Connectomics Informed by Large Language Models

TL;DR

This paper introduces a novel pipeline that leverages large language models to generate neuroanatomical priors, enhancing the accuracy of brain connectome mapping by integrating external knowledge and improving tractography results.

Contribution

It presents a new method for using LLMs to generate quantitative priors for connectomics, addressing scalability and accuracy challenges in brain connectivity mapping.

Findings

LLMs can accurately generate neuroanatomical connectivity priors

Incorporating LLM-derived priors improves connectome accuracy

Enhanced connectome models better predict pathology spread

Abstract

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.