A two-stage RAG-LLM pipeline for roadmapping neuroscience research in operational environments

Neuroscientific research and applications in real world environments are often constrained by limited access, equipment limitations, and high costs. Consequently, robustly selecting constructs to focus effort on is a high-impact task.

This study introduces a two-stage large language model (LLM) pipeline that generates research roadmaps from existing real-world data to inform research priorities in the field, given the state of the art. Stage 1 uses retrieval-augmented generation on operational logs to identify candidate psychological, social, and neurocognitive constructs and map their co-occurrence. Stage 2 evaluates how well these constructs are represented in the literature across four dimensions that commonly inform prioritization: ecological validity, neural correlates, biosensor protocols, and brain computer interface readiness, producing an evidence gap matrix. Pipeline evaluation combined human review, deterministic computations, and human-LLM alignment analysis.

Applying the pipeline to Mars Desert Research Station logs, Stage 1 identified 19 constructs. Stage 2 highlighted critical gaps in neuroimaging and measurement protocols, particularly for social domains, thereby identifying potential research priorities and risks.

This methodology offers a generalizable starting point for synthesizing operational logs into actionable research roadmaps, but the evaluation showed it still required human scrutiny.