Isolating Nonlinear Independent Sources in fMRI with $\beta$-TCVAE Models

TL;DR

This paper adapts the $eta$-TCVAE model to fMRI data to disentangle nonlinear brain sources, successfully recovering meaningful and biologically relevant brain networks.

Contribution

It introduces a novel application of $eta$-TCVAE for nonlinear source separation in real-world fMRI data, demonstrating its effectiveness in capturing interpretable brain components.

Findings

Recovered meaningful nonlinear spatial brain components including default mode network

Latent representations captured coherent brain organization patterns

Validated the approach on real-world neuroimaging data

Abstract

Learning meaningful latent representations from nonlinear fMRI data remains a fundamental challenge in neuroimaging analysis. Traditional independent component analysis, widely used due to its ability to estimate interpretable functional brain networks, relies on a linear mixing assumption for latent sources, limiting its ability to capture the inherently nonlinear and complex organization of brain dynamics. More recently, deep representation learning methods have emerged as promising alternatives for modeling nonlinear latent structure. However, many of these approaches have been evaluated primarily on simulated datasets or natural image benchmarks, with comparatively limited validation on real-world neuroimaging data such as fMRI. In this work, we are motivated by the $\beta$-TCVAE (Total Correlation Variational Autoencoder), a refinement of the $\beta$-VAE framework for learning latent representations without introducing additional hyperparameters during training. We adapt and modify this model to fMRI data for nonlinear source disentanglement, aiming to separate mixed spatial and temporal brain signals into interpretable components. We show that the $\beta$-TCVAE framework can recover meaningful nonlinear spatial components with biological relevance, including well-established intrinsic connectivity networks such as the default mode network. Furthermore, we evaluate the learned representations using functional network connectivity, showing that the latent structure captures coherent and interpretable brain organization patterns. This study provides a pilot investigation that bridges nonlinear representation learning and fMRI analysis.