A long standing goal in neuroscience has been to elucidate the functional organization of the brain. Within higher visual cortex, functional accounts have remained relatively coarse, focusing on regions of interest (ROIs) and taking the form of selectivity for broad categories such as faces, places, bodies, food, or words. Because the identification of such ROIs has typically relied on manually assembled stimulus sets consisting of isolated objects in non-ecological contexts, exploring functional organization without robust a priori hypotheses has been challenging. To overcome these limitations, we introduce a data-driven approach in which we synthesize images predicted to activate a given brain region using paired natural images and fMRI recordings, bypassing the need for category-specific stimuli. Our approach -- Brain Diffusion for Visual Exploration ("BrainDiVE") -- builds on recent generative methods by combining large-scale diffusion models with brain-guided image synthesis. Validating our method, we demonstrate the ability to synthesize preferred images with appropriate semantic specificity for well-characterized category-selective ROIs. We then show that BrainDiVE can characterize differences between ROIs selective for the same high-level category. Finally we identify novel functional subdivisions within these ROIs, validated with behavioral data. These results advance our understanding of the fine-grained functional organization of human visual cortex, and provide well-specified constraints for further examination of cortical organization using hypothesis-driven methods.

翻译：神经科学的一个长期目标是阐明大脑的功能组织。在高级视觉皮层中，功能描述仍然相对粗糙，主要关注感兴趣区域（ROI），并以对诸如面孔、地点、身体、食物或文字等广泛类别的选择性为表现。由于此类ROI的识别通常依赖于由非生态情境下的孤立物体组成的手动构建刺激集，因此，在没有强先验假设的情况下探索功能组织一直具有挑战性。为克服这些局限性，我们引入了一种数据驱动方法，通过配对自然图像和fMRI记录合成预测能激活特定脑区的图像，从而绕过了对特定类别刺激的需求。我们的方法——脑扩散用于视觉探索（"BrainDiVE"）——建立在最新生成方法的基础上，将大规模扩散模型与脑引导图像合成相结合。通过验证我们的方法，我们展示了为已充分表征的类别选择性ROI合成具有适当语义特异性的偏好图像的能力。然后，我们证明BrainDiVE能够表征针对同一高级类别具有选择性的不同ROI之间的差异。最后，我们识别出这些ROI内部的功能亚区，并通过行为数据进行了验证。这些结果推进了我们对人脑视觉皮层细粒度功能组织的理解，并为使用假设驱动方法进一步检验皮层组织提供了明确的约束条件。