little is known about the associations between PD, these neuropsychiatric comorbidities and CD in outpatients. Patients and methods: A nationwide sample of outpatients (n=1,449) was examined by office-based neurologists (n=315) comprising the documentation of the general, neurological status and the degree of CD. The dementia status was clinically rated according to the established DSM-IV criteria. Depression was screened with the Montgomery-Asberg Depression Rating Scale (MADRS). Results: Overall, 18.3% of all patients were care dependent. Even after adjustment for PD severity, patients with depression (OR=2.8; 95% CI 1.8–4.3), dementia (OR=2.7; 95% CI 1.8–4.1) or both (OR=3.9; 95% CI 2.5–60,0) were at higher risk for CD than patients without dementia or depression. Patients aged ≥76 years were fourfold more likely to be care dependent than patients aged ≤65 years (OR=3.5; 95% CI 2.3–5.5). Across all age groups, patients with depression featured the highest increments (from 11.9 to 42.0%). Conclusion: The risk for CD is substantially elevated in outpatients with PD when further neuropsychiatric symptoms are present. The data suggest that depression contributes equally to disability as does dementia.

Small extracellular vesicles (small EVs) are endogenous membrane-enclosed nanocarriers released from essentially all cells. They have been shown to carry proteins, lipids, nucleic acids to transmit biological signals throughout the body, including to the brain. Some evidence has suggested that small EVs can cross the blood-brain barrier (BBB), moving from the peripheral circulation to the central nervous system (CNS). The BBB is a dynamic barrier that regulates molecular trafficking between the peripheral circulation and the CNS. As a result, small EVs have attracted attention for their potential as a novel delivery platform for nucleic acid-based therapeutics across the BBB. Silencing RNAs (siRNAs) are a potent drug class but using “naked” siRNA is not feasible due to their short half-life, vulnerability to degradation and low penetration in cells. Despite the excitement for the development of small EV-based therapeutics, their clinical development is hampered by the lack of reliable methods for packing therapeutics into them. Reshke et al. has shown that cells can be genetically engineered to produce customizable small EVs packaged with siRNA against any protein by integrating the siRNA sequence into the pre- miR-451 structure. Mounting evidence has established that in a misfolded state, α-synuclein becomes insoluble and phosphorylated to form intracellular inclusions in neurons (known as Lewy bodies) which leads to Parkinson’s disease (PD) pathogenesis. Given that increased α-synuclein expression causes familial and idiopathic PD, decreasing its synthesis by using siRNA is an attractive therapeutic strategy. Here, we genetically engineered cells to produce small EVs packaged with siRNA against α-synuclein integrated in the pre-miR451 backbone, tested their ability to cross an in vitro BBB, and deliver its cargo to silence endogenous α-synuclein in neuron- like cells. The therapeutic potential of α-synuclein siRNA delivery by these small EVs was demonstrated by the strong mRNA (60-70%) and protein knockdown (43%) of α-synuclein in neuron-like cells. We also demonstrated that approximately at 4% and 2%, respectively of small EVs-derived from human brain endothelial cells (hCMEC/D3) and human embryonic kidney (HEK293T) were transported cross the in vitro BBB model. Interestingly, we observed that small EVs-derived from HEK293T deliver their cargo to induced brain endothelial cells (iBECs) (~74% α-synuclein mRNA reduction) but their rate of transport across BBB was lower and did not reduce α-synuclein mRNA expression in neuron-like cells, seeded on the far side of the BBB. Small EVs- derived from hCMEC/D3 reduced α-synuclein mRNA (40%) in neuron-like cells across the BBB model. This finding suggests that small EVs derived from different cell sources can undergo different intracellular trafficking routes, providing various opportunities to influence the efficiency of delivery and fate of intracellular cargo. Using small EVs-derived from hCMEC/D3, two different routes of administration, a single bolus intravenous (IV) or intra-carotid (ICD) injection, showed small EVs largely accumulated in the liver, spleen, small intestines and kidneys; and only a small amount of small EVs were detected in the brain. These results indicate that human brain endothelial cells may serve as a promising cell source for CNS treatments based on small EVs.

This thesis consists of four research papers presenting a microdata analysis approach to assess and evaluate the Parkinson’s disease (PD) motor symptoms using smartphone-based systems. PD is a progressive neurological disorder that is characterized by motor symptoms. It is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Both patients’ perception regarding common symptom and their motor function need to be related to the repeated and time-stamped assessment; with this, the full extent of patient’s condition could be revealed. The smartphone enables and facilitates the remote, long-term and repeated assessment of PD symptoms. Two types of collected data from smartphone were used, one during a three year, and another during one-day clinical study. The data were collected from series of tests consisting of tapping and spiral motor tests. During the second time scale data collection, along smartphone-based measurements patients were video recorded while performing standardized motor tasks according to Unified Parkinson’s disease rating scales (UPDRS). At first, the objective of this thesis was to elaborate the state of the art, sensor systems, and measures that were used to detect, assess and quantify the four cardinal and dyskinetic motor symptoms. This was done through a review study. The review showed that smartphones as the new generation of sensing devices are preferred since they are considered as part of patients’ daily accessories, they are available and they include high-resolution activity data. Smartphones can capture important measures such as forces, acceleration and radial displacements that are useful for assessing PD motor symptoms. Through the obtained insights from the review study, the second objective of this thesis was to investigate whether a combination of tapping and spiral drawing tests could be useful to quantify dexterity in PD. More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. The results from this study showed that tapping and spiral drawing tests that were collected by smartphone can detect movements reasonably well related to under- and over-medication. The thesis continued by developing an Approximate Entropy (ApEn)-based method, which aimed to measure the amount of temporal irregularity during spiral drawing tests. One of the disabilities associated with PD is the impaired ability to accurately time movements. The increase in timing variability among patients when compared to healthy subjects, suggests that the Basal Ganglia (BG) has a role in interval timing. ApEn method was used to measure temporal irregularity score (TIS) which could significantly differentiate the healthy subjects and patients at different stages of the disease. This method was compared to two other methods which were used to measure the overall drawing impairment and shakiness. TIS had better reliability and responsiveness compared to the other methods. However, in contrast to other methods, the mean scores of the ApEn-based method improved significantly during a 3-year clinical study, indicating a possible impact of pathological BG oscillations in temporal control during spiral drawing tasks. In addition, due to the data collection scheme, the study was limited to have no gold standard for validating the TIS. However, the study continued to further investigate the findings using another screen resolution, new dataset, new patient groups, and for shorter term measurements. The new dataset included the clinical assessments of patients while they performed tests according to UPDRS. The results of this study confirmed the findings in the previous study. Further investigation when assessing the correlation of TIS to clinical ratings showed the amount of temporal irregularity present in the spiral drawing cannot be detected during clinical assessment since TIS is an upper limb high frequency-based measure.

Eye-tracking is a method for monitoring and measuring eye movements. The technology has had a significant impact so far and new application areas are emerging. Today, the technology is used in the gaming industry, health industry, self-driving cars, and not least in medicine. In the latter, large research resources are invested to investigate the extent to which eye-tracking can help with disease diagnostics. One disease of interest is Parkinson’s disease, a neuro-degenerative disease in which the dopamine production in nerve cells is destroyed. This leads to detoriating nerve signal transmission, which in turn affects the motor skills. One of the affected motor functions associated with PD is the oculomotor function, affecting the eye function. The declination can be observed clinically by physicians, however eye-tracking technology has a high potential here, but it remains to investigate which methodology and which test protocols are relevant to study and to what extent the technology can be used as a diagnostic tool. A novel class of algorithms for finding representations of data is called self-supervised learning (SSL). The class of algorithms seems to have a high potential in terms of categorizing biomarkers. This thesis examines to which extent an SSL network can learn representations of eye-tracking data on Parkinson’s patients, in order to distinguish between healthy and sick, patients on and off medication. The result suggests that the network does not succeed in learning distinct differences between groups. Furthermore, no difference is observed in the result when we in the model take into account the task-specific target information that the subjects are following. Today in the UK approximately 26 percent of Parkinson’s patients are misdiagnosed. In the initial state of the disease, the misdiagnosis is even higher. Potentially, the method can be used as a complement to regular diagnosis in different stages of the disease. This would provide better conditions for the patient as well as for medical and pharmaceutical research. The method also has the potential to reduce physicians’ workload. / Eye-tracking eller ögonrörelsemätning som är den svenska termen, är en metod för att följa och mäta ögats rörelser. Tekniken har fått en betydande genomslagskraft hittills och nya applikationsområden dyker upp titt som tätt. Idag används tekniken inom spelindustrin, hälsa, i självkörande bilar och inte minst inom medicin. Inom det senare läggs idag stora forskningsresurser för att undersöka i vilken utsträckning eye-tracking kan hjälpa till att diagnosticera sjukdomar. En sjukdom av intresse är Parkinson’s sjukdom, vilket är en neurodegenerativ sjukdom där dopaminproduktionen i nervceller förstörs. Det leder till att transmissionen av nervsignaler försämras som i sin tur gör att motoriken påverkas vilket bland annat leder till en nedsättning i ögats motorik. Det är något som man idag kan observera kliniskt, eye-tracking teknik har här en hög potential men det återstår att undersöka vilken metodik och vilka testprotokoll som är relevanta att undersöka och i vilken grad tekniken kan användas som ett diagnostiskt verktyg. En ny typ av algoritmer för att hitta representationer av data kallas för self-supervised learning (SSL), dessa algoritmer verkar ha en hög potential vad gäller kategorisering av biomarkörer. I denna uppsats undersöks i vilken grad ett SSL-nätverk kan lära sig representationer av eye-tracking data på Parkinson’s patienter för att kunna särskilja mellan friska och sjuka, medicinerade och omedicinerade. Resultatet är att nätverket inte lyckas lära sig skiljaktigheter mellan dessa klasser. Vidare noteras ingen skillnad i resultatet då vi i modellen tar hänsyn till de specifika uppgifterna som försökspersonerna fått. Idag får 30 procent av parkinsonpatienterna fel diagnos. I ett initialt tillstånd av sjukdomen är feldiagnosticeringen ännu högre. Potentiellt kan metoden användas som komplement till diagnosticering i olika skeden av sjukdomen. Detta skulle ge bättre förutsättningar för såväl patienten som för den medicinska och farmaceutiska forskningen. Metoden har dessutom potential att minska läkares arbetsbörda.

A striking feature of Parkinson's disease (PD) is that the distal axonal terminals of neurons degenerate prior to the soma, a process referred to as 'dying-back'. Another hallmark of the disease is the pathological accumulation of abnormal protein aggregates in soma and axons. Lysosomes, a critical component of the protein quality control machinery, have thus been thought to be altered in PD. LRRK2 G2019S, a gain-of-kinase-function mutation, is one of PD's most common known causative mutations, and LRRK2-specific small molecule inhibitors have been developed as possible therapeutics. However, LRRK2 G2019S is incompletely penetrant, and its role in axonal degeneration is unclear. LRRK2 phosphorylates a subset of Rab GTPases, including Rab10. Since Rab GTPases are mediators of organelle trafficking, we speculated that LRRK2 G2019S affects the transport of organelles, such as lysosomes, thereby contributing to early PD pathogenesis. Using neural progenitor cell-derived neurons from two LRRK2 G2019S-PD patients; we developed a model of axonal trafficking of lysosomes to characterize the impact of mutant LRRK2 on lysosomal trafficking. In comparison to their isogenic gene-corrected controls, we observed a subtle reduction in mutant axonal lysosomal speed, which could indicate that mutant LRRK2 mildly disrupts retrograde lysosomal transport. We also observed that this trafficking phenotype was only partially rescued by LRRK2 kinase inhibitors, which could indicate the importance of other factors regulating axonal transport. Consistent with this idea, we found that mutant LRRK2 was associated with increased co-localization of phosphorylated Rab10 on a small subset of distal axonal lysosomes. Furthermore, the over-expression of Rab10 only mildly affected lysosomal trafficking in axons. Interestingly, damaging the lysosomal membrane increased LRRK2-dependent Rab10 phosphorylation, leading us to speculate that membrane damage in the axon might induce LRRK2 activity. Since lysosomes have been shown to mediate plasma membrane repair, we speculated that membrane damage might exacerbate LRRK2-dependent phenotypes in distal axons. Axotomy was used to test this idea, and we observed an inconsistent delay in the regrowth of mutant axons after axotomy. Moreover, we identified an association between mutant LRRK2 and the transient increase in lysosomes at the injury site, indicating that LRRK2 G2019S might potentially affect damage-prone distal axons. Since the LRRK2 G2019S-associated phenotypes observed in our assays were relatively mild in one isogenic pair, we were curious about the clinical and genetic phenotypes of the patients from whom the somatic cells for neural progenitor cell generation were sourced. Interestingly, we observed that clinical features of PD, including age-of-onset, motor symptoms, cognitive impairment, and the level of cerebrospinal fluid biomarkers, were heterogeneous between the two patients. Additionally, genetic analysis of specific PD risk-associated loci in MAPT and SNCA revealed that one patient was more at risk of developing PD than the other, indicating influence from genetic factors in addition to LRRK2 G2019S. These factors might affect the axonal phenotypes observed in our assays. Overall, we have developed assays to investigate the effects of LRRK2 G2019S on axonal lysosomes. These assays can potentially be a useful tool to better understand early pathogenesis in heterogeneous PD patients and test targeted therapeutics that can be successful over an eclectic cohort of PD patients, all of whom are diagnosed based on deteriorating motor symptoms.:TABLE OF CONTENTS I LIST OF FIGURES IV LIST OF TABLES VI ABBREVIATIONS VII 1 INTRODUCTION 1 1.1 Neurodegenerative diseases 1 1.2 Parkinson’s disease 2 1.2.1 General Features 2 1.2.2 Phenomenon of “dying back” in PD 6 1.2.3 Contribution of axonal architecture and function to “dying back” 7 1.2.4 Etiology of PD 10 1.2.4.1 Environmental factors 10 1.2.4.2 Genetic factors linked to axonal function 11 1.3 Lysosomes 12 1.3.1 Composition and biogenesis of lysosomes 13 1.3.2 Lysosomes as digestive centers 15 1.3.3 Lysosomes as secretory organelles 18 1.3.4 Lysosomes in PD 20 1.3.4.1 Genetic PD factors linked to lysosomal function 21 1.4 Leucine-rich repeat kinase 2 (LRRK2) 22 1.4.1 LRRK2 domain organization and function 22 1.4.2 Clinical features of PD patients with LRRK2 mutations (LRRK2-PD) 24 1.4.3 LRRK2 animal models 24 1.4.4 LRRK2 induced pluripotent stem cell (iPSC)-based models 25 1.4.5 Animal and iPSC-based models demonstrate a role for LRRK2 in the endo-lysosomal system 27 1.4.6 LRRK2 kinase inhibitors 30 2 AIMS OF THE THESIS 32 3 MATERIALS AND METHODS 33 3.1 Materials 33 3.1.1 Chemicals 33 3.1.2 Purchased kits 34 3.1.3 Plasmids 34 3.1.4 Antibodies 35 3.1.5 Dyes 36 3.1.6 Primers and oligonucleotides 36 3.1.7 Cell culture media and reagents 37 3.1.8 Small molecules 38 3.1.9 Compounds 38 3.1.10 Cell culture media 39 3.1.11 Human Neural Progenitor Cell (NPC) lines 40 3.2 Methods 41 3.2.1 Ethics statement 41 3.2.2 Licenses 41 3.2.3 Information about iPSC and NPC line generation 41 3.2.4 Preparation of cell culture coated plates 41 3.2.5 Maintenance of NPCs 42 3.2.6 Differentiation of NPCs to neurons 42 3.2.7 Preparation of microfluidic chambers 43 3.2.8 Seeding neurons as single cells 44 3.2.9 HEK293T cell culture 45 3.2.10 Treatment of neurons with compounds 45 3.2.11 Genomic DNA isolation 46 3.2.12 Polymerase-Chain Reaction (PCR) 46 3.2.13 Agarose gel electrophoresis 46 3.2.14 Plasmid DNA isolation 46 3.2.15 Lentiviral vector production 47 3.2.16 Lentiviral infection of human neurons 48 3.2.17 Protein isolation and quantification 48 3.2.18 Capillary electrophoresis 49 3.2.19 Axotomy 49 3.2.20 Immunostaining 50 3.2.21 Live cell imaging 51 3.2.22 Quantification of axonal trafficking using kymographs 52 3.2.23 Quantification of axonal trafficking using an object based method 53 3.2.24 Apotome imaging and quantification 54 3.2.25 Confocal imaging and quantification 54 3.2.26 Clinical and biomarker data collection 55 4 RESULTS 57 4.1 Establishing an axonal lysosomal trafficking assay 57 4.1.1 NPCs from LRRK2 G2019S patients and their respective isogenic controls differentiate into neurons 57 4.1.2 Axons can be spatially separated from soma and dendrites 60 4.1.3 Setting up the axonal trafficking assay 62 4.2 Axonal lysosomal trafficking assay detects LRRK2 G2019S associated changes in lysosome movement 65 4.3 Axonal lysosomal trafficking assay detects partial rescue by a small molecule LRRK2 inhibitor 71 4.4 LRRK2 G2019S is associated with an increase in the proportion of lysosomes co-localizing with phosphorylated Rab10 76 4.5 Rab10 over-expression mildly affects lysosomal trafficking in axons 78 4.6 Lysosomal membrane damage increases LRRK2-mediated Rab10 phosphorylation 81 4.7 LRRK2 G2019S is not associated with consistent effects on long-term axonal regrowth after axotomy 82 4.8 LRRK2 G2019S is associated with transient accumulation of lysosomes at the injury site after axotomy 86 4.9 Assessment of clinical, biomarker and genetic data from the LRRK2 G2019S patient donors 88 5 DISCUSSION 92 6 APPENDIX 101 7 SUMMARY 104 8 ZUSSAMENFASSUNG 106 9 BIBLIOGRAPHY 108 10 ACKNOWLEDGEMENTS 136 11 DECLARATIONS 138

ATP is a (co)transmitter and signaling molecule in the CNS. It acts at a multitude of ligand-gated cationic channels termed P2X to induce rapid depolarization of the cell membrane. Within this receptor-channel family, the P2X7 receptor (R) allows the transmembrane fluxes of Na+, Ca2+, and K+, but also allows the slow permeation of larger organic molecules. This is supposed to cause necrosis by excessive Ca2+ influx, as well as depletion of intracellular ions and metabolites. Cell death may also occur by apoptosis due to the activation of the caspase enzymatic cascade. Because P2X7Rs are localized in the CNS preferentially on microglia, but also at a lower density on neuroglia (astrocytes, oligodendrocytes) the stimulation of this receptor leads to the release of neurodegeneration-inducing bioactive molecules such as pro-inflammatory cytokines, chemokines, proteases, reactive oxygen and nitrogen molecules, and the excitotoxic glutamate/ATP. Various neurodegenerative reactions of the brain/spinal cord following acute harmful events (mechanical CNS damage, ischemia, status epilepticus) or chronic neurodegenerative diseases (neuropathic pain, Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis) lead to a massive release of ATP via the leaky plasma membrane of neural tissue. This causes cellular damage superimposed on the original consequences of neurodegeneration. Hence, blood-brain-barrier permeable pharmacological antagonists of P2X7Rs with excellent bioavailability are possible therapeutic agents for these diseases. The aim of this review article is to summarize our present state of knowledge on the involvement of P2X7R-mediated events in neurodegenerative illnesses endangering especially the life quality and duration of the aged human population.

Parkinson’s disease is a brain disorder associated with reduced dopamine levels in the brain, affecting cognition and motor control in the human brain. One of the motor controls that can be affected is eye movements and can therefore be critically affected in patients with Parkinson’s disease. Eye movement can be measured using eye trackers, and this data can be used for analyzing the eye movement characteristics in Parkinson’s disease. The eye movement analysis provides the possibility of diagnostics and can therefore lead to further insights into Parkinson’s disease. In this thesis, feature extraction of clinical relevance in diagnosing Parkinson’s patients from eye movement data is studied. We have used an autoencoder (AE) constructed to learn micro and macro-scaled representation for eye movements and constructed three different models. Learning of the AEs was evaluated using the F1 score, and differences were statistically assessed using the Wilcoxon sign rank test. Extracted features from data based on patients and healthy subjects were visualized using t-SNE. Using the extracted features, we have measured differences in features using cosine and Mahalanobis distances. We have furthermore clustered the features using fuzzy c-means. Qualities of the generated clusters were assessed by F1-score, fuzzy partition coefficient, Dunn’s index and silhouette index. Based on successful tests using a test data set of a previous publication, we believe that the network used in this thesis has learned to represent natural eye movement from subjects allowed to move their eye freely. However, distances, visualizations, clustering all suggest that latent representations from the autoencoder do not provide a good separation of data from patients and healthy subjects. We, therefore, conclude that a micro-macro autoencoder does not suit the purpose of generating a latent representation of saccade movements of the type used in this thesis. / Parkinsons sjukdom är en hjärnsjukdom orsakad av minskade dopaminnivåer i hjärnan, vilket påverkar kognition och motorisk kontroll i människans hjärna. En av de motoriska kontrollerna som kan påverkas är ögonrörelser och kan därför vara kritiskt påverkat hos patienter diagnostiserade med Parkinsons sjukdom. Ögonrörelser kan mätas med hjälp av ögonspårare, som i sin tur kan användas för att analysera ögonrörelsens egenskaper vid Parkinsons sjukdom. Ögonrörelseanalysen ger möjlighet till diagnostik och kan därför leda till ytterligare förståelse för Parkinsons sjukdom. I denna avhandling studeras särdragsextraktion av ögonrörelsedata med en klinisk relevans vid diagnos av Parkinsonpatienter. Vi har använt en autoencoder (AE) konstruerad för att lära sig mikro- och makrosackadrepresentation för ögonrörelser och konstruerat tre olika modeller. Inlärning av AE utvärderades med hjälp av F1-poängen och skillnader bedömdes statistiskt med hjälp av Wilcoxon rank test. Särdragsextraktionen visualiserades med t-SNE och med hjälp av resultatet ifrån särdragsextraktion har vi mätt skillnader med cosinus- och Mahalanobis- avstånd. Vi har dessutom grupperat resultatet ifrån särdragsextraktionen med fuzzy c-means. Kvaliteten hos de genererade klusterna bedömdes med F1- poäng, suddig fördelningskoefficient, Dunns index och silhuettindex.Sammanfattningsvis finner vi att en mikro-makro-autokodare inte passar syftet med att analysera konstgjorda ögonrörelsesdata. Vi tror att nätverket som används i denna avhandling har lärt sig att representera naturlig ögonrörelse ifrån en person som fritt får röra sina ögon.

Parkinson’s disease (PD) is one of the most common neurodegenerative brain disorders, commonly diagnosed and monitored via clinical examinations, which can be imprecise and lead to a delayed or inaccurate diagnosis. Therefore, recent research has focused on finding biomarkers by analyzing brain networks’ neural activity to find abnormalities associated with PD pathology. Brain signals can be measured using Magnetoencephalography (MEG) or Electroencephalogram (EEG), which have demonstrated their practical use in decoding neural activity. Nevertheless, interpreting and labeling human neural activity measured using MEG/EEG is yet a challenging task requiring vast of time and expertise. In addition, there is a risk of introducing bias or omitting important information not recognizable by humans. This thesis investigates whether it is possible to find meaningful features relevant to PD by uncovering the brain signals’ underlying structure using self-supervised learning (SSL), requiring no labels or hand-crafted features. Four experiments on one EEG and one MEG dataset were conducted to evaluate if the features found during the SSL were meaningful, including t-SNE, silhouette coefficient, Kolmogorov-Smirnov test, and classification performance. Additionally, transfer learning between the two datasets was tested. The SSL model, TS-TCC, was employed in this thesis due to its outstanding performance on two other EEGdatasets and its training efficiency. The evaluation of the EEG dataset inferred it was feasible to find meaningful features to distinguish PD from healthy controls to some extent using SSL. However, more investigations of reusing the features in a downstream task are needed. The evaluation of the MEG dataset did not reach the same satisfying result, the proposed reason, among others, was the amount of data. Lastly, transfer learning was unsuccessful in the setting of transforming knowledge from the EEG to the MEG dataset. / Parkinsons sjukdom är en av de mest förekommande neurodegenerativa hjärnsjukdomarna. Vanligtvis diagnostiseras och övervakas sjukdomen via kliniska undersökningar, dessa kan vara diffusa och leda till en fördröjd eller en felaktig diagnos. Den senaste forskning har därför fokuserat på att hitta nya biomarkörer, bland annat genom att analysera hjärnnätverkens neurala aktivitet för att hitta abnormiteter associerade med parkinsons patologi. Magnetoencefalografi (MEG) och elektroencefalogram (EEG) har visat sig vara bra tekniker för att avkoda neural aktivitet och kan därmed användas för att mäta hjärnsignaler. Dessvärre är det en utmanande uppgift att tolka och märka hjärnsignaler, det kräver mycket tid och expertis. Det finns också en risk att märkningen inte blir helt objektiv eller att viktig information som inte är upptäckbar av människor utelämnas. Denna avhandling undersöker om det är möjligt att hitta meningsfulla särdrag relevanta för parkinsons sjukdom medhjälp av självövervakad inlärning (SSL), som varken kräver etiketter eller handgjorda särdrag. För att utvärdera om särdragen funna av SSL är meningsfulla utfördes fyra experiment på ett EEG och ett MEG-dataset. Experimenten inkluderade tSNE, siluettkoefficienten, Kolmogorov-Smirnov-testet och klassificeringsprestanda. Dessutom utvärderades möjligheten att överföra särdrag mellan de två dataseten för att nå bättre resultat. TS-TCC användes som SSL modell i denna avhandling på grund av dess prestanda på två andra EEG-dataset och dess effektivitet när det kommer till träning. Utvärderingen av EEG-datat visade på att det var möjligt att hitta meningsfulla särdrag för att till viss del skilja patienter från friska kontroller. Däremot så behövs vidare undersökning av användandet av särdragen i en klassificerare. Utvärderingen av MEG-datat nådde inte samma tillfredsställande resultat; anledningen kan bland annat vara mängden data. Slutligen, det var inte möjligt att överföra särdrag mellan EEG och MEG-datat för att nå ett bättre resultat.