自动化发现大脑对视觉概念的编码模式，揭示数千种可解释的脑区响应。
BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain
- 通过无监督分解从fMRI数据中挖掘候选脑区模式。
- 识别激发每个模式的自然图像集并生成语言描述，覆盖细粒度视觉概念。
- 适合神经科学、认知计算与脑机接口研究者参考。
理解人类大脑如何表征视觉概念及其在脑区中的编码位置，仍是长期挑战。数十年研究虽有进展，但脑信号复杂且视觉概念空间庞大，多数研究仍为小规模、依赖人工检查，局限于特定脑区和概念，缺乏系统验证。本文提出一种大规模、自动化的全皮层视觉表征发现与解释框架。方法分两阶段：首先利用无监督数据驱动分解法，在fMRI活动中发现候选可解释模式；其次通过识别最强烈激发该模式的自然图像集，并生成其共享视觉意义的自然语言描述来解释模式。为实现规模化，引入自动化流程，测试多种解释候选，分配可信度评分并选择最优描述。框架揭示了跨越多种视觉概念的数千个可解释模式，包括此前未报告的细粒度表征。
原文摘要 · Abstract (English)
Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have advanced our understanding of visual representations, yet brain signals remain large and complex, and the space of possible visual concepts is vast. As a result, most studies remain small-scale, rely on manual inspection, focus on specific regions and concepts, and rarely include systematic validation. We present a large-scale, automated framework for discovering and explaining visual representations across the human cortex. Our method comprises two main stages. First, we discover candidate interpretable patterns in fMRI activity through unsupervised, data-driven decomposition methods. Next, we explain each pattern by identifying the set of natural images that most strongly elicit it and generating a natural-language description of their shared visual meaning. To scale this process, we introduce an automated pipeline that tests multiple candidate explanations, assigns reliability scores, and selects the best description for each voxel pattern. Our framework reveals thousands of interpretable patterns spanning many distinct visual concepts, including fine-grained representations previously unreported.
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