Tractography is a unique method for mapping white matter connections in the brain, but tractography algorithms suffer from an inherent trade-off between sensitivity and specificity that limits accuracy. Incorporating prior knowledge of white matter anatomy is an effective strategy for improving accuracy and has been successful for reducing false positives and false negatives in bundle-mapping protocols. However, it is challenging to scale this approach for connectomics due to the difficulty in synthesising information relating to many thousands of possible connections. In this work, we develop and evaluate a pipeline using large language models (LLMs) to generate quantitative priors for connectomics, based on their knowledge of neuroanatomy. We benchmark our approach against an evaluation set derived from a gold-standard tractography atlas, identifying prompting techniques to elicit accurate connectivity information from the LLMs. We further identify strategies for incorporating external knowledge sources into the pipeline, which can provide grounding for the LLM and improve accuracy. Finally, we demonstrate how the LLM-derived priors can augment existing tractography filtering approaches by identifying true-positive connections to retain during the filtering process. We show that these additional connections can improve the accuracy of a connectome-based model of pathology spread, which provides supporting evidence that the connections preserved by the LLM are valid.

翻译：纤维追踪是一种用于绘制大脑白质连接的特有方法，但纤维追踪算法在敏感性与特异性之间存在固有的权衡，限制了其准确性。整合白质解剖学先验知识是提高准确性的有效策略，已在束路映射协议中成功减少假阳性和假阴性。然而，由于难以综合涉及数千种可能连接的信息，该方法在连接组学中难以规模化应用。本研究开发并评估了一种利用大型语言模型（LLMs）生成连接组学定量先验的流程，该流程基于LLMs对神经解剖学的知识。我们使用源自金标准纤维追踪图谱的评估集对方法进行基准测试，确定了从LLMs中提取准确连接信息的提示技术。进一步探索了将外部知识源整合到流程中的策略，这些知识源可为LLM提供基础依据并提升准确性。最后，我们展示了LLM衍生的先验如何通过识别过滤过程中需保留的真阳性连接，增强现有纤维追踪过滤方法。结果表明，这些额外连接能够提高基于连接组的病理传播模型的准确性，为LLM所保留连接的有效性提供了支持证据。