Scalable Group Level Probabilistic Sparse Factor Analysis

TL;DR

This paper introduces a scalable probabilistic sparse factor analysis model for fMRI data that identifies sparse neural components, models subject-specific noise, and improves interpretability over traditional methods.

Contribution

It presents a novel probabilistic framework for group-level sparse factor analysis with automatic relevance determination and heteroscedastic noise modeling for fMRI analysis.

Findings

Sparse components similar to group ICA

Reduced noise in activation areas

Probabilistic approach handles uncertainties

Abstract

Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a group level scalable probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex noise models than the presently considered.