This master’s thesis deals with diagnostics of prodromal stage of Lewy body disease using microstate analysis. First part of the thesis includes theoretical background which is needed for understanding discussed topics and presented results. This part consists of description of the disease, diagnostic options, electroencephalography, pre-processing of the EEG record and the microstate analysis process. Theoretical background is followed by a practical part of the thesis. In the beginning, there is a chapter about a dataset, used EEG device, and own solution of the pre-processing. Microstate analysis is discussed next, its output parameters were compared between groups with statistical methods. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections.

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.

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京都大学 / 新制・課程博士 / 博士(医学) / 甲第24086号 / 医博第4862号 / 新制||医||1059(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 井上 治久, 教授 渡邉 大, 教授 高橋 淳 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Pesticides have been suggested to play a role in the development of many neurodegerative diseases including Parkinson's disease and Alzheimer's disease. Additionally, it has been suggested that exposure to pesticides and other environmental chemicals during the early stages of life could result in an increased vulnerability to such substances that could lead to neurotoxicity and degeneration late in life. We hypothesized that exposure to mixtures of certain pesticides could change neurotransmitter levels and cellular oxidative stress and that this would be greater in mice exposed early and later in life than mice exposed only as adults. We studied the effects of permethrin (PR) (a pyrethroid type I) and endosulfan (EN) (an organochlorine) on the levels of catecholamines, indolamines, acetylcholinesterase, lipid peroxidation and α-synuclein in the brain of mice. These pesticides have different structures but both are known to modify the kinetics of voltage-sensitive ion channels and calcium ion flux/homeostasis that could affect the release of several neurotransmitters. The study consisted of two experiments: In the first experiment, adult C57Bl/6 mice (7-9 months old) were injected, intraperitoneally, with the following treatments: EN 4.3, 2.15 mg/kg; PR 150, 15 mg/kg and their mixtures EN 4.3 + PR 150 and EN 2.15 + PR 15 mg/kg. Mice were sacrificed 24 hrs after the last injection. In the second experiment, doses consisted of EN 0.7, 1.4 mg/kg, PR 1.5, 15 mg/kg and their mixtures EN 0.7 + PR 1.5 mg/kg and EN 1.4 + PR 15 mg/kg were given to juvenile mice intraperitoneally daily during a period of two weeks from postnatal day 5 to 19. Mice were then, left undisturbed with their dams. Re-challenge was performed when mice were 7-9 months old and dosages of EN 4.3, 2.15 mg/kg, PR 150, 15 mg/kg and their mixtures, EN 4.3 + PR 150 and EN 2.15 + PR 15 mg/kg were given intraperitoneally every other day during a period of two weeks to match the treatments when pesticide exposure was only as adults. Mice were sacrificed 24 hrs after the last injection. The corpora striatum was extracted and analyzed by HPLC for catecholamines (dopamine, DOPAC, homovalinic acid and norepinephrine) and indolamines (serotonin and 5-HIAA). In general low doses of permethrin and endosulfan alone and in combination (EN 2.15 + PR 15 mg/kg) altered the levels of catecholamines and indolamines in both studies with adult mice and mice dosed as juveniles and re-challenged as adults. Catecholamine and indolamines levels were affected to a greater extent in the adult mice than in mice dosed as juveniles and re-challenged as adults, when compared to controls. Acetylcholinesterase was increased under both exposure situations but again adult mice seemed to be more affected than mice dosed as juveniles and re-challenged as adults. Because reactive oxygen species have been implicated in the development of Parkinson's disease, and are known to cause degradation of certain neurotransmitters, we monitored the levels of lipid peroxides in brain cortex as an indicator of free radical tissue damage. The peroxide levels were measured by thiobarbituric acid reactive products (TBARS). Increased levels of lipid peroxides were significant in the low dose treatment groups of the adult study. However, there seemed to be a pattern between the levels of dopamine and DOPAC in the striatum and the levels of peroxidation in cortex. The presence of dopamine metabolites appeared to be related to high levels of peroxidation within the basal ganglia and up-regulation of proteins such as α-synuclein. Western blots of α-synuclein in both experiments of the study showed intense double and triple bands that corresponded to aggregated α-synuclein. In general, when compared with controls, mice dosed as juveniles and re-challenged as adults did not alter the above parameters as much as mice dosed only as adults. Instead, the mice first dosed as juveniles seemed to develop an adaptation response to the later exposure of these pesticides. Taking all these results into account, early exposure and re-challenge with permethrin and endosulfan in this study appeared to induce a protective response against neurochemical changes in the brain of these mice. In addition, low doses of these pesticides and the low dose combination mixture seem to exert an effect on the parameters studied. Therefore, exposure to pesticides such as endosulfan and permethrin and their combinations could make a contribution towards the initiation or aggravation of biochemical neurodegenerative diseases such as Parkinson's and Alzheimer's diseases. / Master of Science

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Le trouble comportemental en sommeil paradoxal (TCSP) idiopathique est caractérisé par une activité motrice indésirable et souvent violente au cours du sommeil paradoxal. Le TCSP idiopathique est considéré comme un facteur de risque de certaines maladies neurodégénératives, particulièrement la maladie de Parkinson (MP) et la démence à corps de Lewy (DCL). La dépression et les troubles anxieux sont fréquents dans la MP et la DCL. L’objectif de cette étude est d’évaluer la sévérité des symptômes dépressifs et anxieux dans le TCSP idiopathique. Cinquante-cinq patients avec un TCSP idiopathique sans démence ni maladie neurologique et 63 sujets contrôles ont complété la seconde édition du Beck Depression Inventory (BDI-II) et le Beck Anxiety Inventory (BAI). Nous avons aussi utilisé le BDI for Primary Care (BDI-PC) afin de minimiser la contribution des facteurs confondant dans les symptômes dépressifs. Les patients avec un TCSP idiopathique ont obtenu des scores plus élevés que les sujets contrôles au BDI-II (9.63 ± 6.61 vs. 4.32 ± 4.58; P < 0.001), au BDI-PC (2.20 ± 2.29 vs. 0.98 ± 1.53; P = 0.001) et au BAI (8.37 ± 7.30 vs. 3.92 ± 5.26; P < 0.001). Nous avons également trouvé une proportion plus élevée des sujets ayant des symptômes dépressifs (4/63 ou 6% vs. 12/55 ou 22%; P = 0.03) ou anxieux (9/50 or 18% vs. 21/43 ou 49%; P = 0.003) cliniquement significatifs. La proportion des sujets ayant des symptômes dépressifs cliniquement significatifs ne change pas en utilisant le BDI-PC (11/55 or 20%) Les symptômes dépressifs et anxieux sont fréquents dans le TCSP idiopathique. L’examen de routine des patients avec un TCSP idiopathique devrait inclure un dépistage systématique des symptômes dépressifs et anxieux afin de les prévenir ou les traiter. / Idiopathic rapid-eye-movement sleep behaviour (iRBD) disorder can be a premotor feature of Parkinson’s disease (PD) or dementia with Lewy bodies (DLB). Depressive and anxiety symptoms are frequent nonmotor features in PD or DLB. We assessed the frequency and severity of depressive and anxiety symptoms in patients with iRBD compared to healthy control subjects. Fifty-five iRBD patients and 63 age and sexmatched healthy subjects were studied. Participants completed the Beck Depression Inventory – Second Edition (BDI-II) and Beck Anxiety Inventory (BAI). We assessed the depressive and anxiety symptoms and compared the proportion of participants with clinically significant depressive or anxiety symptoms. We also used the BDI for Primary Care (BDI-PC) to minimize confounding factors that could overestimate depressive symptoms. iRBD patients scored higher than controls on the BDI-II (9.63 ± 6.61 vs. 4.32 ± 4.58; P < 0.001)), BDI-PC (2.20 ± 2.29 vs. 0.98 ± 1.53; P = 0.001) and BAI (8.37 ± 7.30 vs. 3.92 ± 5.26; P < 0.001). Compared to controls, we found a higher proportion of patients with iRBD with either clinically significant depressive (4/63 or 6% vs. 12/55 or 22% P = 0.03) or anxiety symptoms (9/50 or 18% vs. 21/43 or 49%; P = 0.003). The proportion of iRBD patients with clinically significant depressive symptoms remains unchanged using the BDI-PC (11/55 or 20%). Depressive and anxiety symptoms are frequent features in iRBD. Routine examination of patients with iRBD disorder should include an assessment of depressive and anxiety symptoms in order to prevent or treat them.

Les maladies neurodégénératives représentent un défi sociétal majeur. Trouver des outils pour mieux comprendre et diagnostiquer ces maladies est donc nécessaire. La spectroscopie infrarouge (IR) et Raman semblent être de bons candidats puisqu’elles peuvent caractériser l’état physiopathologique d’un échantillon. Le but de cette thèse a été d’appliquer ces méthodes à l’étude de peptides modèles, de tissus cérébraux et de liquides céphalorachidiens (LCR). Dans le cadre de l’étude des tissus cérébraux, la spectroscopie IR et Raman ont été couplées à la microscopie afin de combiner des informations spectrales et spatiales. Cela a permis de mieux comprendre la formation et le rôle des plaques amyloïdes dans la maladie d’Alzheimer (MA). Egalement, cela a permis de montrer l’intérêt d’utiliser ces méthodes dans des études futures pour suivre l’effet de différents traitements contre la sclérose en plaques. Concernant l’étude des LCR, la spectroscopie IR en mode ATR et la spectroscopie Raman exaltée de surface ont été utilisées afin de mettre en évidence des marqueurs spectroscopiques de la MA et de la maladie à corps de Lewy qui pourraient permettre un diagnostic plus précoce de ces maladies et un diagnostic différentiel entre ces deux. / Neurodegenerative diseases represent a major societal challenge. So, it is necessary to develop new tools for a better understanding and diagnosing of these diseases. Infrared (IR) and Raman spectroscopies seem to be good candidates since they can characterize the physiopathological conditions of a biological sample. The purpose of this thesis was to apply these methods to the study of model peptides, brain tissues and cerebrospinal fluids (CSF). As a part of brain tissue analysis, IR and Raman spectroscopy were coupled to microscopy in order to combine spectral and spatial information. This methodology improved our understanding of the formation and the role of amyloid plaques in Alzheimer’s disease (AD). Moreover, it allowed to demonstrate the potential of these approaches in future studies on the effect of various treatments against multiple sclerosis. Concerning the study of CSF, IR-ATR and surface enhanced Raman spectroscopy were applied to identify spectroscopic markers of AD and Lewy body disease that could enable early diagnosis of these diseases and discrimination between them.