Alignment of low-dimensional representations of brain connectome

Brisbane Convention & Exhibition Centre 

The brain connectome is multidimensional, characterized by multiple axes that reflect the spatial configurations of macroscale organizational principles. These axes can be uncovered using dimensionality reduction algorithms, such as Principal Component Analysis (PCA) and diffusion map embedding, which simplify complex connectivity patterns into interpretable gradients. The resulting low-dimensional representations of brain organization often reveal systematic spatial patterns, such as the hierarchical axis from unimodal to transmodal regions, but they also exhibit substantial variability across individuals. This variability raises critical questions: How can we align these low-dimensional representations to enable meaningful comparisons between individuals, capturing shared features while preserving unique, individual-specific characteristics? Furthermore, how can these alignment methods be generalized to cross-species comparison to uncover conserved and species-specific principles of brain organization?
Building on these questions, this session will introduce linear and nonlinear dimensionality reduction methods, focusing on principal component analysis and diffusion embedding algorithms to capture gradients of the functional connectome. We will highlight the individual variability observed in these gradients and the necessity of aligning them across individuals for meaningful comparisons. We will start with the traditional approach, Procrustes matching, which uses rigid transformations (e.g., rotation, scaling, translation) to align the direction, order, and scale of gradients across individuals. In addition, we will introduce the joint-embedding technique, which embeds multiple connectomes simultaneously to extract shared and aligned components, enabling direct comparisons across individuals and species. Furthermore, we will discuss scalable frameworks for joint-embedding methods, designed to facilitate alignment for large datasets across developmental stages.
Attendees will gain a comprehensive understanding of dimensionality reduction algorithms, the concept of low-dimensional axes, their variability across individuals, and the factors contributing to this variation. Through practical examples and shared resources, participants will learn how to calculate, align, and evaluate gradients for high-dimensional data across diverse populations. The session will also include discussions on best practices for calculating, aligning, and evaluating these gradients in human and nonhuman primate data.