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.

该研究通过结合大规模扩散模型和脑图像合成的方法，介绍了 BrainDiVE（Brain Diffusion for Visual Exploration），该方法有效地从自然图像和功能磁共振成像数据中合成预测激活给定脑区域的图像，并验证了 BrainDiVE 在人类视觉皮层的 fine-grained functional organization 的应用及假设驱动方法的作用。

大规模生成模型的视觉探索：基于脑扩散的皮层发现

Brain Diffusion for Visual Exploration: Cortical Discovery using Large  Scale Generative Models

Causal mapping of the functional organisation of the human brain requires
evidence of \textit{necessity} available at adequate scale only from
pathological lesions of natural origin. This demands inferential models with
sufficient flexibility to capture both the observable distribution of
pathological damage and the unobserved distribution of the neural substrate.
Current model frameworks -- both mass-univariate and multivariate -- either
ignore distributed lesion-deficit relations or do not model them explicitly,
relying on featurization incidental to a predictive task. Here we initiate the
application of deep generative neural network architectures to the task of
lesion-deficit inference, formulating it as the estimation of an expressive
hierarchical model of the joint lesion and deficit distributions conditioned on
a latent neural substrate. We implement such deep lesion deficit inference with
variational convolutional volumetric auto-encoders. We introduce a
comprehensive framework for lesion-deficit model comparison, incorporating
diverse candidate substrates, forms of substrate interactions, sample sizes,
noise corruption, and population heterogeneity. Drawing on 5500 volume images
of ischaemic stroke, we show that our model outperforms established methods by
a substantial margin across all simulation scenarios, including comparatively
small-scale and noisy data regimes. Our analysis justifies the widespread
adoption of this approach, for which we provide an open source implementation:
this https URL

本研究采用深度生成神经网络架构进行损伤缺陷推断的任务，并与现有方法进行比较。结果表明，该模型在所有情况下均显著优于已有方法，包括相对较小和嘈杂的数据情况。