A Foundational fMRI Model for Representing Continuous Brain States

Foundational models have significant potential to advance brain function research, particularly in understanding the dynamics of brain states. However, most existing models process brain signals within fixed time windows, restricting their ability to capture the full temporal complexity of brain activity. In this study, we propose BrainSN (Brain States Network), a novel fMRI foundational model designed to represent continuous brain state information and support diverse downstream tasks. First, leveraging a transformer-based architecture, BrainSN reconstructs input brain states across multiple time scales and predicts future brain activity, effectively capturing both short-term and long-term dependencies. Second, through multiple embeddings and a channel gating module, the model integrates brain state information and applies an attention mechanism to extract critical features. Additionally, we train BrainSN on 1,256 hours of resting-state and naturalistic stimulus fMRI data, enabling it to learn large-scale brain dynamics without relying on task-based paradigms. Without fine-tuning, BrainSN achieves 75.23% and 75.82% accuracy in autism and attention disorder diagnosis tasks, respectively, matching the performance of leading models pretrained on disease-specific data. After fine-tuning, it surpasses these models. In mental state decoding, BrainSN attains 95.31% accuracy without fine-tuning, outperforming the best models trained on large-scale task-based fMRI data. Furthermore, by analyzing BrainSN's embeddings in relation to movie stimuli, we demonstrate that the model effectively captures the semantic content of movie scenes embedded in fMRI signals and is highly sensitive to sequence. These results highlight BrainSN's ability to model brain state dynamics and underscore its potential advantages for clinical diagnosis, treatment evaluation, and cognitive neuroscience research.