多模态神经影像分析通常涉及复杂的, 模态特定预处理工作流程，需要仔细配置, 质量控制, 以及跨异构工具链的协调。除了预处理, 下游统计分析和疾病分类通常需要特定于任务的代码, 评估协议, 和数据格式约定, 在原始采集和可重复的科学分析之间创建了额外的障碍。我们提出 NeuroAgent, 是一个 LLM 驱动的代理框架，可自动执行异构神经影像数据,（包括 sMRI, fMRI, dMRI, 和 PET,）的关键预处理和分析步骤，并支持通过自然语言查询进行交互式下游分析。 NeuroAgent 采用分层多代理架构，具有反馈驱动的生成-执行-验证引擎: 代理自主生成可执行预处理代码, 检测运行时错误并从中恢复, 并验证输出完整性。我们对所有 ADNI 阶段 (CN=1,000, AD=470), 中汇集的 1,470 受试者进行系统评估，其中所有受试者均具有 sMRI 和表格数据,，子集还具有 Tau-PET (n=469), fMRI (n=278), 和 DTI ($n=620$)。跨多个 LLM 后端的管道消融研究表明，有能力的模型达到了高达 100% 意图解析精度,，其中最强的后端 (Qwen3.5-27B) 达到了 84.8% 端到端预处理步骤正确性。自动恢复限制了对需要通过人机环路界面进行人工审查的边缘情况的手动干预。对于使用自动预处理的多模态数据, 进行阿尔茨海默病的 疾病分类，我们的代理整体通过四种模态, 实现了 0.9518 的 AUC，优于所有单模态基线。这些结果表明，NeuroAgent 可以减少神经影像预处理所需的手动工作，并为神经影像研究提供端到端的自动化分析流程。

Multimodal neuroimaging analysis often involves complex, modality-specific preprocessing workflows that require careful configuration, quality control, and coordination across heterogeneous toolchains. Beyond preprocessing, downstream statistical analysis and disease classification commonly require task-specific code, evaluation protocols, and data-format conventions, creating additional barriers between raw acquisitions and reproducible scientific analysis. We present NeuroAgent, an LLM-driven agentic framework that automates key preprocessing and analysis steps for heterogeneous neuroimaging data, including sMRI, fMRI, dMRI, and PET, and supports interactive downstream analysis through natural-language queries. NeuroAgent employs a hierarchical multi-agent architecture with a feedback-driven Generate-Execute-Validate engine: agents autonomously generate executable preprocessing code, detect and recover from runtime errors, and validate output integrity. We evaluate the system on 1,470 subjects pooled across all ADNI phases (CN=1,000, AD=470), where all subjects have sMRI and tabular data, with subsets also having Tau-PET (n=469), fMRI (n=278), and DTI ($n=620$). Pipeline ablation studies across multiple LLM backends show that capable models reach up to 100% intent-parsing accuracy, with the strongest backend (Qwen3.5-27B) reaching 84.8% end-to-end preprocessing step correctness. Automated recovery limits manual intervention to edge cases where human review is required via the Human-In-The-Loop interface. For Alzheimer的 Disease classification using automatically preprocessed multimodal data, our agent ensemble achieves an AUC of 0.9518 with four modalities, outperforming all single-modality baselines. These results show that NeuroAgent can reduce the manual effort required for neuroimaging preprocessing and enable end-to-end automated analysis pipelines for neuroimaging research.

科目:人工智能(cs.AI)

Subjects: Artificial Intelligence (cs.AI)