How the brain supports language across different languages is a basic question in neuroscience and a useful test for multilingual artificial intelligence. Neuroimaging has identified language-responsive brain regions across languages, but it cannot by itself show whether the underlying processing is shared or language-specific. Here we use six multilingual large language models (LLMs) as controllable systems and create targeted ``computational lesions'' by zeroing small parameter sets that are important across languages or especially important for one language. We then compare intact and lesioned models in predicting functional magnetic resonance imaging (fMRI) responses during 100 minutes of naturalistic story listening in native English, Chinese and French (112 participants). Lesioning a compact shared core reduces whole-brain encoding correlation by 60.32% relative to intact models, whereas language-specific lesions preserve cross-language separation in embedding space but selectively weaken brain predictivity for the matched native language. These results support a shared backbone with embedded specializations and provide a causal framework for studying multilingual brain-model alignment.

翻译：大脑如何在不同语言中支持语言处理是神经科学的基本问题，也是对多语言人工智能的有益测试。神经影像学已识别出跨语言的语言响应脑区，但其本身无法揭示底层处理机制是共享还是特定于某种语言。本文以六个多语言大型语言模型（LLMs）为可控系统，通过将跨语言重要或仅对单一语言尤为关键的参数子集归零，创建靶向“计算损伤”。随后比较完整模型与受损模型在100分钟自然故事聆听（母语为英语、汉语和法语的112名参与者）中预测功能性磁共振成像（fMRI）响应的表现。损伤紧凑的共享核心会使完整模型的全脑编码相关性降低60.32%，而语言特异性损伤在保持嵌入空间中跨语言分离性的同时，仅削弱对匹配母语的脑预测能力。这些结果支持存在嵌入专业化的共享骨干网络，并为研究多语言脑-模型对齐提供了因果分析框架。