Background: Lewy body dementia (LBD), consisting of Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB), is known to make up more than 15% of dementia cases at autopsy, however the clinical prevalence rate is reported to be much lower at around 5-6%. Difficulties with diagnosis and/or lack of specific treatments may contribute to this difference. This study investigated the diagnosis and management pathways of LBD and whether inflammation could play a role in the pathophysiology and hence provide a route for future diagnostic and treatment pathways. Methods: Clinical diagnostic rates of LBD in clinics across several NHS trusts in East Anglia were reviewed, followed by an in-depth notes review of patients identified with LBD together with age and gender matched controls. A literature review of the current evidence for inflammation in LBD, preceded a case control study to investigate further. Nineteen DLB patients together with 16 age and gender matched healthy controls underwent [11C]PK11195 PET imaging, and the same cohorts, plus an additional 10 matched control subjects underwent peripheral cytokine analysis. Results: The clinical prevalence rate of LBD was low compared to the known pathology rates, with delays identified in the diagnosis of DLB compared to other dementia subtypes. Delays were also seen between the onset of dementia symptoms and the clinical diagnosis of dementia in Parkinson's disease (PD). The literature review identified studies providing evidence of inflammation in PD but few studies had been carried out in DLB. PET imaging revealed microglial activation negatively correlated with disease severity in DLB, suggesting inflammation occurs early in the disease. DLB patients also showed evidence of differences in cytokine levels compared to healthy controls. Conclusion: The study showed evidence of inflammatory changes in DLB, providing a potential target for treatment and/or biomarkers, that could assist in increasing clinical diagnostic rates.

The protein alpha-synuclein (α-synuclein) accumulates in the brain in disorders such as Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). It is believed that the monomeric form of α-synuclein can adopt a partially folded structure and start to aggregate and form intermediately sized oligomers or protofibrils. The aggregation process can continue with the formation of insoluble fibrils, which are deposited as Lewy bodies. The oligomers/protofibrils have been shown to be toxic to neurons and are therefore believed to be involved in the pathogenesis of the actual diseases.       The overall aims of this thesis were to investigate the properties of α-synuclein oligomers and to generate and characterize antibodies against these species. In addition, the potential for immunotherapy of the α-synuclein oligomer-selective antibodies were evaluated in a transgenic mouse model with α-synuclein pathology. Stable, β-sheet rich α-synuclein oligomers were induced by incubation with either one of the reactive aldehydes 4-hydroxy-2-nonenal (HNE) and 4-oxo-2-nonenal (ONE). The oligomers exhibited distinct morphological properties, although both types were toxic when added to a neuroblastoma cell line. The seeding effects of ONE-induced oligomers were studied in vitro and in vivo. The oligomers induced seeding of monomeric α-synuclein in a fibrillization assay but not in a cell model or when injected intracerebrally in transgenic mice. It seemed, however, as if the oligomers affected α-synuclein turnover in the cell model. By immunizing mice with HNE-induced oligomers antibody producing hybridomas were generated. Three monoclonal antibodies were found to have strong selectivity for α-synuclein oligomers. These antibodies recognized Lewy body pathology in brains from patients with PD and DLB as well as inclusions in the brain from young α-synuclein transgenic mice, but did not bind to other amyloidogenic proteins. Finally, immunotherapy with one of the oligomer/protofibril selective antibodies resulted in lower levels of such α-synuclein species in the spinal cord of α-synuclein transgenic mice. To conclude, this thesis has focused on characterizing properties of α-synuclein oligomers. In particular, antibodies selectively targeting such neurotoxic forms were generated and evaluated for passive immunization in a transgenic mouse model. Such immunotherapy may represent a future treatment strategy against Lewy body disorders.

The theme of this dissertation is to improve the exploration of patient subgroups with a precision medicine lens, specifically using repeated measures data to evaluate longitudinal trajectories of clinical, biological, and lifestyle measures. Our proposed methodological contributions fall into two branches of statistical methodology: undirected graphical models and functional data analysis. In the first part of this dissertation, our goal was to study longitudinal networks of brain imaging biomarkers and clinical symptoms during the time leading up to manifest Huntington's disease diagnosis among patients with known genetic risk of disease. Understanding the interrelationships between measures may improve our ability to identify patients who are nearing disease onset and who therefore might be ideal patients for clinical trial recruitment. Gaussian graphical models are a powerful approach for network modeling, and several extensions to these models have been developed to estimate time-varying networks. We propose a time-varying Gaussian graphical model specifically for a time scale that is centered on an anchoring event such as disease diagnosis. Our method contains several novel components intended to 1) reduce bias known to stem from 𝑙₁ penalization, and 2) improve temporal smoothness in network edge strength and structure. These novel components include time-varying adaptive lasso weights, as well as a combination of 𝑙₁, 𝑙₂, and 𝑙₀ penalization. We demonstrated via simulation studies that our proposed approach, as well as more computationally efficient subsets of our full proposed approach, have superior performance compared to existing methods. We applied our proposed approach to the PREDICT-HD study and found that the network edges did change with time leading up to and beyond diagnosis, with change points occurring at different times for different edges. For clinical symptoms, bradykinesia became well-connected with symptoms from several other domains. For imaging measures, we observed a loss of connection over time among gray matter regions, white matter regions, and the hippocampus. In the second part of this dissertation, we consider time-varying network models for settings in which data are not all Gaussian. We sought to compare longitudinal clinical symptom networks between patients with neuropathologically-defined Alzheimer's disease (AD) vs. neuropathologically-defined Lewy body dementia (LBD), two common types of dementia which can often be clinically misdiagnosed. Given that the clinical measures of interest were largely non-Gaussian, we examined the literature for undirected graphical models for mixed data types. We then proposed an extension to the existing time-varying mixed graphical model by adding time-varying adaptive lasso weights, modeling time in reverse in order to treat neuropathological diagnoses as baseline covariates. The proposed adaptive lasso extension serves a two-fold purpose: they alleviate well-known bias of 𝑙₁ penalization and they encourage temporal smoothness in edge estimation. We demonstrated the improved performance of our extension in simulations studies. Applying our method to the National Alzheimer's Coordinating Center database, we found that the edge structure surrounding the Wechsler Memory Scale Revised (WMS-R) Logical Memory parts IA (immediate recall) and IIA (delayed recall) may contain important markers for discriminant analysis of AD and LBD populations. In the third part of this dissertation, we explored a methodologically distinct area of research from the first two parts, moving from graphical models to functional data analysis. Our goal was to extract meaningful chronotypes, or phenotypes of circadian rhythms, from activity count data collected from accelerometers. Existing approaches for analyzing diurnal patterns using these data, including the cosinor model and functional principal components analysis, have revealed and quantified population-level diurnal patterns, but considerable subject-level variability remained uncaptured in features such as wake/sleep times and activity intensity. This remaining informative variability could provide a better understanding of chronotypes, or behavioral manifestations of one’s underlying 24-hour rhythm. Curve registration, or alignment, is a technique in functional data analysis that separates "vertical" variability in activity intensity from "horizontal" variability in time-dependent markers like wake and sleep times. We developed a parametric registration framework for 24-hour accelerometric rest-activity profiles that are represented as dichotomized into epoch-level states of activity or rest. Specifically, we estimated subject-specific piecewise linear time-warping functions parametrized with a small set of parameters. We applied this method to data from the Baltimore Longitudinal Study of Aging and illustrated how estimated parameters can give a more flexible quantification of chronotypes compared to traditional approaches.

Charles Bonnet syndrome (CBS) is defined as complex persistent visual hallucinations in the absence of mental disorder. It is associated with advanced age and poor vision. It is common, with prevalence estimates of up to 63% among older people with significant visual impairment. CBS would not be diagnosed in the presence of dementia, but its relationship to milder cognitive impairment is unclear. The few studies that have examined this are underpowered and provide contradictory results. There are 16 case reports of dementia emerging in people with a diagnosis of CBS. These cases raise the possibility of an association between impaired insight at diagnosis of CBS and the subsequent development of dementia. This thesis reports the findings of a prospective cohort study which describes changes in cognitive functioning over one year in patients with CBS and age-matched controls. Participants were recruited from low vision and glaucoma assessment clinics. A clinical assessment was carried out by an old age psychiatrist, and participants had a detailed assessment of visual functioning. This thesis also describes the findings of the first study to use voxel-based morphometry (VBM) to investigate changes in volume of grey and white matter in CBS. Participants were recruited from the same clinics as the cohort study, and underwent MRI scanning on a 1.5T scanner, to a protocol designed to produce 1mm3 voxels. Twelve participants with CBS and ten controls were followed up. Two people in the CBS group developed dementia, while none did in the control group. The CBS group showed a mean change in the score on the Addenbrooke’s cognitive examination (ACE-R) of -3.7 points, compared to a change of +1.4 in the control group. This difference was not statistically significant. The CBS participants performed worse on the verbal fluency item of the ACE-R, a difference which was statistically significant. The VBM analysis was conducted on 11 CBS participants and 11 controls. The CBS group showed an increase in grey matter volume in the right cerebellar hemisphere. This difference retained significance after family-wise error correction, non-stationary correction, and ANCOVA to control for the effects of possible confounders. As far as the author is aware, these are the most methodologically robust studies to date to have investigated cognition and morphological brain changes in CBS. The findings of the cohort study were inconclusive. However, the two cases of dementia in CBS patients add weight to the suspicion that this is a clinically important outcome in the condition, and the finding of abnormalities in frontal lobe testing in participants with CBS fits with a theoretical model of visual hallucination generation. Moreover, this type of research appears to be acceptable to a frail and visually disabled population, and studies powered to investigate this issue more fully would be feasible. The VBM findings report the presence of underlying structural brain abnormalities in CBS, in a region not usually associated with visual hallucinations. Possible links with Lewy body dementia, and implications for theories of visual hallucinations, are discussed.

No description available.

Text in English / The present paper analyses the neuroanatomical and neuropsychological backgrounds of art reception and art creation in modern visual art and creative processes. It critically presents two models of aesthetic experience to provide a comprehensive theoretical basis for the discussion. The research purpose is to show that with increasing experience and expertise the referential frame of the aesthetic judgment is changing and that neural processes involved in object recognition provide a starting point for visual aesthetics. Thus, the investigation focuses on constructing and testing neuropsychological theories that fall in the domain called 'neuroaesthetics'. These theories, in turn, serve as a starting point to formulate neural laws of art and aesthetics and aesthetic experience. Some artistic styles, such as expressionism, reflect specific neural processes. Various studies indicate correlations between hemispheric specialisation and art or creativity and show the right hemisphere plays a particular role in it. However, studies exploring the neural correlates of aesthetic preference have yielded mixed results. Furthermore, neuroimaging studies have proved that different categories of modern artworks are processed in different areas of the brain. These diverging results will be discussed in a critical assessment of the two models of aesthetic experience. Besides, the question of identifying exclusive neural correlates of aesthetic preference will be raised. Comparing amateurs and experts has revealed the more reduced the cortical activation, the more efficiently it works. Biological and neuropsychological factors of creativity point out the meaning of the activation level, cognitive inhibition and prefrontal cortex. Divergent thinking differs from convergent thinking in terms of the neural level. Neurodegenerative processes and brain injuries sometimes influence the artistic output surprisingly or even launch it. Lesion studies contributing to understanding art experience will be explained. / Psychology / M.A. (Psychology)

The present paper analyses the neuroanatomical and neuropsychological backgrounds of art reception and art creation in modern visual art and creative processes. It critically presents two models of aesthetic experience to provide a comprehensive theoretical basis for the discussion. The research purpose is to show that with increasing experience and expertise the referential frame of the aesthetic judgment is changing and that neural processes involved in object recognition provide a starting point for visual aesthetics. Thus, the investigation focuses on constructing and testing neuropsychological theories that fall in the domain called 'neuroaesthetics'. These theories, in turn, serve as a starting point to formulate neural laws of art and aesthetics and aesthetic experience. Some artistic styles, such as expressionism, reflect specific neural processes. Various studies indicate correlations between hemispheric specialisation and art or creativity and show the right hemisphere plays a particular role in it. However, studies exploring the neural correlates of aesthetic preference have yielded mixed results. Furthermore, neuroimaging studies have proved that different categories of modern artworks are processed in different areas of the brain. These diverging results will be discussed in a critical assessment of the two models of aesthetic experience. Besides, the question of identifying exclusive neural correlates of aesthetic preference will be raised. Comparing amateurs and experts has revealed the more reduced the cortical activation, the more efficiently it works. Biological and neuropsychological factors of creativity point out the meaning of the activation level, cognitive inhibition and prefrontal cortex. Divergent thinking differs from convergent thinking in terms of the neural level. Neurodegenerative processes and brain injuries sometimes influence the artistic output surprisingly or even launch it. Lesion studies contributing to understanding art experience will be explained. / Psychology / M.A. (Psychology)

La démence à corps de Lewy (DCL) et la maladie de Parkinson (MP) sont des maladies neurodégénératives touchant des milliers de Canadiens et leur prévalence croît avec l’âge. La MP et la DCL partagent la même pathophysiologie, mais se distinguent par l’ordre de manifestation des symptômes : la DCL se caractérise d’abord par l’apparition d’un trouble neurocognitif majeur (démence), tandis que la MP se manifeste initialement par un parkinsonisme. De plus, jusqu’à 80% des patients avec la MP développeront une démence (MPD). Il est désormais établi que le trouble comportemental en sommeil paradoxal idiopathique (TCSPi) constitue un puissant prédicteur de la DCL et la MP. En effet, cette parasomnie, marquée par des comportements indésirables durant le sommeil, est considérée comme un stade prodromal des synucléinopathies, telles que la MP, la DCL et l'atrophie multisystémique (AMS). Ainsi, la majorité des patients atteints d’un TCSPi développeront une synucléinopathie. Malgré les avancées scientifiques, les causes du TCSPi, de la MP et de la DCL demeurent inconnues et aucun traitement ne parvient à freiner ou à arrêter la neurodégénérescence. De plus, ces pathologies présentent une grande hétérogénéité dans l’apparition et la progression des divers symptômes. Face à ces défis, la recherche vise à mieux cerner les phases précoces/initiales et les trajectoires évolutives de ces maladies neurodégénératives afin d’intervenir le plus précocement possible dans leur développement. C’est pourquoi le TCSPi suscite un intérêt majeur en tant que fenêtre d'opportunités pour tester l’efficacité des thérapies neuroprotectrices contre les synucléinopathies, permettant d'agir avant que la perte neuronale ne devienne irréversible. Le TCSPi offre ainsi une occasion unique d'améliorer la détection de la démence et le suivi des individus à haut risque de déclin cognitif. D'où l'importance cruciale de pouvoir généraliser les résultats issus de la recherche sur de petites cohortes à l'ensemble de la population. Sur le plan de la cognition, les études longitudinales sur le TCSPi ont montré que les atteintes des fonctions exécutives, de la mémoire verbale et de l'attention sont les plus discriminantes pour différencier les individus qui développeront une démence de ceux qui resteront idiopathiques. De plus, un grand nombre de patients TCSPi souffrent d’un trouble neurocognitif mineur ou trouble cognitif léger (TCL), généralement considéré comme un stade précurseur de la démence. Les recherches actuelles sur les données cognitives chez cette population offrent des perspectives prometteuses, mais reposent sur des approches statistiques classiques qui limitent leur validation et généralisation. Bien qu'elles offrent une précision élevée (80 à 85%) pour détecter les patients à risque de déclin cognitif, une amélioration est nécessaire pour étendre l'utilisation de ces marqueurs à une plus large échelle. Depuis les années 2000, l'accroissement de la puissance de calcul et l'accès à davantage de ressources de mémoire ont suscité un intérêt accru pour les algorithmes d'apprentissage machine (AM). Ces derniers visent à généraliser les résultats à une population plus vaste en entraînant des modèles sur une partie des données et en les testant sur une autre, validant ainsi leur application clinique. Jusqu'à présent, aucune étude n'a évalué les apports de l'AM pour la prédiction de l'évolution des synucléinopathies en se penchant sur le potentiel de généralisation, et donc d'application clinique, à travers l'usage d'outils non invasifs et accessibles ainsi que de techniques de validation de modèles (model validation). De plus, aucune étude n'a exploré l'utilisation de l'AM associée à des méthodes de généralisation sur des données neuropsychologiques longitudinales pour élaborer un modèle prédictif de la progression des déficits cognitifs dans le TCSPi. L’objectif général de cette thèse est d’étudier l’apport de l’AM pour analyser l’évolution du profil cognitif de patients atteints d’un TCSPi. Le premier chapitre de cette thèse présente le cadre théorique qui a guidé l’élaboration des objectifs et hypothèses de recherche. Le deuxième chapitre est à deux volets (articles). Le premier vise à fournir une vue d'ensemble de la littérature des études ayant utilisé l'AM (avec des méthodes de généralisation) pour prédire l'évolution des synucléinopathies vers une démence, ainsi que les lacunes à combler. Le deuxième volet vise à explorer et utiliser pour la première fois l'AM sur des données cliniques et cognitifs pour prédire la progression vers la démence dans le TCSPi, dans un devis longitudinal. Enfin, le dernier chapitre de la thèse présente une discussion et une conclusion générale, comprenant un résumé des deux articles, ainsi que les implications théoriques, les forces, les limites et les orientations futures. / Lewy body dementia (LBD) and Parkinson's disease (PD) are neurodegenerative diseases affecting thousands of Canadians, and their prevalence increases with age. PD and DLB share the same pathophysiology, but differ in the order of symptom manifestation: DLB is characterized first by the onset of a major neurocognitive disorder (dementia), whereas PD initially manifests as parkinsonism. Moreover, up to 80% of PD patients will go on to develop dementia (PDD). It is established that idiopathic REM sleep behavior disorder (iRBD) is a powerful predictor of DLB and PD. Indeed, this parasomnia, marked by undesirable behaviors during sleep, is considered a prodromal stage of synucleinopathies, such as PD, DLB and multisystem atrophy (MSA). Therefore, the majority of patients with iRBD will develop synucleinopathy. Despite scientific advancements, the causes of iRBD, PD, and DLB remain unknown and no treatment has been able to slow or halt neurodegeneration. Furthermore, these pathologies display great heterogeneity in the onset and progression of various symptoms. Faced with these challenges, research aims to better understand the early/initial stages and the progressive trajectories of these neurodegenerative diseases in order to intervene as early as possible in their development. This is why iRBD garners major interest as a window of opportunities to test the effectiveness of neuroprotective therapies against synucleinopathies, enabling action to be taken before neuronal loss becomes irreversible. iRBD thus provides a unique opportunity to improve dementia detection and monitoring of individuals at high risk of cognitive decline. Hence the crucial importance of being able to generalize results of research on small cohorts to the entire population. In terms of cognition, longitudinal studies on iRBD have shown that impairments in executive functions, verbal memory, and attention are the most discriminating in differencing between individuals who will develop dementia from those who will remain idiopathic. In addition, many iRBD patients suffer from a mild neurocognitive disorder or mild cognitive impairment (MCI), generally considered as a precursor stage of dementia. Current research on cognitive data in this population offers promising prospects, but relies on traditional statistical approaches that limit their validation and generalizability. While they provide high accuracy (80 to 85%) for detecting patients at risk of cognitive decline, improvement is needed to extend the use of these markers to a larger scale. Since the 2000s, increased computational power and access to more memory resources have sparked growing interest in machine learning (ML) algorithms. These aim to generalize results to a broader population by training models on a subset of data and testing them on another, thus validating their clinical application. To date, no study has assessed the contributions of ML for predicting the progression of synucleinopathies, focusing on the potential for generalization, and hence clinical application, through the use of non-invasive, accessible tools and model validation techniques. Moreover, no study has explored the use of ML in conjunction with generalization methods on longitudinal neuropsychological data to develop a predictive model of cognitive deficit progression in iRBD. The general objective of this thesis is to study the contribution of ML in analyzing the evolution of the cognitive profile of patients with iRBD. The first chapter of this thesis presents the theoretical framework that guided the formulation of the research objectives and hypotheses. The second chapter is in two parts (articles). The first aims to provide an overview of the literature of studies that have used ML (with generalization methods) to predict the progression of synucleinopathies to dementia, as well as the gaps that need to be filled. The second part aims to explore and use for the first time ML on clinical and cognitive data to predict progression to dementia in iRBD, in a longitudinal design. Finally, the last chapter of the thesis presents a discussion and a general conclusion, including a summary of the two articles, as well as theoretical implications, strengths, limitations, and future directions.

Le trouble comportemental en sommeil paradoxal idiopathique (TCSPi) précède de plusieurs années le diagnostic d’une maladie synucléinopathique. Dans cette étude, nous cherchions à déterminer si la puissance spectrale relative, les composantes rythmiques et arythmiques des spectres de puissance, ainsi que la connectivité fonctionnelle permettaient d’identifier à un temps de base les patients ayant un TCSPi qui développerait une synucléinopathie lors des suivis cliniques annuels. Un enregistrement EEG au repos et une évaluation neuropsychologique ont été conduits auprès de quatre-vingt-un participants atteints d’un TCSPi (66.89 ± 6.91 ans, 20 femmes) et des évaluations neurologiques annuelles étaient menées afin de définir si les patients montraient des symptômes d’une maladie synucléinopathique. La puissance spectrale standard ainsi qu’une estimation spectrale des composantes rythmiques et arythmiques ont été calculées. Ensuite, la connectivité fonctionnelle globale et entre chaque paire d’électrodes ont été estimée par le weighted Phase Lag Index. Après une durée de suivi de 5.01 ± 2.76 ans, 34 participants ont été diagnostiqués avec une synucléinopathie et 47 sont restés exempts de maladie. Comparativement aux participants avec un TCSPi n’ayant pas converti, ceux ayant converti montraient, lors de l’évaluation de base, une puissance spectrale relative plus élevée dans la bande thêta, une pente de la composante arythmique plus abrupte ainsi qu'une puissance rythmique plus élevée en thêta dans les régions occipitales et temporales ainsi qu’en en bêta1 dans les régions frontales. De plus, les patients TCSPi ayant converti présentaient une hyperconnectivité globale dans la bande bêta, mais une hypoconnectivité dans la bande alpha entre les régions temporo-occipitales gauches lors de l’évaluation de base comparativement à ceux n’ayant pas converti. Les altérations mesurables en EEG au repos lors de l’évaluation de base chez les participants avec TCSPi ayant converti vers une maladie synucléinopathique suggèrent une perturbation des réseaux à grande échelle affectés par la neurodégénérescence précoce des structures sous-corticales. / Idiopathic REM sleep behavior disorder (iRBD) precedes the diagnosis of synucleinopathies by several years. In this study, we aimed to determine whether relative spectral power, rhythmic and arrhythmic components of power spectra, and functional connectivity at baseline could identify patients with iRBD who will develop a synucleinopathy at follow-up. Resting-state EEG recordings and neuropsychological evaluations were conducted on eighty-one participants with iRBD (66.89 ± 6.91 years; 20 women), and annual neurological assessments were performed to define the emergence of synucleinopathy symptoms. Standard spectral power and spectral estimates of rhythmic and arrhythmic components were calculated. Additionally, global and pairwise functional connectivity were estimated using the weighted Phase Lag Index. After a follow-up period of 5.01 ± 2.76 years, 34 participants were diagnosed with a synucleinopathic disorder, while 47 remained disease-free. Compared to patients who did not convert, patients who converted at follow-up exhibited higher relative spectral power in the theta band, steeper slopes of the arrhythmic component, and increased rhythmic power in theta in posterior regions and beta1 in frontal regions at baseline evaluation. Furthermore, participants who converted showed hyperconnectivity in the beta band and hypoconnectivity in the alpha band between left temporo-occipital regions at baseline compared to participants who did not convert. The measurable alterations in resting-state EEG at baseline in participants with iRBD who phenoconverted towards a synucleinopathy suggest disruption of large-scale networks affected by early neurodegeneration of subcortical structures.