Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps
This work addresses challenges in neuroscience meta-analyses for researchers by improving the accuracy of linking text queries to brain images, though it appears incremental as it builds on existing methods like Text2Brain.
The study tackled the problem of mapping open-ended semantic queries to brain activation maps in data-scarce and complex environments by proposing Chat2Brain, which combines large language models with a basic text-to-image model to optimize performance, resulting in anatomically plausible neural activation patterns for more complex tasks.
Over decades, neuroscience has accumulated a wealth of research results in the text modality that can be used to explore cognitive processes. Meta-analysis is a typical method that successfully establishes a link from text queries to brain activation maps using these research results, but it still relies on an ideal query environment. In practical applications, text queries used for meta-analyses may encounter issues such as semantic redundancy and ambiguity, resulting in an inaccurate mapping to brain images. On the other hand, large language models (LLMs) like ChatGPT have shown great potential in tasks such as context understanding and reasoning, displaying a high degree of consistency with human natural language. Hence, LLMs could improve the connection between text modality and neuroscience, resolving existing challenges of meta-analyses. In this study, we propose a method called Chat2Brain that combines LLMs to basic text-2-image model, known as Text2Brain, to map open-ended semantic queries to brain activation maps in data-scarce and complex query environments. By utilizing the understanding and reasoning capabilities of LLMs, the performance of the mapping model is optimized by transferring text queries to semantic queries. We demonstrate that Chat2Brain can synthesize anatomically plausible neural activation patterns for more complex tasks of text queries.