SIM: Surface-based fMRI Analysis for Inter-Subject Multimodal Decoding from Movie-Watching Experiments

TL;DR

This paper introduces a surface-based transformer model combined with contrastive learning to decode and align brain activity with visual and auditory stimuli across individuals, enabling generalisable brain decoding from fMRI data.

Contribution

It presents a novel surface vision transformer approach with multimodal contrastive alignment, improving inter-subject brain decoding and stimulus retrieval capabilities.

Findings

Successfully decoded movie clips from brain activity across individuals.

Captured individual brain activity patterns related to semantic and visual processing.

Enabled stimulus retrieval from cortical activity even for unseen subjects and movies.

Abstract

Current AI frameworks for brain decoding and encoding, typically train and test models within the same datasets. This limits their utility for brain computer interfaces (BCI) or neurofeedback, for which it would be useful to pool experiences across individuals to better simulate stimuli not sampled during training. A key obstacle to model generalisation is the degree of variability of inter-subject cortical organisation, which makes it difficult to align or compare cortical signals across participants. In this paper we address this through the use of surface vision transformers, which build a generalisable model of cortical functional dynamics, through encoding the topography of cortical networks and their interactions as a moving image across a surface. This is then combined with tri-modal self-supervised contrastive (CLIP) alignment of audio, video, and fMRI modalities to enable the retrieval of visual and auditory stimuli from patterns of cortical activity (and vice-versa). We validate our approach on 7T task-fMRI data from 174 healthy participants engaged in the movie-watching experiment from the Human Connectome Project (HCP). Results show that it is possible to detect which movie clips an individual is watching purely from their brain activity, even for individuals and movies not seen during training. Further analysis of attention maps reveals that our model captures individual patterns of brain activity that reflect semantic and visual systems. This opens the door to future personalised simulations of brain function. Code & pre-trained models will be made available at https://github.com/metrics-lab/sim, processed data for training will be available upon request at https://gin.g-node.org/Sdahan30/sim.