Neuroimaging data, often high-dimensional and collected across multiple imaging modalities, is a valuable tool for studying the underlying mechanisms of how the human brain structures, functions, and thus impacts cognition. This dissertation aims to address the challenges of analyzing high-dimensional neuroimaging data, such as the missing data issue in multimodal fusion, the preservation of underlying hierarchical structure between mediators and exposure-by-mediator interactions in model selection with high-dimensional potential mediators, and the false discovery rate control for mediator selection from a high-dimensional candidate set. The first part of this dissertation aims to address the commonly occurring missing data issue during multimodal fusion. Recent advances in multimodal imaging acquisition techniques have allowed us to measure different aspects of brain structure and function. Multimodal fusion, such as linked independent component analysis (LICA), is a popular approach to integrate complementary information. However, these methods are severely limited by the common occurrence of missing data in brain imaging. In the first chapter, we propose a Full Information LICA algorithm (FI-LICA) to handle the missing data problem during multimodal fusion under the LICA framework. Built upon the principle of full information from complete cases, our method utilizes all available information to recover the missing latent information. Our simulation experiments show the ideal performance of FI-LICA compared to current practices. Further, applying to multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, FI-LICA demonstrates better performance in classifying current diagnosis and in predicting the transition of participants with mild cognitive impairment (MCI) to AD, thereby highlighting the practical utility of our proposed method. The second part of this dissertation aims to preserve the underlying hierarchical structure between mediators and exposure-by-mediator interactions during model selection in the high-dimensional mediator settings. In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediators (M) and exposure-by-mediator (X-by-M) interactions. Although several high-dimensional mediation methods can naturally handle X-by-M interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, in the second chapter, we develop the XMInt procedure to select M and X-by-M interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments show promising selection results. Furthermore, we apply our method to ADNI morphological data and examine the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism. The third part of this dissertation aims to control the false discovery rate (FDR) when selecting mediators from a high-dimensional candidate set. Specifically, we formulate a multiple-hypothesis testing framework for mediator selection from a high-dimensional candidate set and propose a method, which extends the recent development in FDR-controlled variable selection with knockoff, to select mediators with FDR control. We show that the proposed method and algorithm achieve finite sample FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with the existing method. Lastly, we demonstrate the method by analyzing data from the Adolescent Brain Cognitive Development (ABCD) study, in which the proposed method selects several resting-state functional magnetic resonance imaging connectivity markers as mediators for the relationship between adverse childhood events and the crystallized composite score in the NIH toolbox.

La maladie d’Alzheimer est actuellement la forme de démence la plus courante. Les causes, les facteurs de risques ainsi que le(s) mécanisme(s) conduisant à l’apparition des symptômes ne sont pas encore clairement connus. Néanmoins, le rôle central du peptide amyloïde (Aβ) dans le développement de la maladie a été démontré au travers de nombreuses recherches et fait actuellement l’unanimité. L’espèce oligomérique d’Aβ est plus précisément pointée doigt comme l’espèce la plus toxique. La formation des oligomers, au cours du processus d’agrégation, conduit à une population hétérogène en termes de taille et morphologies limitant la compréhension actuelle de leur implication dans le processus pathologique ainsi que dans l’initiation de la maladie. <p>Notre étude structurale minutieuse du processus d’agrégation du peptide Aβ démontre la formation d’agrégats dont le degré d’assemblage augmente au cours du temps. Nous avons montré que les agrégats identifiés comme étant des oligomères adoptent une structure en feuillets β antiparallèles. Tandis que l’interconversion de la structure β d’antiparallèle à parallèle conduit à la formation de fibrilles. Sur base de l’interprétation des spectres infrarouges analysés par corrélation à 2 dimensions, nous suggérons que ce changement de conformation est rendu possible grâce aux modifications des liens hydrogènes. En effet, les liens hydrogènes intramoléculaires qui stabilisent la structure antiparallèle des brins β disparaissent en faveur de liens intermoléculaires conduisant à la formation de feuillets β parallèles. De plus, ce changement de conformation requière la rotation des brins β le long de leur axe respectif. <p>Notre travail a pu mettre en avant le rôle central des oligomères dans la pathologie d’une part par leur rôle d’intermédiaires transitoires nécessaires et obligatoires à la formation des fibrilles mais également par la relation étroite qui existe entre leur structure en feuillets β antiparallèles et leur toxicité cellulaire. La modulation et/ou suppression de cette conformation est requise spécifiquement pour réguler leur toxicité et empêcher le processus de mauvais reploiement du peptide conduisant au développement de la maladie. <p>Enfin, nous avons également apporté de nouvelles informations concernant l’implication des membranes biologiques dans le mécanisme de toxicité des oligomères. Nos résultats démontrent que l’interaction du peptide avec un modèle de la membrane biologique ne conduit pas à la déstabilisation de cette dernière. L’hypothèse suggérant la formation de pores et/ou de canaux ioniques comme mécanisme de cytotoxicité est de facto réfutée par notre travail. Néanmoins, nous suggérons que l’interaction du peptide avec les lipides modifie le processus d’agrégation décrit dans la première partie de notre travail. Elle accélère l’étape de nucléation permettant la formation rapide d’oligomères à la surface de la membrane et accentuant ainsi leur probabilité d’interaction avec les protéines membranaires neuronales telles que les récepteurs de neurotransmetteurs./<p>Aggregation of amyloid-β peptides (Aβ1-40 and Aβ1-42) leads to formation of heterogeneous<p>toxic species, oligomers and fibrils, implicated in Alzheimer’s disease. As oligomers were<p>identified as the most cytotoxic entities, our research did focus on their implications in<p>pathology and the Aβ aggregation process which are currently not fully understood.<p>Using ATR-FTIR spectroscopy, we demonstrated that Aβ oligomers adopt an antiparallel β-<p>sheet structure. β-sheet interconversion from antiparallel to parallel seems to be an important<p>step in the Aβ oligomers-to-fibrils transformation. Furthermore, 2-D correlation analysis of<p>infrared spectra recorded during aggregation showed that Aβ isoforms undergo different β-<p>sheet reorganizations explaining their distinct aggregation kinetics. Aβ1-40 misfolding seems<p>to be related to a greater extent of secondary structure changes (increase of β-sheet structure<p>while α-helices and random coil structures content decrease). On the contrary, the same<p>analysis for Aβ1-42 suggests that a possible β-strand ‘rotation’ triggering inter-H bonding<p>formation and stabilizing fibrils may probably explain the antiparallel to parallel β-sheet<p>conversion.<p>We also provided evidence that cytotoxicity is strongly related to the oligomeric antiparallel<p>β-sheet structure of Aβ. The concomitant absence of antiparallel β-sheet structure due to<p>incubation with whey protein-derived peptide hydrolysate strongly suggests that cytotoxicity<p>and β-sheets organization are related.<p>Formation of β-barrel spanning the lipid membrane has been proposed to explain this Aβ<p>structure-toxicity relationship. In the last part of our work, we demonstrated that the<p>interaction of Aβ1-42 with anionic lipid membranes creates and/or stabilizes specific-size<p>oligomers. These oligomers, especially the dodecamer, are known to be the most toxic.<p>Nevertheless, we could not show that these specific oligomers are implicated in membrane<p>destabilization. Further works are needed to separate and study the individual properties of<p>each oligomer. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Indiana University-Purdue University Indianapolis (IUPUI) / Alzheimer disease (AD) results, in part, from the excess accumulation of the amyloid-β peptide (Aβ) as neuritic plaques in the brain. The short Aβ peptide is derived from a large transmembrane precursor protein, APP. Two different proteolytic enzymes, BACE1 and the gamma-secretase complex, are responsible for cleaving Aβ peptide from APP through an intricate processing pathway. Dysregulation of APP and BACE1 levels leading to excess Aβ deposition has been implicated in various forms of AD. Thus, a major goal in this dissertation was to discover novel regulatory pathways that control APP and BACE1 expression as a means to identify novel drug targets central to the Aβ-generating process. MicroRNAs (miRNA) are short, non-coding RNAs that act as post-transcriptional regulators of gene expression through specific interactions with target mRNAs. Global analyses predict that over sixty percent of human transcripts contain evolutionarily conserved miRNA target sites. Therefore, the specific hypothesis tested was that miRNA are relevant regulators of APP and BACE1 expression. In this work, several specific miRNA were identified that regulate APP protein expression (miR-101, miR-153 and miR-346) or BACE1 expression (miR-339-5p). These miRNAs mediated their post-transcriptional effects via interactions with specific target sites in the APP and BACE1 transcripts. Importantly, these miRNA also altered secretion of Aβ peptides in primary human fetal brain cultures. Surprisingly, miR-346 stimulated APP expression via target sites in the APP 5’-UTR. The mechanism of this effect appears to involve other RNA-binding proteins that bind to the APP 5’-UTR. Expression analyses demonstrated that these miRNAs are expressed to varying degrees in the human brain. Notably, miR-101, miR-153 and miR-339-5p are dysregulated in the AD brain at various stages of the disease. The work in this dissertation supports the hypothesis that miRNAs are important regulators of APP and BACE1 expression and are capable of altering Aβ homeostasis. Therefore, these miRNA may possibly serve as novel therapeutic targets for AD.

Indiana University-Purdue University Indianapolis (IUPUI) / Alzheimer's disease (AD) is a national priority, with nearly six million Americans affected at an annual cost of $200 billion and no available cure. A better understanding of the mechanisms underlying AD is crucial to combat its high and rising incidence and burdens. Most cases of AD are thought to have a complex etiology with numerous genetic and environmental factors influencing susceptibility. Recent genome-wide association studies (GWAS) have confirmed roles for several hypothesized genes and have discovered novel loci associated with disease risk. However, most GWAS-implicated genetic variants have displayed modest individual effects on disease risk and together leave substantial heritability and pathophysiology unexplained. As a result, new paradigms focusing on biological pathways have emerged, drawing on the hypothesis that complex diseases may be influenced by collective effects of multiple variants – of a variety of effect sizes, directions, and frequencies – within key biological pathways. A variety of tools have been developed for pathway-based statistical analysis of GWAS data, but consensus approaches have not been systematically determined. We critically review strategies for genetic pathway analysis, synthesizing extant concepts and methodologies to guide application and future development. We then apply pathway-based approaches to complement GWAS of key AD-related endophenotypes, focusing on two early, hallmark features of disease, episodic memory impairment and brain deposition of amyloid-β. Using GWAS and pathway analysis, we confirmed the association of APOE (apolipoprotein E) and discovered additional genetic modulators of memory functioning and amyloid-β deposition in AD, including pathways related to long-term potentiation, cell adhesion, inflammation, and NOTCH signaling. We also identified genetic associations to amyloid-β deposition that have classically been understood to mediate learning and memory, including the BCHE gene and signaling through the epidermal growth factor receptor. These findings validate the use of pathway analysis in complex diseases and illuminate novel genetic mechanisms of AD, including several pathways at the intersection of disease-related pathology and cognitive decline which represent targets for future studies. The complexity of the AD genetic architecture also suggests that biomarker and treatment strategies may require simultaneous targeting of multiple pathways to effectively combat disease onset and progression.