Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!

Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neu- roimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimag- ing data

Metabolic Investigations of the Molecular Mechanisms Associated with Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by fibrillar cytoplasmic aggregates of α-synuclein (i.e., Lewy bodies) and the associated loss of dopaminergic cells in the substantia nigra. Mutations in genes such as α-synuclein (SNCA) account for only 10% of PD occurrences. Exposure to environmental toxicants including pesticides and metals (e.g., paraquat (PQ) and manganese (Mn)) is also recognized as an important PD risk factor. Thus, aging, genetic alterations, and environmental factors all contribute to the etiology of PD. In fact, both genetic and environmental factors are thought to interact in the promotion of idiopathic PD, but the mechanisms involved are still unclear. In this study, we summarize our findings to date regarding the toxic synergistic effect between α-synuclein and paraquat treatment. We identified an essential role for central carbon (glucose) metabolism in dopaminergic cell death induced by paraquat treatment that is enhanced by the overexpression of α-synuclein. PQ "hijacks" the pentose phosphate pathway (PPP) to increase NADPH reducing equivalents and stimulate paraquat redox cycling, oxidative stress, and cell death. PQ also stimulated an increase in glucose uptake, the translocation of glucose transporters to the plasma membrane, and AMP-activated protein kinase (AMPK) activation. The overexpression of α-synuclein further stimulated an increase in glucose uptake and AMPK activity, but impaired glucose metabolism, likely directing additional carbon to the PPP to supply paraquat redox cycling

Analytical strategies in imaging genetics : assessment of potential risk factors for neurodevelopmental domains

Imaging Genetics (IG) aims to test how genetic information influences brain structure and function, cognitive processes and complex neurodevelopmental domains, combining magnetic resonance imaging-based brain features and genetic data from the same individual. IG studies represent an opportunity to deepen our knowledge of the biological mechanisms of neurodevelopmental domains and complex brain disorders. Most studies focus on individual correlation and association tests between a subset of genetic variants (usually single nucleotide polymorphisms, SNPs) and a single measurement of the brain. Despite the great success of univariate approaches, given the current focus of imaging genetic studies in which genome-wide, whole-brain studies should be analyzed, the development of novel statistical methods becomes crucial. The main aim of this thesis consists of investigating genetic determinants of structural brain change, which in turn affect neurodevelopmental domains. We propose the application and development of statistical strategies to improve the assessment of significant relationships associated with neurodevelopmental domains. Specifically, we focus our research efforts on understanding what genomic changes in the cerebral structure allow improvements in the assessment of risk factors associated with Attention-Deficit/Hyperactivity disorder domains, and related cognitive processes such as attention function.Els estudis que combinen la informació genètica i de neuroimatge (IG) pretenen provar com la informació genètica influeix en l'estructura i funció cerebral, en el comportament, i en els dominis del neurodesenvolupament, combinant la informació extreta de ressonàncies magnètiques del cervell i de la informació genètica d'un mateix individu. Els estudis d'IG representen una oportunitat per aprofundir en el coneixement dels mecanismes biològics dels dominis del desenvolupament neurològic. La majoria dels estudis es centren en la correlació individual i en proves d'associació entre un subconjunt de variants genètiques (en general polimorfismes d'un únic nucleòtid, SNPs) i una única mesura d'una regió cerebral. Però, malgrat el gran èxit en l'enfocament univariat, donades les perspectives actuals dels estudis d'IG, en els quals es pretenen analitzar les relacions cerebrals de tot el genoma envers tota la informació del cervell, el desenvolupament de nous mètodes estadístics específics esdevé crucial. L'objectiu principal d'aquesta tesi consisteix a investigar els determinants genètics relacionats amb els canvis estructurals del cervell, que a la vegada, afecten els dominis del neurodesenvolupament. Proposem l'aplicació i el desenvolupament d'estratègies estadístiques per millorar l’avaluació de les relacions biològiques associades als dominis del neurodesenvolupament. Específicament, centrem els nostres esforços de recerca en comprendre quins canvis genètics que influeixen l'estructura cerebral permeten millorar l'avaluació dels factors de risc associats als dominis del trastorn per dèficit d'atenció i hiperactivitat, i a processos cognitius relacionats, com la funció d'atenció

Extending principal covariates regression for high-dimensional multi-block data

This dissertation addresses the challenge of deciphering extensive datasets collected from multiple sources, such as health habits and genetic information, in the context of studying complex issues like depression. A data analysis method known as Principal Covariate Regression (PCovR) provides a strong basis in this challenge.Yet, analyzing these intricate datasets is far from straightforward. The data often contain redundant and irrelevant variables, making it difficult to extract meaningful insights. Furthermore, these data may involve different types of outcome variables (for instance, the variable pertaining to depression could manifest as a score from a depression scale or a binary diagnosis (yes/no) from a medical professional), adding another layer of complexity.To overcome these obstacles, novel adaptations of PCovR are proposed in this dissertation. The methods automatically select important variables, categorize insights into those originating from a single source or multiple sources, and accommodate various outcome variable types. The effectiveness of these methods is demonstrated in predicting outcomes and revealing the subtle relationships within data from multiple sources.Moreover, the dissertation offers a glimpse of future directions in enhancing PCovR. Implications of extending the method such that it selects important variables are critically examined. Also, an algorithm that has the potential to yield optimal results is suggested. In conclusion, this dissertation proposes methods to tackle the complexity of large data from multiple sources, and points towards where opportunities may lie in the next line of research

Mass Spectrometry and Nuclear Magnetic Resonance in the Chemometric Analysis of Cellular Metabolism

The development and awareness of Machine Learning and “big data” has led to a growing interest in applying these methods to bioanalytical research. Methods such as Mass Spectrometry (MS), and Nuclear Magnetic Resonance (NMR) can now obtain tens of thousands to millions of data points from a single sample, due to fundamental instrumental advances and ever-increasing resolution. Simple pairwise comparisons on datasets of this magnitude can obfuscate more complex underlying trends, and does a disservice to the richness of information contained within. This necessitates the need for multivariate approaches that can more fully take advantage of the complexity of these datasets. Performing these multivariate analyses takes high degree of expertise, requiring knowledge of such disparate areas as chemistry, physics, mathematics, statistics, software development and signal processing. As a result, this barrier to entry prevents many investigators from fully utilizing all the tools available to them, instead relying on a mix of commercial and free software, chained together with in-house developed solutions just to perform a single analysis. While there are numerous methods in published literature for statistical analysis of these larger datasets, most are still confined to the realm of theory due to them not being implemented into publicly available software for the research community. This dissertation outlines the development of routines for handling LC-MS data with freely available tools, including the Octave programming language. This presents, in combination with our previously developed software MVAPACK, a unified platform for metabolomics data analysis that will encourage the wider adoption of multi-instrument investigations and multiblock statistical analyses. Advisor: Robert Power

Extending principal covariates regression for high-dimensional multi-block data

This dissertation addresses the challenge of deciphering extensive datasets collected from multiple sources, such as health habits and genetic information, in the context of studying complex issues like depression. A data analysis method known as Principal Covariate Regression (PCovR) provides a strong basis in this challenge.Yet, analyzing these intricate datasets is far from straightforward. The data often contain redundant and irrelevant variables, making it difficult to extract meaningful insights. Furthermore, these data may involve different types of outcome variables (for instance, the variable pertaining to depression could manifest as a score from a depression scale or a binary diagnosis (yes/no) from a medical professional), adding another layer of complexity.To overcome these obstacles, novel adaptations of PCovR are proposed in this dissertation. The methods automatically select important variables, categorize insights into those originating from a single source or multiple sources, and accommodate various outcome variable types. The effectiveness of these methods is demonstrated in predicting outcomes and revealing the subtle relationships within data from multiple sources.Moreover, the dissertation offers a glimpse of future directions in enhancing PCovR. Implications of extending the method such that it selects important variables are critically examined. Also, an algorithm that has the potential to yield optimal results is suggested. In conclusion, this dissertation proposes methods to tackle the complexity of large data from multiple sources, and points towards where opportunities may lie in the next line of research