Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation Models

Neural decoding, the process of understanding how brain activity corresponds to different stimuli, has been a primary objective in cognitive sciences. Over the past three decades, advances in functional Magnetic Resonance Imaging (fMRI) and machine learning have greatly improved our ability to map visual stimuli to brain activity, especially in the visual cortex. Concurrently, research has expanded to decode more complex processes, such as language and memory across the whole brain, using techniques to handle greater variability and improve signal accuracy. We argue that "seeing" involves more than just mapping visual stimuli onto the visual cortex; it engages the entire brain, as various emotions and cognitive states can emerge from observing different scenes. In this paper, we develop algorithms to enhance our understanding of visual processes by incorporating whole-brain activation maps while individuals are exposed to visual stimuli. We utilize transformer-based large-scale fMRI encoders and Image generative models (encoders & decoders) pre-trained on large public datasets, which are then fine-tuned through Image-fMRI contrastive learning. Our models can decode visual experience across the entire cerebral cortex, surpassing the traditional confines of the visual cortex. Using a public dataset (BOLD5000), we first compare our method with state-of-the-art approaches for decoding visual processing and show improved predictive semantic accuracy by 43%. A network ablation analysis suggests that, beyond the visual cortex, the default mode network contributes significantly to stimulus decoding, in line with the proposed role of this network in sense-making and semantic processing.