Deep multivariate autoencoder for capturing complexity in Brain Structure and Behaviour Relationships

TL;DR

This paper introduces a deep multivariate autoencoder model to better understand complex non-linear relationships between brain microstructure and cognitive functions, outperforming traditional linear methods in neuroimaging analysis.

Contribution

The study presents a novel deep autoencoder architecture that captures non-linear brain-behavior relationships and demonstrates its effectiveness using Human Connectome Project data.

Findings

Model outperforms traditional methods in capturing brain-behavior correlations

Autoencoder generalizes well to out-of-sample data

Provides more accurate insights into brain structure and cognition relationships

Abstract

Diffusion MRI is a powerful tool that serves as a bridge between brain microstructure and cognition. Recent advancements in cognitive neuroscience have highlighted the persistent challenge of understanding how individual differences in brain structure influence behavior, especially in healthy people. While traditional linear models like Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS) have been fundamental in this analysis, they face limitations, particularly with high-dimensional data analysis outside the training sample. To address these issues, we introduce a novel approach using deep learninga multivariate autoencoder model-to explore the complex non-linear relationships between brain microstructure and cognitive functions. The model's architecture involves separate encoder modules for brain structure and cognitive data, with a shared decoder, facilitating the analysis of multivariate patterns across these domains. Both encoders were trained simultaneously, before the decoder, to ensure a good latent representation that captures the phenomenon. Using data from the Human Connectome Project, our study centres on the insula's role in cognitive processes. Through rigorous validation, including 5 sample analyses for out-of-sample analysis, our results demonstrate that the multivariate autoencoder model outperforms traditional methods in capturing and generalizing correlations between brain and behavior beyond the training sample. These findings underscore the potential of deep learning models to enhance our understanding of brain-behavior relationships in cognitive neuroscience, offering more accurate and comprehensive insights despite the complexities inherent in neuroimaging studies.