Andrew J Anderson, Adam Turnbull, Feng V Lin
We combined functional magnetic resonance imaging (fMRI) with vision and language artificial intelligence models to characterize neural codes during autobiographical imagination.
The human brain's capacity to imagine visual scenes from memory is thought to rely on the medial temporal subsystem of the default mode network (MT-DMN), yet the neural codes supporting this ability remain poorly understood. We combined functional magnetic resonance imaging (fMRI) with vision and language artificial intelligence models to characterize neural codes during autobiographical imagination. Fifty participants imagined reexperiencing 20 natural scenarios while undergoing fMRI when cued by generic text prompts (e.g., wedding, exercising, and driving). Individual scenes were modeled using Stable Diffusion to generate personalized synthetic images from verbal descriptions of the scenarios imagined, collected beforehand. These depictions were then transformed into image recognition network hidden state representations. Representational similarity analysis revealed that the MT-DMN encoded the participant-specific representational structure of image representations, even when controlling for semantic features derived from a large language model. This effect was absent in other brain networks and during reading without active imagination, identifying the MT-DMN as a core substrate for the visual reconstruction of autobiographical experiences.