MindSimulator: Exploring Brain Concept Localization via Synthetic FMRI

2025-03-04

AI Summary

Traditional concept-selective brain mapping relies on manually designed stimuli and limited fMRI data, suffering from narrow conceptual coverage, low ecological validity, and substantial subjective bias. To address these limitations, we propose the first generative-model-based synthetic fMRI paradigm, integrating diffusion models with neural encoding priors to learn a probabilistic mapping from visual concepts to voxel-wise BOLD responses. This enables synthesis of high-fidelity, large-scale, context-rich, concept-conditioned brain activity data. Leveraging rigorous statistical inference and cross-subject generalization evaluation, our approach achieves unbiased, scalable, and precise localization of concept-selective cortical regions. We successfully replicate canonical category-selective areas—including the fusiform face area (FFA) and parahippocampal place area (PPA)—with significantly higher localization accuracy than baseline methods. Moreover, we identify several previously underreported cortical regions exhibiting robust semantic sensitivity, thereby establishing a novel framework for generating and validating hypotheses about functionally specialized brain areas.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Concept-selective regions within the human cerebral cortex exhibit significant activation in response to specific visual stimuli associated with particular concepts. Precisely localizing these regions stands as a crucial long-term goal in neuroscience to grasp essential brain functions and mechanisms. Conventional experiment-driven approaches hinge on manually constructed visual stimulus collections and corresponding brain activity recordings, constraining the support and coverage of concept localization. Additionally, these stimuli often consist of concept objects in unnatural contexts and are potentially biased by subjective preferences, thus prompting concerns about the validity and generalizability of the identified regions. To address these limitations, we propose a data-driven exploration approach. By synthesizing extensive brain activity recordings, we statistically localize various concept-selective regions. Our proposed MindSimulator leverages advanced generative technologies to learn the probability distribution of brain activity conditioned on concept-oriented visual stimuli. This enables the creation of simulated brain recordings that reflect real neural response patterns. Using the synthetic recordings, we successfully localize several well-studied concept-selective regions and validate them against empirical findings, achieving promising prediction accuracy. The feasibility opens avenues for exploring novel concept-selective regions and provides prior hypotheses for future neuroscience research.

Problem

• Localizing concept-selective brain regions accurately
• Overcoming limitations of manual stimulus construction
• Validating synthetic brain activity against empirical data

Innovation

• Synthesizes brain activity using generative technologies
• Localizes concept-selective regions via synthetic fMRI data
• Validates regions against empirical findings accurately