AI 中文总结
研究针对脑网络分析中现有方法解释性有限等问题，提出BrainAgent智能语言模型框架，将连接组分类设为迭代过程，经特定工具转换、知识检索等步骤生成结构化预测，实验证明其优于基线，为脑网络分析提供实用路径。
AI 中文摘要
脑网络分析对于理解认知和神经系统疾病至关重要，但现有深度学习方法将连接组分析视为图到逻辑分类问题，解释性推理有限。大语言模型（LLMs）为知识密集型科学分析提供了有前景的接口，但直接应用于脑网络仍具挑战。本文提出BrainAgent，一种用于知识增强型脑网络分析的智能语言模型框架。它将连接组分类重新定义为拓扑感知理解、外部检索、推理和反思的迭代过程。具体包括通过特定脑分析工具将原始脑网络转换为紧凑的多级结构描述，检索相关神经科学知识和特定任务案例以支撑推理过程，最后通过反思验证生成结构化预测。在四个公共rs - fMRI数据集上的实验表明，BrainAgent持续优于直接提示和标准推理基线。进一步的消融和可解释性分析证明了各组件的有效性，表明智能语言模型为可解释且基于知识的脑网络分析提供了实用途径。
英文摘要
Brain network analysis is crucial for understanding cognition and neurological disorders, yet existing deep learning methods mainly treat connectome analysis as a graph-to-logit classification problem, offering limited explanatory reasoning. Large language models (LLMs) provide a promising interface for knowledge-intensive scientific analysis, but directly applying general-purpose LLMs to brain networks remains challenging due to the structure-language gap, limited neuroscience grounding, and overconfident positive predictions. In this paper, we propose \textbf{BrainAgent}, an agentic LLM framework for knowledge-enhanced brain network analysis. BrainAgent reformulates connectome classification as an iterative process of topology-aware understanding, external retrieval, reasoning, and reflection. Specifically, it first converts raw brain networks into compact multi-level structural descriptions through brain-specific analysis tools, then retrieves relevant neuroscience knowledge and task-specific cases to ground the reasoning process, and finally generates structured predictions with reflective verification. Experiments on four public rs-fMRI datasets show that BrainAgent consistently improves different closed-source and open-source LLM backbones over direct prompting and standard reasoning baselines. Further ablation and interpretability analyses demonstrate the effectiveness of each component and show that BrainAgent produces more comprehensive, multi-level, and verifiable explanations.These results indicate that agentic LLMs provide a practical route toward interpretable and knowledge-grounded brain network analysis.
