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

Abstract

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 neuroimaging 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 neuroimaging data.

Compositional structural brain signatures capture Alzheimer’s genetic risk on brain structure along the diseasecontinuum

“…Unlike conventional univariate analyses, which often lack the power to detect significant associations due to rigorous multiple testing corrections at brain-wide or genome-wide level, this approach adeptly captures the complex interdependencies among different brain regions. Moreover, CoDA offers distinct advantages over commonly used multivariate methods 19 . While multivariate approaches reduce data dimensionality and identify correlated feature sets 51 , they typically overlook the compositional nature of brain data.…”

“…Similarly, at any level of segmentation, a specific brain area and its segmented regions can be defined as a composition and its components, respectively. Thus, given the interconnected and compositional structure of the brain, it is crucial to analyze brain features in a multivariate way to fully understand the complex interactions and influences across different regions 19 . Recent studies have used compositional approaches in the field of brain imaging due to the compositional structure of the data obtained from structural MRI and anatomical brain segmentation 20 .…”

A fast non-parametric test of association for multiple traits

“…In both cases, we observed stronger associations with the disease among the SNPs affecting hippocampal volumes (Supplementary Figure S27). This highlights the importance of analyzing multi-dimensional MRI-derived endophenotypes to gain insight into complex brain disorders 30 .…”

Inferring the Genetic Influences on Psychological Traits Using MRI Connectivity Predictive Models: Demonstration with Cognition

“…While complete literature reviews of these multisystem brain models are available elsewhere [3, 16], models of outcomes such as pain responsivity [17], sustained attention [18], Alzheimer’s [19], and autism [20] have been developed and replicated across large cohorts. Vilor-Tejedor et al [21] demonstrated a variety of multivariate models that can be used to develop brain phenotypes for genetic studies, including linear combinations like independent component analysis, principal component analysis, multivariate regression approaches, and Bayesian approaches. Interestingly, the authors also noted the wealth of consortium-level imaging data available to develop models.…”

“…Interestingly, the authors also noted the wealth of consortium-level imaging data available to develop models. Their work has demonstrated that imaging is primed to develop and evaluate brain-wide endophenotypes as imaging methodologies advance [21].…”