Time-Resolved fMRI Shared Response Model using Gaussian Process Factor Analysis

TL;DR

This paper introduces S-GPFA, a novel probabilistic model that captures shared temporal brain activity patterns across subjects in fMRI data, improving analysis of multi-subject studies and revealing underlying neural trajectories.

Contribution

The paper presents S-GPFA, a new model that incorporates temporal correlations into shared response modeling for fMRI, enhancing the extraction of common neural dynamics across subjects.

Findings

Successfully recovers ground truth latent structures in simulated data.

Achieves high performance in time-segment matching and inter-subject similarity tasks.

Provides insights into social cognition differences in schizophrenia using multi-site data.

Abstract

Multi-subject fMRI studies are challenging due to the high variability of both brain anatomy and functional brain topographies across participants. An effective way of aggregating multi-subject fMRI data is to extract a shared representation that filters out unwanted variability among subjects. Some recent work has implemented probabilistic models to extract a shared representation in task fMRI. In the present work, we improve upon these models by incorporating temporal information in the common latent structures. We introduce a new model, Shared Gaussian Process Factor Analysis (S-GPFA), that discovers shared latent trajectories and subject-specific functional topographies, while modelling temporal correlation in fMRI data. We demonstrate the efficacy of our model in revealing ground truth latent structures using simulated data, and replicate experimental performance of time-segment matching and inter-subject similarity on the publicly available Raider and Sherlock datasets. We further test the utility of our model by analyzing its learned model parameters in the large multi-site SPINS dataset, on a social cognition task from participants with and without schizophrenia.